WO2001044992A1 - Systeme et procede de mise en correspondance de contextes - Google Patents
Systeme et procede de mise en correspondance de contextes Download PDFInfo
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- WO2001044992A1 WO2001044992A1 PCT/US2000/041713 US0041713W WO0144992A1 WO 2001044992 A1 WO2001044992 A1 WO 2001044992A1 US 0041713 W US0041713 W US 0041713W WO 0144992 A1 WO0144992 A1 WO 0144992A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
Definitions
- the present invention relates to content analysis and, in particular, to correlating accessed data content with target content in Internet and wide network systems.
- Advertisements are often embedded in Internet web pages.
- Internet portals generate significant revenue by the placement of advertising on served web pages.
- a variety of accounting methods are used. For example, ad cost can be correlated to a commitment to place a particular ad a certain number of times. Because such placements are often made randomly, valuations for such placements tend to decline as web "real estate" expands.
- Other, more sophisticated placement schemes rely on operator placement of categorical ads on the basis of search line contents. For example, if an Internet user searches for "Mount Everest" on an Internet portal search page, an operator can push ads related to mountaineering products into the returned pages. Such methods are slow and imprecise however, and fail to contemplate any more than a gross sense of the user's interests and proclivities.
- Search line content matching often fails to identify the goal of such searches.
- the "Mount Everest" search may have actually been run by a professor of physiology with an interest in the effects of oxygen deprivation on mental acuity.
- a more appropriate ad might be directed to medical equipment.
- Another object of the present invention is to provide a system and method which automatically and contextually matches products, advertisements or other content (hereinafter referred to as "offers") to the content on a web page that a user has selected in real-time.
- a further object of the present invention is to provide a system and method as aforesaid, which refines the matches based on business rules established by the web site owner or the system provider, thereby providing offers that are likely to generate selling opportunities to the web site owner.
- Yet a further object of the present invention is to provide a system and method as aforesaid, which additionally refines the matches based on user profile information, thereby providing targeted offers that are likely to generate buying opportunities to the user.
- a still further object of the present invention is to provide a system and method as aforesaid, which dynamically updates the user profile information in accordance with the user's responsive behavior.
- the present invention matches document content with selected products or services and advertisements.
- a client accesses a matching system and gets a return of contextual matches for the content of a document under review or requested by the client.
- a client makes a call to a product matching engine through a JAVA applet, API, or other provided method and receives, in return, data related to products or services that match the contents of a client query, which is composed from the document.
- the matching engine receives a contextual query and an identifier and, in preferred embodiments, a parameter set which may be used to adapt the return to fit particular requirements and returns one or more products, images, services links, and links to buy the referenced items or access other data (hereinafter sometimes collectively referred to as "results" or a “results presentation”).
- results or a “results presentation”
- the behavior is logged and incorporated by the matching system for use in subsequent exercises.
- FIG. 1 is a high-level block diagram depicting a preferred system embodying the present invention
- Fig. 2 A is an example application of the present invention that uses a Java applet
- Fig. 2B depicts an alternative technique employed in accordance with the present invention to pre-match web pages with selected content
- Fig. 3 depicts an embodiment of the present invention
- Fig. 4 shows redirection based upon user behavior in response to matched and presented data
- Fig. 5 is a graphical depiction of a contextual query in the context of the preferred embodiment of Fig. 3;
- Fig. 6 depicts the cache presence analysis of Fig. 3;
- Fig. 7 A depicts data retrieval in accordance with an embodiment of the present invention
- Fig. 7B depicts loading data in accordance with an embodiment of the present invention
- Fig. 8 depicts inventory or catalog loading in accordance with an embodiment of the present invention
- Fig. 9 depicts the process of a template in accordance with an embodiment of the present invention
- Fig. 10 depicts an update of a cache pool in a preferred embodiment
- Fig. 11 illustrates an intelligence engine or match server 14 in accordance with an embodiment of the present invention
- Fig. 12 illustrates an example of a contextual matching process of the present invention
- Fig. 13 is a system diagram of an embodiment of the contextual matching engine of the present invention
- Fig. 14 is a screen shot of the Business Rules Manager
- Fig. 15 is a screen of the Business Rules Manager for customizing business rules
- Fig. 16 is a screen of the Business Rules Manager for selecting promotional items
- Fig. 17 shows a catalog integration method in accordance with an embodiment of the present invention.
