US20220083617A1 - Systems and methods for enhanced online research - Google Patents
Systems and methods for enhanced online research Download PDFInfo
- Publication number
- US20220083617A1 US20220083617A1 US17/537,184 US202117537184A US2022083617A1 US 20220083617 A1 US20220083617 A1 US 20220083617A1 US 202117537184 A US202117537184 A US 202117537184A US 2022083617 A1 US2022083617 A1 US 2022083617A1
- Authority
- US
- United States
- Prior art keywords
- user
- research
- websites
- online research
- keywords
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
- 238000011160 research Methods 0.000 title claims abstract description 241
- 238000000034 method Methods 0.000 title claims abstract description 75
- 230000003542 behavioural effect Effects 0.000 claims description 19
- 230000009471 action Effects 0.000 claims description 12
- 238000001914 filtration Methods 0.000 claims description 11
- 230000000694 effects Effects 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 2
- 230000004044 response Effects 0.000 claims description 2
- 238000004458 analytical method Methods 0.000 abstract description 23
- 238000010586 diagram Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 8
- 238000004891 communication Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 5
- 238000010801 machine learning Methods 0.000 description 5
- 230000006399 behavior Effects 0.000 description 4
- 230000001149 cognitive effect Effects 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- VYZAMTAEIAYCRO-UHFFFAOYSA-N Chromium Chemical compound [Cr] VYZAMTAEIAYCRO-UHFFFAOYSA-N 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 3
- 230000001413 cellular effect Effects 0.000 description 2
- 238000012512 characterization method Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000002372 labelling Methods 0.000 description 2
- 238000003058 natural language processing Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
- G06F16/24578—Query processing with adaptation to user needs using ranking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/248—Presentation of query results
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/2866—Architectures; Arrangements
- H04L67/30—Profiles
- H04L67/306—User profiles
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
Definitions
- the present disclosure generally relates to online research.
- Conventional search engines assume that users know what they are looking for, and that they know what keywords to use to get the information and know the best logical order of keywords. That is, if users enter the right query, then conventional search engines will provide the right answer. The quality of the search results is based on the accuracy of the keywords.
- search results may include a large number of irrelevant contents which need to be checked individually by the user, which may lead to a waste of time. Therefore, there is a need for a better online research system and method whereas research is a consecutive action to search relevant information..
- the present disclosure relates to a method for an enhanced research platform.
- the method can comprise: receiving from a user, by a computer processor, one or more keywords; receiving from a user one or more tags; sending the one or more keywords and the one or more tags to a server; receiving from the server a plurality of curated online research archives, wherein each of the plurality of curated online research archives comprises a list of categorized websites matching the one or more keywords and the one or more tags; ranking the received plurality of curated online research archives based on a relevancy to the keywords and the tags; and displaying the ranked plurality of curated online research archives in a user interface.
- the method can comprise ranking the received plurality of curated online research archives based on an attribute of one of the plurality of curated online research archives, wherein the attribute comprises at least one of: real time trends and popularity, number of links, number of views, or rating.
- the method can comprise ranking the received plurality of curated online research archives based on an attribute of a creator associated with one of the plurality of curated online research archives, wherein the attribute comprises at least one of: number of subscribers of the creator, number of curated online research archives created by the creator, or rating of the creator.
- the method can comprise ranking the received plurality of curated online research archives based on an attribute of a website link associated with one of the plurality of curated online research archives, wherein the attribute comprises at least one of: duration of visit of the website link, number of characters on a webpage associated with the website link, or number of revisit associated with the website link.
- the method can comprise receiving user demographic and behavioral data; sending the received user demographic and behavioral data to the server; receiving a plurality of curated online research archives, wherein each of the plurality of curated online research archives comprises a list of categorized websites matching the one or more keywords, the one or more tags, the user demographic data, and the user behavioral data; and displaying the plurality of curated online research archives in the user interface.
- the method can comprise ranking the plurality of curated online research archives based on relevancy to the keywords, the tags, the user demographic data, and the user behavioral data.
- the user demographic data can comprise at least one of location of the user, gender, age, experience level, education level, or household income.
- each of the plurality of curated online research archives can comprise a title that indicates a research goal, an order of one or more sub-titles that indicate categories for grouping like websites, and an order of websites within each sub-title to indicate progression from an end-to-end research.
- the present disclosure relates to a method for creating a curated online research archive, wherein the curated online research archive comprises a list of websites.
- the method can comprise: receiving, by a computer processor, a title from a user; receiving one or more tags from the user; receiving one or more section names from the user; receiving instruction from a user to start recording search history; recording a list of websites visited by the user based on the received instruction; filtering the recorded list of websites; categorizing the filtered list of websites based on the one or more section names; creating a curated online research archive wherein the curated online research archive comprises the title, the one or more tags, and the categorized list of websites; and outputting the curated online research archive.
- recording the list of websites visited by the user can comprise recording the list of websites with a browser extension.
- filtering the recorded list of websites can comprise: displaying the recorded list of websites to the user; receiving from the user a selection of one or more websites; and removing the one or more websites from the recorded list of websites based on the received selection.
- filtering the recorded list of websites can comprise filtering the recorded list of websites based on a set of pre-defined rules.
- the method can comprise: receiving user demographic data; updating the curated online research archive by adding the received user demographic data to the curated online research archive; and outputting the updated curated online research archive.
- the user demographic data comprises at least one of location of the user, gender, age, experience level, education level, or household income.
- the method can comprise: receiving an instruction of editing from the user; and updating the categorized list of websites in response to the instruction of editing.
- the present disclosure relates to a system for an enhanced research platform, the system comprising: a computer processor; and a non-transitory computer readable storage medium storing computer program instructions, the instructions when executed by the computer processor causing the computer processor to perform steps comprising: receiving from a user one or more keywords; receiving from a user one or more tags; sending the one or more keywords and the one or more tags to a server; receiving from the server a plurality of curated online research archives, wherein each of the plurality of curated online research archives comprises a list of categorized websites matching the one or more keywords and the one or more tags; ranking the received plurality of curated online research archives based on a relevancy to the keywords and the tags; and displaying the ranked plurality of curated online research archives in a user interface.
- FIG. 1 is a block diagram showing an enhanced research system implemented in modules, according to some embodiments of the present disclosure
- FIG. 2 is a flowchart of an exemplary research goal-based method performed by the enhanced research system, according to some embodiments of the present disclosure
- FIG. 3 is a flowchart of an exemplary keywords-based method performed by the enhanced research system, according to some embodiments of the present disclosure
- FIGS. 4A and 4B are flowcharts illustrating an exemplary data flow for the enhanced research system, according to some embodiments of the present disclosure
- FIG. 5 is a flowchart of website-based curated online research archive (or “Pik”) creation method performed by the enhanced research system, according to some embodiments of the present disclosure
- FIG. 6 is a flowchart of browser extension-based Pik creation method performed by the enhanced research system, according to some embodiments of the present disclosure.
- FIGS. 7A and 7B are flowcharts illustrating an exemplary data flow for the enhanced research system, according to some embodiments of the present disclosure
- FIG. 8 is a network diagram depicting a system for implementing the enhanced research system, according to some embodiments of the present disclosure.
- FIG. 9 is a block diagram of an exemplary computing device that may be used to implement exemplary embodiments of the enhanced research system described herein;
- FIGS. 10A-10C is an exemplary workflow for the enhanced research system, according to an example embodiment
- FIGS. 11A-11F illustrate exemplary user interfaces for the enhanced research system, according to an example embodiment.
- FIGS. 12A-12D illustrate exemplary user interfaces for the enhanced research system being used as a Google Chrome Extension, according to an example embodiment.
- FIG. 13 is a block diagram showing functions of a marketplace for Piks, according to some embodiments of the present disclosure.
- a user who needs to find information for their complex online research typically find information through a multitude of venues, ranging from search engines, forums and community boards, and social media and chat.
- One of the most common solutions is utilize search engines to receive quick answers or links to websites containing the information.
- the user may type keywords related to his or her research goal.
- the search results are then populated with links chosen by its algorithm.
- the user may then, based on personal experiences, selectively choose one or more links to browse and continue doing so.
- the user may decide enough information has been gathered.
- the user may also start a new search with new keywords, or give up the process due to a lack of relevant search results.
- Knowledgeable users may also encounter difficulties with the traditional search method.
- a user can save his or her research in order to refer or update to it later, and share or collaborate with others, for personal and professional reasons.
- Typical solutions for such this situation may be keeping tabs of useful sites open, creating spreadsheets and copying and pasting the information into them, or bookmarking relevant sites.
- the browser may crash and the session was not saved.
- the user may also only save the link, but not relevant information to help reference them later. Therefore, there is also a need for a better online research system and method for users with substantial information of the subject.
- the present disclosure describes a system and method of enhance online research.
- a beginner may not need to know what exactly to search as long as he or she has a question or topic in mind.
- the user will be able to view curated online research archives (referred herein as “Piks”) from other creators or via a proprietary algorithm.
- curated online research archives can also be referred as “search journeys”, as described in U.S. Provisional Application 62 / 452 , 040 titled “System and methods for an enhanced research platform,” filed on January 30 , 2017 , the disclosure of which is incorporated herein by reference in its entirety.
- the curated online research archives (or “Piks”) will consist of a comprehensive view of all the sub-topics with links and comments. Since these links have been chosen based on its usefulness, the chance of bouncing is greatly decreased. The user can see the ratings and descriptions of the creator and their Piks to validate the accuracy and usefulness of the research.
- the described system and method can offer two methods to help organize, save, and share their research.
- creators will be able to copy and paste their links and relevant information to their account. Doing so allows them to access the research as long as they have a device able to interact with the internet.
- a browser extension will help automatically save essential links and information with areas for the user to place comments and further organize.
- Described in detail herein is a search system that analyzes user behavior with user modeling and cognitive technology, supports a research goal, and recommends other users' search history or journey who had the same or similar research goal.
- the enhanced research system supports a research goal.
- the enhanced research system parses keywords or search terms entered by a user, and automatically determines the research goal through keyword analysis.
- the enhanced research system may assign a category to each keyword or search term entered by the user.
- the enhanced research system also enables creation of a Pik.
- a “Pik” can be an organized list of search results or content available on the Internet that a user found valuable or relevant to his or her research goal.
- a Pik may include a title that indicates a research goal, a sub-title that indicates categories for grouping like data, content, websites and information, and an order of websites and content within each sub-title to indicate progression from a broad concept to a narrow concept.
- a Pik may be a streamlined report of search activity from start to finish. For example, a user may be interested in buying a drone, and he or she may be a diligent person who likes to perform in-depth research prior to buying a drone.
- the user enters 30 or so keywords in the search box and visits approximately 200 links or search results spending a total of about 5 hours searching.
- the user can save the relevant or valuable links or search results visited as a Pik.
- the user can also categorize the saved links and set a logical order for the links in the Pik.
- a user can create his or her own Pik.
- the enhanced research system automatically creates a Pik by analyzing data collected over time, where the data indicates the relevancy of a search result or content to a user's research goal.
- a Pik may include a category title for content or search results, and an order of content or search results (which may be based on relevancy).
- the system can provide suggestions to a user. For example, when a user creates a title for a research goal, the system can suggest sections to consider. In some embodiments, when a user refers to or edits a Pik, the system can suggest new links to add into one or more sections of the Pik.
- the enhanced research system can analyze user behavior to provide search results and content that is relevant to the user.
- the user behavior may be characterized using machine learning techniques, cognitive technology, user behavior modeling, search history, browsing history, purchase history, and other data that can aid in characterizing a user.
- the enhanced research system can also recommend another user's Pik.
- the enhanced research system analyzes the user characterization and the user's research goal, and identifies existing Piks that match or substantially match the user characterization and the user's research goal.
- the enhanced research system is provided as a plug-in or extension for use with an installed web browser on a computing device.
- the enhanced research system may be provided as an application (e.g., app).
- the user can share his or her Pik with other users of the enhanced research system.
- the user may also edit his or her own Pik.
- the enhanced research system may also include a messaging system, where the user can send messages to other users.
- the sharing of Pik can include a collaborative or social research.
- a user (knowledgeable or not) can start a Pik and have it open to the public to add sections and links.
- a user can invite people (friends, family, etc.) to collaborate on a Pik.
- a user may plan for a group trip and start a Pik of travel research. The user can invite other people who would travel with him or her to collaborate on the Pik.
- Exemplary flowcharts are provided herein for illustrative purposes and are non-limiting examples of methods.
- One of ordinary skill in the art will recognize that exemplary methods may include more or fewer steps than those illustrated in the exemplary flowcharts, and that the steps in the exemplary flowcharts may be performed in a different order than the order shown in the illustrative flowcharts.
- FIG. 1 is a block diagram showing an enhanced research system 100 implemented in modules, according to an example embodiment.
- the system can be implemented in server 830 shown in FIG. 8 , and a client device can access to the system via the Internet.
- the system may be implemented in devices 810 , 820 shown in FIG. 8 .
- the modules include a user profile module 110 , a parsing module 120 , a Pik module 130 , a user analysis module 140 , a recommendation module 150 , and a user interface module 160 .
- the modules may include various circuits and one or more software components, programs, applications, apps or other units of code base or instructions configured to be executed by one or more processors included in devices 810 , 820 .
- one or more of user profile module 110 , parsing module 120 , Pik module 130 , user analysis module 140 , recommendation module 150 , user interface module 160 may be included in server 830 , while others can be provided in the device 810 , 820 .
- user profile module 110 , parsing module 120 , Pik module 130 , user analysis module 140 , recommendation module 150 and user interface module 160 are shown as distinct modules in FIG. 1 , it should be understood that they may be implemented as fewer or more modules than illustrated. It should be understood that any of modules may communicate with one or more components included in system 800 ( FIG. 8 ), such as database(s) (e.g., database(s) 840 ), server (e.g., server 830 ), or devices (e.g., devices 810 , 820 ).
- the user profile module 110 may be a software or hardware-implemented module that may be configured to manage and maintain user profiles for users of the enhanced research system.
