WO2010148410A2 - Utilisation d'indices d'intérêt dans un sujet pour identification et regroupement de contenu par une plateforme activée par sémantique - Google Patents

Utilisation d'indices d'intérêt dans un sujet pour identification et regroupement de contenu par une plateforme activée par sémantique Download PDF

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Publication number
WO2010148410A2
WO2010148410A2 PCT/US2010/039381 US2010039381W WO2010148410A2 WO 2010148410 A2 WO2010148410 A2 WO 2010148410A2 US 2010039381 W US2010039381 W US 2010039381W WO 2010148410 A2 WO2010148410 A2 WO 2010148410A2
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WIPO (PCT)
Prior art keywords
content
user
topic
interest
topics
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PCT/US2010/039381
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English (en)
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WO2010148410A3 (fr
Inventor
Jisheng Liang
Aniruddha Gadre
Will Hunsinger
Chris Jones
Bob Morgan
Nova Spivack
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Evri Inc.
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Application filed by Evri Inc. filed Critical Evri Inc.
Publication of WO2010148410A2 publication Critical patent/WO2010148410A2/fr
Publication of WO2010148410A3 publication Critical patent/WO2010148410A3/fr

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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
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/02—Banking, e.g. interest calculation or account maintenance

Definitions

  • FIG. 1 illustrates an example block diagram of a host server operating semantics-enabled platform which uses indicia of user interest in a topic detected from user devices on the host site or a third party site for identification and aggregation of content from multiple content providers.
  • FIG. 2 depicts an example block diagram of the components of a host server which uses indicia of user interest in a topic detected from user devices on the host site or a third party site for identification and aggregation of content from multiple content providers.
  • FIG. 3 depicts an example block diagram of the components of a mobile device that manages interest subscriptions for a user and presents temporally-relevant content to the user based on the interest subscriptions.
  • FIG. 4 depicts a flow chart illustrating an example process for a mobile device to manage interest subscriptions for a user and to present temporally-relevant content to the user based on the interest subscriptions.
  • FIG. 5 depicts a flow chart illustrating an example process for using indicia of interest in a topic to identify and aggregate, via a semantics-enabled platform, additional content in which a user is potentially interested.
  • FIG. 6 depicts a flow chart illustrating an example process for using a semantic representation of a topic to periodically identify content from multiple content sources for presentation to a user.
  • FIG. 7 depicts a flow chart illustrating an example process for using a semantic representation of a topic to periodically identify content from multiple content sources for presentation to a user.
  • FIG. 8A-8C illustrate example screenshots of a site where indicia of interest relating to a topic can be generated by a user.
  • FIG. 9A-C illustrate example screenshots showing how a user can generate an indicia of interest and how interests can be tracked and combined to generate interest collections.
  • FIG. 10 illustrates an example screenshot of content linked to by the host, the content having a panel through which a user can navigate related content/topics or return to the host site.
  • FIG. 11 illustrates an example screenshot of a third party website having features through which indicia of interest can be generated by a user for use by a connected semantics-enabled platform to identify and aggregate additional content to be presented to the user.
  • FIG. 12A-C depict example screenshots showing multiple search results and/or filtered search results which can be "Followed" by a user to generate indicia of interest.
  • FIG. 12D illustrates a screenshot of an example of a pop-up screen that allows the user to specify the topic/interest to subscribe to.
  • FIG. 13 depicts an example of a third-party web page having content that can be tracked by a user using associated features/mechanisms (e.g., the "Follow” buttons).
  • features/mechanisms e.g., the "Follow” buttons.
  • FIG. 14 illustrates a screenshot of an example web interface for accessing a tracking agent which can be used to manage subscribed topics/interests and to view/browse the content associated with the subscription.
  • FIG. 15A-E depict screenshots of user interfaces on a mobile device showing features (e.g., "FOLLOW” button) allowing users to generate indicia of interest for topics (e.g., channels) and content.
  • features e.g., "FOLLOW” button
  • FIG. 16 shows a diagrammatic representation of a machine in the example form of a computer system within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed.
  • references to one or an embodiment in the present disclosure can be, but not necessarily are, references to the same embodiment; and, such references mean at least one of the embodiments.
  • references in this specification to "one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure.
  • the appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
  • various features are described which may be exhibited by some embodiments and not by others.
  • various requirements are described which may be requirements for some embodiments but not other embodiments.
  • Embodiments of the present disclosure include systems and methods for using indicia of interest in a topic for identification and aggregation of content via a semantics-enabled platform.
  • the present disclosure relates to a mechanism incorporated in a content source (e.g., online content source) that allows a user to track or follow one or more topics related to the user's interests embodied in the content source.
  • a content source e.g., online content source
  • the content source can include online and/or offline content sources including but not limited to, web content (e.g., web page and/or website displayed through a browser), content in an email application, and/or content in other types of application (e.g., multimedia, database, enterprise, desktop, mobile applications, hosted applets, etc.).
  • the content type can include by way of example but not limitation, text, document, audio content, video content, image content, a news article, a comment/review entry, a blog entry, etc.
  • the content source can be identified by a user through a mechanism (e.g., clicking on a button, selecting a tab, selecting a link, or activating any other types of indicators/selectors, etc.) to indicate to the system, interest in one or more topics embodied in the content source or interest in the content source itself.
  • the trackable/followable content can be highlighted or linked in such a manner such that when the cursor moves over the link or content, a pop-up window appears allowing the user to select to track the topic/thing and/or indicate interest.
  • the mechanism e.g., button, tab, indicator, an icon, an image, etc.
  • the mechanism can be implemented using JavaScript, any other type of programming, script, and/or markup language.
  • the mechanism can be automatically configured (e.g., by a web application) or manually added (e.g., by a web developer) in content sources.
  • the mechanism can be configured to be displayed on a web page or web site for each topic, each page, each member/user, each document, each image, or ach video, etc.