- the present invention is readily implemented by presently available communication apparatus and electronic components.
- the invention finds ready application in virtually all commercial communications and/or computer networks, including but not limited to world wide web (Internet), intranet, local area network (LAN), wide area network (WAN), wireless network and wired cable transmission systems.
- Internet world wide web
- LAN local area network
- WAN wide area network
- wireless network wireless network and wired cable transmission systems.
- FIG. 1 there is illustrated a high-level block diagram depicting a matching system 10 in accordance with an embodiment of the present invention.
- a client 12 communicates with a match server (or an intelligence engine) 14 through a signal pathway 16, such as the Internet HTTP protocol. It is appreciated that any signal protocol intelligible to both client 12 and server 14 can be used to operate as signal pathway 16 in the present invention.
- the client 12 makes a call to the match server 14.
- the call from client 12 to match server 14 includes at least a contextual query, i.e., a document or a portion of the document.
- the call includes a parameter set (or business rules) to pare the returned data set by the match server 14.
- the client 12 can establish the parameter set to receive results that are relevant to its business requirements or needs.
- the call can include an ID for verifying that the client 12 is authorized to access or communicate with the match server 14.
- the match server 14 determines that the ID associated with the client 12 is valid (i.e., the client 12 is authorized to access the match server 14), the match server 14 returns to the client 12 a set of matched data or offers, including but not limited to product and service descriptions, advertisements and/or associated links.
- the associated links can include buy links to enable the client's customer to view and/or purchase products or services that are contextually related to the query.
- Fig. 11 there is illustrated an intelligence engine or match server 14 in accordance with an embodiment of the present invention for contextually matching data to content using business rules.
- the match server 14 goes beyond picking up key words from the document, the match server 14 "reads” or analyzes the full context of articles or information using a sophisticated layering of artificial intelligence, neural networking, linguistics and natural language processing.
- the match server 14 conducts the same analysis of the inventory of offers (such as products, services, advertisements, etc.) available to be matched to that content.
- the offers are then ranked in order of relevance to the meaning of the article and the most relevant offers are dynamically served right within the content on the Web page. To the site visitor or user (i.e., customer of client 12), the process is seamless.
- the match server 14 of the present invention presents a buying opportunity to the customer, i.e., an attractively discounted book for "BMW Enthusiasts". If the customer is reading an article about Tiger Woods, the match server 14 displays or presents items such as Nike golf balls or a book on the history of the US Open to the customer. It is appreciated that the intelligence engine 14 of the present invention understands the difference between Tiger Woods, a tiger, and woods.
- the intelligence engine 14 comprises at least a contextual matching engine 210 and an entity extractor 220 for extracting entities, such as names of a person, company, organization, place, product, or the like.
- entity extractor 220 for extracting entities, such as names of a person, company, organization, place, product, or the like.
- the business rules 240 are behavioral constraints applied to matches 230 found by the entity extractor 220 and the contextual matching engine 230 as a result of a query 200. That is, the intelligence engine 14 combines the results from the contextual matching engine 210 and the entity extractor 220 into a single raw, unfiltered list of products 230 not conforming to any business rules.
- the intelligence engine 14 By applying the business rules 240 to the raw list 230, the intelligence engine 14 generates a new, more targeted list 250, which is presented to the client 12. A user profile could even be incorporated into the business rules 240 to further tailor the targeted list 250 for each specific user.
- the targeted product list 250 is a subset of the list 230 that has been filtered through the business rules 240.
- the intelligence engine 14 provides the client 12 with more focused and relevant matches.
- the intelligence engine 14 applies the constraints of the business rules hierarchically.
- the visitor's selection of an article or document may result in a search on the general topic of "business" being performed by the intelligence engine 14.
- the intelligence engine 14 may apply the business rules 230 associated with the client 12 to narrow the search further to "books related to business," thereby focusing and refining the search to target the needs of the client 12.
