WO2021012040A1 - Methods and systems for state navigation - Google Patents
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- WO2021012040A1 WO2021012040A1 PCT/CA2020/051000 CA2020051000W WO2021012040A1 WO 2021012040 A1 WO2021012040 A1 WO 2021012040A1 CA 2020051000 W CA2020051000 W CA 2020051000W WO 2021012040 A1 WO2021012040 A1 WO 2021012040A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24133—Distances to prototypes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/042—Knowledge-based neural networks; Logical representations of neural networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/02—Knowledge representation; Symbolic representation
- G06N5/027—Frames
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
- G06N5/043—Distributed expert systems; Blackboards
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/762—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
Definitions
- TITLE Methods And Systems For State Navigation
- This invention generally relates to information processing, knowledge discovery, artificial general intelligence, and in one aspect relates to autonomous mobile systems and machines such as vehicles, robots, and transportations systems.
- intelligent systems of interest in the industry and people's life such as, decision making, autonomous systems, autonomous moving systems, content generation, control systems, question answering, knowledge discovery, investigation of bodies of knowledge/data
- systems comprising at least one state navigator, in which the systems will respond in various forms in response of an input query or set of data in which a machine or a system changes its state from a current state to a next or a future state.
- Methods are given to enable the systems to navigate through spaces either physical spaces and/or a state space.
- state components of a state space or state components of a system of knowledge, or state components of a body of knowledge, or state components of a body of data, or state components of a universe of state navigation.
- a body of data we call a“composition of state components”.
- state components are grouped in different sets each set is assigned with a predefined order.
- the state components of an intelligent system i.e. state components of a body of knowledge, state components of a composition, state components of a universe corresponding to a composition
- a vector which is corresponded to a column of a participation matrix as will be described in details in the detailed descriptions.
- state navigation is a complete and general case of intelligent actions for which this disclosure aims to address and give one or more solutions, methods, and systems.
- any composition of state components is viewed as an unknown system or system of knowledge from which valuable knowledge can be learnt or extracted by investigation of such compositions.
- the purpose of the investigation is to obtain as much information and knowledge, about such an unknown system, as possible.
- the present invention therefore investigate the“compositions of state components” or a“body” or a“system of knowledge” (as is called from time to time in this disclosure) by providing the investigation methods for identifying the most significant constituent state components for a given body of knowledge or the given compositions in respect to one or more significance aspect/s.
- the significance aspects generally include the“intrinsic significance aspects” and/or“associational/relational significance aspects”.
- VSM/s in short VSM/s in short
- association strength measures or ASM for short
- XY_VSM correlational/associational
- a composition of state components or a body of knowledge is break down to it's constituent state components and labeled /assigned with different orders, from which one or more array of data, respective of the information of the participations of the constituent state components of different orders into each other, are formed.
- the data therefore is used to evaluate various“value significance” values of the constituent state components of the different order according to the disclosed measures of various aspects of significance.
- measure/s are given for valuation of value significances of the state components of the composition based on their significance role which is calculated from the participations pattern/s of the state components of the composition.
- association strength is given from which the relations of state components of the composition can be revealed. Algorithms and formulations and calculation methods are given to evaluate such association strength according to various exemplary association aspects.
- measures are given for evaluating the“causal association strengths” of the state components of different orders to each other or to one or more target state component.
- the causal association strengths are instrumental in knowledge discovery, evidence based decision making, as well as navigating a system’s state into space in an interpretable manner. These measures are also very instrumental in estimating an optimal state-action for an autonomous system.
- methods and system are disclosed for investigation of visual compositions in order to detect, recognize, and classify visual objects and make pluralities of standard data objects corresponding to or representing a plurality of visual objects.
- knowledge retrieval, question answering, and utterances and man-machine conversation is modeled as a space navigating instance.
- the knowledge is gained from a body of knowledge which is considered as sequences of state components of body of knowledge and relationships that is discovered by the teachings of current invention is used to effectively communicate with other agent in a manner which is credible, context aware, informative, and having high degree of relevancy.
- a coupled-mode utterance model is disclosed for continues natural conversation during a converse session.
- the conversation can be aimed at one or more various conversation objectives such as conversation to reveal new knowledge, educational conversation, entertaining conversation and the like using various association strength measures between the state components of one or more system of knowledge.
- an entertaining conversation session can be initiated between machine and a human client by accessing to and investigating/learning from a body of knowledge comprising a large collections of movie scripts or a corpus of novels written by well-known writers.
- the exemplary systems of the current disclosure can learn, through exercising the teaching of current invention, the intricacies and relationships and utterance structures of a spoken natural language such as English language.
- a conversation session can be initiated for medical related knowledge discovery and knowledge retrieval using a system of knowledge comprised of corpuses of medical literature and so on.
- measures are given for evaluating the“relational association strengths” of the state components of different orders to each other or to one or more target state component.
- measures are given for evaluating the“relational value significances” of the state components of different orders to each other or to one or more target state component.
- various measures of the“associational novelty value significances” are given for evaluating novelty value significance in relation to one or more target state components of the composition or the body of knowledge.
- the novelty value is assigned to a predetermined list of state components (e.g. some special words that usually are used to express a novelty or a reasoning or concluding remarks, such as‘therefore, consequently, in spite of, however, but, and the likes.) These are called special significance conveyers to amplify or dampen the significances of such special SCs of a composition in the final output or result.
- state components e.g. some special words that usually are used to express a novelty or a reasoning or concluding remarks, such as‘therefore, consequently, in spite of, however, but, and the likes.
- the present invention provide a unified method and process of investigating the compositions of state components, modeling an unknown system, and obtaining as much worthwhile information and knowledge as possible about the system or the composition or the body of knowledge.
- the obtained knowledge and the derivatives data objects from the body of knowledge or the composition state components then are used in various embodiments to yield practical knowledgeable systems which, for example, can navigate and project through state spaces.
- Fig. 1 shows one exemplary block diagram of a system or a software artifact that generates various outputs from a body of knowledge or a composition according to one embodiment of the present invention.
- FIG. 1a shows the internet as one composition (the largest) trying to describe our universe
- FIG. 1b shows that any other composition can also be viewed as an attempt to describe a smaller universe, i.e. its own universe.
- Fig. 2 shows one exemplary illustration of the concept of association strength of a pair of SCs according to one embodiment of the present invention.
- Fig. 3 shows one exemplary embodiment of a directed asymmetric network or graph corresponding to a composition of state components.
- Fig. 4 shows a block diagram of one preferred embodiment of the method and the algorithm for calculating a number of exemplary“Value Significance Measures” of different types for the state components of a composition according to one embodiment of the present invention.
- Fig. 5 shows one exemplary block diagram of the method and the algorithm of building the State component Maps ( SCM) from the Association Strength Matrix ( ASM) which is built for and from an input composition according to one embodiment of the present invention.
- SCM State component Maps
- ASM Association Strength Matrix
- Figs. 6a, 6b, 6c show the exemplary values and one way of representing the values of the different conveyers of the different types of the“value significance measures”.
- Fig 7 shows one exemplary instance of implementing the formulations and algorithm/s illustrating one way of using the “participation matrix” (PM) and the“association strength matrix” (ASM) to calculate two different types of the associations strength of the SCs of order 2 to the SCs of the order 1, according to one embodiment of the present invention.
- PM participation matrix
- ASM association strength matrix
- Fig. 8 is an schematic view of the system and method of building at least two participation matrixes and calculating VSM for Ith order partition, SC l , to calculate the“Value Significance Measures” (VSM) of other partitions of the compositions, SC l+r , and storing them for further use by the application servers according to one embodiment of the present invention.
- VSM Value Significance Measures
- Fig. 9 a block diagram of an exemplary application and the associated system for ranking, filtering, storing, indexing, clustering the crawled webpages from the internet using“Value Significance Measures” (VSM) according to one embodiment of the present invention.
- VSM Value Significance Measures
- Fig. 10 is an exemplary system of investigating module for investigation of composition of state components providing one or more desired result/data/output according to one embodiment of the present invention.
- Fig. 10-1 is an exemplary application of the current disclosure in investigation and navigation of news content.
- Fig. 11 is a block diagram of an exemplary application for investigation of a body of data or knowledge corresponding to a collection state positions of a system in a predefined state space or state universe..
- Fig 12 is another exemplary system of using the investigator of a body of data or knowledge corresponding to a collection of state positions of a system in a state space or state universe, further having a module or having access to a module which provide contextual data for a given state, according to the present invention.
- Fig 13 is another exemplary system of using the investigator of a body of data or knowledge corresponding to a collection of state positions of a system in a predefined state space or state universe, further having a module or having access to a module which provide contextual data for a given state and one or more visual data investigators further providing contextual data for the given state of the system, according to the present invention.
- Figs. 14-a and 14-b shows an exemplary embodiment of collecting and assembling a body of data from a driven vehicle at time steps wherein the vehicle is equipped with various sensory, processing, control, and communication devices. Data gathered or recorded at any time step is considered as a state component with a predefined order of l, ie. .
- Figs. 15-a and 15-b An exemplary illustration of a body of data or knowledge corresponding to states of a system partitioned over a time sequence and illustrates how this string of data is partitioned to build the corresponding participation matrixes, according to one embodiment of the present.
- Fig. 16 shows high level process flow of preparing/building the instrumental data objects, from a body of knowledge corresponding to the state components of a composition or a body of data, for investigation, state transitioning, and space navigation.
- Figs. 17-1 and 17-2 describes stages of knowledge extraction, state estimation, and optimal state navigations.
- Fig. 18 shows a block diagram of a system for investigation of a Body of knowledge (BOK) comprising one or more collections of images and learn about existence of real world objects from the images by employing the investigation methods of the present invention to identifies and/or classifies and/or clustered and/or building secondary sets/list group of partitions and their corresponding representation data objects, corresponding to the whole and/or newly found partitions of the BOK and storing them in a knowledge base to be used or consulted by other agents, for instance, for identifications of objects in visual compositions such as images or movie frames.
- BOK Body of knowledge
- Fig. 18-1 shows schematically one exemplary illustration of how to partition an image into its constituent visual state components (VSC) of different orders according to one exemplary embodiment of the present invention.
- VSC visual state components
- Figs. 18-2 to 18-4 further illustrates, graphically, the operation of investigation system of Fig. 16 and showing the concepts of primary and secondary partitioning, corresponding secondary data objects, and what the knowledge base of the system of Fig 16 is storing, as a knowledge of the real world, learned from investigating a body of knowledge of collection of images, according to exercising one exemplary embodiment of the investigation system/s of bodies of knowledge of the present invention.
- FIG 19 shows an exemplary application and realization of the disclosed method/s using a neural network in which the connection weight between neurons is adjusted using the various association strengths measures/values according to the teaching of this disclosure.
- Figs. 20-1 , 20-2 shows the concept of coupled utterance model wherein two agents enter into a conversation and navigate through various discourses while keeping/navigating the context along the conversation.
- a system of knowledge here, means a composition or a body of knowledge or a body of data (as will be referred from time to time) in any field, narrow or wide, composed of data symbols such as alphabetical/numerical characters, any array of data, binary or otherwise, or any string of characters/data etc.
- State Components As defined along this disclosure, the constituent parts of the bodies of knowledge are called“State Components” (SCs). The state components further are grouped into different sets assigned or labeled with orders as will be explained in the definition of section of this disclosure.
- a picture or a video frame consists of colored pixels that have participated in a picture to form and convey the information about the picture. Especially some colored pixels of the picture are more significant in that picture. Moreover their combination or the way or the pattern that they participate together in any small parts or segments of that picture are also important in the way the pixels are conveying the information about the picture to an observer's eyes or a camera.
- composition or a body of knowledge could be a string of genetic codes, a DNA string, or a DNA strand, and the like.
- any system, simple or complicated, can be identified and explained by its constituent parts and the relation between the parts.
- any system or body of knowledge can also be represented by network/s or graph/s that shows the connection and relations of the individual parts of the system. The more accurate and detailed the identification of the parts and their relations the better the system is defined and designed and ultimately the better the corresponding tangible systems will function.
- Most of the information about any type of existing or new systems can be found in the body of many textual compositions. Nevertheless, these vast bodies of knowledge are unstructured, dispersed, and unclear for non-expert in the field.
- the present invention is to investigate such bodies of knowledge for various practical purposes. Moreover as will be explained we consider a body of knowledge as a composition of state components of different orders and the system of knowledge is viewed as the navigation trajectories of one or more of state components (possibly of different order) in a state space. Knowing or finding out how and/or when and/or why a state component of particular order is moved from one point (a set of state component of particular order can form a state space and a point in a state space/s is a state component of body of data having a predefined order) to another point, enables us to build machines that can navigate through such space reliably and rationally.
- the purpose of the investigation is to model and gain as much information and knowledge about an unknown system comprised of state components while at least one source of the information about such a system is a given composition of state components wherein the composition is readable by a computer. Therefore, some information about such an unknown system is supposedly embedded in a body of knowledge or system of knowledge or generally in the given composition. The investigator, hence, will have to be able to capture or produce as much knowledge about the system from the information in the given composition.
- the investigation is performed according to at least one important aspect in the investigation of bodies of knowledge (i.e. compositions).
- The“important aspects of the investigation”, can, for example, be one or more of the following objectives:
- Each of these“important aspect” or stages (1, 2,3, and 4 in the above) of the investigation can further be break down to two or more stages or steps or be combined together to perform a desirable investigation goal or to define the“investigation important aspect”.
- the present invention gives a number of such investigation goals and the methods of achieving the desired outcome. Moreover, the present invention provides a variety of tools and investigation methods that enables a user to deal with the task of investigations of compositions of state components for any kind of goals and any types of the composition.
- The“significance aspects”, based on which the significances of the SCs of compositions are defined and calculated, are various that can be looked at.
- one“significance aspect” could be an intrinsic significance of an SC which shows the overall or intrinsic significance of an SC in a body of knowledge.
- Another significance aspect can be considered to be a significant aspect in relation or relative to one or more of the SCs of the body of knowledge.
- Yet another significance aspect is considered to be an intrinsic novelty value of a SC in a body of knowledge or a composition.
- Yet another significance aspect is defined as a relative or relational novelty value of a SC related to one or more of the SCs of the body of knowledge or a composition.
- a“significance aspect” is the orientation that one can use to reason on how to put a significance value on a state component of a composition or a body of knowledge.
- a significance aspect is a qualitative quality that can polarize or differentiate the state components and be used to define value significance measures and consequently suggest or construct various value functions or significance weighting functions on the state components of a composition or a body of knowledge.
- relational value significances are defined here.
- the relational value significances are instrumental in clustering a collection of compositions or clustering partitions of a composition in regards to one or more of a target SC or the parts of the system of knowledge.
- Such a method will speed up the research process and knowledge discovery, and design cycles by guiding the users to know the substantiality of each part in the system. Consequently dealing with all parts of the system based on the value significance priority or any other predetermined criteria can become a systematic process and more yielding to automation.
- a particular case of interest in this disclosure is system of knowledge composed of various types of data and symbols which is gathered by an artisan to use as training or learning material to build autonomous machines of high utility such as autonomous moving robots (e.g. a self-driving car).
- system of knowledge or body of data is gathered.. for instance through recording all types of sensory data, control data, environmental data, visual data command data, conversation, and natural language text or speeches and all types of such conceivable and desired forms of data that are present or relevant during the course of data recording and gathering.