- the user profiles may include information such as username, password, name, geographic location, demographics, user preferences, and other user information.
- the parsing module 120 may be a software or hardware-implemented module that may be configured to identify a research goal by parsing one or more keywords or search terms entered by a user in the enhanced research system.
- the parsing module 120 may categorize each of the keywords or search terms entered by the user into, for example, target, action, purpose, experience level, price range, and others.
- the parsing module 120 may use natural language processing, clustering techniques, auto labeling techniques, and other mechanisms to categorize the keywords or search terms.
- the parsing module 120 may recognize a pattern based on a user's past search history or past entered-keywords, and use the pattern to categorize the instant keywords or search terms entered by a user.
- the categorized keywords or search terms may be referred to as the research goal.
- a user may enter the following as keywords or search terms: “I want to buy a drone for school project.”
- the parsing module 120 may parse the keywords, and categorize them. For example, “drone” may be categorized as ‘target,’ “buy” may be categorized as ‘action,’ “school” may be categorized as ‘purpose.’
- the parsing module 120 may identify an experience level for the research goal based on past search history. For example, the experience level may be set as “beginner” based on the parsing module 120 recognizing from past search history and user activity that the user has beginner level knowledge of drones.
- the user may characterize the keywords or search terms by entering them in the user interface under specific categories.
- the Pik module 130 may be a software or hardware-implemented module that may be configured to store created Piks in a database, manage and maintain Piks, update Piks based on edits made by users, and record sharing of Piks by users with other users.
- the Pik module 130 may manage data and information for each Pik, for example, an organized list of links, search results, or content.
- the Pik module 130 may be configured to automatically generate a Pik by analyzing relevancy of search results with respect to a research goal.
- the Pik module 130 may employ machine learning techniques to analyze large amounts of data, including a number of users who visit particular links or content in view of the keywords searched, an amount of time spent by users on a particular website in view of the keywords searched, and any subsequent actions taken by the user with respect to the website (e.g., saving as bookmark, clicking on another link within the website, completing a purchase on the website, etc.).
- the Pik module 130 can employ machine learning techniques to analyze other data and metrics.
- the user analysis module 140 may be a software or hardware-implemented module that may be configured to characterize users by analyzing various data related to users, and storing the determined characteristics in a database. For example, the user analysis module 140 may analyze data such as a user's search history, user's browsing history, user's purchase habits, user's demographics, user's geographic location, user's social media profile and content, and the like. The user analysis module 140 may employ machine learning techniques or cognitive technology to analyze data and characterize users. In some embodiments, the user analysis module 140 can be implemented in a server which can be accessed by different user with difference user devices. In some embodiments, the user analysis module 140 can be implemented in one user device which is shared by difference users who may have separate profile and/or account on the user device.
- the recommendation module 150 may be a software or hardware-implemented module that may be configured to query a database to generate Pik search results and rank Pik search results based on analyzing the relevancy of each results in view of the research goal identified by the parsing module 120 and the user characteristics identified by the user analysis module 140 .
- the recommendation module 150 may also be configured to query a database to identify Piks that match or substantially match the research goal and the user characteristics.
- the recommendation module 150 may employ mathematical algorithms or techniques that implement user modeling, collaborative filtering, content based filtering, regression modeling, and other mechanisms to provide personalized search results and personalized recommendation of Piks.
- the recommendation module 150 may also analyze information such as popularity of the Pik, timeliness, and other factors when determining if a Pik should be recommended to the user.
- the recommendation module 150 identifies search results and Piks based searching of the keywords and each keyword's assigned category. To illustrate using the previous example of “I want to buy a drone for school project”, the recommendation module 150 , while searching the Internet or querying a database, takes into consideration that “drone” is the target, “buy” is the action, “school” is the purpose, and “beginner” is the experience level. In this manner, the enhanced research system is able to provide more relevant results than conventional search engines.
- each Pik is associated with one or more research goal and stored as such in the database.
- Each Pik may also be associated with user characteristics based on the user who created the Pik.
- the recommendation module 150 ranks the Piks based on how well each matches the research goal and/or the user characteristics. For example, Piks associated with the same research goal as the keywords entered by the user and associated with the same user characteristics as the instant user are ranked higher. The Piks may also be ranked based on other factors including creation time, research trends or popularity.
- the user interface module 160 may be a software or hardware-implemented module that may be configured to manage and display a user interface on device 710 , 720 that enables a user to use the enhanced research system described herein.
- the user interface module 160 may facilitate display of the search results and Piks identified by the enhanced research system. Exemplary user interface screens are described with respect to FIGS. 6A-6F .
- FIG. 2 is a flowchart of an exemplary method 200 performed by the enhanced research system in an example embodiment.
- the parsing module 120 receives keywords from a user via user interface screen provided by the enhanced research system.
- the parsing module 120 determines a research goal by categorizing the keywords entered by the user.
- the parsing module 120 may categorize each of the keywords or search terms entered by the user into categories, such as target, action, purpose, experience level, price range, and others.
- the parsing module 120 may use natural language processing, clustering techniques, auto labeling techniques, and other mechanisms to categorize the keywords or search terms.
- the parsing module 120 may recognize a pattern based on a user's past search history or past entered-keywords, and use the pattern to categorize the instant keywords or search terms entered by a user.
- the user analysis module 140 analyzes user demographic and behavioral data to characterize the user.
- the user analysis module 140 may analyze demographic data such as location of the user, gender, age, experience level, education level, household income, and the like.
- the user analysis module 140 may also analyze data such as a user's search history, user's browsing history, user's purchase habits, user's social media profile and content, and the like.
- the user analysis module 140 may employ machine learning techniques or cognitive technology to analyze data and characterize users.
- the recommendation module 150 queries the database to identify results that match or substantially match the research goal.
- the identified results are Piks.
- the recommendation module 150 also identifies results based on analysis of the user demographic and behavioral data.
- the recommendation module 150 ranks the identified results based each results relevancy to the research goal. The results are also ranked based on analysis of user demographic and behavioral data and determination of relevancy of the results based on the demographic and behavioral data.
- the user interface module 160 displays the ranked results in a user interface (for example, user interface screen 640 , FIG. 6E , described below).
- the recommendation module 150 can also sort the Pik results by date, most viewed, most saved, rankings, or number of links.
- the recommendation module 150 can also filter the Pik results by categories.
- a user can provide one or more keywords and tags, instead of research goals.
- the system can then return one or more Piks to the user based on the identified keywords and tags.
- FIG. 3 is a flow chart showing a keywords and tags-based online research method 300 .
- the system can receive one or more keywords from the user.
- the keywords can include a name of a type of product that the user is interested in.
- the keywords can include a name of a travel destination for a trip that the user is planning for.
- the keywords can include a service that the user may need.
- the system can receive one or more tags from the user.
- a user can type in keywords and then receive the list of Pik results with relevant tags at the top.
- the tags can include a price range for the product that the user is interested in.
- the tags can include a time for the trip that the user is planning for.
- the tags can include the user's demographic data such as location of the user, gender, age, experience level, education level, household income, and the like.
- the system can query the database to identify results that match or substantially match the keywords and tags.
- the identified results are one or more Piks.
- the system also identifies results based on analysis of the user demographic and behavioral data.
- the system can rank the identified results based each results relevancy to the keywords and the tags.
- the results are also ranked based on analysis of user demographic and behavioral data and determination of relevancy of the results based on the demographic and behavioral data.
- the user interface module 160 displays the ranked results in a user interface.
- FIGS. 4A and 4B are flowcharts 400 illustrating an exemplary data flow for the enhanced research system, according to an example embodiment.
- the enhanced research system is provided as a website.
- the flowcharts 400 illustrate data flow between various components, such as the enhanced research system (e.g., website), the API for the enhanced research system, and the database.
- the flowcharts 400 illustrate the data flow when a user navigates to a login page for the enhanced research system, when a user navigates to a search dialog box/page, when a user navigates to the search results page generated by the enhanced research system, and when a user navigates to a Pik page.
- the user can enter his or her credentials associated with the enhanced research system on the enhanced research system website and the application interface (API) can send the login information to the database.
- the user can be navigated to a search page at step 404 .
- the user can type search keywords on the website.
- the user can get search hints from the database through the website and the API.
- the user can also get search options from the database through the website and the API.
- the user can then submit the search query on the website.
- the user can be navigated to a search results page.
- the website can request and display search results from the database.
- the search results can include one or more Piks created by other users.
- the user can choose to view a specific Pik from the search results.
- the website can send this selection to the database to update the number of views for this Pik.
- the user can also click a “like” button and accordingly the number of likes for this Pik can be updated.
- the user can also share this Pik with others via social networks or other channels (e.g., email, texts, etc.) Then the number of shares for this Pik can also be updated.
- the user can provide a rating for the Pik and the average rating of the Pik can be updated.
- the user can save Piks onto his or her account.
- the user can subscribe to a specific creator.
- the present disclosure describes a system and method for a user to create and save his or her Piks.
- the user can create and save a Pik using a website.
- the user can create and save a Pik using a browser extension (e.g., a Google ChromeTM extension.)
- FIG. 5 is a flowchart showing a website-based method 500 of creating a Pik.
- the user can login to the system's website (e.g., “Pikurate” website) with his or her credentials.
- the user can specify a title and one or more tags for the Pik.
- the user can enter contents and links of the Pik on the website.
- the user can copy website links which he or she is visiting and paste them on the system's website.
- the user can copy website contents which he or she likes and paste them on the system's website, and the website contents can be tied to the links that are saved.
- the user can add comments on the system's website.
- the user can add notes about the website links.
- the user can explain why he or she likes the contents.
- the use can save the created Pik in the system.
- FIG. 6 is a flowchart showing a browser extension-based method 600 of creating a Pik.
- the browser extension can (e.g., Google ChromeTM extension, Firefox® extension, etc.) start automatically when the user opens an Internet browser (e.g., Google ChromeTM browser, Firefox® browser, etc.)
- the user can manually enable the browser extension in the browser.
- the browser extension can receive a title and one or more tags from the user.
- the title can include a name of a type of product that the user is interested in.
- the title can include a name of a travel destination for a trip that the user is planning for.
- the title can include a service that the user may need.
- the tags can include a price range for the product that the user is interested in.
- the tags can include a time for the trip that the user is planning for.
- the tags can include the user's demographic and behavioral data such as location of the user, gender, age, experience level, education level, household income, and the like.
- the browser extension can receive the user's instruction to start to record the user's browsing history.
- the user can click a button on the browser extension to start the recording.
- the user can click the button to end the recording.
- the user can also manually save links & comment with extension buttons or hotkeys.
- the recorded browsing history can be filtered.
- the user can manually remove any website links that are not needed.
- the browser extension can filter the list with a pre-defined algorithm.
- the algorithm can filter the list based on how long the user stays on a specific website.
- the algorithm can filter based on actions taken on a specific website, such as clicks, scrolls, etc.
- the algorithm can filter based on the minimum amount of characters on a specific website.
- the algorithm can filter based on keyword analysis.
- the algorithm can filter based on links saved by other users.
- the user can save the created Pik in the browser extension.
- the browser extension can upload it to a system server.
- FIGS. 7A and 7B are flowcharts 700 illustrating an exemplary data flow for the enhanced research system according to an example embodiment.
- the Pik is referred as search journey in FIGS. 7A-7B .
- the enhanced research system is provided as Google ChromeTM Extension.
- the flowcharts 700 illustrate data flow between various components, such as a user's web browser, Internet, the enhanced research system (e.g., Google ChromeTM Extension), the API for the enhanced research system, and the database.
- the flowcharts 700 illustrate the data flow when a user creates a Pik.
- the user can start a browser (e.g., Google ChromeTM browser, Firefox® browser) to search information on the Internet.
- the enhanced research system can include an extension tool in the browser (e.g., Google ChromeTM extensions in Google ChromeTM browser.)
- the extension tool can be activated as the browser runs.
- the extension tool can navigate the user to a login page where the user enters his or her credentials with the enhanced research system. Then the extension tool can send the credentials to the database for authentication. If the login information passes authentication process, the use can be navigated to a Pik page.
- the extension tool can get the current active page data and analyze. It can record the browsing history and send the data to the database.
- the user can start to record a new Pik by clicking a button on the extension tool.
- the user can be asked to enter a name for the new Pik.
- the created new Pik can be set to the active journey.
- the user can be asked to add a category name for the new Pik.
- the user can be asked to add a tag name for the new Pik.
- the user can choose to save the current Pik and the extension tool can send the data to the database.
- the enhanced research system can include a filter to remove unwanted or irrelevant search history.
- a manual filter can be implemented so that the user can manually select which search history and/or record to keep and remove the rest.
- an automatic filter can be implemented with pre-defined algorithm and/or rules. For example, the filter can automatically remove parent webpages and only keep relevant child-webpages. In another example, the filter can automatically detect if the user scrolls down the webpage being visited. If so, the filter can determine the webpage is relevant and keep it in the search record. These relevant webpages are saved into the active section. The user can also manually save the links, comment on them, or highlight and save sections via extension buttons or hotkeys.
- the user can access, edit, organize, and update them later through the extension or a website of the system.
- the system can filter the search history with other criteria including duration of visit by the user, whether it is a search engine results page, mouse action, number of characters on the website, etc.
- FIG. 8 illustrates a network diagram depicting a system 800 for implementing the enhanced research system, according to an example embodiment.
- the system 800 can include a network 805 , multiple devices, for example, device 810 , device 820 , a server 830 , and database(s) 840 .
- Each of the devices 810 , 820 , servers 830 , and database(s) 840 is in communication with the network 805 .
- device 810 , device 820 , server 830 , database(s) 840 can include one or more computer devices 900 as shown in FIG. 9 .
- one or more portions of network 705 may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless wide area network (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a cellular telephone network, a wireless network, a Wi-Fi network, a WiMax network, any other type of network, or a combination of two or more such networks.