- the client 12 may be a book seller utilizing the match server 14 to generate selling opportunities.
- the intelligence engine 14 may recursively apply the business rules to the results of the contextual matching engine 210 and the entity extractor 220.
- the intelligence engine 12 can further refine the results by applying a second business rule limiting the "books" category of the "business” to a sub-category of "best sellers.”
- the returned matches 250 are the product of relevancy scoring leading to the products being targeted to the individuals in a more refined manner.
- the intelligence engine 14 can return matches that are more relevant (i.e., higher return values) to the client 12. That is, more specific filtering by the intelligence engine 14 results in matches that have higher return values, thereby enabling the client 12 to present relevant products to their customers. Accordingly, the business rules 240 provide the client 12 with more selling opportunities because more relevant offers (i.e., products, services, advertisement, etc.) are presented to its customers.
- the inputs 400 comprise both the content that a web site visitor has selected, i.e., the query 200 (Fig.
- the web site visitor may represent a customer of the client 12 while offers may represent products, services, advertisements, promotions, data or other content.
- the client 12 delivers the content from the web page to the intelligence engine 14 automatically through web page mark-up language that the web site owner placed on each page.
- the web site owner can place the contextual tags on every page in its web site or can selectively target pages to display contextually targeted offers. It is appreciated that the contextual tags represent queries 200 or can be converted into queries 200 by the intelligence engine 14.
- the offers to be contextually matched to that content or queries 200 are sent from the database to the intelligence engine 14, where they are then categorized and stored.
- an API can be used to place the contextual tags on the web to display contextually relevant products and special promotions with minimal human intervention and effort.
- a plurality of matched products or items can be displayed, e.g., two contextually matched products, two products from the entity extractor 220 (Fig. 11), and two promotional/default products or items.
- the results presentation 260 (Fig. 11) can be selected from a library of templates or can be customized by the client 12.
- the results presentation 260 can support multiple formats, such as HTML, Java, etc. It is appreciated that each format may support slightly different functionality.
- the functionality of the Java-based results presentation 260 can include the ability to automatically (every n seconds) or manually rotate from one product to the next and back, and also the ability to click on the product offering directly or on the "Buy/More Info" button to go to the purchasing pages of the client 12, i.e., the web site owner, or a merchant.
- the functionality of the client 12 i.e., the web site owner, or a merchant.
- HTML-based results presentation 260 is the same as the Java-based results presentation 260, but the HTML-based results presentation does not provide the rotation capability. Instead the HTML-based product presentation 260 provides the ability to display a random choice of the matched products, services, advertisements, promotions, data, etc., offering diversity to the viewer, i.e., the web site visitor.
- Fig. 2 A illustrates an example of an applet application in accordance with an embodiment of the present invention.
- An applet 24 makes the call 26 to the match server 14.
- a portal 20 can represent any Internet portal, search engine, directory server or database such as Yahoo®.
- the portal 20 responds to a page request 21 from a browser 22 as directed by the web site visitor. It is appreciated that in this example, the browser 22 functions as the client 12 (Fig. 1).
- the browser 22 is shown sequentially as browser 22 A running the requested page as a series of HTML tags.
- the returned page has embedded applet 24 therein.
- the browser 22 loads and runs the applet 24 that includes a query 200 (Fig. 11) defined to be the body of the page, i.e., contextually representative of the page.
- the applet 24 issues a call and query 26 to the match server 14, which performs a match on the body of the page.
- the match server 14 performs a content analysis of the query, i.e., on the body of the page, and selects from content database 28 a set of matched return data 30.
- the match server 14 determines the matched return data 30 in accordance with its internal rules, i.e., contextually matching, entity extraction, etc., and any parameters or business rules 240 included with the call 26.
- a relevancy score can also be returned for operation on by the client or, alternatively, the relevancy score can be used internally by the match server 14 to pare the return data 30 to comply with any business rules 240 conveyed with the call 26.
- the call and query 26 can be directed to a dynamic image generator (not shown) of the match server 14 which dynamically returns the matched information as a graphical image, e.g., a .gif file.