- the current disclosure teaches how one can use these immense data to enable a moving robots, such as a car, derive autonomously by knowing the knowledge of the world and universe and can move from one state to another state along the time (i.e. navigating through its state space to become able to navigate in the physical space-time as we expect from human driven car, or a human).
- the current disclosure is about identifying knowledge, gain knowledge and process knowledge through investigation of large bodies of data and not merely interested in processing data for processing data.
- State Component means generally any string of characters, but more specifically, characters, letters, numbers (e.g. integer, real or complex, Boolean, binary, etc.), words, binary codes, bits, mathematical functions, sound signal tracks, video signal tracks, electrical signals, chemical molecules such as DNAs and their parts, or any combinations of them, and more specifically all such string combinations that indicates or refer to an entity, concept, quantity, and the incidences of such entities, concepts, and quantities.
- SC State Component/s and the abbreviation SC or SCs are used interchangeably.
- the order can be assigned to a group or set of state components usually based on at least one common predefined characteristic of the members of the set. So a higher order SC is a combination of, or a set of, lower order SCs or lower order SCs are members of a higher order SC. Equally one can order the genetic codes in different orders of state components. For instance, the 4 basis of a DNA molecules as the zeroth order SC, the base pairs as the first order, sets of pieces of DNA as the second order, genes as the third order, chromosomes as the fourth order, genomes as the fifth order, sets of similar genomes as the sixth order, sets of sets of genomes as the seventh order and so on. Yet the same can be defined for information bearing signals such as analogue and digital signals representing audio or video information.
- bits electrical One and Zero
- bits can be defined as zeroth order SC
- the bytes as first order
- any sets of bytes as third order
- sets of sets of bytes e.g. a frame, as fourth order SC and so on.
- the pixels with different color can be regarded as first order SC (the RGB values of a pixel can be regarded as zeroth order SCs)
- a set whose members contain two or more number of pixels e.g.
- a segment of a picture can be regarded as SCs of second order, a set whose members composed of two or more such segments as third order SC, a set whose members contain or composed of two or more such third order SCs as fourth order SC, ,a whole frame as fifth order SC, and a number of frames (like a certain period of duration of a movie such as a clip) as sixth order and so on.
- SCs of second order a set whose members composed of two or more such segments
- third order SC a set whose members contain or composed of two or more such third order SCs as fourth order SC
- a whole frame as fifth order SC
- a number of frames like a certain period of duration of a movie such as a clip
- State components can be stored, processed, manipulated, and transported by transferring, transforming, and using matter or energy (equivalent to matter) and hence the SC processing is an instance of physical transformation of materials and energy.
- STATE a state component composed of one or more lower order state components.
- the state refers to the higher order state component in a given set/s of state components.
- state can be defined and/or selected from one or more state components.
- a state of a system of knowledge e.g. a body of data
- a set of lower order state components of the system of knowledge with highest number of members i.e.. the largest set of SCs of the system.
- state transition refers to one or more changes (e.g. replacement of a lower order SC with another lower order SC of a higher order SC, deleting a SC, adding a SC, and any combination of these operations) in a constituent lower order state components of a of higher order state component.
- COMPOSITION is an SC composed of constituent state components of lower or the same order, particularly text documents written in natural language documents, genetic codes, encryption codes, a body of data, numerical values, and strings of numerical values, data files, voice files, video files, and any mixture thereof.
- a collection, or a set, of compositions is also a composition. Therefore a composition is in fact a State Component of particular order which can be broken down to lower order constituent State Components.
- One preferred exemplary composition in this description is a set of data objects containing state components, for example a webpage, papers, documents, books, a set of webpages, sets of PDF articles, multimedia files, or even simply words and phrases.
- compositions and bodies of knowledge are basically the same and are used interchangeably in this disclosure.
- a composition is also an state according the definitions above. Compositions are distinctly defined here for assisting the description in more familiar language than a technical language using only the defined SCs notations.
- a partition of a composition in general, is a part or whole, i.e. a subset, of a composition or a collection of compositions. Therefore, a partition is also a State Component having the same or lower order than the composition as an SC. More specifically in the case of textual compositions, parts or partitions of a composition can be chosen to be characters, words, phrases, any predefined length number of words, sentences, paragraphs, chapters, webpage, documents, etc.
- a partition of a composition is also any string of symbols representing any form of information bearing signals such as audio or videos, texts, DNA molecules, genetic letters, genes, a state of a system in a moment of time, and any combinations thereof.
- partition of a composition in this disclosure is a component of the state of a system, a state of a system (e.g. a vector in the state space of a system), or a number of states of the system under investigation or while running, and the like.
- partitions of a collection of compositions can include one or more of the individual compositions. Partitions are also distinctly defined here for assisting the description in more familiar language than a technical language using only the general SCs definitions.
- SIGNIFICANCE MEASURE assigning a quantity, a number, a feature, or a metric for a SC from a set of SCs so as to assist to distinguishing or selecting one or more of the SCs from the set. More conveniently and in most cases the significance measure is a type of numerical quantity assigned to a partition of a composition. Therefore significance measures are functions of SCs and one or more of other related mathematical objects, wherein a mathematical object can, for instance, be a mathematical object containing information of participations of SCs in each other, whose values are used in the decisions about the constituent SCs of a composition.
- “Relational, and/or associational, and/or novel significances” are one form or a type of the general“significance measures” concept and are defined according to one or more aspects of interest and/or in relation to one or more SCs of the composition.
- FILTRATION/SUMMARIZATION is a process of selecting one or more SC from one or more sets of SCs according to predefined criteria with or without the help of value significance and ranking metric/s.
- the selection or filtering of one or more SC from a set of SCs is usually done for the purposes of representation of a body of data by a summary as an indicative of that body in respect to one or more aspect of interest.
- searching through a set of partitions or compositions, and showing the search results according to the predetermined criteria is considered a form of filtration/summarization.
- finding an answer to a query e.g. question answering, or finding a composition related or similar to an input composition etc. is also a form of searching through a set of partitions and therefore are a form of summarization or filtration according to the given definitions here.
- Embodiments in accordance with the present embodiments may be implemented as an apparatus, method, or computer program product. Accordingly, the present embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.), or an embodiment combining software and hardware aspects that may all generally be referred to herein as a“module” or“system.” Furthermore, the present embodiments may take the form of a computer program product embodied in any tangible medium of expression having computer-usable program code embodied in the medium.
- a computer-readable medium may include one or more of a portable computer diskette, a hard disk, a random access memory (RAM) device, a read-only memory (ROM) device, an erasable programmable read-only memory (EPROM or Flash memory) device, a solid state based storage devices (e.g. SSD, MVNe, etc.), a portable compact disc read-only memory (CDROM), an optical storage device, and a magnetic storage device.
- Computer program code for carrying out operations of the present embodiments may be written in any combination of one or more programming languages. Embodiments may also be implemented in cloud computing environments.
- “cloud computing” may be defined as a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned via virtualization and released with minimal management effort or service provider interaction, and then scaled accordingly.
- configurable computing resources e.g., networks, servers, storage, applications, and services
- a cloud model can be composed of various characteristics (e.g., on-demand self-service, broad network access, resource pooling, rapid elasticity, measured service, etc.), service models (e.g., Software as a Service (“SaaS”), Platform as a Service (“PaaS”), Infrastructure as a Service (“IaaS”), and deployment models (e.g., private cloud, community cloud, public cloud, hybrid cloud, etc.).
- service models e.g., Software as a Service (“SaaS”), Platform as a Service (“PaaS”), Infrastructure as a Service (“IaaS”)
- deployment models e.g., private cloud, community cloud, public cloud, hybrid cloud, etc.
- each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).
- each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations may be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
- These computer program instructions may also be stored in a computer-readable medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.
- the terms“comprises,”“comprising,”“includes,”“including,”“has,”“having,” or any other variation thereof, are intended to cover a non-exclusive inclusion.
- a process, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, article, or apparatus.
- “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).
- any examples or illustrations given herein are not to be regarded in any way as restrictions on, limits to, or express definitions of any term or terms with which they are utilized. Instead, these examples or illustrations are to be regarded as being described with respect to one particular embodiment and as being illustrative only. Those of ordinary skill in the art will appreciate that any term or terms with which these examples or illustrations are utilized will encompass other embodiments which may or may not be given therewith or elsewhere in the specification and all such embodiments are intended to be included within the scope of that term or terms. Language designating such no limiting examples and illustrations includes, but is not limited to:“for example,”“for instance,”“e.g.,” and“in one embodiment.”
- apparatuses and machines a computer process, a computing and data processing systems comprising one or more data processing or computing devices, or as an article of manufacture such as computer-readable storage medium.
- One goal of investigation of a body of data is to learn and extract the knowledge therein in order to utilize that knowledge to build or construct knowledgeable systems capable of, for instance, autonomously make decision, navigate through physical spaces or state spaces, and/or converse and communicate intelligibly with other agents or human.
- This section of present invention discloses a systematic, machine implemented, process efficient and scalable method/s of building, making, and operating knowledgeable machines for variety of tasks and, in a particular example, space (e.g. 4D space or state space) navigators and the corresponding autonomous moving systems with cognition/knowledge of real world.
- space e.g. 4D space or state space
- PC A principal component analysis
- PC A principal component analysis
- the covariance matrix is usually calculated after, preferably, normalizing the data set statistically, (e.g. assuming a normal distribution of values of independent variables (features) of the data set (or the training data) so that the mean of the distribution is zero and the standard deviation and/or its variance to unity).
- the aim of PCA therefore is to find distinguished principal vectors to form a new space with fewer dimensions than the original data set (the principal vectors ideally become the basis of this new space from which all the data points (data vectors) can be decomposed to).
- the principal components are the eigenvectors corresponding to the largest eigenvalues of said covariance matrix which were derived or made or calculated from raw original collection of data.
- the assumption is that principal components are distinguishable so that the interrelationships of the original data can be expressed in their terms clearly without losing much information.
- the aim obviously is to select few principal components that the information of the original collection of data can be efficiently and sufficiently expressed.
- the eigenvalues and eigenvectors of such matrix are also random looking making it hard to select the principal components for such matrix resulting in rendering these methods ineffective in uncontrolled environments and real world situations. Therefore the PCA analysis for large data sets, or bodies of data, is not effective.
- Neural networks building blocks are based on linear regression (e.g. the rule of perceptron) and logistics regression decision making which are trained by using large data sets (usually labeled by human).
- Linear regression involves optimization of a large number of unknown parameters in a predefined types of relationship that eventually can minimize an error or a cost function.
- the possibilities of fitting a regression function to reasonably fit both the training and testing of a large set of data are endless and the hypothesis set for such function is practically infinite.
- a network of combined linear regression blocks e.g. a deep neural network
- a successfully trained neural network shows one of these possibilities that could satisfy the loss function objectives.
- Bayesian networks have also been suggested and promoted to be able to solve the challenge of building intelligent machines.
- Bayesian networks, and Bayesian inferences on the other hand works best if the feature, conditional probabilities, and related priori are provided by experts making the methods and Bayesian networks hard and expensive to adapt or be used in new situations.
- each data-set has to be treated differently with complicated and twisted reasoning which again can potentially make the resulting system unreliable since errors in large Bayesian networks can propagate and give incorrect results and hence unintended consequences. So far there is no successful methods to utilize large data- sets or large bodies of data to build a Bayesian Network efficiently.
- the methods and systems of the present invention can further be used for applications ranging from document classification, search engine document retrieval, news analysis, knowledge discovery and research trajectory optimization, autonomous decision making and navigations, question answering, computer conversation, spell checking, summarization, categorizations, clustering, distillation, automatic composition generation, genetics and genomics, signal and image processing, to novel applications in economical systems by evaluating a value for economical entities, crime investigation, financial applications such as financial decision making, credit checking, decision support systems, stock valuation, target advertising, and as well measuring the influence of a member in a social network, and/or any other problem that can be represented by graphs and for any group of entities with some kind of relations or association.
- compositions of state components is viewed as an unknown system or system of knowledge that the purpose of the investigation is to obtain as much information and knowledge about such an unknown system.
- the present invention therefore investigate the“compositions of state components” or a“body of data” or a“body/system of knowledge” (as is called from time to time in this disclosure) by providing the investigation methods for identifying the most significant constituent state components and their relationships which are conceptualized by various“association strength measures” (ASMs) for a given body of knowledge or the given compositions in respect to one or more significance aspect/s.
- ASMs association strength measures
- the“Participation Matrix” is a matrix indicating the participation of one or more state components of particular order in one or more partitions of the composition.
- PM indicate the participation of one or more lower order SCs into one or more SCs of higher or the same order.
- PM/s are the most important structure of that carries the raw information from which many other important functions, information, features, and desirable parameters/metrics can be extracted.
- the PM is a binary matrix having entries of one or zero and is built for a composition or a set of compositions as the following:
- a desired criteria, in the step 2 above, can be, for instance, to only select the content words, certain values which is corresponded to a state components, or select certain partitions having certain length or, in another instance, selecting all and every word, values, or character strings and/or all the partitions.
- the participating matrix of order Ik i.e. PM lk
- PM lk can also be defined which is simply the transpose of PM kl whose elements are given by:
- PM carries much other useful information.
- a participation matrix in which the entries are the number of time that a particular SC (e.g. a word) is being repeated in another partitions of particular interest (e.g. in a document) one can readily do so by, for instance, the following: wherein the PM_R 15 stands for participation matrix of SCs of order 1 (e.g. words) into SCs of order 5 (e.g. the documents) in which the nonzero entries shows the number of time that a word has been appeared in that document (for simplicity possible repetition of a word in an SC of order 2, e.g sentences, is not accounted for here).
- the COM is a N ⁇ N square matrix. This is the co-occurrences of the state components of order k in the partitions (state components of order / ) within the composition and is (as will be stated in next sections) one indication of the association of SCs of order k evaluated from their pattern of participations in the SCs of order / of the composition.
- the co-occurrence number is shown by which is an
- COM Co-Occurrence Matrix
- COM can also be made binary, if desired, in which case only shows the existence or non-existence of a co-occurrence between any two SC k .
- the“co-occurrence matrix” as defined in this disclosure is that it carries or contain the information of relationship and associations of the SCs of the composition which is further utilized in some embodiments of the present invention.
- the frequency of occurrences and the co-occurrences is defined in view of event/s of interest. In other words the observation of participation of state components of certain order in state comments of higher order (the events).
- the co-occurrences of SCs of order one e.g. words
- the co-occurrences of SCs of order one is their participation, for instance, in composing sentences, i.e. the event of interest, here, is observation of a sentence.
- the co-occurrences of state components can also be obtained by looking at, for instance, co-occurrences of a pair of state components within certain (i.e. predefined) proximities in the composition (e.g. counting the number of times that a pair of state components have co-occurred within certain or predefined distances from each other in the composition. Similarly there are other ways to count the frequency of occurrences of a state components (i.e. the ). However the
- the repeated co-occurrences of a pair of state components within certain proximities is an indication of some sort of association (e.g. a logical relationship) between the pair or else it would have made no sense to appear together in one or more partitions of the composition(i.e. in state components of higher order).