- VPN virtual private network
- LAN local area network
- WLAN wireless LAN
- WAN wide area network
- WWAN wireless wide area network
- MAN metropolitan area network
- PSTN Public Switched Telephone Network
- PSTN Public Switched Telephone Network
- the devices 810 , 820 may comprise, but are not limited to, work stations, computers, general purpose computers, Internet appliances, hand-held devices, wireless devices, portable devices, wearable computers, cellular or mobile phones, portable digital assistants (PDAs), smart phones, tablets, ultrabooks, netbooks, laptops, desktops, computing devices installed in vehicles, vehicle installed user interfaces or user dashboards, multi-processor systems, microprocessor-based or programmable user electronics, game consoles, set-top boxes, network PCs, mini-computers, and the like.
- PDAs portable digital assistants
- smart phones tablets, ultrabooks, netbooks, laptops, desktops, computing devices installed in vehicles, vehicle installed user interfaces or user dashboards, multi-processor systems, microprocessor-based or programmable user electronics, game consoles, set-top boxes, network PCs, mini-computers, and the like.
- Each of devices 810 , 820 can include one or more components described in relation to computing device 800 shown in FIG. 8 .
- Each of devices 810 , 820 may connect to network 705 via a wired or wireless connection.
- Each of devices 810 , 820 may include one or more applications such as, but not limited to, a web browser application, an application based on the enhanced research system described herein, and the like.
- the devices 810 , 820 may perform all the functionalities described herein.
- the enhanced research system may be included on the device 810 , 820 , and the server 830 performs the functionalities described herein.
- the device 810 , 820 may perform some of the functionalities, and server 830 performs the other functionalities described herein.
- Each of the database(s) 840 and server 830 is connected to the network 805 via a wired connection.
- one or more of the database(s) 840 , and server 830 may be connected to the network 805 via a wireless connection.
- server 830 can be (directly) connected to the database(s) 840 .
- Server 830 comprises one or more computers or processors configured to communicate with devices 810 , 820 via network 805 .
- Server 830 hosts one or more applications or websites accessed by devices 810 , 820 and/or facilitates access to the content of database(s) 840 .
- Database(s) 840 comprise one or more storage devices for storing data and/or instructions (or code) for use by server 830 , and/or devices 810 , 820 .
- Database(s) 840 , and/or server 830 may be located at one or more geographically distributed locations from each other or from devices 810 , 820 .
- database(s) 840 may be included within server 830 .
- FIG. 9 is a block diagram of an exemplary computing device 900 that may be used to implement exemplary embodiments of the enhanced research system 100 described herein.
- the computing device 900 includes one or more non-transitory computer-readable media for storing one or more computer-executable instructions or software for implementing exemplary embodiments.
- the non-transitory computer-readable media may include, but are not limited to, one or more types of hardware memory, non-transitory tangible media (for example, one or more magnetic storage disks, one or more optical disks, one or more flash drives), and the like.
- memory 906 included in the computing device 900 may store computer-readable and computer-executable instructions or software for implementing exemplary embodiments of the enhanced research system 100 .
- the computing device 900 also includes configurable and/or programmable processor 902 and associated core 904 , and optionally, one or more additional configurable and/or programmable processor(s) 902 ′ and associated core(s) 904 ′ (for example, in the case of computer systems having multiple processors/cores), for executing computer-readable and computer-executable instructions or software stored in the memory 906 and other programs for controlling system hardware.
- Processor 902 and processor(s) 902 ′ may each be a single core processor or multiple core ( 904 and 904 ′) processor.
- Virtualization may be employed in the computing device 900 so that infrastructure and resources in the computing device may be shared dynamically.
- a virtual machine 914 may be provided to handle a process running on multiple processors so that the process appears to be using only one computing resource rather than multiple computing resources. Multiple virtual machines may also be used with one processor.
- Memory 906 may include a computer system memory or random access memory, such as DRAM, SRAM, EDO RAM, and the like. Memory 906 may include other types of memory as well, or combinations thereof.
- a user may interact with the computing device 900 through a visual display device 918 , such as a computer monitor, which may display one or more graphical user interfaces 922 that may be provided in accordance with exemplary embodiments.
- the computing device 900 may include other I/O devices for receiving input from a user, for example, a keyboard or any suitable multi-point touch interface 808 , a pointing device 910 (e.g., a mouse), a microphone 928 , and/or an optical scanning/capturing device 932 (e.g., a camera, scanner, barcode reader, QR code reader).
- the multi-point touch interface 908 (e.g., keyboard, pin pad, scanner, touch-screen, etc.) and the pointing device 910 (e.g., mouse, stylus pen, etc.) may be coupled to the visual display device 818 .
- the computing device 900 may include other suitable conventional I/O peripherals.
- the optical scanning device 932 can scan optical machine-readable representations associated with physical objects so that the computing device 900 can receive and process the identifier.
- the computing device 900 may also include one or more storage devices 924 , such as a hard-drive, CD-ROM, or other computer readable media, for storing data and computer-readable instructions and/or software that implement exemplary embodiments of the enhanced research system 100 described herein.
- Exemplary storage device 924 may also store one or more databases for storing any suitable information required to implement exemplary embodiments.
- exemplary storage device 924 can store one or more databases 926 for storing information and data to be used by embodiments of the system 100 .
- the databases may be updated manually or automatically at any suitable time to add, delete, and/or update one or more items in the databases.
- the computing device 900 can include a network interface 912 configured to interface via one or more network devices 920 with one or more networks, for example, Local Area Network (LAN), Wide Area Network (WAN) or the Internet through a variety of connections including, but not limited to, standard telephone lines, LAN or WAN links (for example, 802 . 11 , T 1 , T 3 , 56kb, X.25), broadband connections (for example, ISDN, Frame Relay, ATM), wireless connections, controller area network (CAN), or some combination of any or all of the above.
- the computing device 900 can include one or more antennas 930 to facilitate wireless communication (e.g., via the network interface) between the computing device 900 and a network.
- the network interface 912 may include a built-in network adapter, network interface card, PCMCIA network card, card bus network adapter, wireless network adapter, USB network adapter, modem or any other device suitable for interfacing the computing device 900 to any type of network capable of communication and performing the operations described herein.
- the computing device 900 may be any computer system, such as a workstation, desktop computer, server, laptop, handheld computer, tablet computer (e.g., the iPadTM tablet computer), mobile computing or communication device (e.g., the iPhoneTM communication device), internal corporate devices, or other form of computing or telecommunications device that is capable of communication and that has sufficient processor power and memory capacity to perform the operations described herein.
- the computing device 900 may run any operating system 916 , such as any of the versions of the Microsoft® Windows® operating systems, the different releases of the Unix and Linux operating systems, any version of the MacOS® for Macintosh computers, any embedded operating system, any real-time operating system, any open source operating system, any proprietary operating system, or any other operating system capable of running on the computing device and performing the operations described herein.
- the operating system 916 may be run in native mode or emulated mode.
- the operating system 916 may be run on one or more cloud machine instances.
- FIGS. 10A-10C illustrate an exemplary user workflow 1000 for when the enhanced research system is used on a mobile device, according to an example embodiment.
- the enhanced research system may be provided as an app called “Pikurate.”
- the workflow 1000 begins at step 1002 where the homepage for the enhanced research system is displayed.
- the user enters search terms or keywords in the search dialog box, and clicks the search button.
- the workflow 1000 continues to step 1004 where the entered search terms or keywords are parsed and categorized.
- the enhanced research system determines the research goal and automatically assigns categories to each of the keywords or search terms.
- the enhanced research system displays the automatically categorized keywords. For example, “drone” is displayed as the target category and “buy” is displayed as the action category.
- the enhanced research system assigns “beginner” as the experience level category based on analysis of the user's past search history.
- step 1006 the enhanced research system displays Pik results based on analysis and matching of the categorized keywords or search terms.
- the user can click on a category button, which moves the workflow to step 1008 .
- step 1008 the user can save search results of interest by clicking a save button. Clicking on the save button continues the workflow 1000 to step 1016 ( FIG. 10B ).
- step 1016 the user can select or enter a folder name where the search results are saved. Once this information is entered, the workflow returns to step 1006 where the user can continue viewing the Pik results.
- the user can also edit the automatically assigned categories. For example, the user can edit the experience level category by clicking on it at step 1008 .
- the workflow continues to step 1010 ( FIG. 10B ).
- the user can enter a new keyword or select from an available list of keywords for the experience level category. For example, here the user selects “kids” instead of “beginner,” and clicks on the search button. Clicking on the search button displays new Pik results at step 1018 ( FIG. 10C ) that match the updated categorized keywords.
- the user can click on a Pik from the Pik results to display expanded information in the Pik (step 1012 of FIG. 10B ).
- a Pik is an organized list of search results, links or content.
- the user can scroll through the Pik results using a swipe gesture at the mobile device interface.
- the user can click on the links within the Pik. Clicking on the links opens the link at step 1014 ( FIG. 10B ).
- the user can return to step 1012 to view the Pik by clicking on a button at step 1014 .
- the user can click on the search button to edit keywords or enter new keywords.
- the user can click on the search button (e.g., magnifying glass icon), and the workflow continues to step 1020 ( FIG. 10C ) where the user can edit the search terms.
- a user can view options for sharing a Pik.
- the options for sharing displayed on the screen include, for example, share to Twitter, share to Facebook, share to Google +, Share to LinkedIn, and copy URL to share.
- the options also include “Report,” which a user can use to report a Pik if it includes inappropriate information.
- FIGS. 11A-11F illustrate exemplary user interface screens for the enhanced research system, according to an example embodiment.
- the enhanced research system may be provided as a website called “Pikurate.”
- FIG. 11A illustrates an exemplary user interface screen 1100 that displays a login page.
- the user can login into his or her “Pikurate” account by entering account credentials in the login box 1101 . If the user does not have an account with Pikurate, he or she can register an account by clicking the “Sign Up” button 1102 .
- FIG. 11B illustrates an exemplary user interface screen 1110 where the user can enter search terms or keywords in a search box 1111 .
- the user can select specific category under a category menu 1112 .
- the user can enter “awesome digital camera” in the search box 1111 and select “Fun & Photography” from the category menu 1112 .
- the system can provide a number of tags 1113 to the user.
- the user can choose tags that are appealing to him or her.
- the system can then narrow down the results and display them in the result section 1114 .
- FIG. 11C illustrates an exemplary user interface screen 1120 that displays more information for a Pik selected by the user by clicking on one of the Piks displayed in user interface screen 1110 .
- the Pik can include a title 1121 , a brief description 1122 , one or more tags 1123 .
- the system can provide the user with a recommended product 1124 .
- the Pik can include one or more sections or sub-titles 1125 . Under each section or subtitle, there can be a number of website links and comments on the links 1126 .
- FIG. 11D illustrates an exemplary user interface screen 1130 for creating a Pik on the “Pikurate” website.
- a user can enter a title of the Pik 1131 , a brief description 1132 , one or more tags 1133 .
- the user can enter one or more website links and comments 1134 .
- the saved links can be shown in the list 1135 .
- the system can provide suggestions to a user. For example, when a user creates a title for a research goal, the system can suggest sections to consider. In some embodiments, when a user refers to or edits a Pik, the system can suggest new links to add into one or more sections of the Pik.
- FIG. 11E illustrates an exemplary user interface screen 1140 for showing a user's profile page.
- the profile page can include the user's name 1141 , the user's profile photo 1142 and activity data 1143 including rating, number of views, number of shares, etc.
- the profile page can also include a list of Piks 1144 the user has created or viewed.
- the profile page can also include categories for Piks, such as public, private, saved, subscriptions, research requests, research bids, etc.
- FIG. 11F illustrates an exemplary user interface screen 1150 for showing a list of creators.
- the system can provide a creator list 1151 to a user based on one or more search terms entered by the user.
- the creators can be ranked by one or more metrics, such as rating, number of created Piks, number of views, number of shares, etc.
- FIGS. 12A-12D illustrate exemplary user interface screens for the enhanced research system being used as a Google Chrome Extension, according to an example embodiment.
- the enhanced research system may be provided as a website called “Pikurate.”
- FIG. 12A illustrates an exemplary user interface screen 1200 where a Pikurate Chrome Extension icon is displayed on the screen to enable the user to access features of the enhanced research system.
- a user can choose to create a new product Pik by clicking the button 1201 and enter a title of the Pik.
- the user can also create a new general Pik by clicking the button 1202 and enter a title.
- a list of Piks created by the user can be shown in the section 1203 .
- FIG. 12B illustrates an exemplary user interface screen 1210 for adding a new section.
- the user can enter a title of the new section in the box 1211 .
- the user can enter a title of the research snippet in the box 1212 .
- the user can add link label in the box 1213 .
- the user can also add comments in the box 1214 .
- the user can also save the new research snippet by clicking the button 1215 to screenshot the specific area of the website to store for viewing in the future.
- FIG. 12C illustrates an exemplary user interface screen 1220 for recording a user's browsing history.
- the browser extension can start recording the user's browsing history.
- FIG. 12D illustrates an exemplary user interface screen 1230 for recording a user's browsing history.
- the browser extension is recording the user's browsing history
- information of all the recorded websites can be shown in the list 1232 .
- the information can include a title of the website, a link of the website, time duration of visiting, etc.
- the user can click the “Stop Recording” button 1231 to stop the recording and save the results to his or her account.
- the user can manually remove unwanted website from the list 1232 .
- the system can use an algorithm to automatically remove unwanted website from the list 1232 .
- the user can also manually add the link (and its relevant information) during the research process via a button or hotkey.
- the search history, created and saved Piks, and other data is stored at the user's device (e.g., device 810 , 820 ), instead of being stored at a server (e.g., server 830 ).
- a server e.g., server 830
- blockchain technology may be used to store data at the user's device, and this allows for a higher level of protection and privacy of the user's personal search history data.
- all relevant data can be saved at a server (e.g., server 830 .)
- the enhanced research system described herein can be used to create Piks that may be used by corporations to train to employees or personnel. Use of Piks may reduce the learning curve and time for new employees or for new work procedures, since an employee can use a Pik to learn a new skill rather than receiving training from another personnel. Pik can capture the pattern of consumption of the documents to accomplish the tasks and reduce employees' research time.