- a client 12 with ID companyx can used the following sample code:
- the code can be broken down into different functional pieces as delineated in the table below.
- the client 12 is the site owner.
- the client 12 calls the match server 14 through a call 34 which can include a parameter or business rules 240 as described herein with reference to Fig. 2 A.
- the match server 14 performs a contextual analysis and matching exercise with the documents provided with the call 34.
- the match server 14 performs the contextual analysis using the content database 28 and returns pages in which matched data is embedded as depicted by a return 36.
- the embedded pages are returned to the client 12 for storage in a database 38 and for publication to the browser 22 as static pages upon request from a user, as depicted by a publication arrow 40.
- the browser is the site owner.
- the client 12 calls the match server 14 through a call 34 which can include a parameter or business rules 240 as described herein with reference to Fig. 2 A.
- the match server 14 performs a contextual analysis and matching exercise with the documents provided with the call 34.
- the match server 14 performs the contextual analysis using the content database 28 and returns pages in which matched data is embedded as depict
- a variable set of customizable parameters may be passed through the API.
- parameters either in API or other modes such as applets, for example, may have parameters such as: Number of results to return (min, max)
- Client side integration typically involves embedding the call to the API within the pages served by the client web server or including a call to the API from the client page generation engine to embed results before the page is served.
- Some exemplar parameter definitions include:
- Profile business rules selected by the site owner showNum The number of results can be controlled by the client with this parameter. The default value is 1 and the maximum is 3 (currently).
- version The version values (currently) are 0 and 1. 0 sends the results back in HTML format and 1 returns the results back also in HTML format but defines the use of an applet once the page is loaded through the web server. Embedded use: none
- Default is 3000 i.e. 3 sees, flash, #output in flash generator format xml, #extended markup language j ava, #j ava data, not j ava applet(see v3 gui) ⁇ query- URL encoded text to be matched.
- the analysis and matching process all takes place in the intelligence engine or match server 14, where the inputs are processed for entity identification and contextual analysis.
- the meaning, or "context" of each piece of data is used to generate neural net vectors, which are sometimes referred to as feature vectors (feature vectors is used an encompassing term that includes all types of vectors).
- feature vectors is used an encompassing term that includes all types of vectors.
- the vectors from the web site content are then compared to the vectors associated with the inventory, and the resulting matches are scored and ranked.
- the contextual matching engine 210 (fig. 11) provides the necessary facility for automatically matching arbitrary textual content to inventory, thereby making it possible for the clients 12 or web site owners to offer results more closely relevant to their site content.
- the contextual matching engine 210 a web site owner is able to control the matching results.
- the contextual matching engine 210 is composed of three subsystems: (1) the contextual matching server300, which handles client requests and contains the logic for the system; (2) the vector generator 310, which generates document vectors; and (3) a relational database 320, which stores the inventory information.
- the contextual matching server 300 accepts connections on an established port. These connections include, but are not limited to, a text entity (a paragraph, abstract, entire article, etc., specifically on a single topic) to be matched, a metadata query to satisfy, and/or the number of desired returned products.
- the contextual matching server 300 generates a query context vector, or feature vector, using a vector generation algorithm.
- vector based generation algorithms have certain features in common: (1) they all characterize documents based on the presence of keywords; (2) they all associate vectors with these keywords; and (3) they all form document vectors by combining the vectors of the keywords present in the document.
- the present invention provides a novel approach to representing textual documents as high dimensional vectors.
- Such an approach provides an efficient means of indexing document collections, allowing retrieval of documents (querying) based on keywords, grouping related documents (categorization). Additionally, this method supports such conceptual queries and document groupings.
- This method of obtaining document vectors via Explicit Term Co-occurrence proceeds as follows: (1) A set of documents is selected (the corpus) for use in training. (2) The corpus is stemmed, meaning that every word in the corpus is converted into its root form. For example, the word "programmer” is stemmed by the word "program.”
- stemming e.g., Porter's algorithm.
- the method employed in ETC is an aggressive one known as “derivational stemming” which utilizes known word morphologies and greatly reduces the overall stem set for the corpus.
- “Stop words,” which are words that are known to convey little or no semantic value are removed from the corpus.