- each raw of the PM can be stored in a dictionary, or the PM be stored in a list or lists in list, or a hash table, or a SQL database, binary files, compressed data files, or any other convenient objects of any computer programming languages such as Python, C, Perl, Java, R, GO, etc.
- Such practical implementation strategies can be devised by various people in different ways.
- the PM entries (especially for showing the participation of lowest orders SCs of the composition into each other, e.g. a PM 12 ) are binary for ease of manipulation and computational efficiency.
- autonomous mobile systems are systems comprising an array/set of sensory hardware generating a number of sets/vectors/strings of data corresponding to environmental data and/or any other desired sensory data as well as any other forms of data such as commands, conversations, textual data, signals, etc. and/or other desired data by accessing to knowledge repositories and/or through communication facilities which forms one or more sets of state components of predefined orders.
- lower order state components or components of the state space we usually mean any type of data (sensory, controlling, commanding, visual, audio, encrypted strings, strings of characters, numerical values) and/or a content playing a rule in navigation of an autonomous system.
- Each of such events can be characterized, denoted, and/or being represented by a plurality of set of data of various nature.
- a set comprising combinations of one or more of such instances of lower order components forms a set of higher order state components.
- Such machines potentially can perform much better than human considering that the processing speed, memory and storage, and granularity of the data acquisition that the artificial machines have at their disposal is growing very fast while the costs are declining.
- Granularity of data for instance, is in reference to quality and resolution, and spectral width of modem camera lenses, or sensitivity of sensors compared to human sense (e.g. 5 fundamental human sense) and the like.
- one of the objectives of the current disclosure is to make or build or devise such autonomous systems that while can use the benefits of data granularity but still become able to stay rational and behave in a stable and predictable manner as will be pointed throughout the detailed description of the current disclosure.
- association and value significances of the components of the sate space we can calculate the associations between the state vectors (i.e. the points in state space or the corresponding Hilbert space) themselves so that one can quickly and efficiently calculate the best/optimal next state components ( according to some measures of association and significance value and the contextual data surrounding the current state of the system) to make or build smart and rational autonomous systems such as self-driving cars, humanoid and/or autonomous robots, and state-full software artifacts and agents etc.
- This section begins to concentrate on value significance evaluation of a predefined order SCs by several exemplary embodiments of the preferred methods to evaluate the value of an SC of the predetermined order, within a same order set of SCs of the composition, for the desired measure of significance.
- a“value significance measure” various measures of value significances of SCs in a body of knowledge or a composition
- these various measures (usually have intrinsic significances) are grouped in different types and number to distinguish the variety and functionalities of these measures.
- the first type of a“value significance measure” is defined as a function of“Frequency of Occurrences” of is called here and can be given by:
- f 1 (x) might be a liner function (e.g. ax+b), a power/polynomial of x function (e.g. x 3 or x + x 0.53 + x s ), a logarithmic function (e.g. 1/log2(x)), or 1/x function, etc.
- IOP Independent Occurrence Probability
- the independent occurrence probability may conveniently, assuming a single occurrence of an OS k in a partition
- OS l be given by:
- f 2 in Eq. 9 is a predefined function. For instance a (i.e. the number
- c is the co-occurrences of and and f 3 is a predetermined function.
- f 3 is a predetermined function.
- This measure (Eq. 13) once combined with other measures can yet provide other measures. For instance when it is being divided by the of Eq. 7, (e.g. being divided by ), the resultant measure can indicates the diversity of
- this particular combined measure usually gives a high value to the generic words (since generic words can occur with many other words). Once the generic words excluded from the list of SCs of the order k then this measures can quickly identifies the main subject matter of a composition so that it can be used to label a composition or for classification, categorization, clustering, etc.
- index“i” refers to the row number and the index“j” refers to the column number therefore the matrices with only the subscript of“i” usually are the column vectors and the matrices with only the subscript of“j” usually are row vectors.
- the order constituent part that can be use to separate one or more of the these SCs for variety of applications such as labeling, categorization, clustering, building maps, conceptual maps, state component maps, or finding other significant parts or partitions of the composition or the BOK.
- labeling categorization
- clustering building maps
- conceptual maps conceptual maps
- state component maps finding other significant parts or partitions of the composition or the BOK.
- the thereafter can be utilized for scoring, ranking, filtering, and/or be used by other functions and applications based on
- This section look into another important attributes of the state components of a composition that is instrumental and desirable in investigating the composition of state components.
- The“association strength measures” play important role/s in many of the proposed applications and also in calculating and evaluating the different types of“value significance evaluation” of SCs of the compositions.
- the values of an“association strength measure” can be shown as entries of a matrix called herein the“Association Strength Matrix .
- the v and are the values of one of the“value significance measures” of type x and type y of the and respectively, wherein the occurrence of SC k is happening in the partitions that are SCs of order /.
- SC k is happening in the partitions that are SCs of order /.
- the and/or the are from the same type of“value significance measure” and usually are calculated from the participation data of the SC k in the SCs of order /, i.e. the PMs, but generally they can be of different types and possibly calculated from PMs of different bodies of data.
- FIG 2 shows one definition for association of two or more SCs of a composition to each other and shows how to evaluate the strength of the association between each two SCs of composition.
- the“association strength” of each two SCs has been defined as a function of their co-occurrence in the composition or the partitions of the composition, and the value significances of each one of them.
- FIG 2 moreover shows the concept and rational of this definition for association strength according to this disclosure.
- the larger and thicker elliptical shapes are indicative of the value significances, e.g. probability of occurrences, of and in the composition that were driven from the data of PM kl and wherein the small circles inside the area is representing the SC l s of the composition.
- the overlap area shows the common state components of order /, SC l , between the and in which they have co-occurred, i.e. those partitions of the composition that includes both and .
- the co-occurrence e.g. probability of occurrences
- association strength defined by Eq. 16 is not usually symmetric and generally asm . Therefore, one important aspect of the Eq. 16 to be pointed out here is that associations of SC s of the compositions
- an asymmetric“association strength measure” is more rational and better reflects the actual relationship between the SCs of the composition.
- association strength defined by Eq. 16 or more particularly by Eq. 20-1 or 20-2 are not symmetric and generally
- One important aspect of the Eq. 20 which is pointed out is that associations of SCs of
- compositions that have co-occurred in the partitions are not necessarily symmetric and in fact it is argued that asymmetric association strength is more rational and better reflects the actual relationships of SCs of the composition.
- Eq. 20-1 basically says that if a less popular SC co-occurred with a highly popular SC then the association of less poplar SC to the highly popular SC is much stronger than the association of a highly popular SC having the same co-occurrences with the less popular SC. That make sense, since the popular SCs obviously have many associations and are less strongly bounded to anyone of them so by observing a highly popular SC one cannot gain much upfront information about the occurrence of less popular SCs. However observing occurrence of a less popular SC having strong association to a popular SC can tip the information about the occurrence of the popular SC in the same partition, e.g. a sentence, of the composition.
- association strength measures e.g. Eq. 20-1
- finding the real associates of a word e.g. a concept or an entity
- Knowing the associates of words e.g. finding out the associated entities to a particular entity of interest, has many applications in the knowledge discovery and information retrieval. In particular, one application is to quickly get a glance at the context of that concept or entity or the whole composition under investigation.
- Fig. 3 shows a graph or a network of SCs of the composition whose adjacency matrix is the Association Strength Matrix (ASM).
- ASM Association Strength Matrix
- the graph corresponding to the ASM can be shown as a directed and asymmetric graph or network of SCs. Therefore having the ASM one can represent the information of the ASM graphically.
- the graph can transform the information of the graph into an ASM type matrix and use the method and algorithm of this application to evaluate various value significance measures for the nodes of the graph or network.
- Fig. 3 further demonstrate that how any composition of state components can be transformed (using the disclosed methods and algorithms) to a graph or network similar to the one shown in Fig. 3 showing the strength of the bounding between the nodes of the graph.
- association strength concept one can also quickly find out about the context of the compositions or visualize the context by making the corresponding graphs of associations as shown in Fig. 3. Furthermore, the association strengths become instrumental for identifying the real associates of any SC within the composition.
- the composition is large or consist of very many documents one can identify the real associations of any state component of the corresponding universe.
- Such a real association is useful when one wants to research about a subject so that she/he can be guided through the associations to gain more prospects and knowledge about a subject matter very efficiently. Therefore a user or a client can be efficiently guided in their research trajectory to gain substantial knowledge as fast as possible. For instance a search engine or a knowledge discovery system can provide its clients with the most relevant information once it has identified the real associations of the client's query, thereby increasing the relevancy of search results very considerably.
- a service provider providing knowledge discovery assistance to its clients can look into the subjects having high associations strength with the subject matter of the client’s interest, to give guidance as what other concepts, entities, objects etc. should she/he look into to have deeper understanding of a subject of interest or to collect further compositions and documents to extend the body of knowledge related to one or more subject matters of her/his/it' s interest.
- Figures 4, 5 shows a block diagram of one process flow to obtain such data objects to be used for the aforementioned applications such as building state component maps (SCMs)
- SCMs building state component maps
- Fig 6a, 6b, 6c shows spectral representation of association strengths of state components.
- association strength of an SC with other SCs can be represented with a row or column vector and further it can be depicted or regarded as a spectral signature corresponded to each SC.
- Each SC of the body of knowledge therefore will have a spectrum of association value with other SCs of the body of data and depends on the choice of type of the“association strength measure” can have a different depiction.
- the asm vector can also be regarded as relative value significance of a SC in relation to another SC as shown in FIG 7.
- Figs 8, 9 and 10 further shows further applications of such data objects (e.g. VSMs, ASMs, and other data objects of this disclosure) in investigation of bodies of data or bodies of knowledge.
- Eq. 20-5 is a good and sound estimation of the conditional occurrence probability. Further we discovered that for most practical purposes, and based on own experiments especially in investigation of large corpuses, phrase detection, speech recognition, and image investigation we observe that for most of or of the body of knowledge, so that Eq. 20-5 is not in violation of Bayes Theorem.
- Eq 20-5 can also be calculated using frequency of occurrences:
- E is the expected value
- n is the average length of state components of order l,_SC l , of the body of knowledge.
- Eqs. 20-4, 20-5 and/or 20-6 can readily be used for effective knowledge retrieval or question answering, state navigation, content generation, classification, and many other useful applications. However all these applications have similar nature and can be modeled into a state navigator machines/systems.
- Each row (or column) of these association strength matrices or COP matrix can be viewed as an spectrum of association for each state component from which one can be used to extract the knowledge about the relevancy and types of the relevancies of the state components of the same or the higher order.
- an input to the system either as one or more components of the state of the system, or a query in the form of, for instance, a natural language question, we can treat the input as a list of one or more state components of order k, i.e. the , of the body of knowledge.
- ASM and COP therefore could be
- conditional probability of occurrences of state components can be used to evaluate the information content of the partitions of the body of knowledge and consequently estimate the total information content of a BOK.
- conditional probability can be readily used to estimate the probability of components of the next state of the system of knowledge (e.g. an autonomous moving robots) given its current state.
- the informational content of the partitions of the body of data can be very insightful and instrumental for extracting the usable knowledge within a body of data or select the stats of the system which can give the highest insight about the working behavior of a system such as a self-driving car or an autonomous robot or a decision support system artifact.
- conditional entropyOS k is calculated as the following:
- COPM is , Stands for element-wise matrix multiplication, and sumQ is sum of
- the average information content of a sentence (e.g. an average5 ) can be calculated, using computer programs instructions executed by one or more data processing or calculating devices, as the followings:
- H(OS 1 ) is the entropy of independent occurrences of an SC k , calculated from
- the Eq. 20-9 gives the average information (or entropy) of a sentence of the textual body from which, therefore, the higher bound of information content of the body of knowledge or data can be estimated.
- information of an individual sentence can be calculated more precisely from COPM and the information of its constituent SC 1 .
- the information content of any state component of any particular order of the body of knowledge can be estimated or calculated.
- conditional occurrence probabilities can have dependencies on the aspect of association of interest, i.e. the type of association strength measures.
- the ASM of interest further types of association strength measures are also given in supplementary section of this disclosure
- conditional probability of occurrences of state components of certain order knowing the occurrence of an state component of the same order in an SC of higher order, from the concept and definitions and one type of association measures (e.g. from Eq. 20-1) we arrived at Eq. (20-4) and eq.(20-5)).
- this conditional probability of occurrence itself is one measure of association strength between SCs of the composition.
- another type of asm is introduced as the following:
- new state could be a composed sentence or a next navigation control signals for an autonomous moving vehicle or robot.
- the sequence of partitions of a body of knowledge/data can be used to extract some more important relationships between SCs of a system of knowledge.
- things happens one after another and time/sequence play an important role in shaping a system of knowledge. That is directly the results of observation of events which form our understanding of the world.
- Even a textual essay such as script of a talk, a journalistic article, a novel, a movie script, and/or a trajectory of moving objects (big or small) follow a path in transitioning from one state to another.
- the sequence of partitions of a body of data therefore can convey some significant information about the actual inner working of our universe.
- To account for this measure of significance we introduce, at least, a number of more data objects as the followings:
- SPM kl , DPM kl and IPM kl stand for“ Shifted Participation Matrix”,“Differential Participation Matrix” and“Interleaved Participation Matrix” respectively, and t is an integer which basically shifts the columns of the PM kl to the right or left(i.e. to the past or future depend on the sign of t.). In practice the shift could be circular if desired.
- the columns of the PM kl are corresponded to the state components of order / of the system, and the rows are corresponded to the state components of order k which, for example, can refer to quantized values or predefined numerical values of the sensory device arrays and all other such desirable state components such as the textual descriptions (either coded/encrypted or expressed in natural language words, phases, sentences, etc) of the scene from a visual detection and recognition units (e.g. camera/s and/or Lidar/s and/or Radars, and/or GPS data, and/or external sources of information or knowledge etc.).
- a visual detection and recognition units e.g. camera/s and/or Lidar/s and/or Radars, and/or GPS data, and/or external sources of information or knowledge etc.
- the entries of the PM kl are binary in which the value of 1 shows the presence of that particular component of the state (e.g. the actual value of acceleration signal or the actual value of the temperature inside the engine, or the actual values of steering torque, or the actual value of the speed either normalized or absolute values) or else the entry value is zero. Therefore the DPM entries is only nonzero when the components of the state (i+t) has changed relative to state components of sate(i) and it is zero otherwise.
- the new PM i.e. the DPM shows the component that has participated in changing the state and entries will be either +1 or -1, or will be zero if the state components remained the same.
- CASMs which are generally defined by Eq. 16, are instantiated by Eq. 19-1 and more specifically similarly to the ASM deified in Eq. 20-2) are given by:
- CRoss Occurrence Matrix of type 1 to type 5, respectively, that in their matrix form are given by:
- the CASM_ 1 is instrumental in identifying the anticipated changes in state components (e.g. SC k ) given the current state components. This measure therefor can be used to anticipate the likely changes in the components of the SC l , i.e. another state component of order /, given the current state component of order /. More importantly it can identifies the bonds or strengths between the existence of a certain state component and estimate, predict, or anticipate what will be the next state components .
- the term‘Causal’ therefore is appropriate because one can identifies that presence of which components will be followed by changes of other certain state components or changes in some state components have been preceded (or loosely speaking resulted from or caused) by presence of some other state components (i.e. the causal associates). Hence again this type of associations are also asymmetric.