  • the mechanism may be configured differently for different content sources.
  • the mechanism can be added by anyone to any site.
  • the mechanism when activated, can trigger an API call to an external host (e.g., social and/or knowledge networking site) which then hosts the followed interest/topic and searches for any content that matches, and notifies the user, for example, on an ongoing basis.
  • the host e.g., external host including but not limited to social and/or knowledge networking site
  • the content may or may not appear within the content provider's user interface.
  • the third party can host the tracker and also show the content inside ESPN's site, for example, as if it is part of their site, or it can be external.
  • a content provider may place the Follow button on each item or part of an item or concept that can be followed.
  • Content providers can construct a directory or index of things that may be followed via their site or other partner sites. The providers can also recommend things to be followed to users based on their behavior or what they know of the user's interests.
  • Embodiments of the present disclosure describe how users can actively build a topic/channel of interest, using one or more of the semantic ingredients provided by the system. For example, by simply clicking on the 'Follow' button on one or more pages, the user would build a topic of his/her own.
  • the system also allows users follow content (e.g. an article, a set of articles, a piece of content on a web page). In this case, the system can derive the semantic representation from the user-selected content.
  • semantic analysis i.e. NLP and/or named entity tagging
  • the system can then aggregate the entities and relationships from multiple articles in user's selection.
  • structured data i.e. a table on a web page
  • the extraction would depend on how the structure is represented (e.g. in semantic form such as RDF, or in raw HTML which would require some form of scraping).
  • the system can then present the extracted entities, relationships and other semantic representations to the user, such that the user could select from the list and make adjustments.
  • FIG. 1 Illustrates an example block diagram of a host server 100 operating semantics-enabled platform which uses indicia of user interest in a topic detected from user devices 102 A-N on the host site or a third party site for identification and aggregation of content from multiple content providers 108 A-N.
  • the client devices 102A-N can be any system and/or device, and/or any combination of devices/systems that is able to establish a connection with another device, a server and/or other systems.
  • Client devices 102 A-N each typically include a display and/or other output functionalities to present information and data exchanged among the devices 102 A-N and the host server 100.
  • the client devices 102 A-N can be any of, but are not limited to, a server desktop, a desktop computer, a computer cluster, or portable devices including, a notebook, a laptop computer, a handheld computer, a palmtop computer, a mobile phone, a cell phone, a smart phone, a PDA, a Blackberry device, a Treo, and/or an iPhone, etc.
  • the client devices 102 A-N, third party host 110, and content providers 108 A-N of electronic content are coupled to a network 106.
  • the devices 102 A-N and host server 100 may be directly connected to one another.
  • the host server 100 uses user interest in a topic to identify and aggregate content for a specific user.
  • the host server 100 can use the topic to identify additional topics which the user may also be interested in. These additional topics can be identified using keyword-based techniques and/or semantics-based techniques.
  • the host server 100 can use an ontology or taxonomy to identify a hierarchy of topics (e.g., which can be represented as an 'entity' or a 'relationship') which can be used to identify and aggregate additional content for the user.
  • Indicia of interest can be user generated, while browsing a site hosted by the host server 100, or third party sites (e.g., hosted by the third-party host 110), for example, by activating features associated with topics themselves or content related to certain topics.
  • Users can track topics or content via mobile devices as well and leverage the semantics-enabled platform of the host 100 for content recommendation, identification, and/or aggregation. Functions and techniques performed by the host server 100 and the components therein are described in detail with further reference of FIG. 2.
  • the client devices 102A-N can be used by users to generate the indicia detected by the host server 100 which indicates interest in a topic or content.
  • the client devices 102 A-N are generally operable to provide user access (e.g., visible access, audible access) to content identified and/or aggregated in detecting user interest in a topic or piece of electronic content, for example via user interface 104A-N displayed on the display units.
  • the Internet 100 communicate may be a telephonic network, an open network, such as the Internet, or a private network, such as an intranet and/or the extranet.
  • the Internet can provide file transfer, remote log in, email, news, RSS, and other services through any known or convenient protocol, such as, but not limited to the TCP/IP protocol, Open System Interconnections (OSI), FTP, UPnP, iSCSI, NSF, ISDN, PDH, RS-232, SDH, SONET, etc.
  • OSI Open System Interconnections
  • the network 106 can be any collection of distinct networks operating wholly or partially in conjunction to provide connectivity to the client devices 102A-N and the host server 100 and may appear as one or more networks to the serviced systems and devices.
  • communications to and from the client devices 102 A-N can be achieved by, an open network, such as the Internet, or a private network, such as an intranet and/or the extranet.
  • communications can be achieved by a secure communications protocol, such as secure sockets layer (SSL), or transport layer security (TLS).
  • SSL secure sockets layer
  • TLS transport layer security
  • communications can be achieved via one or more wireless networks, such as, but not limited to, one or more of a Local Area Network (LAN), Wireless Local Area Network (WLAN), a Personal area network (PAN), a Campus area network (CAN), a Metropolitan area network (MAN), a Wide area network (WAN), a Wireless wide area network (WWAN), Global System for Mobile Communications (GSM), Personal Communications Service (PCS), Digital Advanced Mobile Phone Service (D-Amps), Bluetooth, Wi-Fi, Fixed Wireless Data, 2G, 2.5G, 3G networks, enhanced data rates for GSM evolution (EDGE), General packet radio service (GPRS), enhanced GPRS, messaging protocols such as, TCP/IP, SMS, MMS, extensible messaging and presence protocol (XMPP), real time messaging protocol (RTMP), instant messaging and presence protocol (IMPP), instant messaging, USSD, IRC, or any other wireless data networks or messaging protocols.
  • LAN Local Area Network
  • WLAN Wireless Local Area Network
  • PAN Personal area network
  • CAN Campus area network
  • MAN Metropolitan area network
  • the host server 100 may include internally or be externally coupled to, one or more of any of, a user repository 128, a knowledge repository 130, and/or a user content repository (not shown).