- Every unique stem word in the corpus is identified, and the frequencies of occurrence of these words are computed.
- a list of keywords is compiled from the stem words and then arranged from the most frequently appearing stems to the least frequently appearing stems. This list can be chosen to be of any desired size, or a metric system based on the upper and lower frequencies can be used.
- a term/document matrix is computed. The rows of this matrix represent the keywords in our list and the columns represent the documents in the corpus. The values at each position are "weights" for each keyword with respect to each document. The weights used in ETC are simply the number of times each keyword appears in each document. For example, given a simple corpus consisting of two documents and a keyword set consisting of two stem words, one might obtain the following term/document matrix:
- This matrix indicates that the first keyword appears once in the first document and twice in the second, while the second keyword does not appear at all in the first document and only occurs once in the second.
- this matrix will be much larger, as the corpus generally consists of many documents (this number is denoted as N) and the keyword set will consist of many keywords (this number is denoted as M).
- the resulting term/document matrix will therefore have MxN elements.
- a term co-occurrence matrix is computed. This matrix represents the number of times in the corpus, pairs of words occur in the same document. Unlike the term/document matrix, the columns and the rows represent keywords. Hence, the matrix is MxM. For example, from the term/document matrix above, the following term co-occurrence matrix is generated:
- This matrix is interpreted as follows: the first term occurs with itself three times, the first and second term occur together once, and the second term occurs with itself once.
- co-occurrence matrix is symmetric about the main diagonal and values on the main diagonal represent the total number of times a keyword appears in the corpus. Another useful property not evident in this example is that this matrix tends to be sparse, that is, most of the values are zero, for typical keyword sets. This is a consequence of the fact that most terms do not appear in conjunction with most of the other terms. Sparsity allows for compact storage using one of many sparse matrix representations known from the literature. (8) Unit length vectors are then generated for each keyword using the term co-occurrence matrix. This is accomplished by computing the eigen values of the co-occurrence matrix and their associated eigen vectors.
- the eigen vectors associated with the K largest eigen values (K « M) are used as column vectors to form a new MxK matrix.
- the rows of this matrix are then normalized to unit length, and are used as the K dimensional vectors for the keywords.
- Appropriate values of K can be determined empirically or by computing the ratio of the Kth largest eigen value to the largest eigen value and choosing the value of K that provides the desired ratio. (9)
- a vector is generated by taking a weighted sum of the keyword vectors for the keywords appearing in the document:
- wl ... wn are the weights of each term with respect to the document and VI ... Vn are the keyword vectors.
- the weights can be computed using any of several rules, and represent the relative frequency of the terms in the document.
- the document vector, or feature vector must be compared with database vectors, or feature vectors.
- a naive approach to product matching would be to compare the document vector to the vectors for every product in the database. This process becomes more burdensome as the number of products in the database grows. Fortunately, the database vectors are not smoothly distributed throughout the vector space, but rather, tend to "clump" together, leaving vast empty spaces between the clumps, or clusters as they are commonly known.
- N is determined empirically to provide the best compromise between match quality and computational burden.
- the contextual matching server 300 compares the query context vector to the pre-determined item context vectors to narrow the search to focus on products that are most likely to be relevant to the selected text.
- the query context vector is compared to the center vector of each cluster and the clusters with center vectors closest to the query context vector are selected. It is appreciated that these clusters IDs are then used to narrow the scope of products returned by the original metadata query as issued by the client 12. In other words, this narrowing qualification is added to the original metadata query to form an SQL query.
- the contextual matching server 300 returns N most relevant to the client or the E-commerce applet 12, along with their associated relevance.
- the user can, as an option, set the matching process to return results based on the default matching, or based upon user-defined business rules 240. If the user selects results based on general topic matching, then the system will employ the predefined (default) business rules 240. Whereas, if the user selects results based on business rules 240, then the system will pass the text to the vector generation subsystem 310.
- the vector generation subsystem 310 employs user-defined business rules 240 to return results that are filtered, i.e., biased towards the business rules or instructions 240.
- the business rules 240 are applied on the front end of the contextual analysis process to define the inventory sources to match to and the minimum relevancy score acceptable for contextual matches.