- CASM_ 1 a“Causal value significance measure” of type one, CVSM_1, for state components of order k as the following:
- The“Causal Association Strength Measure” of type 2, CASM_2 kk (p, q ), is instrumental in identifying the state components (specially the lowest order state components) that change with each other which might be due to a common cause or multiple factors. It is noticed that factors here, as can be appreciated, are in fact the state components that their changes affects significant changes in state components of higher order. For instance, appearance of certain state components of order 1 (one) in a i th state components of order 2 (two) will coincides with changes in some of the state components of order 1 in the (i+q) th state component of order 2.
- our first order state component are the discretized or quantized numerical values of the parameters of interest (e.g. the voltage of acceleration control signal, the steering control signal, the GPS info, speedometer data, odometer data, textual data, or some encrypted strings, all output data of sensory device and the all the desired info from visual recognition unite such as the recognition of red light signals, the distance from an intersection, presence of pedestrian, 4G, 5G communication signals and symbols, etc.) and the state components of order 2 are vectors with binary values showing the presence or absence of that particular state components of order one in that instance of time. Consequently the PM 12 is, therefore, a matrix whose columns are corresponded to state components of order 2 and each row correspond to one of state components of order 1.
- the parameters of interest e.g. the voltage of acceleration control signal, the steering control signal, the GPS info, speedometer data, odometer data, textual data, or some encrypted strings, all output data of sensory device and the all the desired info from visual recognition unite such as the recognition of red light signals, the distance from an intersection
- DPM will be a matrix with entries of -1, 0, or 1.
- An entry of 1 in i th index/row of each column j of DPM 12 (q) shows the appearance of i th state components of order one, 1, in q columns after j th column of PM 12 , ie.
- a -1 entry in the ith location/index of j th column of DPM 12 (q) shows the disappearance of i th state components of order 1 in q column after j th columns of PM 12
- an entry of 0 in the i th location of j th columns of DPM 12 ( q) shows no change in the presence or absence of i th state component of order 1 in q column after j th columns of PM 12 .
- the resultant Causal COM from DPM can have entries with negative values, positive and zero values wherein each indicates different meaning.
- a highly negative causal co- occurrences, that is calculated from DPM, between two state components of order 1 is an indication of mutual exclusiveness ( whenever one component appears the other will disappear) whereas a highly positive causal co-occurrences shows highly dependent relationship between the two components, whereas a zero causal co-occurrence between two state components does not provide much information without further investigation ( it could be that they never change with each other or consequent to other or they do change independently, i.e. statistically independent from each other, which needs more closer look).
- the CASM_3 kk (p, q) is instrumental in identifying the causal association of changing state components in a way to anticipate changes of state components with each other regardless of the type of their association whether they are changing in mutually exclusive manner) in which observing a change in certain state components will almost ensure the disappearance of other certain components) or highly dependent manner (in which changes in certain state components will almost ensure changes in other certain state components).
- high value entries of CASM_ 3 kk (p, q ) indicate knowledge worthy relations between the corresponding state components. That is, a high casm_3 kk (p, q ) individual entry of CASM_3 kk (p, q) indicates that there are some interesting noteworthy relationships between SC k and SC k regardless of their type of relationship. Therefore this measure will quickly extract the knowledge about SC k of the system of knowledge which play a significant role in the behavior of the corresponding system (the system that has produced such data).
- the CASM_A kk (p, q) is instrumental in identifying the causal association of changing lower order state components in a way to anticipate changes in SC k given the context of both higher state components of and which is mostly similar in nature to CASM_1 kk (p, q).
- the measure not only give upfront knowledge and information about changing low state components in view of two or more higher state components but also can indicate the presence or the context of new state that the system will enter into. Again this measure alone or in conjunction with other measure/s can ensure certain level of sanity of navigation by confirming or anticipating the general context of the future state (e.g. state ).
- CASM_5 kk (p, q ) carry the knowledge and information about the contextually, i.e. the smooth-ness or proximity, or anticipation of future higher order states.
- association strength of low order state components calculated using“Interleaved Participation Matrix”, IPM can be interpreted as a measure of how appearance of a low state component (e.g. words or phrases) in a higher consecutive state components (e.g. next sentences) can steer, shift, or navigate the context of the subject matter of the text as it being composed.
- a low state component e.g. words or phrases
- a higher consecutive state components e.g. next sentences
- SCs an“induction property” for SCs, and shows that how certain words can influence or cause the following sentences being composed semantically.
- this measure of association is instrumental in, for instance, building a conversational system which can interact with another client (e.g. a human user or a conversant agent) to ensure the continuity and sanity of the conversation and preserving the context of conversation while also producing informative and knowledge worthy utterance.
- the term“Causal” here is used to indicate a probability of Causal relationships between state comments as opposed to a concrete factual causal relationships, as in reality there cannot be found one to one causal relationship between any two state complements.
- a cause and effect event cannot be conceived in a universe with only two lower order state components and there should be at least one more component in order to see an event taking place. Therefore the knowledge of at least one more state component is needed to infer a causal relationship between any two state components. This is especially more true in worthwhile non-trivial challenges of real life such as medical, engineering, economics, and generally well being of societies.
- the sanity and rationality can be extracted from the investigation of body of the data, as explained along this specifications, collected from the state transitions of the system in real world and from the behavior of an intelligent sane being in navigating such systems.
- An exemplary way of building such system of knowledge, corresponding to the state space or universe of an autonomous mobile system, according to this disclosure, is by collecting all possible and desired types of data (such as sensory data, environmental data, visual or equivalent data, system control data, commanding data, communication data, conversing data, user interface data, etc.) from some real situation by, for example, recoding all such data during a 100 hours of driving a car in various situations. For instance such data is recorded while driving and interacting with a vehicle in city traffic, highway traffic, urban traffic, downtown traffic, drop of, pick up, with and without human inputs both verbally or physically and the like.
- data such as sensory data, environmental data, visual or equivalent data, system control data, commanding data, communication data, conversing data, user interface data, etc.
- each event i.e. a state component of higher order, /
- state components of order k such as:
- One or more array of data or data files corresponding to one or more visual scene of an event can be gathered from Cameras, Lidars, Radars etc.
- One or more set of state components corresponding to the description of the visual scene of the even. For example a one or more list of encrypted or natural language textual data (e.g. an English language paragraph text) which describe the visual scene of the event.
- encrypted or natural language textual data e.g. an English language paragraph text
- One or more array of data corresponding to values of controlling signals at an event.
- One or more array of data corresponding to communicating devices such as 5G/6G wireless data, external data, municipalities data and the like that can be accessed during gathering the data.
- each value of a sensory signal corresponding to a sensor can be represented by a row in the matrix, and each event is represented by a column (see Fig 15-b).
- these data objects are used for navigating through states or for transitioning from one state to another state in such a way that the transition is consistent with a such transition that is expected from a an intelligent/cognition-able/conscious being such as a trained human being or any other similar being.
- association strengths and conditional probability of occurrences to estimate the most rational other SC k of and make a decision as how transit/move the system from into or navigate through its space.
- the system can further make sure to have made a right decision by evaluating or estimating the higher order state components (.e.g. ) to gain upfront information about the possible future events and states
- Kalman filtering can also be used to further solidify the decisions about the components of next state of the system.
- the system can proceed to navigate or transition into its next state (e.g. decide to accelerate, decelerate, steer left gradually, steer right gradually, or keep driving steadily, etc.)
- the current state vector i.e. the SC l
- the system can proceed to transit to the next state and keep continuing its trajectory through space-time, or its universe of body of knowledge.
- an electromagnetic wave/signal e.g. a laser light or a microwave signal
- some of its properties e.g. the permittivity
- the simulation is done by solving Maxwell' s equations for such environment and the data gathered accordingly along different steps of the propagation of the wave.
- Maxwell equation to simulate the wave proportion but rather we use the dataset gathered from the first run and calculate the data objects of interests (e.g. VSMs, and ASMs, CASMs, and COPs) and from one or more initial state data (e.g. the initial wave distribution at some point along the propagation axis and other data corresponding to the propagating environment properties at that points,) we were able to project the next wave distribution states very accurately and efficiently as the propagating environment properties varied along the propagation axis.
- This test confirms that from the data of the first run simulation our space navigation system become knowledgeable about the behaviors of wave propagation in the environments so that without consulting with governing equations (i.e. the Maxwell wave propagation equations) it become able to accurately predict, project and navigate the wave through the propagation environment.
- the state transitioning can also be calculated or estimated for a block of state components or any other higher state order state components, therefore higher state components can provide a context within which the prediction or estimation of the participation of lower order state components can be checked and re-evaluated again.
- state components with assigned order of 1 are the actual discretized and quantized values of all types of sensory data, control data, actuators, and natural language vocabulary that described the scene (outputted from visual investigation units) and other desired and conceivable forms of participating sate components etc.
- state components that assigned with order 2 is comprises of values corresponding to the values of the state components of order 1 (i.e. the presence or absence state components of order 1 which in a participation matrix forms a sparse columns of the corresponding PM 12 ) that are recoded and stored in time steps of 1 ms (or any other desired time steps), i.e.
- any one or more state components of order k there is usually a large number of state components of order / (l>k) that show strong association (consensus, novel, more informative, most probable etc.) and can be projected as the next higher order states. Therefore after each initial prediction more targeted and relevant knowledge is identified that can be used to refine the decision if desired.
- stimulatory environments with decision from a human decision maker or driver
- the body of knowledge can be enriched significantly.
- Such a stimulatory system can also be used to create novel scenarios and record the state components for this improbable scenarios in order to ensure that the real system have the knowledge to deal with as many possible scenarios as possible.
- Such systems can be used also for training a human operator such as in navigating an airplane or other mission critical machineries when human decision making for any reasons (e.g. legal requirements) is preferred.
- the resulting autonomous space or state navigation could also be used for training in any profession (Law, Medical, technical jobs, etc.) or similarly educational purposes such as in variety of schools and universities.
- system of present invention can extract the knowledge from the body of data/knowledge in order to have enough knowledge of the world (e.g. in an unsupervised manner) to deal with real world events and change their state rationally (not stochastically) in a predictable manner as the time evolves while having known the trace or the reasons for making such decisions to transition from an origin state to the destined state.
- those skilled in the art can store, process or represent the information of the data objects of the present application (e.g. list of state components of various order, participation matrix or matrices, association strength matrix or matrices, and various types of associational, relational, novel, and causal matrices, various value significance measures, co- occurrence matrix/matrices, and other data objects introduced herein) or other data objects as introduced and disclosed in this disclosure (e.g.
- the PMs, ASMs, SCMs or co-occurrences of the state components, COMs, etc. can be represented by a matrix, sparse matrix, table, database rows, NoSQL databases, JSON, dictionaries and the like which can be stored in various forms of data structures.
- each part, section, or any subset of the objects of the current disclosure such as a PM, ASM, SCM, CASM, RNVSM, NVSM, and the like or the state component lists and index, or knowledge database/s can be represented and/or stored in one or more data structures such as one or more dictionaries, one or more cell arrays, one or more row/columns of an SQL database, or by any implementation of NoSQL database/s of different technologies or methods etc., one or more filing systems, one or more lists or lists in lists, hash tables, tuples, string format, zip format, CSV files, sequences, sets, counters, JSON, or any combined form of one or more data structure, or any other convenient objects of any computer programming languages such as Python, C, Perl, Java., JavaScript etc.
- Such practical implementation strategies can be devised by various people in different ways.
- the processing units or data processing devices e.g. CPUs
- the processing units or data processing devices must be able to handle various collections of data. Therefore the computing or data processing units to implement the system have compound processing speed equivalent of one thousand million or larger than one thousand million instructions per second and a collective memory, or storage devices (e.g. RAM), that is able to store large enough chunks of data to enable the system to carry out the task and decrease the processing time significantly compared to a single generic personal computer available at the time of the present disclosure.”
- the computing or executing system includes or has processing device/s such as graphical processing units for visual computations that are for instance, capable of rendering, synthesizing, and demonstrating the content (e.g. audio or video or text) or graphs/maps of the present invention on a display (e.g.
- the methods, teachings and the application programs of the presents invention can be implement by shared resources such as virtualized machines and servers (e.g. VMware virtual machines, Amazon Elastic Beanstalk, e.g. Amazon EC2 and storages, e.g. Amazon S3, and the like etc.
- specialized processing and storage units e.g. Application Specific Integrated Circuits ASICs, field programmable gate arrays (FPGAs) and the like
- ASICs Application Specific Integrated Circuits
- FPGAs field programmable gate arrays
- the data communication network to implement the system and method of the present invention carries, transmit, receive, or transport data at the rate of 10 million bits or larger than 10 million bits per second;”
- “storage device,“storage”,“memory”, and“computer-readable storage medium/media” refers to all types of no-transitory computer readable media such as magnetic cassettes, flash memories cards, digital video discs, random access memories (RAMSs), Bernoulli cartridges, optical memories, read only memories (ROMs), Solid state discs, Sild State derives (SSD/s) and the like, with the sole exception being a transitory propagating signal.”
- Figs. 18 to 18-4 Similar to other type of bodies of knowledge or data and the investigation methods presented here, there are shown in Figs. 18 to 18-4, one or more unit/s that can use a premade body of data (a collections of very many images or video frames or make a body of knowledge in real time) to make sense and learn about the real world environments and the knowledge contained in the visual scenes to learn about valuable state components of the universe and their relationships by the same methods that were described in above sections.
- a premade body of data a collections of very many images or video frames or make a body of knowledge in real time
- the visual investigator can recognize the boundaries or edges of objects/areas in a visual scene, separate the objects, and make a new PM from the detected edges for further and more detailed investigation of such detected visual objects by using the methods of current disclosure with assigning value significance to“Visual State Components” (VSCs) and from computed associations of state components, and decide about the objects of high values in the scene and their relationships with each other.
- VSCs Value significance to“Visual State Components”
- the standard representation of visual objects are corresponding data objects (e.g. one or more PMs) that can be shown and stored or transport by standard participation matrices.
- PMs for instance are the ones that have a predefined number of VSCs of certain order.
- an standard PM can be the participation matrix PM 12 in which the row are corresponded to standard pixels, e.g. 2 24 true color SVGA or 2 8 or VGA etc., or a predefined subset of standard pixels.
- higher order Visual SCs can also be standardized and used for representing all visual objects with PMs of standard size for at least one of the dimensions of the PM.
- the system of image processing is basically the system of Fig 16, wherein, as shown in Figs 18, 18-1, 18-2, 18-3, and 18-4, exemplary illustrations are given as how to apply the methods of this disclosure to process image data and gain the knowledge about such bodies of knowledge, i.e. in this instance a collection of images.
- the system of image processing can detect, recognize, and classify related or similar images, through calculating various Association and Significance values of State components of visual nature and order.
- a SCs of order k is in fact composed of 2 2(k-2) (for k >2) pixels.
- VSCs in building“Visual State Components”, VSCs, of an image all the desired combinations of VSCs of different orders can be identified and kept for analyzing the image. For instance for a given VSCs of order /, the VSCs of order k within that VSC can be all the combinations of VSCs of order k. As an example, if we assign an order of 3 to every 3 pixels strip (i.e. aligned horizontally like an strip) then we can have two VSC of two pixels (e.g VSC assigned with order 2) and similarly if we assign an order of 3 to every 4 pixels strip then we would have 3 VSC of two pixels and so on.