  • the repositories can store software, descriptive data, images, system information, drivers, and/or any other data item utilized by other components of the host server 100 and/or any other servers for operation.
  • the repositories may be managed by a database management system (DBMS), for example but not limited to, Oracle, DB2, Microsoft Access, Microsoft SQL Server, PostgreSQL, MySQL, FileMaker, etc.
  • DBMS database management system
  • the repositories can be implemented via object-oriented technology and/or via text files, and can be managed by a distributed database management system, an object-oriented database management system (OODBMS) (e.g., ConceptBase, FastDB Main Memory Database Management System, JDOInstruments, ObjectDB, etc.), an object-relational database management system (ORDBMS) (e.g., Informix, OpenLink Virtuoso, VMDS, etc.), a file system, and/or any other convenient or known database management package.
  • OODBMS object-oriented database management system
  • ORDBMS object-relational database management system
  • the host server 100 is able to provide data to be stored in the user repository 128, the knowledge repository 130, and the user content repository and/or can retrieve data stored in the user repository 128, the knowledge repository 130, and/or the user content repository.
  • the user repository 128 can store user information, user profile, user tags, user interests, any user-generated content (if applicable), user privacy settings, user device information, hardware information, etc.
  • the knowledge repository 130 can include, by way of example but not limitation, sets of ontologies, taxonomies and/or folksonomies that can be used by the host server 100 for determining the semantic type or attribute type of topics or interests, etc.
  • the sets of ontologies, taxonomies and/or folksonomies can be used by the host server 100 in identifying semantic types associated with metadata identified from content sources describing objects and/or the content embodied therein.
  • the ontology set can also be used to identify the type of semantic relationship that exists between the objects/embodied content and the identified semantic types for use by the server 100 in identifying and aggregating content based on user interest in a given topic or set of topics.
  • the knowledge repository 130 in some instances, can also include, additional sources of ontologies, user-defined ontologies, customized ontologies, dictionaries, thesauruses, and/or encyclopedias, etc.
  • location identifier patterns and their associated semantic types and/or attributes are stored in the knowledge repository 130.
  • FIG. 2 depicts an example block diagram of the components of a host server 200 which uses indicia of user interest in a topic detected from user devices on the host site or a third party site for identification and aggregation of content from multiple content providers.
  • the host server 200 includes a network interface 202, a user interest tracker
  • a type detector module 206 a recommendation module 208, a content identifier 210, a presentation module 212, and/or a mobile platform module 214.
  • the host server 200 is coupled to a user repository 228 and/or a knowledge repository 230.
  • the user repository 228 and the knowledge repository 230 have been described with further reference to the example of FIG. 1.
  • each module in the example of FIG. 2 can include any number and combination of sub-modules, and systems, implemented with any combination of hardware components (e.g., ASICS, FPGAs, memory units and/or processors) and/or software modules.
  • hardware components e.g., ASICS, FPGAs, memory units and/or processors
  • One embodiment of the host server 200 includes a user interest tracker 204.
  • the user interest tracker 204 can be any combination of software agents and/or hardware components that are able to detect, identify, determine, track, manage, and/or update user interest in topics, content, types of content, etc.
  • the user interest tracker 204 can manage user information such as user information or user profile information including but not limited to, demographic information, interests, level of education, profession, hobbies, etc. User information can be static or dynamically updated by the system.
  • the user module 204 can also detect user action that indicates interest or potential interest in a given topic or piece of content. User interest in a group of topics can be grouped into a collection. For example, a user can create a collection of interest in the topic "Oil Spill" having sub-topics "BP" and "Barack Obama". One embodiment includes a collection manager to track the user's collections of interests.
  • the host server 200 can automatically identify sub-topics based on a given topic specified to be of interest to the user. For example, the host 200 can identify sub-topics "BP" and "Barack Obama" in response to identification of user interest in the topic "Oil Spill”.
  • user interest tracker 204 detects indicia of interest (e.g., generated by a user). Such indicia can be explicit or implicit. For example, the user can activate an indicator on a webpage or content displayed via other modes (e.g., email, desktop application, SMS, webpage via a mobile device, and/or cell phone application via a mobile device) to indicate interest in a certain topic or interest in the piece of content.
  • User selectable or activatable indicators e.g., as shown in the examples in the screenshots of FIG. 8-15
  • Such indicators may also be presented via a search engine in conjunction with one or more search results (e.g., FIG. 12A-D).
  • the user interest tracker 204 can detect implicit indicia. For example, tracker 204 can track the user's tagging/bookmarking habits, web browsing habits, time spent on a webpage, website, search queries, etc. to identify implicit indicia of user interest.
  • the user interest tracker 204 can track browsing behavior such as the frequency with which a user views a particular page or visits a particular site, how active a user is on a site/page (e.g., via browsing, commenting, and/or otherwise interacting with pages or objects), the user's postings on a networking or messaging utility, the user's updates on a networking/messaging utility, the user's shared items with other users, etc.
  • One embodiment of the host server 200 includes a type detector module
  • the module 206 can be any combination of software agents and/or hardware components able to detect the semantic type (e.g., entity, relationship, etc.) of a selected topic of interest.
  • the type detector module 206 assigns a semantic type to topics that can be identified by users as being of interest.
  • the type detector module 206 can also locate, search, detect, identify, find, retrieve, collect, analyze, and/or aggregate metadata associated electronic objects and/or their embodied content, specifically content that the user has indicated explicit or implicit interest in.
  • the metadata can be used by the server 200 to extract topics that a user may be interested in based on specified interest in a piece of content (e.g., article, news feed, Tweet, etc.)
  • metadata associated with objects/embodied content can include, by way of example but not limitation, data regarding or relating to the object/embodied content from any information sources internal and/or external to the host server (e.g., host server 200 of FIG. 2).