- creators of Piks can be reviewed by other users and ranked based on the quality of their Piks and/or other attributes.
- a creator with high ranking can be designated as a “trusted creator.”
- Piks created by a “trusted creator” can be listed in the front page of the search results to be shown to a user.
- FIG. 13 is a block diagram showing a marketplace 1300 for Piks, according to some embodiments of the present disclosure.
- the marketplace herein refers to the requesting, bidding, selling, and buying paid Piks.
- marketplace 1300 can include a creation and editing part 1310 , a sharing and selling part 1320 , and a searching and requesting part 1330 .
- a user or “creator” can conduct as online research and create a Pik with the systems and methods described in the present disclosure.
- the sharing and selling part 1320 once the user has created their curated online research, he/she can save it onto their account for a variety of uses, ranging from public, private, paid, and drafts.
- the saved research can be accessed for future reference and updated. It can also be shared to specific users, to the Pikurate community, to others via social media, text, and email. In addition, the user has the opportunity to lock their research which can only be viewed when the requester pays the amount asked.
- the sharing of Pik can include a collaborative or social research. For example, a user (knowledgeable or not) can start a Pik and have it open to the public to add sections and links.
- a user can invite people (friends, family, etc.) to collaborate on a Pik. For example, a user may plan for a group trip and start a Pik of travel research. The user can invite other people who would travel with him or her to collaborate on the Pik.
- a user can share or post his or her request for Pik in the marketplace.
- the request can specify the user's detailed research needs such as target, action, etc.
- Other users or creators of Piks can view and bid for the request.
- the user requesting a Pik can compensate another user who provides the Pik through regular payment channels (e.g., credit cards, bank accounts, third party online payment systems, etc.)
- users can purchase internal system credits or points to be used for payment of Piks.
- public and paid research can be searched for with keywords, as well as sorting and filtering options.
- the recommended results may vary depend on factors from the algorithm that can consist of demographic and behavioural data, content and tag analysis, and real-time big data.
- the user will be able to browse various curated online research from the homepage and their subscriptions to other users. If the desired research cannot be found, then the user has the opportunity to place a request for it. Other users can now bid on the opportunity to conduct the research and the requester can make the final decision on which creator to move forward with.
Abstract
Systems and methods for an enhanced online research are described. In exemplary embodiments, the enhanced research platform receives one or more keywords, and determines a research goal by parsing each of the keywords and identifying a category for each of the keywords. The enhanced research platform then queries a database to identify results matching the keywords based on analysis of the keywords and the research goal, and displays the identified results in a user interface.
Description
- This application claims priority to and the benefit of U.S. Provisional Patent Application 62/452,040 titled “Systems and methods for an enhanced search platform”, filed on Jan. 30, 2017, the disclosure of which is incorporated herein by reference in its entirety.
- The present disclosure generally relates to online research.
- Conventional search engines assume that users know what they are looking for, and that they know what keywords to use to get the information and know the best logical order of keywords. That is, if users enter the right query, then conventional search engines will provide the right answer. The quality of the search results is based on the accuracy of the keywords.
- However, if the user does not have enough information on the subject, he or she may not be able to use accurate keywords to start the search. Therefore, the search results may include a large number of irrelevant contents which need to be checked individually by the user, which may lead to a waste of time. Therefore, there is a need for a better online research system and method whereas research is a consecutive action to search relevant information..
- In one aspect, the present disclosure relates to a method for an enhanced research platform. The method can comprise: receiving from a user, by a computer processor, one or more keywords; receiving from a user one or more tags; sending the one or more keywords and the one or more tags to a server; receiving from the server a plurality of curated online research archives, wherein each of the plurality of curated online research archives comprises a list of categorized websites matching the one or more keywords and the one or more tags; ranking the received plurality of curated online research archives based on a relevancy to the keywords and the tags; and displaying the ranked plurality of curated online research archives in a user interface.
- In some embodiments, the method can comprise ranking the received plurality of curated online research archives based on an attribute of one of the plurality of curated online research archives, wherein the attribute comprises at least one of: real time trends and popularity, number of links, number of views, or rating.
- In some embodiments, the method can comprise ranking the received plurality of curated online research archives based on an attribute of a creator associated with one of the plurality of curated online research archives, wherein the attribute comprises at least one of: number of subscribers of the creator, number of curated online research archives created by the creator, or rating of the creator.
- In some embodiments, the method can comprise ranking the received plurality of curated online research archives based on an attribute of a website link associated with one of the plurality of curated online research archives, wherein the attribute comprises at least one of: duration of visit of the website link, number of characters on a webpage associated with the website link, or number of revisit associated with the website link.
- In some embodiments, the method can comprise receiving user demographic and behavioral data; sending the received user demographic and behavioral data to the server; receiving a plurality of curated online research archives, wherein each of the plurality of curated online research archives comprises a list of categorized websites matching the one or more keywords, the one or more tags, the user demographic data, and the user behavioral data; and displaying the plurality of curated online research archives in the user interface. In some embodiments, the method can comprise ranking the plurality of curated online research archives based on relevancy to the keywords, the tags, the user demographic data, and the user behavioral data. In some embodiments, the user demographic data can comprise at least one of location of the user, gender, age, experience level, education level, or household income.
- In some embodiments, each of the plurality of curated online research archives can comprise a title that indicates a research goal, an order of one or more sub-titles that indicate categories for grouping like websites, and an order of websites within each sub-title to indicate progression from an end-to-end research.
- In another aspect, the present disclosure relates to a method for creating a curated online research archive, wherein the curated online research archive comprises a list of websites. In some embodiments, the method can comprise: receiving, by a computer processor, a title from a user; receiving one or more tags from the user; receiving one or more section names from the user; receiving instruction from a user to start recording search history; recording a list of websites visited by the user based on the received instruction; filtering the recorded list of websites; categorizing the filtered list of websites based on the one or more section names; creating a curated online research archive wherein the curated online research archive comprises the title, the one or more tags, and the categorized list of websites; and outputting the curated online research archive. In some embodiments, recording the list of websites visited by the user can comprise recording the list of websites with a browser extension.
- In some embodiments, filtering the recorded list of websites can comprise: displaying the recorded list of websites to the user; receiving from the user a selection of one or more websites; and removing the one or more websites from the recorded list of websites based on the received selection. In some embodiments, filtering the recorded list of websites can comprise filtering the recorded list of websites based on a set of pre-defined rules.
- In some embodiments, the method can comprise: receiving user demographic data; updating the curated online research archive by adding the received user demographic data to the curated online research archive; and outputting the updated curated online research archive. In some embodiments, the user demographic data comprises at least one of location of the user, gender, age, experience level, education level, or household income.
- In some embodiments, the method can comprise: receiving an instruction of editing from the user; and updating the categorized list of websites in response to the instruction of editing.
- In another aspect, the present disclosure relates to a system for an enhanced research platform, the system comprising: a computer processor; and a non-transitory computer readable storage medium storing computer program instructions, the instructions when executed by the computer processor causing the computer processor to perform steps comprising: receiving from a user one or more keywords; receiving from a user one or more tags; sending the one or more keywords and the one or more tags to a server; receiving from the server a plurality of curated online research archives, wherein each of the plurality of curated online research archives comprises a list of categorized websites matching the one or more keywords and the one or more tags; ranking the received plurality of curated online research archives based on a relevancy to the keywords and the tags; and displaying the ranked plurality of curated online research archives in a user interface.
- To assist those of skill in the art in making and using an enhanced research system and associated methods, reference is made to the accompanying figures. The accompanying figures, which are incorporated in and constitute a part of this specification, illustrate one or more embodiments of the invention and, together with the description, help to explain the invention. Illustrative embodiments are shown by way of example in the accompanying drawings and should not be considered as limiting. In the figures:
-
FIG. 1 is a block diagram showing an enhanced research system implemented in modules, according to some embodiments of the present disclosure; -
FIG. 2 is a flowchart of an exemplary research goal-based method performed by the enhanced research system, according to some embodiments of the present disclosure; -
FIG. 3 is a flowchart of an exemplary keywords-based method performed by the enhanced research system, according to some embodiments of the present disclosure; -
FIGS. 4A and 4B are flowcharts illustrating an exemplary data flow for the enhanced research system, according to some embodiments of the present disclosure; -
FIG. 5 is a flowchart of website-based curated online research archive (or “Pik”) creation method performed by the enhanced research system, according to some embodiments of the present disclosure; -
FIG. 6 is a flowchart of browser extension-based Pik creation method performed by the enhanced research system, according to some embodiments of the present disclosure. -
FIGS. 7A and 7B are flowcharts illustrating an exemplary data flow for the enhanced research system, according to some embodiments of the present disclosure; -
FIG. 8 is a network diagram depicting a system for implementing the enhanced research system, according to some embodiments of the present disclosure; -
FIG. 9 is a block diagram of an exemplary computing device that may be used to implement exemplary embodiments of the enhanced research system described herein; -
FIGS. 10A-10C is an exemplary workflow for the enhanced research system, according to an example embodiment; -
FIGS. 11A-11F illustrate exemplary user interfaces for the enhanced research system, according to an example embodiment; and -
FIGS. 12A-12D illustrate exemplary user interfaces for the enhanced research system being used as a Google Chrome Extension, according to an example embodiment. -
FIG. 13 is a block diagram showing functions of a marketplace for Piks, according to some embodiments of the present disclosure. - A user who needs to find information for their complex online research typically find information through a multitude of venues, ranging from search engines, forums and community boards, and social media and chat. One of the most common solutions is utilize search engines to receive quick answers or links to websites containing the information. The user may type keywords related to his or her research goal. The search results are then populated with links chosen by its algorithm. The user may then, based on personal experiences, selectively choose one or more links to browse and continue doing so. During the browsing process, the user may decide enough information has been gathered. The user may also start a new search with new keywords, or give up the process due to a lack of relevant search results.
- Conventional search engines assume that users know what they are looking for, and that they know what keywords to use to get the information and know the best logical order of keywords. That is, if users enter the right query, then conventional search engines will provide the right answer. However, a user's initial or perceptual needs can be different from actual needs. There is a difference in what users think they want versus what they actually need. Users may also struggle to find the right keywords to obtain the right information. Some topics may require basic research just to understand relevant jargon. Users may follow an illogical order of keywords, lengthening their search time.
- There are several areas for improvement in this process. First, the user needs to know the appropriate keywords to search to find the best results. A novice may not know the jargon necessary to produce the search results he/she needs in the beginning. He or she may not know what's the best keywords to search first, second, third, etc. Without the correct order, the knowledge obtained may be confusing. Second, as the basics of the algorithm is known to marketers, they have used that to their advantage. By manipulating the metadata, the title, and content, the website can be high on the search result list without being the most useful for the user's research. This leads to the third issue of bouncing, when users click on non-essential links, leave, and ultimately, waste their time. Finally, there is the issue of accurate and comprehensive knowledge. It is difficult for beginners to know that their research is complete without knowing all the components of the topic beforehand and that the information they are reading is accurate. Their “feeling” of what is correct and complete may lead to poor decisions in the future. Therefore, a better online research system and method is needed for users with limited information of the subject.
- Knowledgeable users may also encounter difficulties with the traditional search method. A user can save his or her research in order to refer or update to it later, and share or collaborate with others, for personal and professional reasons. Typical solutions for such this situation may be keeping tabs of useful sites open, creating spreadsheets and copying and pasting the information into them, or bookmarking relevant sites. However, there are plenty of possibilities of failure. For example, the browser may crash and the session was not saved. The user may also only save the link, but not relevant information to help reference them later. Therefore, there is also a need for a better online research system and method for users with substantial information of the subject.
- The present disclosure describes a system and method of enhance online research. In some embodiments, a beginner may not need to know what exactly to search as long as he or she has a question or topic in mind. The user will be able to view curated online research archives (referred herein as “Piks”) from other creators or via a proprietary algorithm. In some embodiments, curated online research archives can also be referred as “search journeys”, as described in U.S. Provisional Application 62/452,040 titled “System and methods for an enhanced research platform,” filed on January 30, 2017, the disclosure of which is incorporated herein by reference in its entirety. The curated online research archives (or “Piks”) will consist of a comprehensive view of all the sub-topics with links and comments. Since these links have been chosen based on its usefulness, the chance of bouncing is greatly decreased. The user can see the ratings and descriptions of the creator and their Piks to validate the accuracy and usefulness of the research.
- For a knowledgeable user (referred herein as a “creator”), the described system and method can offer two methods to help organize, save, and share their research. First, creators will be able to copy and paste their links and relevant information to their account. Doing so allows them to access the research as long as they have a device able to interact with the internet. Secondly, a browser extension will help automatically save essential links and information with areas for the user to place comments and further organize.
- Research Goal-based Online Research
- Described in detail herein is a search system that analyzes user behavior with user modeling and cognitive technology, supports a research goal, and recommends other users' search history or journey who had the same or similar research goal.
- In exemplary embodiments, the enhanced research system supports a research goal. In an example embodiment, the enhanced research system parses keywords or search terms entered by a user, and automatically determines the research goal through keyword analysis. The enhanced research system may assign a category to each keyword or search term entered by the user.
- In some embodiments, the enhanced research system also enables creation of a Pik. As used herein, a “Pik” can be an organized list of search results or content available on the Internet that a user found valuable or relevant to his or her research goal. A Pik may include a title that indicates a research goal, a sub-title that indicates categories for grouping like data, content, websites and information, and an order of websites and content within each sub-title to indicate progression from a broad concept to a narrow concept. A Pik may be a streamlined report of search activity from start to finish. For example, a user may be interested in buying a drone, and he or she may be a diligent person who likes to perform in-depth research prior to buying a drone. So the user enters 30 or so keywords in the search box and visits approximately 200 links or search results spending a total of about 5 hours searching. Via the enhanced research system, the user can save the relevant or valuable links or search results visited as a Pik. The user can also categorize the saved links and set a logical order for the links in the Pik.