- VSCs of an image in multiple combinations (e.g by sliding the VSCs in one or both directions in the image) of VSCs that can make up or reconstruct the image.
- VSCs For higher order VSCs of square or rectangular shape the possible combinations of pixels and the resulting possible lower order VSCs increases and consequently the resulting PMs become much larger and so the demand for storage and the processing power also increases.
- lower order VSCs e.g. VSCs of order 1 to 3
- VSCs of order 1 to 3 can be standardized and all other possible higher order VSCs can be expressed by its lower order constituents VSCs.
- higher order VSCs of the image are the partitions of the image and limited variations of partitioning are considered rather than all possible variations whereas each partition then can be expressed by its constituents lower order standardized VSCs.
- lists of VSCs of particular order defined for visual objects can be a set (all identical SCs represented with one of such) or be listed as they appear in the picture.
- Setting the ordered state components of the picture will make the PMs less data intensive resulting faster processing and shortening the image processing task thereof. Furthermore sometimes said setting can also enhance the functionality of the process and lessen the clutters. For instance, if the desired function of the process is to categorize the visual objects, setting the VSCs may help to reduce unnecessary noise beside the data processing effect.
- index of that SCs in a PM also bears the geometrical information of that SCs (partitions of the picture) in the picture.
- the indices of the corresponding matrix are in fact an indication of geometrical shape of the objects in the scene as the indices i and j can be interpreted as the coordinates of the VSCs of an image in a two dimensional plan. Therefore when a visual SC of order k is signified as important (according to one or more significance aspects, e.g. novelty) then several of such identified objects show similar behavior and significance values and therefore can be grouped together and from the coordinates (ie. the indices of significant SCs) and he boundaries of the such significant objects in the scene can be recognized and detected as shown in FIG 18-1-18-4.
- the index of the state components bears a very important information about a picture and can be used geometrically to characterize a picture.
- the ratio of the j index of significant VSCs of order 3 of the picture can be used as further information to characterize the picture.
- New data objects and Matrix/es can be constructed to convey the information of some of the selected VSCs of certain order of the image frame/picture respect to each other.
- geometrical information and/or their ratio can be normalized so that they can be used for comparing to other processing needs (identifying a picture in a standard way from a group of other pictures).
- the data objects of the present invention can be adequately described as being a representation of points in a Hilbert space and linear transformations of the data objects does not have drastic effect on the quality and continuity of the investigation results.
- Most other transformation such as rotating an image, i.e. rotating the data of its corresponding participation matrix, or other mathematical operations on the data objects
- Most other transformation also would not cause a discontinuity type of effect on the behavior of the result of desired data, e.g the result of a novelty detection or finding significant partitions/segments or edge detection etc, of an image.
- the disclosed image processing method is much more robust and process efficient than the image processing with neural networks, or deep learning, convolutions neural nets, and classical image processing methods.
- the result of investigation of visual compositions can be used to build more efficient and compact neural networks than building a heuristically large neural network.
- the data objects that are generated after investigations of a body knowledge, composed of a number of images can be used to initialize the neural networks for further training. Since the data of the investigation results (e.g. ASMs, VSMs, COPs, RASMs and other data objects of this disclosure) like) are obtained from existing and real images (or in general exhibiting state components rather than randomly possibly existing State components) a deep learning network built and initialized (by using the data of the presented investigation method of compositions of state components) is more likely to converge, and converge faster.
- the process is efficient in doing intelligent actions and decision making based on a received or input image/picture.
- Another advantage of using the present invention as a method of image processing in application ranging from computer vision, navigation, categorization, content generation, gaming and many more, is that the method/s is less sensitive to the orientation and angle and almost invariant since many data objects are built during the investigation that are assigned to segments of deferent sizes of the image. Accordingly by using one or more of these data objects or a combination of different ASM/VSM measures and the information that are extracted from the images during the investigation process, one can assign a distinguishable signature to an input images.
- the visual investigator first identifies the areas of the image that have some significances (e.g. VSSs that poses high novelty value significance or any other measure of significances) and collect these areas as secondary higher order VSCs. For instance the boundaries of an objects such as a cat in an image is identified as a secondary VSC and so one. Referring to Fig. 18-2 here, then one or more secondary PMs are build. Then the investigator further investigate (using various VSMs and ASMs, and other derivative data objects corresponding to the image) these secondary high ordered visual objects and try to find more valuable areas in that secondary VSCs, and yet recognize more objects in that VSC and repeat the process until no significant areas is discovered.
- VSSs that poses high novelty value significance or any other measure of significances
- these sub areas are represented by standard PMs and is labeled either with human input or automatically labeled.
- Automatic labeling can be done with assigning a unique string of character to each of the final detected visual objects.
- a standard representation of a visual object is obtained and indexed. Accordingly the visual investigator can acquire the knowledge about very many objects of real world and index and calcifies them in standard forms of data objects (e.g. sets of standard VSCs of different order and their respective participation matrices of various order). The method is illustrated and described in Figs in 18-3 and 18-4.
- autonomous robots for robot visions, autonomous robots, intelligent expert (e.g. medical assistant robots), autonomous or semi- autonomous transportation robots (e.g. self-driving car, truck, drone, self-flying objects, etc.).
- intelligent expert e.g. medical assistant robots
- autonomous or semi- autonomous transportation robots e.g. self-driving car, truck, drone, self-flying objects, etc.
- a system or machine that comprises the image processing/investigation of the present disclosure can issue further instructions or signals to be used by other systems or parts (e.g. another machine, software, robot, intelligent being etc).
- Such systems/machines can therefore achieve a cognition and understanding of their surroundings and environment.
- using the present disclosure s method of investigation of compositions, such systems and machines are capable of conversing and exchanging data and knowledge not only with other machines but also with human by conversing with human clients through human consumable languages or content such as voice or machine generated multimedia content.
- One particular use of the methods and algorithm of this disclosure is to rank the images based on relational value significances using association strengths values of State components of different order (see supplementary section of this disclosure).
- An interesting system is for image recognition when ranking an input image as how that could be related to an state components. For example how an image is close or contain certain object or living thing etc. or, for instance, whether there is a tree in the image.
- the system of FIG 10 comprising data processing or graphical processing units have the details of a tree picture along with partition as number of sets of state components of predefined order as been illustrated in FIG 18-1.
- a data processing apparatus comprising one or more computing or data processing devices, and to evaluate or score or rank the relevancy of an input image/picture to a target or desired image/picture, category, concept, function, signal, or instructing a machine or order a machine to perform a desired task or operations. For example how closely an input image or picture is related to certain entity/ies, like a cat, a tree, a house, a car, a passenger, a movable objects (as the target State component), or when there are very number of images then use the method for classification and categorization of images.
- the image/pictures can be preprocessed by known digital signal processing to do for example, rotate the input picture once or more with certain angle, change the orientation, resize the image/picture to a predefined pixel size, or a desired height and width, or predefined dimension (e.g. every picture transformed or re scaled, or resizes to 320 * 320 pixels or to a 1000 by 1000 pixels, or one Mega pixels etc.)
- the range of possible combinations (R, G, B), with or without the pixel depth data can be changed or reduced.
- the image/picture can be transformed to gray scale only, or range of pixel color be reduced to a desired number of colors, e,g. from 256 ⁇ 256 ⁇ 256 number of colors be reduced to 16 ⁇ 16 ⁇ 16 number of colors or the like.
- VGM Visual Geometrical Matrix
- pairs (x p , y p ) and (x q , y q ) are the coordinates of the significant points (i.e. point/area of p and point/area of q) or area p and q of the image, respectively, which themselves are functions of the indices of their respective VSC.
- the standard characteristic matrix or the VGM as we called it is generally sparse and only have nonzero values for really important and significant point/areas or VSCs of the image (significant according to one or more significance measure as described before.) It is also evident that the Eq. 38 II-III- V-1 is just one way of defining the standard characteristic matrix or the VGM.
- a computer vision system is built using the one or more of the investigation methods of this disclosure or using the data objects of the investigator to interpret and track the novelty to their corresponding state components (e.g. a cat is moving near a tree) in order to build a computer vision system to be used in systems requiring vision cognitions (e.g. using in humanoid Robots and/or self-deriving car/robots or drowns security systems etc.)
- the data volume of a picture frame or an image file is way larger than the data of an average text file. Accordingly the processing time of an image frame especially if it is a high definition image, is considerably higher. Also consider that usually the image in some scenarios or embodiments is processed with a large number of other pictures of the same category or a diverse group or number of images. Therefore, in one exemplary method, application, and system of image processing with teachings of this disclosure we use graphic processing units, each having one or more processing cores, coupled with enough random access computer readable memories (e.g. RAMs) to accelerate the computing speed.
- RAMs random access computer readable memories
- One or more graphic processing units are programed to receive an image frame, for instance from a video port, process the image, encoded image data to partition the image and extract the constituent state components of different orders, build the participation matrix/es, build one or more“association strength matrix” (ASM) between state components of the said image.
- ASM “association strength matrix”
- the ASM could be calculated for state components of the same order or different order, each order corresponds to partition or a segments of various size of the image (as described before). Further building data structures corresponding to value significance of the portions of at least one order. Further calculate other data objects of various type such as RASM, RNASM, VSMs, NVSMs, and any other desired data objects expressed by Eq. 1-65 to investigate the image or group of images as outlined in FIG 10 for example.
- processing units further execute the instructions by the processing units to do at least one of the exemplary applications disclosed in this disclosure (such as clustering a large number of images into one or more categories, novelty detection, summarization, recognition, tagging, transforming to text, reconstruction of an image with certain desired features, construction of other images, new image creation etc.) or further process the image to do other desirable functions based on the data of the investigation results.
- the processing units further, or when coupled with other processing devices, can control other machines, artificial limbs, robots or decide on further actions and/or executing other functions and processing.
- association strength measure As:
- This particular association strength measure can reveal a strong relationship from a less significant SC to the one who has co-occurred the most and is a useful measure to hunt for some types of novelty.
- This association strength measure usually is useful for discovering the real association of two important or significant SCs of the composition.
- This measure can be defined to hunt for mutual associations bonds such as word phrases as the following:
- This measure of association strength i.e. Eq. 40-1
- Another symmetric association strength measure is defined as: .
- This measure of association strength (i.e. Eq. 40-2) is also symmetric and gives a high value to those associations that are can give high value information about each other,
- association strength measures which are found to be instrumental in analyzing and investigation of a composition of state components.
- Eq. 16 it can be seen that there could be defined, synthesized and calculate numerous other association strength measures.
- association strength measures there could be defined, synthesized and calculate numerous other association strength measures.
- Eq. 16 can be further generalized as:
- c is a constant and indicates an element-wise multiplication of two vectors and wherein Eqs. 7, 10, 18-1, 19-1, were combined to derive the Eq. 42.
- association strength measures Also importantly from the one or more of the“association strength measures” one can go on and define a measure for evaluating the hidden association strength of SC of order k even further by:
- type x3“association strength measure” which is basically a N x N matrix.
- the Eq. 43 takes into account the transformative or hidden association of SCs of order k (e.g. words of a textual composition or BOK) from one asm measure and combines with the information of another or the same asm measure to gives another measure of association that is not very obvious or apparent from the start.
- This type of measure therefore takes into account the indirect or secondary associations into account and can reveal or being used to suggest new or hidden relationships between the SCs of the compositions and therefore can be very instrumental in knowledge discovery and research.
- Eq. 43 can, in fact, be interpreted as“cross-association strength” between state components in general with the same or different association strength measure in mind.
- association strength measure CROSS_ASM for short which is defined as:
- ASM is one of the desired types of the association matrix and“T” stands for matrix transposition operation and “x” indicates matrix multiplications.
- Eq. 44 is one particular case for the general concept of“cross-association strength measures” which is described, defined, represented, and calculated by Eq. 43. It is understood that CROSS _ASM (or any other objects of mathematical and data objects this disclosure) can further be processed or go through other mathematical operations when desired.
- any desired matrix of this disclosure can be, and very frequently is desirable, to become column normalized, or row normalized (i.e. the norm or the length of each column or row of the desired matrix is unity).
- the multiplications and/or products of the matrices sometime are element-wise and sometimes are inner products and sometimes are normalized inner products of the vectors of the corresponding Hilbert space.
- a very important, useful, and quick use of exemplary“association strength measures” of Eq. 17 -26 and“cross association strength measures” of Eq. 44 is to find the real associates of a word, e.g. a concept or an entity, from their pattern of usage in the partitions of textual compositions. Knowing the associates of words, e.g. finding out the associated entities to a particular entity of interest, finds many applications in the knowledge discovery and information retrieval. In particular, one application is to quickly get a glance at the context of that concept or entity or the whole composition under investigation. The choice and the evaluation method of the association strength measure is important for the desired application. Furthermore, these measures can be directly used as a database of semantically associated words or SCs in meaning or semantic.
- composition under investigation is the entire (or even a good part of) content of Wikipedia
- entity e.g. a word, concept, noun, etc.
- association strength measures As mentioned befor, from the “association strength measures” one can also obtain and derive various other “value significance measures” which poses more of intrinsic type of significances. For instance the (e.g. Eq. 20-26) was used to determine the association strength measures.
- Eq. 20-1 we can use Eq. 20-2 to find out which SC the given SC, say , is highly “associated to” (assume it was found out to be the ).
- Eqs. 43 and 44 which is an important tool for knowledge discovery. For instance this measure can be used to hunt for the subject matters that can in fact be highly related, but one cannot find their relations in the literature explicitly.
- association strength values are important for many applications.
- One or more of such applications is to cluster or to find hidden relationships between the partitions of the compositions.
- the asm i®J ⁇ of the lower order SCs can show the association strength of the higher order SCs of the composition thereby to use them for clustering, categorization, scoring, ranking and in general filtering and manipulating the higher order SCs.
- order k which is a MxN matrix and shows the degree that an SC of order / (e.g. the i l th sentence of the composition) is associated or is related to a particular SC of order k (e.g. to the j k th word of the composition) .
- SC but also is related to other constituent lower order SCs of the higher order SC.
- association strength between the SCs of order / e.g. an association strength measure between sentences of a textual composition
- This matrix is particularly useful to find or select the higher order SCs of the composition or the partitions (e.g. sentences or paragraphs, or documents), that are highly associated with each other. In some applications, though, it would be desirable, for instance, to find out the partitions that have the least amount of associations with any other partitions etc.
- one or more of these“related associations measures” can be used (either normalized or not) to define and/or synthesize new RASMs.
- Eqs. 45-48 make it easy to find the partitions of the compositions that have the highest relatedness or highest relative association with a keyword or the other way around etc. Therefore a computer implemented method utilizing these formulations can essentially filters out the most related parts or partitions of a composition in relation to a target keyword.
- One immediate application is for scoring the relatedness of group of documents to a subject matter or a keyword.
- Another immediate application of the computer implemented method, utilizing the concept of and the formulation, for instance, is to cluster and separate partitions of a BOK or a large corpus/s, etc into sets of partitions that are related to a particular subject matter.
- the relatedness is measured by one or more of the above measures and partitions that exhibited an association strength value greater (or sometimes smaller) than a predetermined threshold to a particular SC, can be grouped or clustered together. Further these data can be readily used to build a neural network type system (for learning, reasoning etc.) whose edge/connection weights can be obtained from the data of association strengths of the state components (e.g.