- the business rules 240 are applied again after the contextual analysis to sort the relevant matches based on predefined priorities, such as profit margin, release date and best sellers. These rules then define the display criteria, set promotions that should be served in addition to contextual matches, and set default offers when no contextual matches were found to meet the site criteria.
- the profile-based personalization i.e., process of learning the user's buying habits, can also be integrated into the contextual matching process of the present invention.
- the related product information is embedded in the contextual tag and dynamically delivered back to the site. These matches are now cached on that page so each time a site visitor calls that page the relevant offers are dynamically served within the page. Whenever the page content, inventory of offerings or business rules are updated, the content is resent to the intelligence engine 14 for new matches.
- the client site can view detailed reports to see their results and optimize those results by applying their market and merchandising expertise to edit or build new business rules 240, set promotions, and if desired, supervise the matches.
- a dashboard 410 is provided to help the users, such as the web site owners, manage their accounts and retrieve reporting information.
- the dashboard 410 is comprised of three main components: a business rules manager, a report manager and an inventory manager.
- a tool for Supervised Matching is also provided.
- the Report Manager allows users to view all of the key metrics for their account, such as measures for calculating contextual matches, promotional matches, and an aggregate of both contextual and promotional Matches.
- the default for reporting will be the past 30 days. There will also be an option to the users to select the date range they want to view and recalculate the figures.
- the reporting can be modified to provide an overview of the performance of the entire site based upon a defined Business Rule or based upon a specific page that is defined by the users.
- profile manager allows the users to access information about their profile configurations.
- profile can be made up the following elements:
- the users select the Business Rules Manager from the Dashboard 410, they will be able to configure the commerce offerings for their site.
- the users can select one of the preconfigured Business Rules selections, or design a tailor-made business rule for themselves.
- a screen shot of the Business Rules Manger is illustrated in Fig. 14.
- the user clicks on an existing Business Rule the merchant, products and promos for that rule will be displayed in the text box to the right. If the users want to select the highlighted rule, they can click the Apply button and it will take them to the page with the look and feel configurations. For users who do not select one of the predefined Business Rule sets, they can configure customized Business Rule by click the New Rules button.
- customers In order for customers to define a Business Rules set 240, they must first enter a name for their configurations. After they have entered a name in the designated text box, the users can select different Product Categories from the available merchants. Each merchant will have the applicable Product Categories in a separate column.
- Each category has a corresponding subcategory select box.
- the selection box allows the users to choose more than one subcategory for each corresponding category.
- the users can enter the Relevance score. This is a percentage that determines the minimum relevancy score of the product to the context of the content.
- the final configuration for the Business Rules set is the determination of the sort order.
- the users can rank the priority of Relevance, Price and Best Seller for the products they have selected. Since there are three sorting selections, the rank order is 1, 2 or 3 with 1 being the highest rank. If the user enters the same number in more than one drop down, an error message is received.
- This ranking can be done for each individual category, but applies to all products within the selected subcategories. If the users do not select a sort order, they can leave the drop down menus at the blank default.
- a screen shot of the Business Rules Manager for selecting promotions is shown Fig. 16.
- the users When all the inputs have been approved, the users will be taken to the Promotions section of the Business Rules Manager.
- the promotions will be limited to the categories that were selected by the users in the pre-generated and customer profile configurations.
- the users select a product category from the drop down list and enter a keyword to narrow the search.
- the users will check the boxes for the promotional items they want to add to the business rules profile. When they have selected all the desired items, they click the "OK" button and will be returned to the Promotions page.
- Fig. 3 there is illustrated another embodiment of the match server 14 of the present invention.
- the client 12 generates a call and query 44.
- the authentication at ensuing process block 46 is the first process at what has been previously shown as the matching server 14. If authentication of the client or user ID fails, an exception is generated and the process ends as depicted by process block 47.
- the contextual query e.g., the body of a web page
- the query is analyzed as being either text or binary.
- a text query is simply a word or series of words such as sentences or paragraphs.
- a binary query is composed of images, sound, compiler code, executable code, compressed data or other similarly binary representational data.
- the query is coded for comparison purposes. It is appreciated that a variety of methods can be used to code the query data, such as, checksum.
- a cache 50 is examined to determine if data corresponding to the checksum or code has previously been matched by the match server 14 and, if so, the page is immediately sent to the client 12 on return line 51.
- the request and the matched data are logged in a database log 56. If the checksum or code is not found in the cache, a call is made to relevancy process 54 for analysis of the query.
- a variety of known techniques for relevancy scoring can be used, such as Resource Miner.
- the relevancy process 54 determines a relevancy score based on any parameters or business rules 240 from client 12 or other rules generated internally in the match server 14.
- the rule set is depicted as process block 52. Parameters could include rules or requirements such as the number of books or cars to be matched or, the parameter list could include either with or without literal numeric limits, a relevancy score limit from which only those found matches that achieve a relevancy score greater than x are to be returned.
- Match selections from relevancy process 54 may also be pared by the rule set at process block 52 to sculpture the return to known or learned data concerning the user by the client 12. For example, previous buying habits of the user or customer of the client 12 stored in the database log 56 may be contemplated in the output from relevancy process 54. Alternatively or in addition, categorical inclinations such as demographic propensities may be contemplated to return items of likely interest to those in the demographic group of the user for the client 12. Changes in user interests as exhibited in previously logged web page access or purchase decisions may also be used to modify the rules to adapt to changing interests of the user at client 12. Observed factors such as page dwell time may further refine return data to correspond to user interests and inclinations. Internal rules may be devised in conjunction with the database log 56.
- the relevancy process block 54 can include a relevancy engine along with analytical tools devised to contemplate data from the database log 56.
- the contextual database inventory block 58 is searched for relevant matches.
- the database 58 is selectively loaded by catalog load 59 according to business relationships between the owner of match server 14 and the web site owner who wish to place or display the product or services listing.
- the database 58 can also be selectively loaded to fit any dimension such as climate (i.e., winter, summer), season (i.e., Christmas), demographics (retired, generation X), financial status or other overlay to business contexts.
- the template formatter 60 fits the return material in the returned page.
- the cache 50 is updated through an update cache 62 and the transaction is logged in the database log 56 and the return page is sent to the client 12.
- responsive behavior such as logging on a "buy" site is logged from the click capture line from the client 12, shown as line 64 in Fig. 3
- the client i.e., the web site owner, determines the content criteria, such as inventory, and product matching criteria.
- the product inventory can come from merchants, provider of the match server 14 and/or affiliates.
- Contextual analysis Product descriptions are filtered, analyzed, and indexed in advance. Neural network techniques are used to identify concepts in product descriptions, and duplicates with different media and product families are identified (i.e.: paperback vs. hardback, items of different color, etc.). 3. Matching: The body of the text of each web page is used as the query.
- Neural network and linguistic pattern techniques are used to identify contextual matches in real time, and the context of the content is matched to the context of the inventory.
- Result serving Matches can be placed on any page, on any site. As pages are served, appropriate products are delivered automatically. Results are presented in a number of convenient ways, such as alongside content as HTML, a gif, Java or Java script or as a pop-up.
- the matching processing system of the present invention works with any content, any product inventory, any user profile, any Web site, and it can link/interface with any transaction engine
- Figs. 3 and 4 combine to show redirection based upon user behavior in response to matched and presented data returned to client 12 on return line 51 (Fig. 3).
- process block 64 Fig. 4
- client 12 is returned with a requested list of products and services, options for user-interaction such as loading, reloading,
- URL links 66, viewing and stopping, etc. are redirected through match server 14 for behavioral analysis and tracking of user actions.
- the results of such analysis are deposited in database log 56. These statistics are used to formulate behavioral trends on how, when, and where the list of returned results are used, and to provide financial analysis in the case of transaction or fulfillment reporting.
- Fig. 5 is a graphical depiction of the contextual query of process block 44 of Fig. 3.
- the query ID and list of parameters are analyzed.
- ID 70 and the context of the query itself condition the entire contextual analysis. If no other parameters are with the query, they may be set to default values P,, P 2 and P N can be null or, if desired, restrictions used, as client set parameters are used, as elements of a BOOLEAN AND with the context.
- Fig. 6 depicts the cache presence analysis of process block 50 of Fig. 3.
- the query's checksum 74 is used by process block 76 to lookup through DB lookup 76 an existing list of results already present in cache pool 80 that match the coding results from contextual query analysis 48 (Fig. 3). If an appropriate list exists, those results associated with the list are found as indicated by process block 78 and returned to client 12 as indicated by block 82. The request and return are logged in the database log 56. If not appropriate list is found in cache pool 80, the query is sent on to the relevancy matching process as indicated by process block 84.
- Fig. 7 A depicts retrieval from contextual database 58. After filtering through filter process 52 shown in Fig. 3, the now filtered query is depicted at block 86. A call 88 is made to the contextual inventory and raw data results 90 are returned to relevancy process 54. Fig. 7B depicts loading data for passage to template formatter 60 of Fig. 3.
- Raw data 90 of Fig. 7 A is normalized at relevancy process 54 as indicated by block 92. It is then stored in temporary storage 94 by loader process 96 until a return set is completed. The entire temporary storage 94 then replaces the current database for production and update as shown by block 98.
- Fig. 8 depicts inventory or catalog loading of process 59 of Fig. 3.
- inventory providers send raw formats of items for inclusion. Preferably, the inventory providers can select the frequency of updates to keep the data current.
- the components are identified and marked or coded at block 102 and sent to process block 104 for normalization and process blocks 106 and 108 for data integrity verification. It is appreciated that the components can represent merchant name/id, category name, URL, and the like.
- Fig. 17 illustrates a catalog integration method in accordance with an embodiment of the present invention.
- the process starts with the catalog or inventory.
- each product is classified by the specific category to which it belongs.
- a category is a specific, individual description of a product.
- a catalog contains different categories of products.
- the Hand-held Electronic Organizer category is part of the Computer catalog.
- the inventory is stored within a database using this architecture.
- the catalogs have to provide the following information:
- the database is queried one product at a time and fed through a neural network to produce a context vector. After its meaning is produced by a neural network, it is then stored within the neural network's own database. At this point, we have a complete cognition of each piece of the entire inventory.
- the next phase involves developing an API that will wrap around the neural network database of products for retrieval.
- the API handles the request from a client, re-routes it to the neural network database and analyzes it to match the appropriate products that relate to the initial query.
- the API is responsible for checking for data integrity of both incoming and outgoing traffic, caching certain data for optimization and providing statistical data for reporting and re-tuning procedures.
- Fig. 9 depicts the process of template formatter 60 of Fig. 3.
- formatter 60 takes in raw data 110 and formats specific parameters 112 which can either be pre-loaded or conveyed to match server 14 with a query.
- the format parameters are components of style dimensions 114, 114,, 114 2 , 114 3 , 114 N .
- the resultant return set and request is logged in the database log 56 and returned to client 12 on return line 51.
- Fig. 10 depicts the update of cache pool 80 of previous Fig. 6. When the cache is updated, the code or checksum is calculated and a key value pointing to the associated list of products 118. The key value and list of products are added to cache pool 80.
- programs written to route the client 12 requests, analyze and filter data are written in Java and C++.
- Java is used in the match server 14 with the Java Servlet standard. This provides quick start threading and improved scalability to enable a round robin server when loads increase. This reduces development time and ensures a reliable mechanism.
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Abstract
Priority Applications (1)
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AU27489/01A AU2748901A (en) | 1999-12-15 | 2000-10-31 | Context matching system and method |
Applications Claiming Priority (2)
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US17097499P | 1999-12-15 | 1999-12-15 | |
US60/170,974 | 1999-12-15 |
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WO2001044992A1 true WO2001044992A1 (fr) | 2001-06-21 |
WO2001044992A9 WO2001044992A9 (fr) | 2002-08-01 |
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Application Number | Title | Priority Date | Filing Date |
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PCT/US2000/041713 WO2001044992A1 (fr) | 1999-12-15 | 2000-10-31 | Systeme et procede de mise en correspondance de contextes |
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