  • the metadata can also be internal in a sense that it is collected from the object itself or the host of the object (e.g., the same host of a web page).
  • metadata is collected from content sources hosted by various external servers, the host server (e.g., the host server 200 of FIG. 2), and/or a combination of the above.
  • web page metadata is collected from data embedded in
  • the metadata can be collected from electronic content including one or more of, bookmarks, bookmarked content, blog articles, tweets, updates, comments, networking sites (e.g., social networking sites, professional networking sites, knowledge networking sites, Digg, Twine, Delicious, Facebook, MySpace, etc.), networking utilities (e.g.,
  • Twitter mobile networking
  • real time/non-real time messaging e.g., web-based and/or mobile-device based
  • any content having information about the object e.g., web page.
  • the type detector module 206 can determine a semantic type and/or type of semantic relationship to which metadata or a tag corresponds. Generally, the type detector module 206 can identify the semantic type for metadata or the tag selected for use in tracking user interest and aggregating/identifying content based on user interest.
  • semantic types of content in objects can be partially or fully automatically determined by the system or specified by an end user.
  • the semantic type can be automatically determined through topic detection, natural language processing (NLP), speech processing, latent semantics indexing, etc.
  • Semantic types can also be defined by the end user through tagging or annotating the object (e.g., web page) through a user interface in which the object is provided for access.
  • the type detector module 206 detects the semantic entity type or relationship type by mapping the metadata/tag to an ontology or taxonomy set. If the ontological class of a content source (e.g., a web page) is known (e.g., that a given webpage is about restaurants), NLP can be used to map the tags/metatags in the page with an ontology for restaurants. Similarly, on a web page for prescription drugs, if there is a node in the XML template of the web page that maps to "dosage", then node can be mapped to the dosage property of the ontology for prescription drugs.
  • a content source e.g., a web page
  • NLP can be used to map the tags/metatags in the page with an ontology for restaurants.
  • node can be mapped to the dosage property of the ontology for prescription drugs.
  • module 205 includes a semantic object generator which can generate a semantic object to represent a given topic.
  • the semantic object generator can represent the topic as one or more of: 1) individual entities, 2) categories/facets of entities, 3) groups or collections of entities (e.g. summer new movies, Los Angeles Lakers team members), 4) relationships between entities and/or facets, and 5) keywords and key phrases. Users can create a topic interest using any combination of the above items as ingredients.
  • the user interface provided by the system allows users to create their topics of interest.
  • the system allows identify their preference of content or content sources, either explicitly (users give ratings) or implicitly (by analyzing user click log).
  • the users can, via the user interface, identify certain content sources that they prefer for a particular topic/channel (e.g. for topics on celebrity gossips, TMZ.com might be a preferred source).
  • the system can give more weighting to content from preferred sources.
  • users could give us feedback on particular pieces of content. For example, we given option to users to rate individual returned article (Like/Don't Like, Thumps up/down, 5-star rating, etc.). Given the feedback, the system can adjust the ranking of the returned content to better tailor to the user's interest.
  • the host server 200 includes a topic recommendation module 208.
  • the topic recommendation module 208 can be any combination of software agents and/or hardware components able to recommend potential topics of interest to the user based on detected indicia of interest in a particular topic.
  • the recommendation module 208 can identify related topics (e.g., which can be represented as entities or relationships) using ontologies stored in the knowledge repository 230, for example.
  • the topic hierarchy module identifies a hierarchy of topics (entities or relationships) using a known topic of interest.
  • the host 200 can automatically add the related topics and use them in identifying related content to be recommended/pushed to the user.
  • the recommendation module 208 can also prompt the user to determine whether the user is interested in any of these identified related topics.
  • the recommendation module 208 identifies related topics by a) using ontologies, and/or b) using NLP/text mining on a corpus of text.
  • the topic of interest can be represented in the system as semantic entities and/or relationships, which can be linked to entries in the one or more ontologies.
  • an entity can include one or more associated facets, which include finely grained characteristics of entities such as types, categories, and/or characteristics, etc.
  • Example facets include actor, politician, basketball player, nation, drug, automobile, and the like.
  • the module 208 can organize the facets into a hierarchical taxonomy, using the "is-a" relation (e.g. facet 'basketball player' is-a 'sports athlete').
  • entities can have properties in the ontology, such as properties that specify relationships with other entities (e.g. a person's spouse, birth place, etc; for a musician, his/her band, music label, top albums and singles, etc; for a NFL football player, the college and professional teams he played for, etc.). Therefore, given an entity associated with the topic of interest, the recommender 208 can identify its facets and relations from the ontology.
  • the module 208 traverses the facet hierarchy to detect, for example, more general or granular facets as related topics. Also, we could traverse the relationship links of the entity in order to identify or detect other related entities (for example, given a NBA basketball player, his current team can be identified; from the team, the team's league/conference can be identified, other team members can be identified, coaches can be identified, etc.).
  • the properties and relationships stored in the ontology can be static, and can be updated periodically (e.g. when players change team).
  • the module 208 can extract more dynamic and temporal relationships, by, for example, using text mining and NLP techniques to mine the text documents (i.e. news feeds, blogs, web pages, etc.). For example, given Kobe Bryant, a basketball player of NBA's Los Angeles Lakers, other entities that are related to him in the ontology can be identified, at a given point in time. For example, during the NBA finals, the related entities that can be identified can include his team members, as well as the Lakers' opponent, Boston Celtics, and its top players, such as Kevin Garnett and Ray Allen.
  • text mining and NLP techniques to mine the text documents (i.e. news feeds, blogs, web pages, etc.). For example, given Kobe Bryant, a basketball player of NBA's Los Angeles Lakers, other entities that are related to him in the ontology can be identified, at a given point in time. For example, during the NBA finals, the related entities that can be identified can include his team members, as well as the Lakers' opponent, Boston Celtics, and
  • the ontologies can be populated from multiple structured and semi- structured sources (e.g. Wikipedia).