- In one embodiment, a user can create his or her own Pik. In other embodiments, the enhanced research system automatically creates a Pik by analyzing data collected over time, where the data indicates the relevancy of a search result or content to a user's research goal. A Pik may include a category title for content or search results, and an order of content or search results (which may be based on relevancy). In some embodiments, the system can provide suggestions to a user. For example, when a user creates a title for a research goal, the system can suggest sections to consider. In some embodiments, when a user refers to or edits a Pik, the system can suggest new links to add into one or more sections of the Pik.
- In some embodiments, the enhanced research system can analyze user behavior to provide search results and content that is relevant to the user. The user behavior may be characterized using machine learning techniques, cognitive technology, user behavior modeling, search history, browsing history, purchase history, and other data that can aid in characterizing a user.
- The enhanced research system can also recommend another user's Pik. In an example embodiment, the enhanced research system analyzes the user characterization and the user's research goal, and identifies existing Piks that match or substantially match the user characterization and the user's research goal.
- In an example embodiment, the enhanced research system is provided as a plug-in or extension for use with an installed web browser on a computing device. For use on a mobile device, the enhanced research system may be provided as an application (e.g., app).
- In some embodiments, the user can share his or her Pik with other users of the enhanced research system. The user may also edit his or her own Pik. The enhanced research system may also include a messaging system, where the user can send messages to other users. In this manner, the enhanced research system provides a collaborative environment for users to find, learn and share knowledge and information. In some embodiments, the sharing of Pik can include a collaborative or social research. For example, a user (knowledgeable or not) can start a Pik and have it open to the public to add sections and links. In some embodiments, a user can invite people (friends, family, etc.) to collaborate on a Pik. For example, a user may plan for a group trip and start a Pik of travel research. The user can invite other people who would travel with him or her to collaborate on the Pik.
- Exemplary flowcharts are provided herein for illustrative purposes and are non-limiting examples of methods. One of ordinary skill in the art will recognize that exemplary methods may include more or fewer steps than those illustrated in the exemplary flowcharts, and that the steps in the exemplary flowcharts may be performed in a different order than the order shown in the illustrative flowcharts.
-
FIG. 1 is a block diagram showing anenhanced research system 100 implemented in modules, according to an example embodiment. In some embodiments, the system can be implemented inserver 830 shown inFIG. 8 , and a client device can access to the system via the Internet. In some embodiments, the system may be implemented indevices FIG. 8 . The modules include a user profile module 110, aparsing module 120, aPik module 130, a user analysis module 140, arecommendation module 150, and auser interface module 160. The modules may include various circuits and one or more software components, programs, applications, apps or other units of code base or instructions configured to be executed by one or more processors included indevices module 120,Pik module 130, user analysis module 140,recommendation module 150,user interface module 160 may be included inserver 830, while others can be provided in thedevice module 120,Pik module 130, user analysis module 140,recommendation module 150 anduser interface module 160 are shown as distinct modules inFIG. 1 , it should be understood that they may be implemented as fewer or more modules than illustrated. It should be understood that any of modules may communicate with one or more components included in system 800 (FIG. 8 ), such as database(s) (e.g., database(s) 840), server (e.g., server 830), or devices (e.g.,devices 810, 820). - The user profile module 110 may be a software or hardware-implemented module that may be configured to manage and maintain user profiles for users of the enhanced research system. The user profiles may include information such as username, password, name, geographic location, demographics, user preferences, and other user information.
- The
parsing module 120 may be a software or hardware-implemented module that may be configured to identify a research goal by parsing one or more keywords or search terms entered by a user in the enhanced research system. Theparsing module 120 may categorize each of the keywords or search terms entered by the user into, for example, target, action, purpose, experience level, price range, and others. Theparsing module 120 may use natural language processing, clustering techniques, auto labeling techniques, and other mechanisms to categorize the keywords or search terms. In an example embodiment, theparsing module 120 may recognize a pattern based on a user's past search history or past entered-keywords, and use the pattern to categorize the instant keywords or search terms entered by a user. The categorized keywords or search terms may be referred to as the research goal. - For example, a user may enter the following as keywords or search terms: “I want to buy a drone for school project.” The
parsing module 120 may parse the keywords, and categorize them. For example, “drone” may be categorized as ‘target,’ “buy” may be categorized as ‘action,’ “school” may be categorized as ‘purpose.’ In an example embodiment, theparsing module 120 may identify an experience level for the research goal based on past search history. For example, the experience level may be set as “beginner” based on theparsing module 120 recognizing from past search history and user activity that the user has beginner level knowledge of drones. - In an example embodiment, the user may characterize the keywords or search terms by entering them in the user interface under specific categories.
- The
Pik module 130 may be a software or hardware-implemented module that may be configured to store created Piks in a database, manage and maintain Piks, update Piks based on edits made by users, and record sharing of Piks by users with other users. ThePik module 130 may manage data and information for each Pik, for example, an organized list of links, search results, or content. - In an example embodiment, the
Pik module 130 may be configured to automatically generate a Pik by analyzing relevancy of search results with respect to a research goal. ThePik module 130 may employ machine learning techniques to analyze large amounts of data, including a number of users who visit particular links or content in view of the keywords searched, an amount of time spent by users on a particular website in view of the keywords searched, and any subsequent actions taken by the user with respect to the website (e.g., saving as bookmark, clicking on another link within the website, completing a purchase on the website, etc.). In some embodiments, thePik module 130 can employ machine learning techniques to analyze other data and metrics. - The user analysis module 140 may be a software or hardware-implemented module that may be configured to characterize users by analyzing various data related to users, and storing the determined characteristics in a database. For example, the user analysis module 140 may analyze data such as a user's search history, user's browsing history, user's purchase habits, user's demographics, user's geographic location, user's social media profile and content, and the like. The user analysis module 140 may employ machine learning techniques or cognitive technology to analyze data and characterize users. In some embodiments, the user analysis module 140 can be implemented in a server which can be accessed by different user with difference user devices. In some embodiments, the user analysis module 140 can be implemented in one user device which is shared by difference users who may have separate profile and/or account on the user device.
- The
recommendation module 150 may be a software or hardware-implemented module that may be configured to query a database to generate Pik search results and rank Pik search results based on analyzing the relevancy of each results in view of the research goal identified by theparsing module 120 and the user characteristics identified by the user analysis module 140. Therecommendation module 150 may also be configured to query a database to identify Piks that match or substantially match the research goal and the user characteristics. Therecommendation module 150 may employ mathematical algorithms or techniques that implement user modeling, collaborative filtering, content based filtering, regression modeling, and other mechanisms to provide personalized search results and personalized recommendation of Piks. Therecommendation module 150 may also analyze information such as popularity of the Pik, timeliness, and other factors when determining if a Pik should be recommended to the user. - The
recommendation module 150 identifies search results and Piks based searching of the keywords and each keyword's assigned category. To illustrate using the previous example of “I want to buy a drone for school project”, therecommendation module 150, while searching the Internet or querying a database, takes into consideration that “drone” is the target, “buy” is the action, “school” is the purpose, and “beginner” is the experience level. In this manner, the enhanced research system is able to provide more relevant results than conventional search engines. - In an example embodiment, each Pik is associated with one or more research goal and stored as such in the database. Each Pik may also be associated with user characteristics based on the user who created the Pik. In an example embodiment, the
recommendation module 150 ranks the Piks based on how well each matches the research goal and/or the user characteristics. For example, Piks associated with the same research goal as the keywords entered by the user and associated with the same user characteristics as the instant user are ranked higher. The Piks may also be ranked based on other factors including creation time, research trends or popularity. - The
user interface module 160 may be a software or hardware-implemented module that may be configured to manage and display a user interface ondevice 710, 720 that enables a user to use the enhanced research system described herein. Theuser interface module 160 may facilitate display of the search results and Piks identified by the enhanced research system. Exemplary user interface screens are described with respect toFIGS. 6A-6F . -
FIG. 2 is a flowchart of anexemplary method 200 performed by the enhanced research system in an example embodiment. Atstep 202, theparsing module 120 receives keywords from a user via user interface screen provided by the enhanced research system. Atstep 204, theparsing module 120 determines a research goal by categorizing the keywords entered by the user. Theparsing module 120 may categorize each of the keywords or search terms entered by the user into categories, such as target, action, purpose, experience level, price range, and others. Theparsing module 120 may use natural language processing, clustering techniques, auto labeling techniques, and other mechanisms to categorize the keywords or search terms. In an example embodiment, theparsing module 120 may recognize a pattern based on a user's past search history or past entered-keywords, and use the pattern to categorize the instant keywords or search terms entered by a user. - At
step 206, the user analysis module 140 analyzes user demographic and behavioral data to characterize the user. For example, the user analysis module 140 may analyze demographic data such as location of the user, gender, age, experience level, education level, household income, and the like. The user analysis module 140 may also analyze data such as a user's search history, user's browsing history, user's purchase habits, user's social media profile and content, and the like. The user analysis module 140 may employ machine learning techniques or cognitive technology to analyze data and characterize users. - At
step 208, therecommendation module 150 queries the database to identify results that match or substantially match the research goal. In an example embodiment, the identified results are Piks. In an example embodiment, therecommendation module 150 also identifies results based on analysis of the user demographic and behavioral data. - At
step 210, therecommendation module 150 ranks the identified results based each results relevancy to the research goal. The results are also ranked based on analysis of user demographic and behavioral data and determination of relevancy of the results based on the demographic and behavioral data. Atstep 212, theuser interface module 160 displays the ranked results in a user interface (for example, user interface screen 640,FIG. 6E , described below). In some embodiments, therecommendation module 150 can also sort the Pik results by date, most viewed, most saved, rankings, or number of links. In some embodiments, therecommendation module 150 can also filter the Pik results by categories. - Keywords and Tags-based Online Research
- In some embodiments, a user can provide one or more keywords and tags, instead of research goals. The system can then return one or more Piks to the user based on the identified keywords and tags.
-
FIG. 3 is a flow chart showing a keywords and tags-basedonline research method 300. Atstep 302, the system can receive one or more keywords from the user. For example, in one embodiment, the keywords can include a name of a type of product that the user is interested in. In one embodiment, the keywords can include a name of a travel destination for a trip that the user is planning for. In one embodiment, the keywords can include a service that the user may need. - At
step 304, the system can receive one or more tags from the user. In some embodiments, a user can type in keywords and then receive the list of Pik results with relevant tags at the top. At the Pik search result page, there will be a list of relevant tags, that the user can choose to help filter out their result pages. For example, in one embodiment, the tags can include a price range for the product that the user is interested in. In one embodiment, the tags can include a time for the trip that the user is planning for. In one embodiment, the tags can include the user's demographic data such as location of the user, gender, age, experience level, education level, household income, and the like. - At
step 306, the system can query the database to identify results that match or substantially match the keywords and tags. In one embodiment, the identified results are one or more Piks. In one embodiment, the system also identifies results based on analysis of the user demographic and behavioral data. - At
step 308, the system can rank the identified results based each results relevancy to the keywords and the tags. The results are also ranked based on analysis of user demographic and behavioral data and determination of relevancy of the results based on the demographic and behavioral data. Atstep 310, theuser interface module 160 displays the ranked results in a user interface. -
FIGS. 4A and 4B areflowcharts 400 illustrating an exemplary data flow for the enhanced research system, according to an example embodiment. In this example, the enhanced research system is provided as a website. Theflowcharts 400 illustrate data flow between various components, such as the enhanced research system (e.g., website), the API for the enhanced research system, and the database. Theflowcharts 400 illustrate the data flow when a user navigates to a login page for the enhanced research system, when a user navigates to a search dialog box/page, when a user navigates to the search results page generated by the enhanced research system, and when a user navigates to a Pik page. - At
step 402, the user can enter his or her credentials associated with the enhanced research system on the enhanced research system website and the application interface (API) can send the login information to the database. If the authentication is finished, the user can be navigated to a search page atstep 404. The user can type search keywords on the website. The user can get search hints from the database through the website and the API. The user can also get search options from the database through the website and the API. The user can then submit the search query on the website. Atstep 406, the user can be navigated to a search results page. The website can request and display search results from the database. The search results can include one or more Piks created by other users. Atstep 408, the user can choose to view a specific Pik from the search results. Then the website can send this selection to the database to update the number of views for this Pik. The user can also click a “like” button and accordingly the number of likes for this Pik can be updated. The user can also share this Pik with others via social networks or other channels (e.g., email, texts, etc.) Then the number of shares for this Pik can also be updated. In some embodiments, the user can provide a rating for the Pik and the average rating of the Pik can be updated. In some embodiments, the user can save Piks onto his or her account. In some embodiments, the user can subscribe to a specific creator. - Creation of a Pik
- The present disclosure describes a system and method for a user to create and save his or her Piks. In some embodiments, the user can create and save a Pik using a website. In some embodiments, the user can create and save a Pik using a browser extension (e.g., a Google Chrome™ extension.)