- association strength data structures usually in the form a matrix, therefore are instrumental to build such cognitive networks for variety of tasks in general and for building neural nets in particular.
- the training iteration and the resource needed to train a neural net is significantly reduced using the information of the association strengths (and various other data objects or data structures introduced in this disclosure) of the state components obtained by investigating a body of knowledge as taught through this disclosure.
- FIG 10-1 shows the procedure in which using the concept of“value significance” a number of head category are selected from those SCs exhibiting the highest value significances, and consequently using the“related association strength measure” concept it was possible to separate the very many different news feeds into different categories automatically with satisfactory accuracy.
- RVSM relative or“relational value significance measures”
- RVSM Relational value significance measure
- the RVSM can simply be the association strengths of to a target or the j k th
- Eq. 49 once executed, will assign values to OS l in which it amplifies the importance or significance values of the partitions (e.g. sentences) of the composition that contains the SCs (e.g. words) that have the highest association strength to the target (i.e.
- a target keyword thereby to provide an instrument, i.e. a filtering function, for scoring and consequently selecting one or more highly related partitions to an .
- Eq. 49 can also be written in a matrix form wherein the is a M by N matrix indicating the relative
- first type relational value significance measure e.g. can be shown by RVSM_1 notation.
- the RVSM_ 1 therefore, following the Eqs. 27 and 31, can be given in the matrix form as:
- RVSM_ 2 notation a second type relative value significance measure (e.g. can be shown by RVSM_ 2 notation).
- the value of association to other and column indicates the value of being association with by others. Therefore the is indicative of a degree that an SC of order /, , (e.g. sentences) containing the SCs of order k, OS k (e.g. the words) that are used to explain or express or provide information regarding the target (i.e. containing the words that are highly associated with the target SC). Whereas the is indicative of a degree that an (e.g sentences) containing the OS k (e.g. the words)
- the target SC for which the target is used or participated to explain or express or provide information about them (i.e. containing the words that the target SC is highly associated with).
- these measures can be instrumental to, for example, representing a body of knowledge with the highest relational value significance or to summarize a composition. To do so one can simply select one or more partition of the BOK that scored the highest for these measures in order to present it as summary of a composition.
- the retrieved documents or the parts thereof should be the most relevant document and partition to a target SC which could be a keyword or set of keywords or even a composition itself.
- a target SC which could be a keyword or set of keywords or even a composition itself.
- value significance measures can readily be applied using the method of this discloser to retrieve and present the most relevant part (e.g. a word, a sentence, a paragraph, a chapter, a document) to the sought after subject matter or in response to a query.
- NVSM novelty value significance measures
- compositions yet other value significance measures are introduced and explored herein.
- this aspect of investigation in some instances it would become desirable to have found the words or the partitions of a composition expressing novel information about one or more subject matter/s.
- an instrument or a function to measure a novelty value of a subject matter e.g. an SC of the composition
- a novelty measure for the partitions it would become practical to spot the novel information and/or the partitions of the composition carrying novel information in the context of that compositions or a set of compositions or generally a body of knowledge ( BOK) as we defined before.
- NVSM novelty value significance measures
- the first step is to define what constitute a novelty in the context of a BOK and identify different aspects that there is into a novelty investigation.
- Novelty is an attribute that is related to newness, surprising factors, entropy, not being well known, not seen before, and unpredictability.
- this attributes depends very much on the context and in relations to other state components of the compositions. For instance something which is new in one domain or context might be an obvious thing in another domain. Or something that is new now, it might become vey well known fact after sometimes.
- novelty of the news is very much related to the time of the news being broken and how many other news agencies have published the same news story. Therefore the novelty should be measured in relation to the context, time, and other partitions of the compositions.
- we look for novelty or novelties in the given composition for investigation and since we can treat time and/or a time stamp as an SC our method of investigation, therefore, would also work for time-related compositions such as news, as well.
- a valuable novelty occurrence is relational (i.e. more than one SC is participated where the novelty occurs) which should be investigated in the context of a composition.
- a body of knowledge BOK
- One of the situations is a novel relationship between two or more SCs in which case there could yet be envisioned at least two notable and important situations.
- a type of“relational novelty value significance measure” can be assigned to spot a novel or less known relationship between two important SCs.
- the relational novel value should be high because the two significant SCs are less seen with each other in a part or partitions of a composition or a BOK. Therefore the desired“relational novel significance measure” should be proportional to the value significances of each of the SCs and be inversely proportional to their“association strength bond”.
- Another situation of novel relationship between two or more SCs is a type of novelty between two SCs in which the novelty reveals less known information about one important SC of the interest (e.g. a target keyword, a high value significance subject of a BOK, etc.), regardless the significance of the other SCs.
- the intrinsic value of the target SC e.g. an intrinsic vsm
- the less known associations can be a guide to find the novel part or partitions or statement of a relationship between a significant SC with other SCs of the composition.
- this type of novelty value should be proportional to the value significance of the second SC, e.g. a target SC, and be inversely proportional to the value significance of the less significant SC and also be inversely proportional to their co- occurrences so that:
- significance and relational novelty value should be inversely proportional to the significances, i.e. VSMs, of each of the SCs and also proportional to their co- occurrences so that:
- This measure can be used to spot a highly novel relationship between two less known SCs but with even less credibility than .
- This measure can be used to spot the noise like partitions that might be irrelevant to the context of the BOK but might be essential to be looked at such as crime investigation or financial analysis, fraud detections and the like.
- This measure also can be used to filter out the irrelevant or noisy part of the composition, or be used in data compression, image compression and the like.
- a measure of relational novelty value can be defined based on their association strengths to each other as:
- the co-occurrence is one of the measures and indications of the associations between a pair of SC then the can further be generalized as a function of individual values significances of the SCs and their association
- g 2 is a predefined or predetermined function.
- pair-wise value significances can be used in combination and perhaps with various weight to achieve the same filtering effect for a set of SCs. For instance
- a 1 , a 2 , and a 3 are predetermined weighting functions such as or
- x 1 , x 2 and x 3 are indications of the type of the rnvsm (e.g. Eq. 39-45) and“ ” is the indication of one or more combination of the first SC to the particular target SC.
- Eq. 47 in just one of the notable situations of novelty occurrence and in another instance it might become more useful to multiply the pair-wise to each other.
- This measure of novelty gives a high value to the relational novelty of those pairs that exhibit strong hidden association correlation but they are not explicitly strongly bonded. This measure is particularly useful for detecting hidden relationships between two SCs of interest, i.e. and and can be used to spot the cases worthy of further research and investigation (e.g.
- association novelty value significance measure ANVSM
- y1 and y2 indicates the types and numbers of the“value significance measure” used in this formula.
- the proportionality factor can be adjusted to account for normalization of the vectors when desired.
- Eq. 51 can be re written in matrix form in general terms which is more useful as:
- Eq. 51, 52 and 53 are generally the exemplary cases of the general form of:
- g 3 is predetermined or predefined function and y1, y2, x1... x4 etc refer to the selected type of the respective kind and type of the“value significance measure”.
- y1, y2, x1... x4 etc refer to the selected type of the respective kind and type of the“value significance measure”.
- NVSM intrinsic“novelty value significance measure”
- the first measure of novelty of course can be derived and defined based on the independent probability of occurrence so that:
- h is a predetermined function such as h 1 (x) be a liner function (e.g. ax+b), power of x (e.g. x 3 or x 0.3 ), logarithmic (e.g. alog2(x)), 1/x, etc wherein a or b might be SCalar constant or a vector.
- h 1 (x) be a liner function (e.g. ax+b), power of x (e.g. x 3 or x 0.3 ), logarithmic (e.g. alog2(x)), 1/x, etc wherein a or b might be SCalar constant or a vector.
- c might be a scalar or a constant vector. In another instance it might be defined as :
- b is a constant and c could be constant or a vector.
- c can be an auxiliary vector that when multiplies to other vectors it suppresses or dampen the value of particular SCs of the compositions such as the generic words in a textual composition.
- the novelty is observed in relation or combination with other SCs since novelty could occurs in a context and therefore in relation to other state components.
- the stand alone or the intrinsic“novelty value significance value” in this case is defined as sum of the novelty that an SC will have with a desired number of other SCs.
- NVSM type 2 can be defined as:
- pair-wise novelty measures are summed over the column (i.e. the j subscript).
- any combination of them can still serve as an intrinsic measure of novelty of the SCs of the composition as:
- h is predetermined function and y is the type and number of the particular used into building other types of
- the second measure of significance is defined in terms of the “cumulative association strength” of each SC. This measure can carry the important information about the usage pattern and co- occurrence patterns of an SC with others. So the second value significance measure for an is defined versus the
- the latter quantity or number shows the net amount of importance of and SC in terms of association strengths exchanges or forces.
- This quantity can be visualized by a three dimensional graph representing the quantity . A positive number
- conditional entropy is proposed and is applicable here to be used for evaluation of such important value measure. Therefore, we can use the defined conditional occurrence probabilities (COP) to define and calculate“Conditional Entropy Measures ( CEMs )” as another value significance measure.
- COP conditional occurrence probabilities
- H j stands for Shannon-defined type entropy that operates on j index only.
- Eq. 84 any other basis for logarithm can also be used and stands for first type“Conditional Entropy Measure” and is to distinguish the first type entropy
- H j stands for Shannon-defined type entropy that operates on j index only again, and wherein stands for the second
- the real information of the composition in terms of bits (wherein bit is a unit of information according to he Information Theory) which could be considered as yet another measure of value significance for the whole composition or the partitions therein. For instance, this measure can be used to evaluate the merits of a document among many other similar or any collection of documents.
- the information value of the SCs or the partitions (by addition the individual information of the its constituent SCs) is a very good and familiar measure of merit and therefore can be another good quantity as an indication of value significance.
- Conditional Entropy Measures i.e. CEM1, CEM2, DCEM are calculated according to Eq. 11, 12, and 13.
- Fig. 4a compares these different measures of significance for an exemplary textual input composition.
- the VSMs have been evaluated for a short text, actually a research paper, as an example to illustrate the normalized various measures of value significances disclosed in this invention.
- the SCs of the first order are the words and the second order SCs are the sentences of the text. These data have been calculated from the PM 12 of the exemplary text. This is only to demonstrate the calculation and implementation of the method and algorithm and an exemplary illustrating figure for representing the VSMx (x is 1, 2, 3, ..etc).
- the results for large bodies of knowledge and corpuses must be more well pronounced and having more meaningful interpretations.
- the resulting similar figures for different compositions can be substantially different from the depicted exemplary figures presented here.
- more figures and curves can be made which could be substantially different and/or show various other functions, values, and other desired parameters.
- the association matrix could be regarded as the adjacency matrix of any graphs such as social graphs or any network of anything.
- the graphs can be built representing the relations between the concepts and entities or any other desired set of SC s in a special area of science, market, industry or any“body of knowledge”.
- the method becomes instrumental at identifying the value significance of any entity or concept in that body of knowledge and consequently be employed for building an automatic ontology.
- the and other mathematical objects can be very instrumental in knowledge discovery and research trajectories prioritizations and ontology building by indicating not only the important concepts, entities, parts, or partitions of the body of knowledge but also by showing their most important associations.
- the VSM has many useful and important applications, for instance the words of a composition with high normalized VSM can be used as the automatic extraction of the keyword and relatedness for that composition. In this way a plurality of compositions and document can be automatically and much more accurately be indexed under the keywords in a database.
- search engines, webpage retrieval, and many more applications such as marketing, knowledge discovery, target advertisement, market analysis, market value analysis of economical enterprises and entities, market research related areas such as market share valuation of products, market volume of the products, credit checking, risk management and analysis, automatic content composing or generation, summarization, distillation, question answering, and many more.
- the parameters, vectors, and matrices of the present invention are transformation of the information hidden in the participation matrix which can be used for different applications with ease, convenience and efficiency to investigate various aspects of interests in the BOK such as extracting the most significant parts or partitions, finding the highly associated concepts or parts and partition, finding the novel part/s or partition/s of the BOK, finding the best piece of informative part of the composition, clustering and categorization of the partitions of the composition or the BOK, ranking and scoring partitions of a composition based on their relatedness to a subject matter (e.g. a query), excluding one or more partitions or SCs of the BOK or suppressing their role in the analysis, and numerous other application.
- a subject matter e.g. a query
- the mathematical objects and data arrays can be easily transformed to other forms, filtered out the desired part or segment of a matrix, amplify or suppress the role of one or more of the SCs of the composition and/or their values being altered numerically without needing to manipulate the input composition string or file.
- the matrices or vectors being normalized in order to make the comparisons more meaningful in the context of the BOK. Accordingly one or more of such mathematical objects and data arrays (vectors, matrices etc.) can and might be desired to become column or row normalized or further being multiplied by other matrices or vectors as a mask or filter etc.
- all these matrices can be regarded as an adjacency matrix for a corresponding graph wherein the matrix carry the data of the connectivity between the nodes or objects of the graph. Therefore, from these connectivity matrixes one can proceed to calculate a corresponding eigenvalue equation/s in order to estimate and calculate other types of desirable value significance measure or in general any type of value significance.
- These measures of value calculated from the corresponding eigenvalue equations of the matrices are generally indication of intrinsic significance values of the SCs.
- one or more of these matrices have been used to calculate the significance values of the SCs of the composition based on their centralities of the corresponding node in the graph that could be represented by that matrix.
- the centrality value can be, for instance, be the values of largest eigenvector of the eigenvalue.
- VSM values e.g vectors
- these vectors or filter can be designed in such a way to amplify the significances of proper sentences of compositions written in a particular natural language such as English.
- the objective can be to give significance to particular types of partitions of the composition having of particular feature/s, attribute/s, or form/s.
- These pre-assigned vectors are called“special cases conveyers” herein or“significance value conveyer vectors” as shown in FIG 6c, that can be used solely or in combinations with other VSM value vectors to obtain the desired functionality from the investigation.
- These conveyers are assigned and used based upon the goal of investigation.
- the special conveyers can be designed and altered for various stage of the process and can be used in different stages of calculations and processes.
- the participation matrix can, for instance, routinely being transformed to other types of objects or participation matrices by operating one or more vector or matrices on the PM. For example one can multiply the PM by a diagonal matrix (M by M) from the right side whose diagonal values are the reciprocal of the number of constituent SCs of order k in the partitions or the higher order SC of order / (i.e norml column normalization of a matrix).
- The“resulting PM” matrix will become a column normalized PM and values of the entries will become the weighted participation factor.
- the PM matrix can be multiplied from the left side by a diagonal matrix ( N by N) whose entries are a vector that will put a value on the SC of the order k so that their participation weight will be altered.
- the diagonal of the left matrix is one except for some particular words (such as the generic words of a natural language) for which the corresponding entries are suppressed (e.g. replaced with 0.1) then the role of those particular words (e.g. the generic words) in the computations will be suppressed as well, without having to manipulate the original string of the compositions in order to achieve the same goal of suppressing the role of generic words.
- auxiliary vectors i.e. filters
- filters can be built to dampen the significance of particular SCs of the composition by multiplying those vectors on the resulting vector objects such as one or more of the different types and number of the“value significance measures” vectors or matrices.
- the method/s can conveniently be used for compositions of different nature such as data file compositions, e.g. audio or video signals, DNA string investigation, textual strings and text files, corporate reports, corporate databases, etc.
- data file compositions e.g. audio or video signals
- DNA string investigation e.g. DNA string investigation
- textual strings and text files e.g. textual strings and text files
- corporate reports e.g. corporate databases
- the investigation method disclosed herein can be readily used to investigate image and video files, such as spotting a novelty in an image or picture or video, edge detection in an image, feature/s extraction, compression of image and video signals, and manipulating the image etc.
- the disclosed methods of the present invention can readily be applied in applications such as, artificial intelligence, neural network training and learning, network training, machine learning, computer conversation, approximate reasoning, as well as computer vision, robotic vision, object tracking etc.
- the disclosed frame work along with the algorithms and methods enables the people in various disciplines, such as artificial intelligence, robotics, information retrieval, search engines, knowledge discovery, genomics and computational genomics, signal and image processing, information and data processing, encryption and compression, business intelligence, decision support systems, financial analysis, market analysis, public relation analysis, and generally any field of science and technology to use the disclosed method/s of the investigation of the compositions of state components and the bodies of knowledge to arrive the desired form of information and knowledge desired with ease, efficiency, and accuracy.
- disciplines such as artificial intelligence, robotics, information retrieval, search engines, knowledge discovery, genomics and computational genomics, signal and image processing, information and data processing, encryption and compression, business intelligence, decision support systems, financial analysis, market analysis, public relation analysis, and generally any field of science and technology to use the disclosed method/s of the investigation of the compositions of state components and the bodies of knowledge to arrive the desired form of information and knowledge desired with ease, efficiency, and accuracy.
- those skilled in the art can store, process or represent the information of the data objects of the present application (e.g. list of state components of various order, list of subject matters, participation matrix/ex, association strength matrix/ex, and various types of associational, relational, novel, matrices, co-occurrence matrix, participation matrices, and other data objects introduced herein) or other data objects as introduced and disclosed (e.g. association value spectrums, state component map, state component index, list of authors, and the like and/or the functions and their values, association values, counts, co-occurrences of state components, vectors or matrix, list or otherwise, and the like etc.) of the present invention in/with different or equivalent data structures, data arrays or forms without any particular restriction.
- data objects of the present application e.g. list of state components of various order, list of subject matters, participation matrix/ex, association strength matrix/ex, and various types of associational, relational, novel, matrices, co-occurrence matrix, participation matrices, and other data objects introduced herein
- the PMs, ASMs, SCM or co-occurrences of the state components etc. can be represented by a matrix, sparse matrix, table, database rows, dictionaries and the like which can be stored in various forms of data structures.
- each layer of the a Pm, ASM, SCM, RNVSM, NVSM, and the like or the state component index, or knowledge database/s can be represented and/or stored in one or more data structures such as one or more dictionaries, one or more cell arrays, one or more row/columns of an SQL database, one or more filing systems, one or more lists or lists in lists, hash tables, tuples, string format, zip format, sequences, sets, counters, or any combined form of one or more data structure, or any other convenient objects of any computer programming languages such as Python, C, Perl, Java., JavaScript etc.
- Such practical implementation strategies can be devised by various people in different ways.
- the processing units or data processing devices e.g. CPUs
- the processing units or data processing devices must be able to handle various collections of data. Therefore the computing units to implement the system have compound processing speed equivalent of one thousand million or larger than one thousand million instructions per second and a collective memory, or storage devices (e.g. RAM), that is able to store large enough chunks of data to enable the system to carry out the task and decrease the processing time significantly compared to a single generic personal computer available at the time of the present disclosure.”
- the state navigation methods introduced here by building various data objects from one or more data set or body of knowledge can be used in various applications, mostly in making knowledgeable machines that can navigate through spaces both state space and physical spaces.
- the applications includes autonomous moving machines, such as vehicles, and robots, as well as machines with utterance ability by navigating through semantic space or knowledge space or their representative universes.
- the goal of the investigation is to produce a useful data, information, and knowledge from a given or accessed composition/s, according to at least one aspect of significance or the goal/s of the investigation.
- the result of the investigation can be represented in various forms and presentation style and various devices of modern information technology (private or public cloud computing, wired or wireless connections, etc.).
- the interaction between a client and an investigator, employing one or more of the disclosed algorithms, can be facilitated through various forms of data network accessibility to an investigator through various interfaces such as web interfaces, or data transferring facilities.
- the result of the investigation can be displayed or provided in various forms such as interactive page/device environment, graphs, reports, charts, summaries, maps, interactive navigation maps, email, image, video compositions, voice or vocal compositions, different nature composition such as transformation of a textual composition to visual or vice versa, encoded data, decoded data, data files, etc.
- a goal of investigation can be to finding out the SCs of the composition scoring significant enough novelty value in the context of the given BOK or an assembled BOK wherein the SCs of the composition can be words, phrases, sentences, paragraphs, lines, document or the like for the BOK under investigation.
- Another exemplary goal of investigation can be to get a summary of the credible statements from a BOK or to modify a part or partitions of a composition (e.g. a document, an image, a video clip etc.).
- another instance of investigation can be to obtain a map of relations between the most significant parts or partitions of the BOK.
- a patent attorney, inventor, or an examiner can use the disclosed method to plan his/her claim drafting by investigation the application disclosure and get the most valuable or novel part of the disclosure to draft the claims.
- the method can be used for examining the application in comparison to one or more collection of one or more patent application disclosures.
- an intelligent being e.g. a software bot/robot a humanoid, a machine, or an appliances
- a provider of such services e.g. conversing and doing tasks, or entertaining, or assisting in knowledge discover etc.
- FIG 1 it depicts one general flow process and the system that can provide one or more exemplary investigation's result, as services, utilizing the algorithms and the methods of the present invention.
- the required variables or the mathematical or data objects e.g. the matrices and the vectors values etc
- building the various filter one can design, synthesize, and compose an output according to her/his/it’ s need or goal of investigation or informational requirements and for an input composition. For example if one applications calls for getting the most credible and valuable partitions of an input compositions then she/he/it must chose (or select through an interface) the corresponding filter (i.e.
- the suitable XY_VSM/s and algorithm/s for which to obtain such a credible glance or summary of the composition.
- the user or the designer of such system and service can synthesize the suitable filter, using the tools, measures and methods of the present invention to provide the desired response, output or the service.
- the input composition is used to build or generate the one or more participation matrices while the state components of different orders are grouped, listed, and kept in the short term or more permanent storage media.
- the actual SCs or the partitions usually are used at the end of the processing and calculations of the desired quantity or quantities, when they are fetched again based on their corresponding value for one or more measures of the values introduced in previous sections. Accordingly after having the PM/s the system will calculate the desired mathematical objects such as COM, ASM/ s, the desired VSM/s, one or more RASM if needed for the desired service , one or more RVSM/ s if needed for the service, one or more of NVSM/ s, or RNVSM/s or ANVSM/s if desired and so on.
- These data objects are used to synthesize the required filter to provide the desired functionality once it operated on the PM.
- the output is further investigated for selection of suitable SCs of the composition for further processing or re-composing or presentation.
- the output can be presented in predetermined form/s or format, such as a file, displaying on a web-interface or an interactive web-interface, encoded data in a particular format for using by another system or software agent, sending by email, being displayed in a mobile device, projector and the like over a network, or sent to a client over the internet and the like.
- the desired mode of operation is to find out the novel partitions of the composition exhibiting enough novelty value while having enough significance then the corresponding filter will use the RNVSM of the Eq. 39 for finding, scoring and consequently selection of the suitable partitions for this requested service.
- composition data are transformed or transported into participation matrix/matrices then we only deal with numerical calculations that will determine the value of the members of the listed SCs and (based on their index in the list or based on their row or column number in the participation matrix) once the value for the corresponding measure was calculated then those SCs that exhibited the desirable value or range of values are selected by the selector or a composer that provide the output data or content, e.g. as service, according to predetermined formats for that service.
- association strength measure/s In references to FIG 2 now, it involves the conceptualization of the association strength measure/s. As exemplified several times along the disclosure the concept and values of“association strength measure/s” plays an important role in investigation of the composition of state components as well as providing the data that is valuable itself. That is, knowing the association strength of SCs to each other is important and can be used to build many other applications especially in artificial intelligence applications.
- FIG 2 it is shown one general form of conceptualizing and defining the association strength measures and consequently calculating the association strength values for those measures.
- the association strength of the SCs of order k that have co-occurred in one or more SCs of order / is given by a function of their number of co-occurrence and the value/s respective of one or more of the“value significance measure/s” (e.g independent probability of occurrence).
- the“value significance measure/s” e.g independent probability of occurrence
- any composition of state components can in principal be represented by a graph which in this preferred embodiment shown as an asymmetric graph.
- the exemplified graph is corresponded to one of the exemplary “association strength matrix”, i.e. an ASM, as representative of its adjacency matrix.
- the nodes represent the desired group of SCs and the edge or arrows show the link between the associated nodes and the values on the edges are representative of the association strength from one node to the connected one.
- This figure is to graphically exemplify and depicts that compositions of state components and a network of state components can basically be investigated and dealt with in the same manner according to the teachings of the present invention.
- FIG 4 there is shown again another embodiment for the process of calculating various value significance measures in more details.
- the data of the input composition is transformed to calculable quantities and data from which, employing the above methods and formulations, the desired value significance measures are calculated and/or are stored in the storage areas for further use or being used by other processes or programs or clients.
- FIG 5 therefore shows the block diagram of one basic exemplary embodiment in which it demonstrates a method of using the association strengths matrix ( ASM) to build an “State component Map ( OSM )” or a graph.
- ASM association strengths matrix
- OSM tate component Map
- the map is not only useful for graphical representation and navigation of an input body of knowledge but also can be used to evaluate the value significances of the SCs in the graph. Utilization of the ASM introduced in this application can result in better justified State component Map ( OSM) and the resultant calculated significance value of the SCs.
- OSM State component Map
- the association strength matrix could be regarded as the adjacency matrix of any graphs such as social graphs or any network of anything.
- the graphs can be built representing the relations between the concepts and entities or any other desired set of SCs in a special area of science, market, industry or any“body of knowledge”.
- the method becomes instrumental at identifying the value significance of any entity or concept in that body of knowledge and consequently be employed for building an automatic ontology.
- the and other mathematical objects can be very instrumental in knowledge discovery and research trajectories prioritizations and ontology building by indicating not only the important concepts, entities, parts, or partitions of the body of knowledge but also by showing their most important associations.
- values of different types of value significance measures can be shown as a vector in a multidimensional space.
- XY_VSM/s in general are matrices that might also carry the relational value significances but still any row or column (as shown in FIG 6 a) of them can be shown as discrete vectors in a multidimensional space. These discreet vectors can also be treated as discrete signals in which they can be further be used for investigation of the compositions.
- XY_VSM Some types of XY_VSM, that are intrinsic, are vectors (e.g. FIG 6b) for which they can readily be used to weigh other SCs or the partitions of the composition. Also shown in FIG 6c are some of the vectors that might be“special conveyer vectors” labeled with“significance conveyer vectors” in the FIG 6c and are usually predefined or predetermined that can be used for filtering out and/or dampening or amplifying and/or shaping/synthesizing the VSMs of one or more of the predetermined SCs of the composition. FIG 6c demonstrate that special conveyer vectors or VSM have basically the same characteristics as other XY-VSM except the values might have been set in advance.
- FIG 7 shows one way of demonstrating (e.g. schematically) how two exemplary value significance vectors can be extracted from an exemplary“association strength matrix” (asm) which in this instance are also shown to be used to evaluate the associations of SCs of order / (e.g. sentences) to particular SC of order k (e.g. a word or keyword or phrase).
- exemplary“association strength matrix” asm
- SCs of order / e.g. sentences
- SC of order k e.g. a word or keyword or phrase
- an SC of order / can be selected by the investigator based on its strength of association to one or more SCs of the order k.
- the calculation and the selection method of SCs of order / can find an important application in document retrieval, question answering, computer conversation, in which a suitable answer or output is being south from a knowledge repository (e.g. a given composition) in response to the input query or composition.
- a knowledge repository e.g. a given composition
- an input statement or a query is parsed to its constituent SCs of order k and from the association strength matrix (which might be constructed from and for said knowledge repository) then the mostly related partitions of the stored composition (i.e.
- the knowledge repository is retrieved in response of an input query which is a conversational statement or a question.
- the mostly related partition of the knowledge repository can be the partition (SC of order /) that has scored the highest average or cumulative association to the constituent SCs of the input query.
- the mostly related partition of the knowledge repository might have scored the highest, for example, after multiplication of the association strength vectors of the SCs of the input query in the association strength matrix that have been built from the knowledge repository.
- FIG 8 shows, in schematic, a block diagram of an exemplary system as well as the process of further clarification as how to use the“value significances” data of one or more SCs of particular order to evaluate and calculate the one or more“value significances” of SCs of another order using the one or more XY_VSM and one or more participations matrix.
- the XY in the FIG 8 is the indication, and can be replaced with the desired type and number combination, of the desired“value significance measure”. Therefore XY_VSM in FIG 8 can be replaced with any of the different types of the“value significance measures” (such as RVSM, NVSM, ARASM, RSVM, etc.).
- the data objects can be stored, if desired, for later use so that the pre- calculated data and objects are pre-made and can easily be retrieved for the corresponding compositions and the desired application.
- the pre-made stored data can be used to accelerate and speeding up the process of composition investigation in a system that provide such a service/s to one or more clients.
- FIG 9 shows an instance of clustering and ranking, and sorting of a number of webpages fetched from the internet for example, by crawling the internet. This is to demonstrate the process of indexing and consequently easily and efficiently finding the relevant information related to a keyword or a subject matter. This is the familiar but very important application and example of the present invention to be used in search engines. As seen after crawling a number of webpage or documents from the internet (or from any other repository in fact) the pages/documents/compositions are investigated so that the associations of the desired part or partitions of such collections are calculated to other desired SCs of the collection of the compositions.
- special SCs can be selected for which the association strength of pages are to be calculated.
- special SCs can be the content words such as nouns or named entities. Nevertheless there would be no limitation on the selection or choice of the target SC and they can basically be all possible types of words, or even sentences and higher orders partitions.
- SCs of high value significance can be identified so that the whole composition (i.e. the whole collection of the documents or pages) can be clustered or categorized into bodies of knowledge under one or more target subject matter or head categories (e.g. the high value SCs of lower order, such as words or phrases).
- the target SCs could usually be the keywords or phrases, or the words or any combinations of the characters, such as dates, special names, etc.
- the target SCs of such composition could be the extracted sentences, phrases, paragraphs, or even a whole document and the like.
- a service provider system such as a search engine, question answering or computer conversing, which comprises or having access to the system of FIG 9, receives a query from a user
- the system can simply parse the input query and extract all or some of the words of the input query (i.e. the SCs of order one ) then by having calculated the associations strength of one can easily calculate the association strength of each of the documents
- the vector e.g. the association strength of a words to each other obtained from the investigation of the crawled repository of webpages consisting one or more webpages/documents
- the common association vector uses the common association vector to identify the most related or associated documents, or sentences to the input query by multiplying the common association spectrum with the respective participation matrix (e.g. PM 15 for document retrieval and PM 12 for question answering or conversation as an example).
- the respective participation matrix e.g. PM 15 for document retrieval and PM 12 for question answering or conversation as an example.
- the engine can return for instance the document or the web-page that composed of the partitions of high novelty values, either intrinsic or relative, to the target SC/s. Therefore the engine can also filters out and present the documents or webpages that have most relevancy to the desired“significance aspect” based on the user preferences. So if novelty or credibility or information density of a document, in the context of a BOK, is important for the user then these services can readily be implemented in light of the teachings of the present invention.
- FIG 10 shows schematically a system of composition investigations that can provide numerous useful data and information to a client or user as a service.
- Such output or services in principal can be endless once combined in various modes for different application.
- FIG 10 a few of the exemplary and important and desirable outputs are illustrated.
- the FIG 10 illustrates a block diagram system composed of an investigator and/or analyzer and/or a transformer and/or a service provider that can receive or access a composition and provide a plurality of data or content as output.
- the investigator in fact implement at least one of the algorithms of calculating one of the measures in order to assign a value on the part or partitions of the compositions and based on the assigned value process one or more of the partitions or SCs of the particular order as an output in the form of a service or data.
- the output could be simply one or more tags or SC/s that the input composition can be characterized with, i.e. significant keywords of the composition.
- the significant keywords or labels are selected based on their values corresponding to at least one of the aspectual XY_VSM, i.e. one of the value significance measures.
- the output or outcome of the investigator of FIG 10, could be to provide the partitions of the input composition which have exhibited intrinsic value significances of above a predetermined threshold.
- Another output could be the novel parts or the SCs of the compositions that scored a predetermined level of a particular type of novelty value significance.
- the output could be the noisy part of a composition or a detected spam in a collection of compositions etc.
- FIG 10 Several other output or services of the system of FIG 10 are depicted in the FIG 10 itself which are, in light of the foregoing, self explanatory.
- FIG 10-1 shows another instance and application of the present invention in which the process, methods, algorithms and formulations used to investigate a number of news feeds and/or news contents automatically and present the result to a client.
- the news are being first categorized automatically through finding the significant head-categories and consequently clustering and bunching the news into or under such significant head-categories and then select one or more partitions of such cluster to represent the content of that clustered news to a reader.
- Head-categories can simply being identified, by evaluating at least one of the significance measures introduced in the present invention, from those SCs that have exhibited a predetermined level of significance.
- the predetermined level of significance can be set dynamically depends on the compositions of the input news.
- a navigable state component map/s can accurately being built and accompany the represented news.
- Various display method can be used to show the head-categories and their selected representative piece of news or part of the piece of the news so that make it easy to navigate and get the most important and valuable news content for the desired category.
- the categorization can be done in more than one steps wherein there could be a predetermined or automatic selection of major categories and then under each major category there could be one or more subcategories so that the news are highly relevant to the head category or the sub-categories or topics.
- the computing or executing system includes or has processing device/s such as graphical processing units for visual computations that are for instance, capable of rendering and demonstrating the graphs/maps of the present invention on a display (e.g.
- the methods, teachings and the application programs of the presents invention can be implement by shared resources such as virtualized machines and servers (e.g. VMware virtual machines, Amazon Elastic Beanstalk, e.g. Amazon EC2 and storages, e.g. Amazon S3, and the like etc.
- virtualized machines and servers e.g. VMware virtual machines, Amazon Elastic Beanstalk, e.g. Amazon EC2 and storages, e.g. Amazon S3, and the like etc.
- specialized processing and storage units e.g. Application Specific Integrated Circuits ASICs, system/s on a chip, field programmable gate arrays (FPGAs) and the like
- ASICs Application Specific Integrated Circuits
- FPGAs field programmable gate arrays
- the data communication network to implement the system and method of the present invention carries, transmit, receive, or transport data at the rate of 10 million bits or larger than 10 million bits per second;”
- “Furthermore the terms“storage device,“storage”,“memory”, and“computer-readable storage medium/media” refers to all types of no-transitory computer readable media such as magnetic cassettes, flash memories cards, digital video discs, random access memories (RAMSs), Bernoulli cartridges, optical memories, read only memories (ROMs), Solid state discs, and the like, with the sole exception being a transitory propagating signal.
- the processes and systems of FIGs. 8 to 20-2 can be an on premises system, an intelligent being, or a network system of computation and processing, storage medium, displays and interfaces, and the associated software.
- FIGs. 8 to 20-2 can be a remote system providing the service in the form of cloud environment for one or more clients providing one or more the services mentioned above.
- the system can be a combination of an on premises private cloud/machine computation facilities connected to a public cloud service provider.
- public and/or private and/or hybrid cloud computing environment (either distributed or central, on premises or remote, private or public or hybrid) is known to the skilled to art and the disclosed methods of investigations of compositions of state components can be performed in variety of topologies which is regarded as service provider system employing one or more of the generating methods/s of output data respective of one or more of the disclosed methods of the investigation of a composition of state components.
- An interesting mode of service is when for an input composition and after investigation the system yet provides further related compositions or bodies of knowledge to be looked at or being investigated further in relation to the one or more aspect of the input composition investigation.
- Another service mode is that the system provides various investigation diagnostic services for the input composition from user.
- Another mode of use is when an intelligent being make connection or communicate with the system of composition investigation (i.e. the brain) by way of communication networks to provide desired services (e.g. conversing, telling stories, talking, instructing, providing consultancy, generating various content, manufacturing, etc.).
- desired services e.g. conversing, telling stories, talking, instructing, providing consultancy, generating various content, manufacturing, etc.
- the currently disclosed method/s and system/s is implemented within the intelligent being or used to realize new intelligent beings.
- the method and the associated system can be used as a platform so that the user can use the core algorithms of the composition investigation to build other applications that need or use the service of such investigation. For instance a client might want to have her/her website being investigated to find out the important aspects of the feedback given by their own users, visitors or clients.
- the methods and systems of the present invention can be employed to provide a human computer conversation and/or computer/computer conversation such as chat-bots, automatic customer care, question answering, fortunetelling, consulting or any general any type of kind of conversation.
- a user might want to use the service of the such system and platform to compare and investigate her/his created content to find out the most closely related content available in one or more of such content repositories (e.g. a private or public, or subscribed library or knowledge database etc.) or to find out the score of her/his creation in comparison to the other similar or related content. Or to find out the valuable parts of her/his creation, or find a novel part etc.
- content repositories e.g. a private or public, or subscribed library or knowledge database etc.
- a network of objects is considered a composition and vice versa. Accordingly the methods of investigation disclosed here are applied to build new applications, services and products. Accordingly a network of state components can be a representative for a composition and vice versa.
- artificial neural networks are therefore a form or a representative of a composition of state components itself whose associations of its state components ( e.g. connections between nodes of the network) are to be known.
- the popularity of the neural networks and the so-called deep learning is due to its potential ability to train a network of connecting nodes to become able to map a certain set of data (e.g an input dada) to a desired set of data (e.g. the output data).
- connection weight between nodes of a neural network is obtained by various training algorithms and processing which are generally rooted in stochastic gradient decent type of algorithms.
- an exemplary multilayer neural network comprises of a number of neurons in each layer.
- the whole network can have very many layers (e.g. hidden layers to provide extra degrees of freedom for optimization) each node can be considered or assigned with an state components of predefined order (e.g. such as each node in the first layer can be represented of a textual word) the second layer.
- Each node e.g. a neuron or perceptron
- Each node in each layer is connected to a number of other nodes in its preceding layer and to a number of nodes on its consequent layer.
- the role of neural network is to learn the impact of each input/neuron to other neuron in other layers either directly or indirectly (through hidden layers).
- N x M maps the N inputs of the network in Fig 19 to the desired number of outputs (e.g M).
- A1, A2, ...An are matrixes with dimensions specified in the above equations.
- Each intermediate matrix can be corresponded to the connections of nodes of adjacent layers. These intermediate matrixes show the connection and the weight of the connections between nodes of adjacent layer or back propagating connections from other layers.
- Computationally and in practice training of a neural network starts/initialized with a randomly populated matrixes and the values are changes and varied through various computational algorithms until the desired results are achieved satisfactorily.
- Such desired results from the network could be that the network become able to classify an image correctly with high degree of probability, or distinguishes an audio signal and extract or convert the audio signal to its corresponded or equivalent text, and/or translating text/voice between languages etc.
- each of these intermediate, matrices that will collectively make the whole neural network to perform a task, are to be fund which is the goal of neural networks learning algorithms. It is conceptually easy to see that if a node (i.e. a neuron) is connected to/from another node so they would have some sort of relationships and or, using the terms of this disclosure, some types of associations and relationship with each other.
- a node i.e. a neuron
- nodes of the first layer are corresponded to State components of order k and the nodes of a second layer are corresponded or representatives of State components of order l ( k and l can be the same or equal) and the next layer is corresponded or representatives of state components of order l+1 and so on.
- nodes of the first layer of a neural network can be regarded or been representative of textual words of a natural language such as words of English languages as input to a system of networks of nodes (e.g. Neural Networks, the so called deep learning neural nets, or any other network of objects with some data processing function).
- neural network Without going into the details of shortcoming of such training and drawbacks of neural network to perform intelligent tasks, here it is aimed to use the data objects (e.g. various association strength matrices, various significance values etc.) of this disclosure which are obtained or built by exercising the teachings of this disclosure to build a neural networks both in hardware or software shape with the initial connections and weights are obtained by calculating for example ASM of different types and order and if it is needed further train the neural network to even function better.
- Said neural network further can be implemented as various classes/types of recurrent neural networks, convolutional neural networks, recursive neural networks, neural history compressor, feed forward neural networks and the like.
- Using the data of associations from this disclosure therefore can reduce the size of the neural network significantly.
- the data e.g. the entries of ASM matrix or connection weight between the nodes
- further adjustments to improve the performance of the artificial neural network would converge much quicker while the performance of the whole network (as an artificial brain) would be significantly enhances.
- the neural network Since we have introduced various data objects and various types of associations and relationships between the state components of a composition or very large set of compositions the neural network become programmable and therefore the designer of such systems has control and insight into to working mechanics of the artificial intelligent system (e.g. a robot or self-driving car/robot etc) which employs an artificial network of state components (e.g. neural network). In this way the designer of such system have advance knowledge and expectation form the system whereas currently the neural networks are trained by brute forces and sheer processing power of processing devices such as NVidia graphical processing accelerators.
- the disclosure introduces an artificial intelligent system which uses the various data objects of/from the investigator of Figs 1 or 10 to build and train further a network of state components (a neural net is an instance of network of state components) to perform intelligent tasks and to implement machine learning by investigating one or more bodies of knowledge to learn about the world.
- a network of state components a neural net is an instance of network of state components
- AI system e.g. the hardware or software system
- Such a system then is incorporated into mechanical systems such as special purpose or general purpose robots and intelligent systems and machines.
- An ASM can define a Hilbert space in which each row or column is a point in that space or a numerical vector. In such spaces excitation of one point can cause to excite other points of that space.
- Fig. 20-1 schematically illustrates the conversation of tow conversant agents. To build such conversant agents, one can use the methods of the current disclosure to build a knowledgeable machines capable of meaningful and context aware utterance.
- the knowledgeable system or machine have acquired the knowledge from the investigation of large bodies of textual knowledge (other forms of knowledge can also be transformed to textual bodies of knowledge) by exercising the methods of the present invention to acquire the knowledge about the state component of the real world through the literature and have built the derivative data objects (i.e. various PMs, VSMS, ASMs, COPs RASMs, CASMs, etc.) that make it possible to make the machine be knowledgeable.
- the knowledgeable machine/system comprises, (among other parts and hardware and software) or has access to these data objects which obtained by processing large enough body of textual data according to the teachings of this disclosure.
- the knowledgeable system can assemble or compute an“association strength spectrum” for this utter (e.g. from asm spectrum of the utter constituents words) to find or compose a most relevant and appropriate response to the first utter according to some desired kind of conversation.
- an“association strength spectrum” for this utter (e.g. from asm spectrum of the utter constituents words) to find or compose a most relevant and appropriate response to the first utter according to some desired kind of conversation.
- By“desired kind of conversation” we mean the type of conversation such as being entertaining, or being informative, or being argumentative etc.
- deferent types of data objects can be used (such as which ASM, or COP or CAUSAL ASM or which type of VSM.) For instance is conversation is going to be the most informative response which gives the highest knowledge then it might be more appropriate to use COP as the ASM, and if the conversation is going to be for new discovery and argumentative, perhaps a CAS AL type ASM is used to find best suited response to the first agent utterances ft is become evident that various kinds of conversation can be combined to make a new kind of conversation such as both entertain and informative, and the like.
- the knowledgeable system e.g. as the second conversant agent
- the second agent utterances can take into accounts previous conversations with the same or weighted influence on the response utter as shown in Fig. 20-2. In this case the spectrums of the previous utterance will be accounted for in the spectrum of the future utterances.
- the disclosed frame work along with the algorithms and methods enables the people in building knowledgeable machines and more particularly machines and systems with autonomous navigation abilities in the desired space/s.
- various disciplines such as artificial intelligence, robotics, information retrieval, search engines, knowledge discovery, genomics and computational genomics, signal and image processing, information and data processing, encryption and compression, business intelligence, decision support systems, financial analysis, market analysis, public relation analysis, and generally any field of science and technology to use the disclosed method/s of the investigation of the compositions of state components and the bodies of knowledge to arrive the desired form of information and knowledge desired with ease, efficiency, and accuracy.
- the data processing operations e.g. vector/matrix manipulations, manipulating data structures, association spectrums calculations and manipulation, etc.
- ASICS Application Specific Integrated Circuits
- FPGA Field-Programmable Gate Arrays
- system-on-chip based on any computing and data processing device manufacturing platforms and technologies, such as silicon based, III-IV semiconductors, and quantum computing artifacts to name a few.
- the invention also provides a unified and integrated method and systems for investigation of compositions of state components.
- the method can be implemented language independent and grammar free.
- the method is not based on the semantic and syntactic roles of symbols, words, or in general the syntactic role of the state components of the composition. This will make the method very process efficient, applicable to all types of compositions and languages, and very effective in finding valuable pieces of knowledge embodied in the compositions.
- Several valuable applications and services also were exemplified to demonstrate the possible implementation and the possible applications and services. These exemplified applications and services were given for illustration and exemplifications only and should not be construed as limiting application.
- the invention has broad implication and application in many disciplines that were not mentioned or exemplified herein but in light of the present invention's concepts, algorithms, methods and teaching, they becomes apparent applications with their corresponding systems to those familiar with the art.
- the system and method have numerous applications in autonomous state navigators, knowledgeable machines, knowledge discovery, knowledge visualization, content creation, signal, image, and video processing, genomics and computational genomics and gene discovery, finding the best piece of knowledge, related to a request for knowledge, from one or more compositions, artificial intelligence, realization of artificially or new intelligent begins, computer vision, computer or man/machine conversation, approximate reasoning, as well as many other fields of science and generally state component processing.
- the invention can serve knowledge seekers, knowledge creators, inventors, discoverer, as well as general public to investigate and obtain highly valuable knowledge and contents related to their subjects of interests.
- the method and system thereby, is instrumental in increasing the speed and efficiency of knowledge retrieval, discovery, creation, learning, problem solving, and accelerating the rate of knowledge discovery to name a few.
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