  • a batch updating process is applied periodically that pulls all the entries from a particular source. Such batch update could also be run by demand on a certain type or group of entities. For example, before major events like Soccer World Cup, we could update all the entities of the facet 'soccer player'.
  • the system employs an automated and adaptive updating process, by monitoring popular or emerging entities. The goal is that, when a new entity is emerging (e.g. a new musician suddenly becomes very famous, like Susan Boyle), or an entity's popularity rises due to changing property (e.g. sports players are traded to a new team, a person dies, etc.), the system can pull the latest data from the source and update the entity in the ontology in a timely manner.
  • the sources of entity that indicate popularity that can be monitored include, by way of example: Frequency counts in our own index of news articles, as well as the timeline (in order to detect emerging entities that have rising popularity); User clicks on entities in a most recent time interval; Wikipedia page view counts (Wikipedia traffic statistics on hourly basis are publicly available from sources such as http://stats. grok.se); Number of most recent mentions in Tweets, through Twitter API; Social networks sites such as Facebook (e.g. Facebook's 'Like' counts); Popular/trending queries from search engines such as Google and Yahoo.
  • the system can select a set of top N entities based on a 'Zeitgeist' popularity measure computed by combining the factors listed above. For those identified popular/trending entities, the system can identify the latest data from proper sources, and add new entries or update existing entities' properties in the ontologies immediately. Similarly, the system can compute the Zeitgeist popularity scores on certain categories (i.e. facets) or groups of entities (by aggregating popularity of individual entities that belong to the same group), and update entities by facet or group.
  • One embodiment of the host server 200 includes a content identifier 210.
  • the content identifier 210 can be any combination of software agents and/or hardware components able to detect, identify, retrieve, select, content from multiple sources based on a user's topic of interest, an indicia of a user's interest in a topic, and/or topics identified by the server as being related to the user's topic of interest.
  • the content identifier 210 uses a semantic representation of one or more topics to identify content from various sources (e.g., websites, news sites, social networking sites, feeds, etc.).
  • the content that is aggregated can include, news, articles, quotes, comments, video, images, tweets, etc.
  • the content that is aggregated can be temporally relevant.
  • the server 200 can periodically aggregate content and update the database such that it is identifying up-to-date information for recommendation or presentation to users.
  • temporally relevant content is identified using the timing module.
  • the content identifier 210 has access to location data of the user or the user's device; the location module can then further identify or filter aggregated content that is geographically relevant to the user.
  • the content identifier module 210 can perform an indexing process, and can support the search of the indexed content using semantic queries.
  • Content can be acquired from news feeds, blog feeds, and by crawling the web.
  • the system can apply, for example, text analysis and/or NLP process (i.e. entity tagging and disambiguation) to recognize entities and relationships mentioned in the text.
  • the system can link instances of the entity mentions to the corresponding entries in one or more ontolologies and store such semantic annotations of text in an efficient index structure.
  • the content identifier 210 can retrieve the processed content using entities and/or relationships as queries.
  • Example processes are described in related US Patent No. 7,526,425 entitled “Method and system for extending keyword searching to syntactically and semantically annotated data” and US Patent Application 20090144609 entitled “NLP -based entity recognition and disambiguation", the contents of which are incorporated by reference herein.
  • the content identifier 210 can use the APIs (e.g., public APIs) to retrieve content (e.g. using YouTube search APIs to retrieve relevant videos).
  • the identifier 210 uses external APIs to retrieve tweets (e.g, via Twitter search API).
  • Content can be pulled periodically (e.g., every 30 sec, every few minutes, every hour, every few hours, every few days, etc.). In one embodiment, content is pulled every few minutes to ensure temporal relevance of the data.
  • the content is added to the indexing pipeline, which can include, applying semantic analysis to identify entities and relationships in the content, hi addition, the indexing process can include storing of the semantic annotations in an efficient indexing structure.
  • the system Given the semantic representation of user's interest, the system can perform one or more queries against the index, and return a list of content tanked by relevance and timeliness. For content that is being accessed directly via third-party APIs, the semantic representation can be translated by the content identifier 210 into a query form recognizable to the third-party API.
  • the host server 200 includes a presentation module 212.
  • the presentation module 212 can be any combination of software agents and/or hardware components able to generate a user interface to present the aggregated content to the user.
  • the aggregated content can be presented in a fashion such that the user is able to view and select the content based on topic. For example, if the user indicates interest in 'basketball', and the host server 200 also identifies content related more specifically to the NBA league, Kobe Bryant or the NBA finals, the user interface and provide the content such that the user can select to selectively access content based on topic, for example, as illustrated in the example screenshot of. FIG. 9C. The example of FIG. 9C illustrates that under the 'Oil Spill' collection, the user can select to access content related to 'Barack Obama' or 'BP'.
  • One embodiment of the host server 200 includes a mobile platform module
  • the mobile platform module 214 can be any combination of software agents and/or hardware components able to provide an interface of the host server 200 such that it is compatible with a mobile device (e.g., the mobile device in the example of FIG. 3).
  • the mobile platform module 214 can adapt the user interface to fit a mobile device screen.
  • the module 214 adjusts the position and/or form of features (e.g., "follow" button) that the user can use to generate indicia of interest in a topic for easier access.
  • mobile devices may be equipped with location sensors which can provide the module 214 with the location data.
  • the module 214 can format or process this data to be used by the host 200 in aggregating or filtering content such that they are also geographically relevant to the user's location.
  • each module in the example of FIG. 2 can include any number and combination of sub-modules, and systems, implemented with any combination of hardware and/or software modules.
  • the host server 200 although illustrated as comprised of distributed components (physically distributed and/or functionally distributed), could be implemented as a collective element.
  • some or all of the modules, and/or the functions represented by each of the modules can be combined in any convenient or known manner.
  • the functions represented by the modules can be implemented individually or in any combination thereof, partially or wholly, in hardware, software, or a combination of hardware and software.
  • FIG. 3 depicts an example block diagram illustrating the components of a mobile device 300 which is able to manage interest subscriptions for user and also present content (e.g., including temporally-relevant content) to the user based on the subscriptions.
  • content e.g., including temporally-relevant content
  • mobile device 300 includes a network interface 302, a processing unit 304, a memory unit 306, a storage unit 308, and/or a location sensor 310. Additional or less units or modules may be included.
  • the data received from the network interface 302 can be input to the processing unit 304.
  • the processing unit 304 can include one or more processors, CPUs, microcontrollers, FPGAs, ASICs, DSPs, or any combination of the above. Data that is input to the mobile device 300 can be processed by the processing unit 304 and output to a display.
  • One embodiment of the mobile device 300 further includes a memory unit
  • the memory unit 306 and a storage unit 308 are, in some embodiments, coupled to the processing unit 304.
  • the memory unit can include volatile and/or non- volatile memory.
  • the memory unit has stored thereon instructions which when executed by the processor, cause the processor to perform a method for managing interest subscriptions for a user and presenting temporally-relevant content to the user based on the interest subscriptions, for example, by subscribing the user to an interest in response to detecting activation of an indicator via the mobile device, receiving the temporally-relevant content related to multiple topics identified based on the interest subscription via a semantics-enabled platform, and/or displaying the temporally- relevant content via the mobile device, such that the user is able to selectively access the temporally-relevant content identified by the semantics-enabled platform by group.
  • FIG. 4 depicts a flow chart illustrating an example process for a mobile device to manage interest subscriptions for a user and to present temporally-relevant content to the user based on the interest subscriptions.
  • process 402 the user is subscribed to an interest in response to detecting activation of an indicator, via a mobile device.
  • the interest subscription is managed for the user.
  • process 406 the temporally-relevant content related to multiple topics identified based on the interest subscription is received via a semantics-enabled platform.
  • the multiple topics can include a topic that is treated as an entity or a relationship in the semantics-enabled platform.
  • the content is displayed via the mobile device, such that the user is able to selectively access the content identified by the semantics-enabled platform by topic.
  • the temporally-relevant content is displayed to be categorized based on topic and is selectively accessible to the user by topic.
  • the mobile device further includes a location sensor and thus the temporally-relevant content received from the semantics-enabled platform includes content that is also spatially relevant to a location of the mobile device.
  • the content can be filtered for content that is spatially or geographically relevant to the user based on his current location.
  • FIG. 5 depicts a flow chart illustrating an example process for using indicia of interest in a topic to identify and aggregate, via a semantics-enabled platform, additional content in which a user is potentially interested.
  • indicia of interest in a topic are detected.
  • the indicia of interest can be generated by the user via a site hosted by the semantics-enabled platform.
  • the indicia can also be generated by the user via third party sites linked to the semantics enabled platform.
  • process 504 a hierarchy of topics, identified using the topic in which the user is interested, is presented to the user.
  • the user is enabled to select from the hierarchy of topics, additional topics of interest, hi process 508, an aggregate group of topics of interest to the user is formed based on the selection.
  • the aggregate group of topics of interest to the user is stored in the semantics-enabled platform.
  • process 512 the content from multiple content sources is identified using the semantic representation.
  • the content that is identified or later presented is typically temporally relevant.
  • the content is presented in an aggregated fashion to the user via a user device, such that the user is able to select content associated with a particular topic of the aggregate group of topics for access.
  • the content is presented via a site hosted by the semantics-enabled platform, in chronological order; wherein, the content presented on the site includes a link to a full version of the content.
  • the content can be presented via a desktop application, hi one embodiment, a mechanism is provided to share the content for access via a different platform, for example, with other users.
  • FIG. 6 depicts a flow chart illustrating an example process for using a semantic representation of a topic to periodically identify content from multiple content sources for presentation to a user.
  • indicia of interest in a topic are received from a user.
  • the indicia of user interest in the topic can be generated via activation of a user-perceptible indicator depicted on a website, for example.
  • the indicator can be presented in conjunction with content or with a topic on a website or other data delivery mechanisms.
  • the indicia of user interest in the topic can be received on a website hosted in the semantic platform or on a third party website linked to the semantic platform.
  • the user can generate the indicia from a desktop application or using a mobile device (e.g. using a phone application, the Internet, or via SMS).
  • a semantic representation of the topic in the semantics- enabled platform is generated.
  • the topic can include an entity or a relationship between entities.
  • a hierarchy of topics is identified using the topic in which the user is potentially interested, for example, using an ontology or taxonomy.
  • the additional content is periodically identified from multiple content sources (e.g., multiple websites including news sites) using the semantic representation.
  • process 608 the additional content in an aggregated fashion to the user via a user device, such that the user is able to view and select the additional content identified in response to the indicia of user interest in the topic.
  • process 610 a selection of the multiple topics that are in the collection from the user.
  • process 612 content is identified from multiple content sources for a collection of multiple topics.
  • process 614 the thus identified content is presented in an aggregated fashion, such that the user is able to view and select the content in the collection and also such that the user is able to select a subset of the content associated with one of the multiple topics.
  • FIG. 7 depicts a flow chart illustrating an example process for using a semantic representation of a topic to periodically identify content from multiple content sources for presentation to a user.
  • process 702 interest of a user in one or more topics is determined based on activation of an indicator associated with a content source.
  • the indicator can be in the form of a clickable button or tab.
  • the content source e.g., a web page, search results page, email, feed, status update, voice message, SMS, etc.
  • the content source e.g., a web page, search results page, email, feed, status update, voice message, SMS, etc.
  • a notification for notifying the user is generated in response to detecting an event associated with the content source and/or identifying other content sources related to the one or more topics.
  • the event can include, one or more of, a notification for notifying the user in response to identifying additional content sources related to the topic of interest.
  • multiple interests of the user are determined based on activation of indicators associated with multiple content sources.
  • One embodiment includes, determining multiple interests of the user based on activation of indicators associated with multiple content sources.
  • a notification for notifying the user is generated in response to identifying additional content sources related to the topic of interest.
  • FIG. 8A-8C illustrate example screenshots of a site where indicia of interest a topic can be generated by a user.
  • the follow button 802 is an example of a mechanism through which a user can generate indicia of interest in a topic (e.g., "FICA World Cup”).
  • the share button 804 can be used to share the content through other sites (e.g., social networking sites).
  • FIG. 8C illustrates how the filter button 806 can be used to select the type of content (e.g., articles, quotes, images, tweets) that the user wants to access.
  • FIG. 9A-C illustrates example screenshots showing how a user can generate an indicia of interest and how interests can be tracked and combined to generate interest collections.
  • the example of FIG. 9A shows that the user is FOLLOWING 902 the topic "NBA Finals" (e.g., the user has selected the follow button to indicate interest in the particular topic).
  • FIG. 9B illustrates, for example, under the panel 906, topics that the user is following, or has specified interest in.
  • FIG. 9C illustrates a specific collection of interests created by a user relating to "oil spill". The collection includes several topics each of which can be selected by a user. An example of the list of topics is shown under panel 916 for the collection "oil spill" 914. The user can create a new collection using the button 910.
  • FIG. 10 illustrates an example screenshot of content linked to by the host, the content having a panel 1002 through which a user can navigate to related content/topics or return to the host site.
  • the related topics/content depicted in the panel 1002 can be identified and aggregated based on indicia of user interest in a topic.
  • the panel 1002 allows the user to have convenient access to the collection of related content from various sources while accessing content from a third party site.
  • FIG. 11 illustrates an example screenshot of a third party website having features 1102 through which indicia of interest can be generated by a user for use by a connected semantics-enabled platform (e.g., the host server 100 in the example of FIG. 1) to identify and aggregate additional content to be presented to the user.
  • a connected semantics-enabled platform e.g., the host server 100 in the example of FIG. 1
  • FIG. 12 A-C depict example screenshots showing multiple search results and/or filtered search results which can be "Followed" by a user to generate indicia of interest. Each of the search results can be followed, for example.
  • the activation mechanism e.g., the "Follow” button
  • the mechanism can be associated with each individual search result.
  • FIG. 12 A-C show that the search results can be filtered and each filtered search result can also be individually associated with an activation mechanism.
  • the mechanism through which users can subscribe to content or topics of interest can include multiple indicators (e.g., buttons or other types of selectors) per web page.
  • a web page can include one button associated with the main topic of the web page.
  • FIG. 12 A illustrates a screenshot of an example web page with multiple buttons (e.g., the "Follow" button) for use by a user to subscribe to topics/interests through tracking content in the web page.
  • the web page includes buttons associated with subtopics.
  • the web page can include a button associated with each item (e.g., each article, each comment, each posting, each video, each audio track, etc.).
  • each interest that is followed can be set to be tracked via a different device, application, and/or mechanism or they can be tracked the same way (e.g., the notifications can be received via the same mechanism and/or the content can be received via the same mechanism.
  • the interests can be tracked via email, SMS, RSS, online message, a call, etc.
  • each interest can have its own filtering/processing rules or share the same filtering rules.
  • the user can subscribe to the content source and/or specific topics embodied therein. For example, if the user selects a particular content source (e.g., by activating a selector/indicator), the system can identify various topics included in the piece of content that the user may wish to track. The user may automatically be subscribed to each of the various topics automatically identified in the content source. In some embodiments, the system presents the identified topics and allows the user to select the topics and/or type of content for which the user wishes to receive notifications.
  • the indicator/selector e.g., a button such as the "Follow” button in the example of FIG. 12A
  • the system can identify various topics included in the piece of content that the user may wish to track. The user may automatically be subscribed to each of the various topics automatically identified in the content source. In some embodiments, the system presents the identified topics and allows the user to select the topics and/or type of content for which the user wishes to receive notifications.
  • the system can identify some topics related to the particular topic for which the user may automatically or selectively subscribe to.
  • users can subscribe to specific terms including but not limited to concepts, keywords, etc.
  • users can subscribe to topics by indicating interests in strings, images, symbols, audio, and/or video content, etc.
  • interests/topics that users can subscribe to are defined by search queries and/or search queries with filtering rules (e.g., semantic filtering rules, social filtering rules, chronological filtering rules, relevancy filtering rules, other types of filtering rules).
  • the system can notify the user about updates/revisions/additions to the content source or "events" associated with the content source. For example, the user can be notified of a revision to the subscribed content source, a new comment/review of the content source, a new file, a new book-mark of the content source, etc.
  • an event can include, a new search result for a given search query, new articles in a newswire or RSS feed, new email, new mailbox, etc.
  • the user can also be alerted about activity of the interest and/or the content source.
  • the user can be notified or alerted of new or other sources having content that matches or relates to the subscribed topics/interests.
  • the user can be notified via one or more communication mechanisms including but not limited to, an online message, an email, RSS, an SMS text message, a link, an icon, a sound, a web-based reader/application, a desktop reader/application, social networking site, knowledge networking site, Twitter, Facebook, an API, and/or any other form of syndicating notifications, etc.
  • the notifications can be of any length and can include data/content of various levels of detail and various data types.
  • the user can be notified of content or new content having topics that are related to the topics of the originally identified content source. For example, if a user has subscribed to a content source related to scuba diving, the system can, in addition to notifying the user about content related to scuba diving, can also notify the user about content related to snorkeling.
  • the scope of the notification may be determined by the source content.
  • the scope of notification can be modified by the user through the subscription via the content source. For example, the user can narrow or broaden the scope of related topics that they wish to be notified of for a given subscription.
  • the user can also edit the subscription to track additional or different topics or interests.
  • FIG. 12D illustrates a screenshot of an example of a pop-up screen that allows the user to specify the topic/interest to subscribe to, in response to receiving indicia of user interest in a topic or content.
  • a selector/indicator e.g., the "Follow” button
  • the pop-up screen may automatically be depicted to allow the user to select the topics/interests to subscribe to (e.g., check the topics/interests to subscribe to and click "Follow”).
  • Multiple options can be presented to the user.
  • the options presented to the user are selected using content extracted from the page or link associated with the activated selector (e.g., button).
  • the user can also indicate that none of the presented options are of interest and to not subscribe (click "No Thanks") to any of the topics/interests.
  • FIG. 13 depicts another example of a third-party web page having content that can be tracked by a user using associated features/mechanisms (e.g., the "Follow” buttons 1304).
  • associated features/mechanisms e.g., the "Follow” buttons 1304.
  • FIG. 14 illustrates a screenshot of an example web interface for accessing a tracking agent which can be used to manage subscribed topics/interests and to view/browse the content associated with the subscription.
  • the follow buttons can be used to indicate interest in a topic, article, or piece of content.
  • the present disclosure relates to a tracker for a user to manage and/or access the topics/interests subscribed to by a user.
  • the tracker can be accessible via a web interface through a web browser.
  • the tracker may also be accessible through a desktop application to manage the notifications and/or track content that is both online and/or offline.
  • the tracker can also allow the user to manage the notifications/interests and/or associated content from different websites and/or applications. A user can thus use the tracker to view/browse the subscribed topics/interests and the associated content/notifications.
  • the tracker can be used to sort the associate content/notifications by various interests. For example, the user can select to view the content/notifications associated with specific interests in the tracker.
  • the tracker can be used by the user to search within the collection of content matching the user's interest/topic subscriptions or to search within the content matching a particular interest/topic subscription.
  • the tracker sends out notifications or enables notifications to be retrieved by request of the user or an application.
  • a user can access notifications and/or the associated content through an external application (e.g., email, RSS, API of a social networking site including but not limited to Twitter, Facebook, etc., API of a knowledge networking site, Short Messaging Services, etc.) using the tracker.
  • an external application e.g., email, RSS, API of a social networking site including but not limited to Twitter, Facebook, etc., API of a knowledge networking site, Short Messaging Services, etc.
  • FIG. 15A-E depict screenshots of user interfaces on a mobile device showing features (e.g., "FOLLOW” button) allowing users to generate indicia of interest for topics (e.g., channels) and content.
  • features e.g., "FOLLOW” button
  • FIG. 16 shows a diagrammatic representation of a machine in the example form of a computer system 1600 within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed.
  • the machine operates as a standalone device or may be connected (e.g., networked) to other machines.
  • the machine may operate in the capacity of a server or a client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment.
  • the machine may be a server computer, a client computer, a personal computer (PC), a tablet PC, a laptop computer, a set-top box (STB), a personal digital assistant (PDA), a cellular telephone, an iPhone, a Blackberry, a processor, a telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine.
  • PC personal computer
  • PDA personal digital assistant
  • machine-readable medium or machine-readable storage medium is shown in an exemplary embodiment to be a single medium, the term “machine-readable medium” and “machine-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions.
  • the term “machine- readable medium” and “machine-readable storage medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the presently disclosed technique and innovation.
  • routines executed to implement the embodiments of the disclosure may be implemented as part of an operating system or a specific application, component, program, object, module or sequence of instructions referred to as "computer programs.”
  • the computer programs typically comprise one or more instructions set at various times in various memory and storage devices in a computer, and that, when read and executed by one or more processing units or processors in a computer, cause the computer to perform operations to execute elements involving the various aspects of the disclosure.
  • machine-readable storage media machine-readable media, or computer-readable (storage) media
  • recordable type media such as volatile and non-volatile memory devices, floppy and other removable disks, hard disk drives, optical disks (e.g., Compact Disk Read-Only Memory (CD ROMS), Digital Versatile Disks, (DVDs), etc.), among others, and transmission type media such as digital and analog communication links.
  • CD ROMS Compact Disk Read-Only Memory
  • DVDs Digital Versatile Disks
  • transmission type media such as digital and analog communication links.
  • the words “comprise,” “comprising,” and the like are to be construed in an inclusive sense, as opposed to an exclusive or exhaustive sense; that is to say, in the sense of "including, but not limited to.”
  • the terms “connected,” “coupled,” or any variant thereof means any connection or coupling, either direct or indirect, between two or more elements; the coupling of connection between the elements can be physical, logical, or a combination thereof.

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Abstract

La présente invention concerne un système et un procédé pour améliorer l'attribution de comptes débiteurs à une pluralité de parties de recouvrement. Le mode de réalisation préféré est capable d'optimiser la façon par laquelle des entités individuelles de performances sont attribuées au recouvrement de comptes débiteurs individuels passibles de poursuites par un créancier. Une solution d'analyse utilise des algorithmes pour analyser les données rassemblées et pour noter chaque partie de recouvrement sur la base des caractéristiques des parties individuelles de recouvrement, des comptes débiteurs, du créancier, des données acquises à l'extérieur et des contraintes sur toutes les parties concernées. Le système et le procédé sont également capables d'améliorer une note de crédit d'un emprunteur individuel en fonction du risque encouru lors d'un octroi de crédit à cet emprunteur particulier sur la base de l'aptitude au recouvrement par défaut.
PCT/US2010/039381 2009-06-19 2010-06-21 Utilisation d'indices d'intérêt dans un sujet pour identification et regroupement de contenu par une plateforme activée par sémantique WO2010148410A2 (fr)

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