-
FIG. 5 is a flowchart showing a website-basedmethod 500 of creating a Pik. Atstep 502, the user can login to the system's website (e.g., “Pikurate” website) with his or her credentials. Atstep 504, the user can specify a title and one or more tags for the Pik. Atstep 506, the user can enter contents and links of the Pik on the website. In some embodiments, the user can copy website links which he or she is visiting and paste them on the system's website. In some embodiments, the user can copy website contents which he or she likes and paste them on the system's website, and the website contents can be tied to the links that are saved. Atstep 508, the user can add comments on the system's website. In some embodiments, the user can add notes about the website links. In some embodiments, the user can explain why he or she likes the contents. Atstep 510, the use can save the created Pik in the system. -
FIG. 6 is a flowchart showing a browser extension-basedmethod 600 of creating a Pik. Atstep 602, the browser extension can (e.g., Google Chrome™ extension, Firefox® extension, etc.) start automatically when the user opens an Internet browser (e.g., Google Chrome™ browser, Firefox® browser, etc.) In some embodiment, the user can manually enable the browser extension in the browser. - At
step 604, the browser extension can receive a title and one or more tags from the user. For example, in one embodiment, the title can include a name of a type of product that the user is interested in. In one embodiment, the title can include a name of a travel destination for a trip that the user is planning for. In one embodiment, the title can include a service that the user may need. In one embodiment, the tags can include a price range for the product that the user is interested in. In one embodiment, the tags can include a time for the trip that the user is planning for. In one embodiment, the tags can include the user's demographic and behavioral data such as location of the user, gender, age, experience level, education level, household income, and the like. - At
step 606, the browser extension can receive the user's instruction to start to record the user's browsing history. In some embodiments, the user can click a button on the browser extension to start the recording. In some embodiments, the user can click the button to end the recording. In some embodiments, the user can also manually save links & comment with extension buttons or hotkeys. - At
step 608, the recorded browsing history can be filtered. In some embodiments, the user can manually remove any website links that are not needed. In some embodiment, the browser extension can filter the list with a pre-defined algorithm. In one embodiment, the algorithm can filter the list based on how long the user stays on a specific website. In another embodiment, the algorithm can filter based on actions taken on a specific website, such as clicks, scrolls, etc. In another embodiment, the algorithm can filter based on the minimum amount of characters on a specific website. In another embodiment, the algorithm can filter based on keyword analysis. In another embodiment, the algorithm can filter based on links saved by other users. - At
step 610, the user can save the created Pik in the browser extension. In some embodiments, if the user wants to share the created Pik, the browser extension can upload it to a system server. -
FIGS. 7A and 7B areflowcharts 700 illustrating an exemplary data flow for the enhanced research system according to an example embodiment. The Pik is referred as search journey inFIGS. 7A-7B . In this example, the enhanced research system is provided as Google Chrome™ Extension. Theflowcharts 700 illustrate data flow between various components, such as a user's web browser, Internet, the enhanced research system (e.g., Google Chrome™ Extension), the API for the enhanced research system, and the database. Theflowcharts 700 illustrate the data flow when a user creates a Pik. - At
step 702, the user can start a browser (e.g., Google Chrome™ browser, Firefox® browser) to search information on the Internet. The enhanced research system can include an extension tool in the browser (e.g., Google Chrome™ extensions in Google Chrome™ browser.) The extension tool can be activated as the browser runs. The extension tool can navigate the user to a login page where the user enters his or her credentials with the enhanced research system. Then the extension tool can send the credentials to the database for authentication. If the login information passes authentication process, the use can be navigated to a Pik page. The extension tool can get the current active page data and analyze. It can record the browsing history and send the data to the database. Atstep 704, the user can start to record a new Pik by clicking a button on the extension tool. The user can be asked to enter a name for the new Pik. The created new Pik can be set to the active journey. Atstep 706, the user can be asked to add a category name for the new Pik. Atstep 708, the user can be asked to add a tag name for the new Pik. Atstep 710, the user can choose to save the current Pik and the extension tool can send the data to the database. - In some embodiments, the enhanced research system can include a filter to remove unwanted or irrelevant search history. In one embodiment, a manual filter can be implemented so that the user can manually select which search history and/or record to keep and remove the rest. In one embodiment, an automatic filter can be implemented with pre-defined algorithm and/or rules. For example, the filter can automatically remove parent webpages and only keep relevant child-webpages. In another example, the filter can automatically detect if the user scrolls down the webpage being visited. If so, the filter can determine the webpage is relevant and keep it in the search record. These relevant webpages are saved into the active section. The user can also manually save the links, comment on them, or highlight and save sections via extension buttons or hotkeys. The user can access, edit, organize, and update them later through the extension or a website of the system. In some embodiments, the system can filter the search history with other criteria including duration of visit by the user, whether it is a search engine results page, mouse action, number of characters on the website, etc.
-
FIG. 8 illustrates a network diagram depicting asystem 800 for implementing the enhanced research system, according to an example embodiment. Thesystem 800 can include anetwork 805, multiple devices, for example,device 810,device 820, aserver 830, and database(s) 840. Each of thedevices servers 830, and database(s) 840 is in communication with thenetwork 805. In some embodiments,device 810,device 820,server 830, database(s) 840 can include one ormore computer devices 900 as shown inFIG. 9 . - In an example embodiment, one or more portions of network 705 may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless wide area network (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a cellular telephone network, a wireless network, a Wi-Fi network, a WiMax network, any other type of network, or a combination of two or more such networks.
- The
devices - Each of
devices computing device 800 shown inFIG. 8 . Each ofdevices devices devices - In other embodiments, the enhanced research system may be included on the
device server 830 performs the functionalities described herein. In yet another embodiment, thedevice server 830 performs the other functionalities described herein. - Each of the database(s) 840 and
server 830 is connected to thenetwork 805 via a wired connection. Alternatively, one or more of the database(s) 840, andserver 830 may be connected to thenetwork 805 via a wireless connection. Although not shown,server 830 can be (directly) connected to the database(s) 840.Server 830 comprises one or more computers or processors configured to communicate withdevices network 805.Server 830 hosts one or more applications or websites accessed bydevices server 830, and/ordevices server 830 may be located at one or more geographically distributed locations from each other or fromdevices server 830. -
FIG. 9 is a block diagram of anexemplary computing device 900 that may be used to implement exemplary embodiments of the enhancedresearch system 100 described herein. Thecomputing device 900 includes one or more non-transitory computer-readable media for storing one or more computer-executable instructions or software for implementing exemplary embodiments. The non-transitory computer-readable media may include, but are not limited to, one or more types of hardware memory, non-transitory tangible media (for example, one or more magnetic storage disks, one or more optical disks, one or more flash drives), and the like. For example,memory 906 included in thecomputing device 900 may store computer-readable and computer-executable instructions or software for implementing exemplary embodiments of the enhancedresearch system 100. Thecomputing device 900 also includes configurable and/orprogrammable processor 902 and associatedcore 904, and optionally, one or more additional configurable and/or programmable processor(s) 902′ and associated core(s) 904′ (for example, in the case of computer systems having multiple processors/cores), for executing computer-readable and computer-executable instructions or software stored in thememory 906 and other programs for controlling system hardware.Processor 902 and processor(s) 902′ may each be a single core processor or multiple core (904 and 904′) processor. - Virtualization may be employed in the
computing device 900 so that infrastructure and resources in the computing device may be shared dynamically. Avirtual machine 914 may be provided to handle a process running on multiple processors so that the process appears to be using only one computing resource rather than multiple computing resources. Multiple virtual machines may also be used with one processor. -
Memory 906 may include a computer system memory or random access memory, such as DRAM, SRAM, EDO RAM, and the like.Memory 906 may include other types of memory as well, or combinations thereof. - A user may interact with the
computing device 900 through avisual display device 918, such as a computer monitor, which may display one or moregraphical user interfaces 922 that may be provided in accordance with exemplary embodiments. Thecomputing device 900 may include other I/O devices for receiving input from a user, for example, a keyboard or any suitable multi-point touch interface 808, a pointing device 910 (e.g., a mouse), amicrophone 928, and/or an optical scanning/capturing device 932 (e.g., a camera, scanner, barcode reader, QR code reader). The multi-point touch interface 908 (e.g., keyboard, pin pad, scanner, touch-screen, etc.) and the pointing device 910 (e.g., mouse, stylus pen, etc.) may be coupled to the visual display device 818. Thecomputing device 900 may include other suitable conventional I/O peripherals. - As described herein, the
optical scanning device 932 can scan optical machine-readable representations associated with physical objects so that thecomputing device 900 can receive and process the identifier. - The
computing device 900 may also include one ormore storage devices 924, such as a hard-drive, CD-ROM, or other computer readable media, for storing data and computer-readable instructions and/or software that implement exemplary embodiments of the enhancedresearch system 100 described herein.Exemplary storage device 924 may also store one or more databases for storing any suitable information required to implement exemplary embodiments. For example,exemplary storage device 924 can store one ormore databases 926 for storing information and data to be used by embodiments of thesystem 100. The databases may be updated manually or automatically at any suitable time to add, delete, and/or update one or more items in the databases. - The
computing device 900 can include anetwork interface 912 configured to interface via one ormore network devices 920 with one or more networks, for example, Local Area Network (LAN), Wide Area Network (WAN) or the Internet through a variety of connections including, but not limited to, standard telephone lines, LAN or WAN links (for example, 802.11, T1, T3, 56kb, X.25), broadband connections (for example, ISDN, Frame Relay, ATM), wireless connections, controller area network (CAN), or some combination of any or all of the above. In exemplary embodiments, thecomputing device 900 can include one ormore antennas 930 to facilitate wireless communication (e.g., via the network interface) between thecomputing device 900 and a network. Thenetwork interface 912 may include a built-in network adapter, network interface card, PCMCIA network card, card bus network adapter, wireless network adapter, USB network adapter, modem or any other device suitable for interfacing thecomputing device 900 to any type of network capable of communication and performing the operations described herein. Moreover, thecomputing device 900 may be any computer system, such as a workstation, desktop computer, server, laptop, handheld computer, tablet computer (e.g., the iPad™ tablet computer), mobile computing or communication device (e.g., the iPhone™ communication device), internal corporate devices, or other form of computing or telecommunications device that is capable of communication and that has sufficient processor power and memory capacity to perform the operations described herein. - The
computing device 900 may run anyoperating system 916, such as any of the versions of the Microsoft® Windows® operating systems, the different releases of the Unix and Linux operating systems, any version of the MacOS® for Macintosh computers, any embedded operating system, any real-time operating system, any open source operating system, any proprietary operating system, or any other operating system capable of running on the computing device and performing the operations described herein. In exemplary embodiments, theoperating system 916 may be run in native mode or emulated mode. In an exemplary embodiment, theoperating system 916 may be run on one or more cloud machine instances. - User Interface
-
FIGS. 10A-10C illustrate anexemplary user workflow 1000 for when the enhanced research system is used on a mobile device, according to an example embodiment. In an example embodiment, the enhanced research system may be provided as an app called “Pikurate.” Theworkflow 1000 begins atstep 1002 where the homepage for the enhanced research system is displayed. The user enters search terms or keywords in the search dialog box, and clicks the search button. Upon clicking of the button, theworkflow 1000 continues to step 1004 where the entered search terms or keywords are parsed and categorized. As described above, the enhanced research system determines the research goal and automatically assigns categories to each of the keywords or search terms. Atstep 1004, the enhanced research system displays the automatically categorized keywords. For example, “drone” is displayed as the target category and “buy” is displayed as the action category. In an example embodiment, the enhanced research system assigns “beginner” as the experience level category based on analysis of the user's past search history. - If the user is satisfied, he or she can click the search button, which continues the workflow to step 1006. At
step 1006, the enhanced research system displays Pik results based on analysis and matching of the categorized keywords or search terms. The user can click on a category button, which moves the workflow to step 1008. Atstep 1008, the user can save search results of interest by clicking a save button. Clicking on the save button continues theworkflow 1000 to step 1016 (FIG. 10B ). Atstep 1016, the user can select or enter a folder name where the search results are saved. Once this information is entered, the workflow returns to step 1006 where the user can continue viewing the Pik results. - At
step 1008, the user can also edit the automatically assigned categories. For example, the user can edit the experience level category by clicking on it atstep 1008. At this point, the workflow continues to step 1010 (FIG. 10B ). Atstep 1010, the user can enter a new keyword or select from an available list of keywords for the experience level category. For example, here the user selects “kids” instead of “beginner,” and clicks on the search button. Clicking on the search button displays new Pik results at step 1018 (FIG. 10C ) that match the updated categorized keywords. - The user can click on a Pik from the Pik results to display expanded information in the Pik (
step 1012 ofFIG. 10B ). As described above, a Pik is an organized list of search results, links or content. The user can scroll through the Pik results using a swipe gesture at the mobile device interface. Atstep 1012, the user can click on the links within the Pik. Clicking on the links opens the link at step 1014 (FIG. 10B ). The user can return to step 1012 to view the Pik by clicking on a button atstep 1014. - At any time, the user can click on the search button to edit keywords or enter new keywords. For example, at step 1018 (
FIG. 10C ) the user can click on the search button (e.g., magnifying glass icon), and the workflow continues to step 1020 (FIG. 10C ) where the user can edit the search terms. - At step 1022 (
FIG. 10C ), a user can view options for sharing a Pik. The options for sharing displayed on the screen include, for example, share to Twitter, share to Facebook, share to Google +, Share to LinkedIn, and copy URL to share. The options also include “Report,” which a user can use to report a Pik if it includes inappropriate information. -
FIGS. 11A-11F illustrate exemplary user interface screens for the enhanced research system, according to an example embodiment. In an example embodiment, the enhanced research system may be provided as a website called “Pikurate.” -
FIG. 11A illustrates an exemplaryuser interface screen 1100 that displays a login page. The user can login into his or her “Pikurate” account by entering account credentials in thelogin box 1101. If the user does not have an account with Pikurate, he or she can register an account by clicking the “Sign Up”button 1102. -
FIG. 11B illustrates an exemplary user interface screen 1110 where the user can enter search terms or keywords in asearch box 1111. The user can select specific category under acategory menu 1112. For example, the user can enter “awesome digital camera” in thesearch box 1111 and select “Fun & Photography” from thecategory menu 1112. Based on the entered search terms and selected category, the system can provide a number oftags 1113 to the user. The user can choose tags that are appealing to him or her. The system can then narrow down the results and display them in theresult section 1114. -
FIG. 11C illustrates an exemplaryuser interface screen 1120 that displays more information for a Pik selected by the user by clicking on one of the Piks displayed in user interface screen 1110. In some embodiments, the Pik can include atitle 1121, abrief description 1122, one ormore tags 1123. In some embodiments, if the Pik is related to a product research, the system can provide the user with a recommended product 1124. In some embodiments, the Pik can include one or more sections or sub-titles 1125. Under each section or subtitle, there can be a number of website links and comments on thelinks 1126. -
FIG. 11D illustrates an exemplaryuser interface screen 1130 for creating a Pik on the “Pikurate” website. A user can enter a title of thePik 1131, abrief description 1132, one ormore tags 1133. The user can enter one or more website links and comments 1134. The saved links can be shown in thelist 1135. In some embodiments, the system can provide suggestions to a user. For example, when a user creates a title for a research goal, the system can suggest sections to consider. In some embodiments, when a user refers to or edits a Pik, the system can suggest new links to add into one or more sections of the Pik. -
FIG. 11E illustrates an exemplaryuser interface screen 1140 for showing a user's profile page. The profile page can include the user'sname 1141, the user'sprofile photo 1142 andactivity data 1143 including rating, number of views, number of shares, etc. The profile page can also include a list ofPiks 1144 the user has created or viewed. The profile page can also include categories for Piks, such as public, private, saved, subscriptions, research requests, research bids, etc. -
FIG. 11F illustrates an exemplaryuser interface screen 1150 for showing a list of creators. The system can provide acreator list 1151 to a user based on one or more search terms entered by the user. The creators can be ranked by one or more metrics, such as rating, number of created Piks, number of views, number of shares, etc. -
FIGS. 12A-12D illustrate exemplary user interface screens for the enhanced research system being used as a Google Chrome Extension, according to an example embodiment. In these examples, the enhanced research system may be provided as a website called “Pikurate.”FIG. 12A illustrates an exemplaryuser interface screen 1200 where a Pikurate Chrome Extension icon is displayed on the screen to enable the user to access features of the enhanced research system. A user can choose to create a new product Pik by clicking thebutton 1201 and enter a title of the Pik. The user can also create a new general Pik by clicking thebutton 1202 and enter a title. A list of Piks created by the user can be shown in thesection 1203. -
FIG. 12B illustrates an exemplaryuser interface screen 1210 for adding a new section. The user can enter a title of the new section in thebox 1211. The user can enter a title of the research snippet in thebox 1212. The user can add link label in thebox 1213. The user can also add comments in thebox 1214. The user can also save the new research snippet by clicking thebutton 1215 to screenshot the specific area of the website to store for viewing in the future. -
FIG. 12C illustrates an exemplaryuser interface screen 1220 for recording a user's browsing history. When the user clicking the “Begin Recording”button 1221, the browser extension can start recording the user's browsing history. -
FIG. 12D illustrates an exemplaryuser interface screen 1230 for recording a user's browsing history. When the browser extension is recording the user's browsing history, information of all the recorded websites can be shown in thelist 1232. The information can include a title of the website, a link of the website, time duration of visiting, etc. The user can click the “Stop Recording”button 1231 to stop the recording and save the results to his or her account. In some embodiments, the user can manually remove unwanted website from thelist 1232. In some embodiments, the system can use an algorithm to automatically remove unwanted website from thelist 1232. In some embodiments, the user can also manually add the link (and its relevant information) during the research process via a button or hotkey. - In an example embodiment, the search history, created and saved Piks, and other data is stored at the user's device (e.g.,
device 810, 820), instead of being stored at a server (e.g., server 830). In this embodiment, blockchain technology may be used to store data at the user's device, and this allows for a higher level of protection and privacy of the user's personal search history data. In some embodiments, once the user publishes a Pik, all relevant data can be saved at a server (e.g.,server 830.) - As an example use, the enhanced research system described herein can be used to create Piks that may be used by corporations to train to employees or personnel. Use of Piks may reduce the learning curve and time for new employees or for new work procedures, since an employee can use a Pik to learn a new skill rather than receiving training from another personnel. Pik can capture the pattern of consumption of the documents to accomplish the tasks and reduce employees' research time.
- In some embodiments, creators of Piks can be reviewed by other users and ranked based on the quality of their Piks and/or other attributes. In one embodiment, a creator with high ranking can be designated as a “trusted creator.” In another embodiment, Piks created by a “trusted creator” can be listed in the front page of the search results to be shown to a user.
- Marketplace
-
FIG. 13 is a block diagram showing amarketplace 1300 for Piks, according to some embodiments of the present disclosure. The marketplace herein refers to the requesting, bidding, selling, and buying paid Piks. In some embodiments,marketplace 1300 can include a creation andediting part 1310, a sharing and sellingpart 1320, and a searching and requestingpart 1330. In the creation andediting part 1310, a user (or “creator”) can conduct as online research and create a Pik with the systems and methods described in the present disclosure. - Referring to the sharing and selling
part 1320, once the user has created their curated online research, he/she can save it onto their account for a variety of uses, ranging from public, private, paid, and drafts. The saved research can be accessed for future reference and updated. It can also be shared to specific users, to the Pikurate community, to others via social media, text, and email. In addition, the user has the opportunity to lock their research which can only be viewed when the requester pays the amount asked. In some embodiments, the sharing of Pik can include a collaborative or social research. For example, a user (knowledgeable or not) can start a Pik and have it open to the public to add sections and links. In some embodiments, a user can invite people (friends, family, etc.) to collaborate on a Pik. For example, a user may plan for a group trip and start a Pik of travel research. The user can invite other people who would travel with him or her to collaborate on the Pik. - Referring to the searching and requesting
part 1330, a user can share or post his or her request for Pik in the marketplace. The request can specify the user's detailed research needs such as target, action, etc. Other users or creators of Piks can view and bid for the request. In one embodiment, the user requesting a Pik can compensate another user who provides the Pik through regular payment channels (e.g., credit cards, bank accounts, third party online payment systems, etc.) In another embodiment, users can purchase internal system credits or points to be used for payment of Piks. - In some embodiments, public and paid research can be searched for with keywords, as well as sorting and filtering options. The recommended results may vary depend on factors from the algorithm that can consist of demographic and behavioural data, content and tag analysis, and real-time big data. Furthermore, the user will be able to browse various curated online research from the homepage and their subscriptions to other users. If the desired research cannot be found, then the user has the opportunity to place a request for it. Other users can now bid on the opportunity to conduct the research and the requester can make the final decision on which creator to move forward with.
- In describing exemplary embodiments, specific terminology is used for the sake of clarity. For purposes of description, each specific term is intended to at least include all technical and functional equivalents that operate in a similar manner to accomplish a similar purpose. Additionally, in some instances where a particular exemplary embodiment includes a plurality of system elements, device components or method steps, those elements, components or steps may be replaced with a single element, component or step. Likewise, a single element, component or step may be replaced with a plurality of elements, components or steps that serve the same purpose. Moreover, while exemplary embodiments have been shown and described with references to particular embodiments thereof, those of ordinary skill in the art will understand that various substitutions and alterations in form and detail may be made therein without departing from the scope of the invention. Further still, other embodiments, functions and advantages are also within the scope of the invention.
Claims (20)
1. A computer-implemented method for providing an enhanced research platform, the method comprising:
receiving, by a computer processor over a network, one or more keywords, the one or more keywords having been generated based on information provided on a computing device associated with a user;
receiving, by the computer processor over the network, one or more tags, the one or more tags having been selected on the computing device;
determining, by the computer processor, an experience level of the user by analyzing past search history of the user;
identifying, by the computer processor, a plurality of curated online research archives stored in a database that match the one or more keywords and the one or more tags, wherein each of the plurality of curated online research archives is associated with a research topic and comprises a list of websites ordered to indicate a progression in research from a broad concept to a narrow concept, the websites having been added to the curated online research archive by one or more users as being relevant to the research topic associated with the curated online research archive;
ranking, by the computer processor, each of the plurality of curated online research archives based on a relevancy of the curated online research archive to the one or more keywords, the one or more tags, the experience level of the user, or any combination thereof;
providing, by the computer processor, the ranked plurality of curated online research archives for display in a user interface on the computing device;
receiving, by the computer processor, an indication of a selection of one of the ranked plurality of curated online research archives; and
providing, by the computer processor, the ordered list of websites of the selected curated online research archive for display in the user interface on the computing device.
2. The method of claim 1 , further comprising ranking the identified plurality of curated online research archives based on an attribute of one of the plurality of curated online research archives, wherein the attribute comprises real time trends and popularity, number of links, number of views, rating, or any combination thereof.
3. The method of claim 1 , further comprising ranking the identified plurality of curated online research archives based on an attribute of a creator associated with one of the plurality of curated online research archives, wherein the attribute comprises number of subscribers of the creator, number of curated online research archives created by the creator, rating of the creator, or any combination thereof.
4. The method of claim 1 , further comprising ranking the identified plurality of curated online research archives based on an attribute of a website link associated with one of the plurality of curated online research archives, wherein the attribute comprises duration of visit of the website link, number of characters on a webpage associated with the website link, number of revisit associated with the website link, or any combination thereof.
5. The method of claim 1 , wherein each of the websites is categorized with one or more keywords, and each of the plurality of curated online research archives further comprises one or more tags, user demographic data, and user behavioral data, further comprising,
receiving user demographic and behavioral data; and
identifying the plurality of curated online research archives based on a matching of the received one or more keywords, the received one or more tags, the received demographic data, and the received behavioral data to the one or more keywords, the one or more tags, the user demographic data, and the user behavioral data associated with the plurality of curated online research archives.
6. The method of claim 5 , further comprising: ranking the plurality of curated online research archives based on relevancy to the received one or more keywords, the received one or more tags, the received user demographic data, and the received user behavioral data to the one or more keywords, the one or more tags, the user demographic data, and the user behavioral data associated with the plurality of curated online research archives.
7. The method of claim 5 , wherein the received user demographic data comprises at least one of a location, gender, age, education level, or household income of a user, and the user demographic data associated with a curated online research archive comprises at least one of a location, gender, age, experience level, or household income of a creator of the curated online research archive.
8. The method of claim 1 , wherein each of the plurality of curated online research archives comprises a title that indicates a research topic, an order of one or more sub-titles that indicate categories for grouping like websites, and an order of websites within each sub-title to indicate progression from an end-to-end research.
9. The method of claim 1 , wherein determining the experience level of the user is determined by further analyzing activity of the user.
10. A computer-implemented method for creating a curated online research archive, wherein the curated online research archive comprises a list of websites, the method comprising:
receiving, by a computer processor, a title based on input from a user;
receiving, by the computer processor, one or more tags based on input from the user;
receiving, by the computer processor, one or more section names based on input from the user;
receiving, by the computer processor, an indication that the user has made a selection to start recording a search history of a web browser;
recording, by the computer processor, a list of websites visited by the web browser based on the received indication using a web browser extension for the web browser;
receiving, by the computer processor, an indication that the user has made a selection to stop recording the search history of the web browser;
filtering, by the computer processor, the recorded list of websites using the web browser extension based on a set of pre-defined rules;
categorizing, by the computer processor, the filtered list of websites based on the one or more section names using the web browser extension;
receiving, by the computer processor, a display order based on input from the user;
receiving, by the computer processor, a comment associated with a website in the filtered list of websites based on input from the user;
creating, by the computer processor, a curated online research archive wherein the curated online research archive comprises the title, the one or more tags, the comment, the categorized list of websites, or any combination thereof; and
outputting, by the computer processor, the curated online research archive.
11. The method of claim 10 , further comprising filtering the recorded list of websites based on an amount of time the user spent on each of the websites in the list.
12. The method of claim 10 , wherein filtering the recorded list of websites comprises:
providing the recorded list of websites for display to the user;
receiving from the user a selection of one or more websites; and
removing the one or more websites from the recorded list of websites based on the received selection.
13. The method of claim 10 , wherein filtering the recorded list of websites comprises removing parent web-pages from the recorded list of websites.
14. The method of claim 10 , further comprising:
receiving user demographic data; and
updating the curated online research archive by adding the received user demographic data to the curated online research archive.
15. The method of claim 14 , wherein the user demographic data comprises at least one of a location, gender, age, education level, or household income of the user.
16. The method of claim 10 , further comprising:
receiving an instruction of editing from the user; and
updating the categorized list of websites in response to the instruction of editing.
17. A system for providing an enhanced research platform, the system comprising:
a computer processor; and
a non-transitory computer readable storage medium storing computer program instructions, which, when executed by the computer processor, cause the computer processor to perform a method, the method comprising:
receiving, over a network, keywords, the keywords having been generated based on characters input on a computing device associated with a user;
parsing the keywords to identify an action, a target, and a purpose;
determining a research goal based on the identified action, target, and purpose;
determining, by the computer processor, an experience level of the user by analyzing past search history of the user;
identifying a plurality of curated online research archives that match the determined research goal, wherein each of the plurality of curated online research archives is associated with a research topic and comprises user demographic data and a list of websites, the websites having been added to the curated online research archive by one or more other users as being relevant to the research topic associated with the curated online research archive, wherein each of the websites is categorized with one or more keywords, and each of the plurality of curated online research archives further comprises user demographic data;
ranking each of the plurality of curated online research archives based on a relevancy of the curated online research archive to the determined research goal and the experience level of the user;
providing the ranked plurality of curated online research archives for display in a user interface on the computing device;
receiving an indication of a selection of one of the ranked plurality of curated online research archives; and
providing the ordered list of websites of the selected curated online research archive for display in the user interface on the computing device,
wherein the computer processor is further configured to:
receive user demographic data; and
identify the plurality of curated online research archives based on a matching of the received one or more keywords, and the received demographic data to the one or more keywords, and the user demographic data associated with the plurality of curated online research archives;
wherein the computer processer is further configured to ranked plurality of curated online research archives based on a relevancy of the received one or more keywords, and the received demographic data to the one or more keywords, or the user demographic data associated with the plurality of curated online research archives.
18. The system of claim 17 , wherein the received user demographic data comprises at least one of a location, gender, age, education level, or household income of a user, and the user demographic data associated with a curated online research archive comprises at least one of a location, gender, age, experience level, education level, or household income of a creator of the curated online research archive.
19. The system of claim 17 , wherein each of the plurality of curated online research archives comprises a title that indicates the research topic, an order of one or more sub-titles that indicate categories for grouping like websites, and an order of websites within each sub-title to indicate progression from an end-to-end research.
20. The system of claim 17 , wherein determining the experience level of the user is determined by further analyzing activity of the user.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US17/537,184 US20220083617A1 (en) | 2017-01-30 | 2021-11-29 | Systems and methods for enhanced online research |
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201762452040P | 2017-01-30 | 2017-01-30 | |
US15/882,913 US11250083B2 (en) | 2017-01-30 | 2018-01-29 | Systems and methods for enhanced online research |
US17/537,184 US20220083617A1 (en) | 2017-01-30 | 2021-11-29 | Systems and methods for enhanced online research |
Related Parent Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US15/882,913 Continuation US11250083B2 (en) | 2017-01-30 | 2018-01-29 | Systems and methods for enhanced online research |
Publications (1)
Publication Number | Publication Date |
---|---|
US20220083617A1 true US20220083617A1 (en) | 2022-03-17 |
Family
ID=62979698
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US15/882,913 Active US11250083B2 (en) | 2017-01-30 | 2018-01-29 | Systems and methods for enhanced online research |
US17/537,184 Abandoned US20220083617A1 (en) | 2017-01-30 | 2021-11-29 | Systems and methods for enhanced online research |
Family Applications Before (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US15/882,913 Active US11250083B2 (en) | 2017-01-30 | 2018-01-29 | Systems and methods for enhanced online research |
Country Status (4)
Country | Link |
---|---|
US (2) | US11250083B2 (en) |
KR (2) | KR102581333B1 (en) |
CN (1) | CN110235121B (en) |
WO (1) | WO2018140883A1 (en) |
Families Citing this family (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11762927B2 (en) * | 2018-10-23 | 2023-09-19 | Zeta Global Corp. | Personalized content system |
US11625532B2 (en) * | 2018-12-14 | 2023-04-11 | Microsoft Technology Licensing, Llc | Dynamically generated content understanding system |
CN114003793A (en) * | 2021-10-29 | 2022-02-01 | 苏州城室科技有限公司 | Automatic questionnaire generation method based on picture forced selection method |
Citations (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20080301174A1 (en) * | 2007-03-30 | 2008-12-04 | Albert Mons | Data structure, system and method for knowledge navigation and discovery |
US20090049019A1 (en) * | 2005-12-16 | 2009-02-19 | Nextbio | Directional expression-based scientific information knowledge management |
US20090222400A1 (en) * | 2005-12-16 | 2009-09-03 | Nextbio | Categorization and filtering of scientific data |
US20100058210A1 (en) * | 2008-01-02 | 2010-03-04 | Simon Johnson | Online Investing |
US20120198073A1 (en) * | 2011-01-27 | 2012-08-02 | Computenext Inc. | Dynamically organizing cloud computing resources to facilitate discovery |
US20160042299A1 (en) * | 2014-08-06 | 2016-02-11 | Kaybus, Inc. | Identification and bridging of knowledge gaps |
US9262520B2 (en) * | 2009-11-10 | 2016-02-16 | Primal Fusion Inc. | System, method and computer program for creating and manipulating data structures using an interactive graphical interface |
US20160098405A1 (en) * | 2014-10-01 | 2016-04-07 | Docurated, Inc. | Document Curation System |
US9331973B1 (en) * | 2015-04-30 | 2016-05-03 | Linkedin Corporation | Aggregating content associated with topics in a social network |
US20170039527A1 (en) * | 2015-08-06 | 2017-02-09 | Clari, Inc. | Automatic ranking and scoring of meetings and its attendees within an organization |
US20170076046A1 (en) * | 2015-09-10 | 2017-03-16 | Roche Molecular Systems, Inc. | Informatics platform for integrated clinical care |
US9996623B1 (en) * | 2014-06-27 | 2018-06-12 | Pubsonic, Inc. | Computer-implemented method of carrying out a search for information available over a network |
US11720575B2 (en) * | 2015-01-16 | 2023-08-08 | Rakuten Group, Inc. | Computer database access system and method for categorizing by style ranking |
Family Cites Families (36)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8554775B2 (en) | 1999-04-13 | 2013-10-08 | Semmx, Inc. | Orthogonal corpus index for ad buying and search engine optimization |
US20020091991A1 (en) * | 2000-05-11 | 2002-07-11 | Castro Juan Carlos | Unified real-time microprocessor computer |
US7536323B2 (en) * | 2003-03-26 | 2009-05-19 | Victor Hsieh | Online intelligent multilingual comparison-shop agents for wireless networks |
CA2523480C (en) * | 2003-04-25 | 2014-05-27 | Xm Satellite Radio Inc. | System and method for providing recording and playback of digital media content |
US20050149538A1 (en) | 2003-11-20 | 2005-07-07 | Sadanand Singh | Systems and methods for creating and publishing relational data bases |
US20050160082A1 (en) * | 2004-01-16 | 2005-07-21 | The Regents Of The University Of California | System and method of context-specific searching in an electronic database |
US7774378B2 (en) * | 2004-06-04 | 2010-08-10 | Icentera Corporation | System and method for providing intelligence centers |
WO2007052285A2 (en) * | 2005-07-22 | 2007-05-10 | Yogesh Chunilal Rathod | Universal knowledge management and desktop search system |
US9201979B2 (en) * | 2005-09-14 | 2015-12-01 | Millennial Media, Inc. | Syndication of a behavioral profile associated with an availability condition using a monetization platform |
WO2007115224A2 (en) * | 2006-03-30 | 2007-10-11 | Sri International | Method and apparatus for annotating media streams |
US8880402B2 (en) * | 2006-10-28 | 2014-11-04 | General Motors Llc | Automatically adapting user guidance in automated speech recognition |
US7856433B2 (en) * | 2007-04-06 | 2010-12-21 | Yahoo! Inc. | Dynamic bid pricing for sponsored search |
EP2031819A1 (en) * | 2007-09-03 | 2009-03-04 | British Telecommunications Public Limited Company | Distributed system |
US11159909B2 (en) * | 2008-02-05 | 2021-10-26 | Victor Thomas Anderson | Wireless location establishing device |
US8244721B2 (en) * | 2008-02-13 | 2012-08-14 | Microsoft Corporation | Using related users data to enhance web search |
US8364664B2 (en) * | 2008-05-12 | 2013-01-29 | Enpulz, L.L.C. | Web browser accessible search engine that identifies search result maxima through user search flow and result content comparison |
EP2304667A4 (en) * | 2008-06-03 | 2011-08-10 | Just Parts Online Inc | System and method for listing items online |
US8515937B1 (en) * | 2008-06-30 | 2013-08-20 | Alexa Internet | Automated identification and assessment of keywords capable of driving traffic to particular sites |
US20110035375A1 (en) * | 2009-08-06 | 2011-02-10 | Ron Bekkerman | Building user profiles for website personalization |
US9684683B2 (en) | 2010-02-09 | 2017-06-20 | Siemens Aktiengesellschaft | Semantic search tool for document tagging, indexing and search |
US8768934B2 (en) * | 2010-06-15 | 2014-07-01 | Chacha Search, Inc | Method and system of providing verified content |
US9436764B2 (en) * | 2010-06-29 | 2016-09-06 | Microsoft Technology Licensing, Llc | Navigation to popular search results |
US20120096389A1 (en) * | 2010-10-19 | 2012-04-19 | Ran J Flam | Integrated web-based workspace with curated tree-structure database schema |
WO2012160567A1 (en) * | 2011-05-20 | 2012-11-29 | Yogesh Chunilal Rathod | A system and method for providing unified active search engine based on search result item specific identified, dynamic, contextual & accessible active links. |
US8762227B1 (en) * | 2011-07-01 | 2014-06-24 | Amazon Technologies, Inc. | Automatic product groupings for merchandising |
US20140172864A1 (en) * | 2011-07-08 | 2014-06-19 | Annie Shum | System and method for managing health analytics |
US9195771B2 (en) * | 2011-08-09 | 2015-11-24 | Christian George STRIKE | System for creating and method for providing a news feed website and application |
US10096033B2 (en) * | 2011-09-15 | 2018-10-09 | Stephan HEATH | System and method for providing educational related social/geo/promo link promotional data sets for end user display of interactive ad links, promotions and sale of products, goods, and/or services integrated with 3D spatial geomapping, company and local information for selected worldwide locations and social networking |
US20130275429A1 (en) * | 2012-04-12 | 2013-10-17 | Graham York | System and method for enabling contextual recommendations and collaboration within content |
US20140082645A1 (en) * | 2012-09-14 | 2014-03-20 | Peter Stern | Apparatus and methods for providing enhanced or interactive features |
US8935272B2 (en) * | 2013-03-17 | 2015-01-13 | Alation, Inc. | Curated answers community automatically populated through user query monitoring |
US9489460B2 (en) * | 2013-06-26 | 2016-11-08 | Michael Wexler | System and method for generating expert curated results |
US9626361B2 (en) * | 2014-05-09 | 2017-04-18 | Webusal Llc | User-trained searching application system and method |
US9396483B2 (en) * | 2014-08-28 | 2016-07-19 | Jehan Hamedi | Systems and methods for determining recommended aspects of future content, actions, or behavior |
US20160149956A1 (en) * | 2014-11-21 | 2016-05-26 | Whip Networks, Inc. | Media management and sharing system |
US20180135122A1 (en) * | 2016-11-11 | 2018-05-17 | OneOme LLC | Systems and methods for genotype-derived drug recommendations |
-
2018
- 2018-01-29 CN CN201880009248.0A patent/CN110235121B/en active Active
- 2018-01-29 US US15/882,913 patent/US11250083B2/en active Active
- 2018-01-29 KR KR1020227019194A patent/KR102581333B1/en active IP Right Grant
- 2018-01-29 WO PCT/US2018/015750 patent/WO2018140883A1/en active Application Filing
- 2018-01-29 KR KR1020197025624A patent/KR102477245B1/en active IP Right Grant
-
2021
- 2021-11-29 US US17/537,184 patent/US20220083617A1/en not_active Abandoned
Patent Citations (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20090049019A1 (en) * | 2005-12-16 | 2009-02-19 | Nextbio | Directional expression-based scientific information knowledge management |
US20090222400A1 (en) * | 2005-12-16 | 2009-09-03 | Nextbio | Categorization and filtering of scientific data |
US20080301174A1 (en) * | 2007-03-30 | 2008-12-04 | Albert Mons | Data structure, system and method for knowledge navigation and discovery |
US20100058210A1 (en) * | 2008-01-02 | 2010-03-04 | Simon Johnson | Online Investing |
US9262520B2 (en) * | 2009-11-10 | 2016-02-16 | Primal Fusion Inc. | System, method and computer program for creating and manipulating data structures using an interactive graphical interface |
US20120198073A1 (en) * | 2011-01-27 | 2012-08-02 | Computenext Inc. | Dynamically organizing cloud computing resources to facilitate discovery |
US9996623B1 (en) * | 2014-06-27 | 2018-06-12 | Pubsonic, Inc. | Computer-implemented method of carrying out a search for information available over a network |
US20160042299A1 (en) * | 2014-08-06 | 2016-02-11 | Kaybus, Inc. | Identification and bridging of knowledge gaps |
US20160098405A1 (en) * | 2014-10-01 | 2016-04-07 | Docurated, Inc. | Document Curation System |
US11720575B2 (en) * | 2015-01-16 | 2023-08-08 | Rakuten Group, Inc. | Computer database access system and method for categorizing by style ranking |
US9331973B1 (en) * | 2015-04-30 | 2016-05-03 | Linkedin Corporation | Aggregating content associated with topics in a social network |
US20170039527A1 (en) * | 2015-08-06 | 2017-02-09 | Clari, Inc. | Automatic ranking and scoring of meetings and its attendees within an organization |
US20170076046A1 (en) * | 2015-09-10 | 2017-03-16 | Roche Molecular Systems, Inc. | Informatics platform for integrated clinical care |
Also Published As
Publication number | Publication date |
---|---|
WO2018140883A1 (en) | 2018-08-02 |
KR102581333B1 (en) | 2023-09-20 |
CN110235121A (en) | 2019-09-13 |
KR20190108624A (en) | 2019-09-24 |
KR102477245B1 (en) | 2022-12-12 |
US20180218084A1 (en) | 2018-08-02 |
US11250083B2 (en) | 2022-02-15 |
CN110235121B (en) | 2023-10-27 |
KR20220082114A (en) | 2022-06-16 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US10728203B2 (en) | Method and system for classifying a question | |
JP5358442B2 (en) | Terminology convergence in a collaborative tagging environment | |
US10713666B2 (en) | Systems and methods for curating content | |
US10607235B2 (en) | Systems and methods for curating content | |
US9396485B2 (en) | Systems and methods for presenting content | |
US20220083617A1 (en) | Systems and methods for enhanced online research | |
US11675824B2 (en) | Method and system for entity extraction and disambiguation | |
US8463648B1 (en) | Method and apparatus for automated topic extraction used for the creation and promotion of new categories in a consultation system | |
US11080287B2 (en) | Methods, systems and techniques for ranking blended content retrieved from multiple disparate content sources | |
US11151618B2 (en) | Retrieving reviews based on user profile information | |
US11232522B2 (en) | Methods, systems and techniques for blending online content from multiple disparate content sources including a personal content source or a semi-personal content source | |
US8954868B2 (en) | Guided profile editing system | |
US10255277B2 (en) | Crowd matching translators | |
US20170098180A1 (en) | Method and system for automatically generating and completing a task | |
US20220405485A1 (en) | Natural language analysis of user sentiment based on data obtained during user workflow | |
US11216735B2 (en) | Method and system for providing synthetic answers to a personal question | |
US10929905B2 (en) | Method, system and machine-readable medium for online task exchange | |
US20110197137A1 (en) | Systems and Methods for Rating Content | |
WO2024097372A1 (en) | Systems and methods for improved search and interaction with an online profile |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
STPP | Information on status: patent application and granting procedure in general |
Free format text: DOCKETED NEW CASE - READY FOR EXAMINATION |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: NON FINAL ACTION MAILED |
|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION |