US20120191716A1 - System and method for knowledge retrieval, management, delivery and presentation - Google Patents

System and method for knowledge retrieval, management, delivery and presentation Download PDF

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US20120191716A1
US20120191716A1 US13/168,785 US201113168785A US2012191716A1 US 20120191716 A1 US20120191716 A1 US 20120191716A1 US 201113168785 A US201113168785 A US 201113168785A US 2012191716 A1 US2012191716 A1 US 2012191716A1
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semantic
information
knowledge
web
agent
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US13/168,785
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Nosa Omoigui
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Nosa Omoigui
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Priority to PCT/US2002/020249 priority Critical patent/WO2003001413A1/en
Priority to USPCT/US02/20249 priority
Priority to US10/367,825 priority patent/US6946715B2/en
Priority to PCT/US2004/004574 priority patent/WO2004075299A1/en
Priority to USPCT/US04/04574 priority
Priority to USPCT/US04/04674 priority
Priority to PCT/US2004/004674 priority patent/WO2004075466A2/en
Priority to PCT/US2005/005329 priority patent/WO2005103883A1/en
Priority to USPCT/US05/05329 priority
Priority to US11/462,688 priority patent/US20070081197A1/en
Priority to US11/505,261 priority patent/US20070038610A1/en
Priority to US11/561,320 priority patent/US20070260580A1/en
Priority to US11/829,880 priority patent/US20090254510A1/en
Priority to US11/931,659 priority patent/US20080147716A1/en
Priority to US11/931,793 priority patent/US20080147788A1/en
Priority to US13400308A priority
Priority to US20669508A priority
Priority to US20665608A priority
Priority to US12/358,224 priority patent/US20100070448A1/en
Application filed by Nosa Omoigui filed Critical Nosa Omoigui
Priority to US13/168,785 priority patent/US20120191716A1/en
Publication of US20120191716A1 publication Critical patent/US20120191716A1/en
Abandoned legal-status Critical Current

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    • HELECTRICITY
    • H01BASIC ELECTRIC ELEMENTS
    • H01LSEMICONDUCTOR DEVICES; ELECTRIC SOLID STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H01L27/00Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate
    • H01L27/14Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including semiconductor components sensitive to infra-red radiation, light, electromagnetic radiation of shorter wavelength or corpuscular radiation and specially adapted either for the conversion of the energy of such radiation into electrical energy or for the control of electrical energy by such radiation
    • H01L27/144Devices controlled by radiation
    • H01L27/146Imager structures
    • H01L27/14643Photodiode arrays; MOS imagers
    • H01L27/14645Colour imagers
    • HELECTRICITY
    • H01BASIC ELECTRIC ELEMENTS
    • H01LSEMICONDUCTOR DEVICES; ELECTRIC SOLID STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H01L27/00Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate
    • H01L27/14Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including semiconductor components sensitive to infra-red radiation, light, electromagnetic radiation of shorter wavelength or corpuscular radiation and specially adapted either for the conversion of the energy of such radiation into electrical energy or for the control of electrical energy by such radiation
    • H01L27/144Devices controlled by radiation
    • H01L27/146Imager structures
    • HELECTRICITY
    • H01BASIC ELECTRIC ELEMENTS
    • H01LSEMICONDUCTOR DEVICES; ELECTRIC SOLID STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H01L27/00Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate
    • H01L27/14Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including semiconductor components sensitive to infra-red radiation, light, electromagnetic radiation of shorter wavelength or corpuscular radiation and specially adapted either for the conversion of the energy of such radiation into electrical energy or for the control of electrical energy by such radiation
    • H01L27/144Devices controlled by radiation
    • H01L27/146Imager structures
    • H01L27/14601Structural or functional details thereof
    • H01L27/1463Pixel isolation structures
    • HELECTRICITY
    • H01BASIC ELECTRIC ELEMENTS
    • H01LSEMICONDUCTOR DEVICES; ELECTRIC SOLID STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H01L27/00Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate
    • H01L27/14Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including semiconductor components sensitive to infra-red radiation, light, electromagnetic radiation of shorter wavelength or corpuscular radiation and specially adapted either for the conversion of the energy of such radiation into electrical energy or for the control of electrical energy by such radiation
    • H01L27/144Devices controlled by radiation
    • H01L27/146Imager structures
    • H01L27/14643Photodiode arrays; MOS imagers
    • H01L27/14645Colour imagers
    • H01L27/14647Multicolour imagers having a stacked pixel-element structure, e.g. npn, npnpn or MQW elements
    • HELECTRICITY
    • H01BASIC ELECTRIC ELEMENTS
    • H01LSEMICONDUCTOR DEVICES; ELECTRIC SOLID STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H01L27/00Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate
    • H01L27/14Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including semiconductor components sensitive to infra-red radiation, light, electromagnetic radiation of shorter wavelength or corpuscular radiation and specially adapted either for the conversion of the energy of such radiation into electrical energy or for the control of electrical energy by such radiation
    • H01L27/144Devices controlled by radiation
    • H01L27/146Imager structures
    • H01L27/14601Structural or functional details thereof
    • H01L27/14609Pixel-elements with integrated switching, control, storage or amplification elements
    • HELECTRICITY
    • H01BASIC ELECTRIC ELEMENTS
    • H01LSEMICONDUCTOR DEVICES; ELECTRIC SOLID STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H01L31/00Semiconductor devices sensitive to infra-red radiation, light, electromagnetic radiation of shorter wavelength or corpuscular radiation and specially adapted either for the conversion of the energy of such radiation into electrical energy or for the control of electrical energy by such radiation; Processes or apparatus specially adapted for the manufacture or treatment thereof or of parts thereof; Details thereof
    • H01L31/0248Semiconductor devices sensitive to infra-red radiation, light, electromagnetic radiation of shorter wavelength or corpuscular radiation and specially adapted either for the conversion of the energy of such radiation into electrical energy or for the control of electrical energy by such radiation; Processes or apparatus specially adapted for the manufacture or treatment thereof or of parts thereof; Details thereof characterised by their semiconductor bodies
    • H01L31/0352Semiconductor devices sensitive to infra-red radiation, light, electromagnetic radiation of shorter wavelength or corpuscular radiation and specially adapted either for the conversion of the energy of such radiation into electrical energy or for the control of electrical energy by such radiation; Processes or apparatus specially adapted for the manufacture or treatment thereof or of parts thereof; Details thereof characterised by their semiconductor bodies characterised by their shape or by the shapes, relative sizes or disposition of the semiconductor regions
    • H01L31/035236Superlattices; Multiple quantum well structures
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02ATECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The present invention is directed to an integrated implementation framework and resulting medium for knowledge retrieval, management, delivery and presentation. The system includes a first server component that is responsible for adding and maintaining domain-specific semantic information and a second server component that hosts semantic and other knowledge for use by the first server component that work together to provide context and time-sensitive semantic information retrieval services to clients operating a presentation platform via a communication medium. Within the system, all objects or events in a given hierarchy are active Agents semantically related to each other and representing queries (comprised of underlying action code) that return data objects for presentation to the client according to a predetermined and customizable theme. This system provides various means for the client to customize and “blend” Agents and the underlying related queries to optimize the presentation of the resulting information.

Description

    PRIORITY CLAIM
  • This application is a continuation of and claims priority to co-pending U.S. patent application Ser. No. 12/358,224 filed Jan. 22, 2009 which is a continuation of U.S. patent application Ser. Nos. 11/505,261 filed Aug. 16, 2006, 11/462,688 filed Aug. 4, 2006, 11/561,320 filed Nov. 17, 2006, 11/829,880 filed Jul. 27, 2007, 11/931,659 filed Oct. 31, 2007; 11/931,793 filed Oct. 31, 2007, 12/134,003 filed Jun. 5, 2008, 12/206,695 filed Sep. 8, 2008, and 12/206,656 filed Sep. 8, 2008.
  • This application also claims priority to U.S. Provisional Patent Application No. 60/970,498 filed Sep. 6, 2007. This application also claims priority to U.S. Provisional Patent Application No. 60/820,606 filed Jul. 27, 2006. This application also claims priority to U.S. Provisional Patent Application No. 60/681,892 filed May 16, 2005. U.S. patent application Ser. No. 11/127,021 filed May 10, 2005; which application claims priority to U.S. Provisional Application Ser. Nos. 60/569,663 (Attorney Docket No. NERV-1-1007) and/or U.S. Provisional Application Ser. No. 60/569,665 (Attorney Docket No. NERV-1-1008).
  • This application claims priority to U.S. application Ser. No. 10/179,651 (Attorney Docket No. FORE-1-1001) filed Jun. 24, 2002, which application claims priority to U.S. Provisional Application No. 60/360,610 (Attorney Docket No. NERV-1-1003) filed Feb. 28, 2002 and/or to U.S. Provisional Application No. 60/300,385 (Attorney Docket No. FORE-1-1002) filed Jun. 22, 2001. This application also claims priority to U.S. Provisional Application No. 60/447,736 (Attorney Docket No. NERV-1-1004) filed Feb. 14, 2003. This application also claims priority to PCT/US02/20249 (Attorney Docket No. FORE-11-1001) filed Jun. 24, 2002.
  • This application claims priority to U.S. application Ser. No. 10/781,053 (Attorney Docket No. NERV-1-1006) filed Feb. 17, 2004, which application is a Continuation-In-Part of U.S. application Ser. No. 10/179,651 filed Jun. 24, 2002, which claims priority to U.S. Provisional Application No. 60/360,610 filed Feb. 28, 2002 and/or to U.S. Provisional Application No. 60/300,385 filed Jun. 22, 2001. This application also claims priority to U.S. Provisional Application No. 60/447,736 filed Feb. 14, 2003. This application also claims priority to PCT/US02/20249 filed Jun. 24, 2002. This application also claims priority to PCT/US2004/004380 (Attorney Ref. No. NERV-11-1012) and/or U.S. application Ser. No. 10/779,533 (Attorney Ref. No. NERV-1-1005), both filed Feb. 14, 2004. This application claims priority to PCT/US04/004674 (Attorney Docket No. NERV-11-1013) filed Feb. 14, 2004, which application is a Continuation-In-Part of U.S. application Ser. No. 10/179,651 filed Jun. 24, 2002, which claims priority to U.S. Provisional Application No. 60/360,610 filed Feb. 28, 2002 and/or to U.S. Provisional Application No. 60/300,385 filed Jun. 22, 2001. This application also claims priority to U.S. Provisional Application No. 60/447,736 filed Feb. 14, 2003. This application also claims priority to PCT/US02/20249 filed Jun. 24, 2002. This application also claims priority to PCT/US2004/004380 (Attorney Ref. No. NERV-11-1012) and/or U.S. application Ser. No. 10/779,533 (Attorney Ref. No. NERV-1-1005), both filed Feb. 14, 2004.
  • All of the foregoing applications are hereby incorporated by reference in their entirety as if fully set forth herein.
  • COPYRIGHT NOTICE
  • This disclosure is protected under United States and International Copyright Laws. © 2002-2009 Nosa Omoigui. All Rights Reserved. A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.
  • BACKGROUND OF THE INVENTION
  • Knowledge is now widely recognized as a core asset for organizations around the world, and as a tool for competitive advantage. In today's connected, information-based world, knowledge-workers must have access to the knowledge and the tools they need to make better, faster, and more-informed decisions to improve their productivity, enhance customer relationships, and to make their businesses more competitive. In addition, industry observers have touted “agility” and the “real-time enterprise” as important business goals to have in the information economy.
  • Many organizations have begun to realize the value of disseminating knowledge within their organizations in order to improve products and customer service, and the value of having a well-trained workforce. The investments businesses are making in e-Learning and corporate training provides some evidence of this. Companies have also invested in tools for content management, search, collaboration, and business intelligence. Companies are also spending significant resources on digitizing their business processes, particularly with respect to acquiring and retaining customers.
  • However, many knowledge/learning and customer-relationship assets are still stored in a diverse set of repositories that do not understand each other's language, and as a result are managed and interacted with as independent islands of information. As such, what many organizations call “knowledge” is merely data and information. The information economy in large part is a struggle to find a way to provide context, meaning and efficient access to this ever increasing body of data and information. Or, stated differently, to turn the mass of available data and information into usable knowledge.
  • Information has been long accessible in a variety of forms, such as in newspapers, books, radio and television media, and in electronic form, with varying degrees of proliferation. Information management and access changed dramatically with the use of computers and computer networks. Networked computer systems provide access throughout the system to information maintained at any point along the system. Users need only establish the requisite connection to the network, provide proper authorization and identify the desired information to obtain access.
  • Information access further improved with the advent of the Internet, which connects a large number of computers across diverse geography to provide access to a vast body of information. The most wide spread method of providing information over the Internet is via the World Wide Web. The Web consists of a subset of the computers or Web servers connected to the Internet that typically run Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), GOPHER or other servers. Web servers host Web pages at Web sites. Web pages are encoded using one or more languages, such as the original Hypertext Markup Language (HTML) or the more current eXtensible Markup Language (XML) or the Standard Generic Markup Language (SGML). The published specifications for these languages are incorporated by reference herein. Web pages in these formatting languages may be accessed by Internet users via web browsing software such as Microsoft's Internet Explorer or Netscape's Navigator.
  • The Web has largely been organized based on syntax and structure, rather than context and semantics. As a result, information is typically accessed via search engines and Web directories. Current search engines use keyword and corresponding search techniques that rely on textual or basic subject matter information and indices without associated context and semantic information. Unfortunately, such searching methods produce thousands of largely unresponsive results; documents as opposed to actionable knowledge. Advanced searching techniques have been developed to focus queries and improve the relevance of search results. Many such techniques rely on historical user search trends to make basic assumptions as to desired information. Alternatively, other search techniques rely on categorization of Web sites to further focus the search results to areas anticipated to be most relevant. Regardless of the search technique, the underlying organization of searchable information is index-driven rather than context-driven. The frequency or type of textual information associated the document determines the search results, as opposed to the attributes of the subject matter of the document and how those attributes relate to the user's context. The result is continued ambiguity and inefficiency surrounding the use of the Web as a tool for acquiring actionable knowledge.
  • In enterprises around the world today, the Web is the information platform for knowledge-workers. And there lies the problem. The Web as we know it is a platform for data and information while its users operate at the level of “knowledge.” This disconnect is a very fundamental one and cannot be understated. The Web, in large measure, has fulfilled the dream of “information at your fingertips.” However, knowledge-workers demand “knowledge at your fingertips” as opposed to mere “information at your fingertips.” Unfortunately, today's knowledge-workers use the Web to browse and search for documents—compilations of data and information—rather than actual knowledge relevant to their inquiry. To achieve improved knowledge requires providing proper context, meaning and efficient access to data and information, all of which are missing with the traditional Web.
  • Efforts have been made to achieve the goal of “knowledge at your fingertips.” One example is a new concept for information organization and distribution referred to as the Semantic Web. The Semantic Web is an extension of the current Web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. While conceptually a significant step forward in supporting improved context, meaning and access of information on the Internet, the Semantic Web has yet to find successful implementation that lives up to its stated potential.
  • Both the current Web and the Semantic Web fail to provide proper context, meaning and efficient access to data and information to allow users to acquire actionable knowledge. This is partially a problem related to the ways in which Today's Web and the contemplated Semantic Web are structured or, in other words, related to their technology layers. As shown in FIG. 1, Today's Web, for example, which is a hypertext medium, provides the three technology layers, which include “dumb” links, or links having no context-sensitivity, time-sensitivity, etc. Present conceptualizations of the Semantic Web, also referred to as a “semantic hypermedia,” provide for five technology layers, as shown in FIG. 2. As explained in greater detail below, there are serious limitations associated with each of the technology layer structures.
  • In addition, various properties must be present in a comprehensive information management system to provide an integrated and seamless implementation framework and resulting medium for knowledge retrieval, management and delivery. A non-exhaustive list of these properties include: Semantics/Meaning; Context-Sensitivity; Time-Sensitivity; Automatic and intelligent Discoverability; Dynamic Linking; User-Controlled Navigation and Browsing; Non-HTML and Local Document Participation in the Network; Flexible Presentation that Smartly Conveys the Semantics of the Information being Displayed; Logic, Inference, and Reasoning; Flexible User-Driven Information Analysis; Flexible Semantic Queries; Read/Write Support; Annotations; “Web of Trust”; Information Packages (“Blenders”); Context Templates, and User-Oriented Information Aggregation. Each of these properties will be discussed below in the context of their application to both Today's Web and the Semantic Web.
  • Semantics/Meaning
  • Today's Web lacks semantics as an intrinsic part of the platform and user experience. Web pages convey only textual and graphical data rather than the semantics of the data they contain. As a result, users cannot issue semantic queries such as those that one might expect with natural language—for example, “find me all books less than hundred pages long, about Latin Jazz, and published in the last five years.” To be able to process such a query, a Web site or search engine must “know” it contains books and must be able to intelligently filter its contents based on the semantics of the query request. Such a query is not possible on the Web today. Instead, users are forced to rely on text-based searches. These searches usually result in information overload or information loss because the user is forced to pick search terms that might not match the text in the information base. In the aforementioned example, a user might pick the search term “Books Latin Jazz” and hope that the search engine can make the connection. The user is usually then left to independently filter the search results. This sort of text-based search also implies that terms that might convey the same meaning. In the above example, results from search terms such as “Books on South or Central American Jazz” or “Publications on Jazz from Latino Lands” might be ignored during the processing of the search query.
  • The lack of semantics also implies that Today's Web does not allow users to navigate based on they way humans think. For example, one might want to navigate a corporate intranet using the organizational structure. For example, from people to the documents they create to the experts on that documents to the direct reports of those experts to the distribution lists the direct reports are members of to the members of the distribution lists to the documents those members created, etc. This “web” is semantic and is based on actual information classification (“things”) and not just “pages” as Today's Web is.
  • The lack of semantics also has other implications. First, it means that the Web is not programmable. With semantics, the Web can be consumed by Smart Agents that can make sense of the pages and the links and then make inferences, recommendations, etc. With Today's Web, the only “Agent” that can make inferences is the human brain. As such, the Web does not employ the enormous processing power that computers are capable of—because it is not represented in a way that computers can understand.
  • The lack of semantics also implies that information is not actionable. A search engine does not “understand” the results it spits out. As such, once a user receives search results, he or she is “on his or her own.” Also, a web browser does not “understand” the information it is displaying and as such cannot do smart things with the information. With semantics in place, a smart display, for example, will “know” that an event is an event and might do interesting things like check if the event is already in the user's calendar, display free/busy information, or allow the user to automatically insert the event into his/her calendar thereby making the information actionable. Information presented without semantics is not actionable or might require that the semantics be inferred, which might result in an unpleasant user experience.
  • The Semantic Web seeks to address semantics/meaning limitations with Today's Web by encoding information with well-defined semantics. Web pages on the Semantic Web include metadata and semantic links to other metadata, thereby allowing search engines to perform more intelligent and accurate searches. In addition, the Semantic Web includes ontologies that will be employed for knowledge representation, thereby allowing a semantic search engine to interpret terms based on meaning and not merely on text. For example, in the previous example, Latin Jazz ontology might be employed on a Semantic Web site and would allow a search engine on the site to “know” that the terms “Books on South or Central American Jazz” or “Publications on Jazz from Latino Lands” have the same meaning as the term “Books on Latin Jazz.” While conceptually overcoming many of the deficiencies with Today's Web, there has not to date been a successful implementation of a well-defined data model providing context and meaning, including in particular the necessary semantic links, ontologies, etc. to provide for additional characteristics such as context-sensitivity and time-sensitivity.
  • Context-Sensitivity
  • Today's Web lacks context-sensitivity. The implication of a lack of context is that Today's Web is not personal. For example, documents in accessible storage are independently static and therefore stupid. Information relevant to the subject matter of the document has already been published, is being newly published, or will soon be published. Because the document in storage is static, however, there is no way to dynamically associate its subject matter with this relevant information in real-time. Stated differently, users have no way to dynamically connect their private context with external information in real-time. Information sources (such as the document) that form context sit in their own islands, totally isolated from other relevant information sources. This results in information and productivity losses.
  • The primary reason for this is that Today's Web is a presentation-oriented medium designed to present views of information to a dumb client (e.g., remote computer). The client has virtually no role to play in the user experience, aside from merely displaying what the server tells it to display. Even in cases where there is client-side code (like Java applets and ActiveX controls), the controls usually do one specific thing and do not have coordinated action with the remote server such that code on the client is being orchestrated with code on the server.
  • From a productivity standpoint, the implication of this is that knowledge-workers and information consumers are totally at the mercy of information authors. Today, knowledge-workers have portals that are maintained and updated to provide custom views of corporate information, external data, etc. However, this is still very limiting because knowledge-workers are completely helpless if nothing dynamically and intelligently connects relevant information in the context of their task with information that users have access to.
  • If a knowledge-worker does not see a link to a relevant piece of information on his of her portal, of if a friend or colleague does not email him or her the link, the information gets dropped; information does not connect with or adapt to the user context or the context in which it is displayed. Likewise, it is not enough to just notify a user that new data for an entire portal is available and shove it down to their local hard drive. It lacks a customizable presentation with context sensitive alert notifications.
  • The Semantic Web suffers from the same limitations as Today's Web when it comes to context-sensitivity. On the Semantic Web, users are likewise at the mercy of information authors. The Semantic Web itself will be authored, but the authoring will include semantics. As a result, users are still largely on their own to locate and evaluate the relevance of available information. The Semantic Web, as a standalone entity, will not be able to make these dynamic connections with other information sources.
  • Time-Sensitivity
  • Today's Web lacks time-sensitivity. The Web platform (e.g., browser) is a dumb piece of software that merely presents information, without any regard to the time-sensitivity of the information. The user is left to infer time sensitivity or do without it. This results in a huge loss in productivity because the Web platform cannot make time-sensitive connections in real-time. While some Web sites focus on presenting time-sensitive information, for example, by indexing information past a predetermined date, the Web browser itself has no notion of time-sensitivity. Instead, it is left to individual Web sites to include time-sensitivity in the information they display in their own island. In other words, there is no axis of time on a Web link.
  • The Semantic Web, like Today's Web, also does not address time-sensitivity. A Semantic Web can have semantic links that do not internalize time. This is largely because the Semantic Web implicitly has no notion of software Web services that address context and time-sensitivity.
  • Automatic and Intelligent Discoverability
  • Today's Web lacks automatic and intelligent discoverability of newly created information. There is currently no way to know what Web sites started anew today or yesterday. Unless the user is notified or the user serendipitously discovers a new site when he or she does a search, he or she might not have any clue as to whether there are any new Web sites or pages. The same problem exists in enterprises. On Intranets, knowledge-workers have no way of knowing when new Web sites come up unless informed via some external means. The Web platform itself has no notion of announcements or discovery. In addition, there is no context-sensitive discovery to determine new sites or pages within the context of the user's task or current information space.
  • The Semantic Web, like Today's Web, does not address the lack of automatic discoverability. Semantic Web sites suffer from the same problem—users either will have to find out about the existence of new information sources from external sources or through personal discovery when they perform a search.
  • Dynamic Linking
  • Today's Web employs a pure network or graph “data structure” for its information model. Each Web page represents a node in the network and each page can contain links to other nodes in the network. Each link is manually authored into each page. This has several problems. First, it means that the network needs to be maintained for it to have continuous value. If Web pages are not updated or if Web page or site authors do not have the discipline to add links to their pages based on relevance, the network loses value. Today's Web is essentially prone to having dead links, old links, etc. Another problem with a pure network or graph information model is that the information consumer is at the mercy of—rather than in control of—the presentation of the Web page or site. In other words, if a Web page or site does not contain any links, the user has no recourse to find relevant information. Search engines are of little help because they merely return pages or nodes into the network. The network itself does not have any independent or dynamic linking ability. Thus, a search engine can easily return links to Web pages that themselves have no links or dead, stale or irrelevant links. Once users obtain search results, they are on their own and are completely at the mercy of whether the author of the returned pages inserted relevant, time-sensitive links into the page.
  • The Semantic Web suffers from the same problem as Today's Web because the Semantic Web is merely Today's Web plus semantics. Even though users will be able to navigate the network semantically (which they cannot currently do with the Web), they will still be at the mercy of how the information has been authored. In other words, the Semantic Web is also dependent on the discipline of the authors and hence suffers from the same aforementioned problems of Today's Web. If the Semantic Web includes pages with ontologies and metadata, but those pages are not well maintained or do not include links to other relevant sources, the user will still be unable to obtain current links and other information. The Semantic Web, as currently contemplated, will not be a smart, dynamic, self-authoring, self-healing network.
  • User-Controlled Navigation and Browsing
  • With Today's Web, the user has no control over the navigation and browsing experience, but rather is completely at the mercy of a Web page and how it is authored with links (if any). As shown with reference to prior art FIG. 3, Today's Web consists of “dumb links,” or statically authored generic links that are wholly dependent on continuous maintenance to be navigable.
  • The Semantic Web suffers from a similar problem as Today's Web in that there is no user-controlled browsing. Instead, as shown with reference to prior art FIG. 4, the Semantic Web consists of “dumb links,” further including semantic information and metadata. However, the Semantic Web links remain equally dependent on continuous maintenance to be navigable.
  • Non-HTML and Local Document Participation in the Network
  • Another problem with Today's Web is the requirement that only documents that are authored as HTML can participate in the Web, in addition to the fact that those documents have to contain links. The implication is that other information objects like non-HTML documents (e.g., PDF, Microsoft Word, PowerPoint, and Excel documents, etc.)—especially those on users” hard drives—are excluded from the benefits of linking to other objects in the network. This is very limiting, especially since there might be semantic relevance between information objects that are not HTML and which do not contain links.
  • Furthermore, search engines do not return results for the entire universe of information since vast amount of content available on the web is inaccessible to standard web crawlers. This includes, for example, content stored in databases, unindexed file repositories, subscription sites, local machines and devices, proprietary file formats (such as Microsoft Office documents and email), and non-text multimedia files. These form a vast constellation of inaccessible matter on the Internet, referred to as “the invisible Intranet” inside corporations. Today's Web servers do not provide web crawler tools that address this problem.
  • The Semantic Web also suffers from this limitation. It does not address the millions of non-HTML documents that are already out there, especially those on users” hard drives. The implication is that documents that do not have RDF metadata equivalents or proxies cannot be dynamically linked to the network.
  • Flexible Presentation that Smartly Conveys the Semantics of the Information being Displayed
  • Today's Web does not allow users to customize or “skin” a Web site or page. This is because Today's Web servers return information that is already formatted for presentation by the browser. The end user has no flexibility in choosing the best means of displaying the information—based on different criteria (e.g., the type of information, the available amount of real estate, etc.)
  • The Semantic Web does not address the issue of flexible presentation. While a semantic Web site conceptually employs RDF and ontologies, it still sends HTML to the browser. Essentially, the Semantic Web does not provide for specific user empowerment for presentation. As such, a Semantic Web site, viewed by Today's Web platform, will still not empower the user with flexible presentation. Moreover, despite industry movement towards XML, only a new platform can dictate that data will be separated from presentation and define guidelines for making the data programmable. Authors building content for the Semantic Web either return XML and avoid issues with presentation entirely, or focus their efforts on a single presentation style (vertical industry scenario) for rendering. Neither approach allows the Semantic Web to achieve an optimum degree of knowledge distribution.
  • Logic, Inference and Reasoning
  • Because Today's Web does not have any semantics, metadata, or knowledge representation, computers cannot process Web pages using logic and inference to infer new links, issue notifications, etc. Today's Web was designed and built for human consumption, not for computer consumption. As such, Today's Web cannot operate on the information fabric without resorting to brittle, unreliable techniques such as screen scraping to try to extract metadata and apply logic and inference.
  • While the Semantic Web conceptually uses metadata and meaning to provide Web pages and sites with encoded information that can be processed by computers, there is no current implementation that is able to successfully achieve this computer processing and which illustrates new or improved scenarios that benefit the information consumer or producer.
  • Flexible User-Driven Information Analysis
  • Today's Web lacks user-driven information analysis. Today's Web does not allow users to display different “views” of the links, using different filters and conditions. For example, Web search engines do not allow users to test the results of searches under different scenarios. Users cannot view results using different pivots such as information type (e.g., documents, email, etc.), context (e.g., “Headlines,” “Best Bets,” etc.), category (e.g., “wireless,” “technology,” etc.) etc.
  • While providing a greater degree of flexible information analysis, the Semantic Web does not describe how the presentation layer can interact with the Web itself in an interactive fashion to provide flexible analysis.
  • Flexible Semantic Queries
  • Today's Web only allows text-based queries or queries that are tied to the schema of a particular Web site. These queries lack flexibility. Today's Web does not allow a user to issue queries that approximate natural language or incorporate semantics and local context. For example, a query such as “Find me all email messages written by my boss or anyone in research and which relate to this specification on my hard disk” is not possible with Today's Web.
  • By employing metadata and ontologies, the conceptual Semantic Web allows a user to issue more flexible queries than Today's Web. For example, users will be able to issue a query such as “Find me all email messages written by my boss or anyone in research.” However, users will not be able to incorporate local context. In addition, the Semantic Web does not define an easy manner with which users will query the Web without using natural language. Natural language technology is an option but is far from being a reliable technology. As such, a query user interface that approximates natural language yet does not rely on natural language is required. The Semantic Web does not address this.
  • Read/Write Support
  • Today's Web is a read-only Web. For example, if users encounter a dead link (e.g., via the “404” error), they cannot “fix” the link by pointing it to an updated target that might be known to the user. This can be limiting, especially in cases where users might have important knowledge to be shared with others and where users might want to have input as to how the network should be represented and evolve.
  • While the Semantic Web conceptually allows for read/write scenarios as provided by independent participating applications, there is no current implementation that provides this ability.
  • Annotations
  • Today's Web has no implicit support for annotations. And while some specific Web sites support annotations, they do so in a very restricted and self-contained way. Today's Web medium itself does not address annotations. In other words, it is not possible for users to annotate any link with their comments or additional information that they have access to. This results in potential information loss.
  • While the Semantic Web conceptually allows for annotations to be built into the system subject to security constraints, there is no current implementation that provides this ability.
  • “Web of Trust”
  • Today's Web lacks seamless integration of authentication, access control, and authorization into the Web, or what has been referred to as a “Web of Trust.” With a Web of Trust, for example, users are able to make assertions, fix and update links to the Web and have access control restrictions built in for such operations. On Today's Web, this lack of trust also means that Web services remain independent islands that must implement a proprietary user subscription authorization, access control or payment system. Grand schemes for centralizing this information on 3rd party servers meet with consumer and vendor distrust because of privacy concerns. To gain access to rich content, asset users must log in individually and provide identity information at each site.
  • While the Semantic Web conceptually allows for a Web of Trust, there is no current implementation that provides for this ability.
  • Information Packages (Blenders)
  • Neither Today's Web nor the Semantic Web allows users to deal with related semantic information as a whole unit by combining characteristics of potentially divergent semantic information to produce overlapping results (for example, like creating a custom, personal newspaper or TV channel).
  • Context Templates
  • Neither Today's Web nor the Semantic Web allows users to independently create and map to specific and familiar semantic models for information access and retrieval.
  • User-Oriented Information Aggregation
  • Today's Web lacks support for user-oriented information aggregation. The user can only access one Web site or one search engine at a time, within the context of one browsing session. As such, even if there is context or time-sensitive information on other information sources that relate to the information that the user is currently viewing, those sources cannot be presented in a holistic fashion in the current context of the user's task.
  • The Semantic Web also suffers from a lack of user-oriented information aggregation. The medium itself is an extension of Today's Web. As such, users will still access one site or one search engine at a time and will not be able to aggregate information across information repositories in a context or time-sensitive manner.
  • Given the growing demand for “knowledge at your fingertips” as well as the deficiencies in Today's Web and the conceptual Semantic Web, many of which are noted above, there is a need for a new and comprehensive system and method of knowledge retrieval, management and delivery.
  • The general background to this invention is described in my co-pending parent applications (including U.S. application Ser. No. 11/505,261 filed Aug. 15, 2006, which is a continuation of U.S. application Ser. No. 10/179,651 filed Jun. 24, 2002, and all the applications listed above), which are all incorporated by reference herein.
  • The following application is incorporated by reference as if fully set forth herein: U.S. application Ser. No. 11/127,021 filed May 10, 2005. Preferred embodiments of the present invention are directed in part to a semantically integrated knowledge retrieval, management, delivery and/or presentation system. Preferred embodiments of the present invention and system include several additional improved features, enhancements and/or properties, including, without limitation, semantic advertisements, spider RSS integration, pivot views, watch lists, context extraction methods, context ranking methods, client duplication management methods, a server data and index model, improved metadata indexing methods, adaptive ranking methods, and content transformation methods.
  • The following application is incorporated by reference as if fully set forth herein: U.S. application Ser. No. 11/383,736 filed May 16, 2006. The explosive growth of digital information is increasingly impeding knowledge-worker productivity due to information overload. Online information is virtually doubling every year and/or most of that information is unstructured—usually in the form of text. Traditional search engines have been unable to keep up with the pace of information growth primarily because they lack the intelligence to “understand,” semantically process, mine, infer, connect, and/or contextually interpret information in order to transform it to—and/or expose it as—knowledge. Furthermore, end-users want a simple yet powerful user-interface that allows them to flexibly express their context and/or intent and/or be able to “ask” natural questions on the one hand, but which also has the power to guide them to answers for questions they wouldn't know to ask in the first place. Today's search interfaces, while easy-to-use, do not provide such power and/or flexibility.
  • Now that the Web has reached critical mass, the primary problem in information management has evolved from one of access to one of intelligent retrieval and/or filtering. Computer users are now faced with too much information, in various formats and/or via multiple applications, with little or no help in transforming that information into useful knowledge.
  • Search engines such as Google™ provide some help in filtering information by indexing content based on keywords. Google™, in particular, has gone a step further by mining the hypertext links in Web pages in order to draw inferences of relevance based on page popularity. These techniques, while helpful, are far from sufficient and/or still leave end-users with little help in separating wheat from chaff. The primary reason for this is that current search engines do not truly “understand” what they index or what users want. Keywords are very poor approximations of meaning and/or user intent. Furthermore, popularity, while useful, is no guarantee of relevance: Popular garbage is still garbage.
  • Furthermore, knowledge has multiple axes, and/or search is only one of those axes. Knowledge-workers also wish to discover information they might not know they need ahead of time, share information with others (especially those that have similar interests), annotate information in order to provide commentary, and/or have information presented to them in a way that is contextual, intuitive, and/or dynamic—allowing for further (and/or potentially endless) exploration and/or navigation based on their context. Even within the search axis, there are multiple sub-axes, for instance, based on time-sensitivity, semantic-sensitivity, popularity, quality, brand, trust, etc. The axis of choice depends on the scenario at hand.
  • Search engines are appropriately named because they focus on search. However, merely improving search quality without reformulating the core goal of search will leave the information overload problem unaddressed.
  • SUMMARY OF THE INVENTION
  • The present invention is directed in part to an integrated and seamless implementation framework and resulting medium for knowledge retrieval, management, delivery and presentation. The system includes a server comprised of several components that work together to provide context and time-sensitive semantic information retrieval services to clients operating a presentation platform via a communication medium. The server includes a first server component that is responsible for adding and maintaining domain-specific semantic information or intelligence. The first server component preferably includes structure or methodology directed to providing the following: a Semantic Network, a Semantic Data Gatherer, a Semantic Network Consistency Checker, an Inference Engine, a Semantic Query Processor, a Natural Language Parser, an Email Knowledge Agent and a Knowledge Domain Manager. The server includes a second server component that hosts domain-specific information that is used to classify and categorize semantic information. The first and second server components work together and may be physically integrated or separate.
  • Within the system, all objects or events in a given hierarchy are active Agents semantically related to each other and representing queries (comprised of underlying action code) that return data objects for presentation to the client according to a predetermined and customizable theme or “Skin.” This system provides various means for the client to customize and “blend” Agents and the underlying related queries to optimize the presentation of the resulting information.
  • The end-to-end system architecture of the present invention provides multiple client access means of communication between diverse knowledge information sources via an independent Semantic Web platform or via a traditional Web portal (e.g., Today's Web access browser) as modified by the present invention providing additional SDK layers that enable programmatic integration with a custom client.
  • The methodology of the present invention is directed in part to the operational aspects of the entire system, including the retrieval, management, delivery and presentation of knowledge. This preferably includes securing information from information sources, semantically linking the information from the information sources, maintaining the semantic attributes of the body of semantically linked information, delivering requested semantic information based upon user queries and presenting semantic information according to customizable user preferences. Alternative embodiments of the methodology of the present invention are directed to the operation of Agents representing queries that are used with server-side and client-side applications to enable efficient, inferential-based queries producing semantically relevant information.
  • The present invention is directed in part to a semantically integrated knowledge retrieval, management, delivery and presentation system, as is more fully described in my co-pending parent application (U.S. application Ser. No. 10/179,651 filed Jun. 24, 2002). The present invention and system includes several additional improved features, enhancements and/or properties, including, without limitation, Entities, Profiles and Semantic Threads, as are more fully described in the Detailed Description below.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • The preferred and alternative embodiments of the present invention are described in detail below with reference to the following drawings.
  • FIG. 1 is a table showing the technology layers of Today's Web.
  • FIG. 2 is a table showing the technology layers of the conceptual Semantic Web.
  • FIG. 3 is a diagram showing user navigation to links in Today's Web.
  • FIG. 4 is a diagram showing user navigation to links in the conceptual Semantic Web.
  • FIG. 5 is a screenshot showing a sample Information Agent Results Pane in accordance with the present invention.
  • FIG. 6 shows the technology platform stacks of Today's Web and the Information Nervous System of the present invention.
  • FIG. 7 is a diagram showing an overview of the system of the present invention.
  • FIG. 8 is a diagram showing the end-to-end system architecture for the Information Nervous System of the present invention.
  • FIG. 9 is a diagram showing the system architecture for the Knowledge Integration Server (KIS) of the Information Nervous System of the present invention.
  • FIG. 10 is a comparison between the high-level descriptive platform layers of Today's Web and the equivalents (where applicable) in the Information Nervous System of the present invention.
  • FIG. 11 illustrates the preferred embodiment of the Information Nervous System and illustrates the heterogeneous, cross-platform context for the present invention.
  • FIGS. 12-14 show exemplar screenshots of aspects of the Blender Wizard user interface according to a preferred embodiment of the present invention.
  • FIG. 15 is an exemplar pane of a Breaking News Agent user interface.
  • FIG. 16 illustrates a preferred embodiment showing the Open Agent dialog of the present invention.
  • FIGS. 17-19 illustrate the Tree View of a sample Semantic Environment involving the Open Agent dialog.
  • FIG. 20 shows the Agent schema of the preferred embodiment of the present invention.
  • FIG. 21 shows the AgentTypeIDs of the preferred embodiment of the present invention.
  • FIG. 22 shows the AgentQueryTypeIDs of the preferred embodiment of the present invention.
  • FIG. 23 illustrates sample semantic queries that correspond to Agent names showing how server-side Agents are preferably configured on the KIS of the present invention.
  • FIG. 24 is a diagram showing an overview of the KIS of the present invention.
  • FIG. 25 is a diagram showing a sample Semantic Network directed towards an enterprise situation in accordance with the present invention.
  • FIG. 26 is a table showing the preferred schema of the Object type in accordance with the present invention.
  • FIG. 27 shows the SemanticLinks table of the present invention.
  • FIG. 28 is a table showing predicate type IDs of the preferred embodiment of the present invention.
  • FIG. 29 is a table showing the preferred user object schema made in accordance with the present invention.
  • FIG. 30 is a table showing MailingAddressTypeIDs preferably associated with the User (person) object schema.
  • FIG. 31 is a table of the preferred category object schema made in accordance with the present invention.
  • FIG. 32 is a table of the preferred document object schema made in accordance with the present invention.
  • FIG. 33 shows the Print Media Type IDs of the preferred embodiment.
  • FIG. 34 shows the preferred FORMATTYPEID.
  • FIG. 35 shows the preferred email message list object schema made in accordance with the present invention.
  • FIGS. 36 and 37 are exemplar tables showing the email distribution list and email public folder object schemas, respectively, of a preferred embodiment of the present invention.
  • FIG. 38 shows the preferred PublicFolderTypeID of the present invention.
  • FIG. 39 shows the preferred event object schema message list object schema made in accordance with the present invention.
  • FIG. 40 shows the events types of a preferred embodiment of the present invention.
  • FIG. 41 shows the preferred media object schema message list object schema made in accordance with the present invention.
  • FIG. 42 shows the media types of a preferred embodiment of the present invention.
  • FIGS. 43-45 illustrate additional samples showing how objects are categorized and utilized in the preferred embodiment of the present invention.
  • FIG. 46 is an object graph showing mapping of raw email XML metadata to the Semantic Network according to the present invention.
  • FIGS. 47-53 are exemplar screenshots showing aspects of Agent management by the KIS.
  • FIG. 54 shows a sample user interface illustrating an information object displayed in the Information Agent Results Pane.
  • FIG. 55 shows an example of a balloon popup associated with an Intrinsic Semantic Link showing an email sample according to the present invention.
  • FIG. 56 shows an example of a balloon popup associated with a Verb user interface according to the present invention.
  • FIG. 57 shows an example of a balloon popup associated with a Deep Information Mode user interface according to the present invention.
  • FIGS. 58 and 59 are illustrations showing an exemplar Semantic Environment according to the present invention.
  • FIGS. 60-68 provide exemplar screenshots of an Information Agent according to a preferred embodiment of the present invention.
  • FIGS. 69-71 provide exemplar balloon popup menus associated with the Smart Lens feature of an Information Agent according to the present invention.
  • FIG. 72 shows a sample of a variant of the balloon popup menu of FIG. 71 showing the relatedness measure of the two objects.
  • FIGS. 73-75 show sample tables illustrating the behaviors and relational contains objects types predicates when using Smart Lenses.
  • FIG. 76 is a user interface sample illustrating semantic results Player/Preview Control according to the present invention.
  • FIG. 77 is a user interface sample showing the semantic results of a Blender.
  • FIGS. 78 and 79 illustrate exemplar functionality mappings of the present invention.
  • FIG. 80 illustrates a user interface showing Agent results and corresponding Context Palettes according to the present invention.
  • FIG. 81 shows a sample Smart Recommendations popup context Results Pane according to the present invention.
  • FIG. 82 is a table showing the technology layers of the Information Nervous System of the present invention.
  • FIG. 83 illustrates dynamic linking and user-controlled navigation and browsing according to a preferred embodiment of the present invention.
  • The preferred and alternative embodiments of the present invention are described in detail below with reference to the following drawings.
  • FIG. 1 is a partial screenshot overview and FIG. 2 is an expansion of a dialog box of FIG. 1 for a scenario of a Patent Examiner using the preferred embodiment in a prior art search, a screenshot of where “Magnetic Resonance Imaging” occurs in a Pharmaceuticals taxonomy.
  • FIG. 3 shows the Sharable Smart Request System Interaction, which is the binary document format that encapsulates the SQML buffer with the smart request and also illustrates how the extension handler opens a document.
  • FIG. 4 is a partial screenshot overview of document files, showing an illustration of two .REQ documents (titled ‘Headlines on Reuters™ Related to My Research Report (Live)’ and ‘Headlines on Reuters™ (as of Jan. 21, 2003, 08 17 AM)’ on the far right) with a registered association in the Windows™ shell.
  • FIG. 5 is a Diagram Illustrating the Text-to-Speech Object Skin and shows an illustration of an email message being rendered via a text-to-speech object skin.
  • FIG. 6 is a Diagram Illustrating a Text-to-Speech Request Skin.
  • FIG. 7 is a Diagram Illustrating Knowledge Modeling for a Pharmaceuticals Company Example.
  • FIG. 8 is a Diagram Illustrating Client Component Integration and Interaction Workflow.
  • FIGS. 9-11 show three different views of the Explore Categories dialog box.
  • FIGS. 12 and 13 show sample screenshots of the Dossier Smart Lens in operation.
  • FIG. 14 shows how the server-side semantic query processor processes incoming semantic queries (represented as SQML).
  • FIG. 15 illustrates the semantic browser showing two profiles (the default profile named “My Profile” and a profile named “Patents”). Observe how the user is able to navigate his/her knowledge worlds via both profiles without interference.
  • FIG. 16A-C illustrate how a user would configure a profile (to create a profile, the user will use the “Create Profile Wizard” and the profile can then be modified via a property sheet as shown in other Figures).
  • FIG. 17 shows how a user would select a profile when creating a request with the “Create Request Wizard.”
  • FIG. 18 shows a screenshot with the ‘Smart Styles’ Dialog Box illustrating some of the foregoing operations and features.
  • FIG. 19 illustrates the “Smart Request Watch” Dialog Box.
  • FIG. 20 illustrates a Watch Window displaying Filtered Smart Requests (e.g., Headlines on Wireless). FIG. 20 is an Illustration of the Watch Window with a Current Smart Request Title (e.g., “Breaking News”).
  • FIG. 21 illustrates Entity views displayed in the Semantic Browser.
  • FIGS. 22A and 22B show the UI for the Knowledge Community Subscription.
  • FIG. 23 illustrates a semantic thread object and its semantic links.
  • FIGS. 24 through 46B are additional screen shots further illustrating the functions, options and operations as described in the Detailed Description.
  • FIG. 47 as a sample semantic image for Pharmaceuticals/Biotech industry (DNA helix).
  • FIG. 48 is an illustration of a semantically appropriate image visualization for the Breaking News context template.
  • FIG. 49 is a Visualization—Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc. (Headlines).
  • FIG. 50 is a Visualization—Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc. (Two people working at a desk).
  • FIG. 51 illustrates a semantic “Newsmaker” Visualization or Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
  • FIG. 52 illustrates a semantic “Upcoming Events” Visualization—Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
  • FIG. 53 is a Visualization—Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc. (Petri Dish).
  • FIG. 54 illustrates a semantic “History” Visualization—Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
  • FIG. 55 illustrates a semantic Visualization—Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc. (Spacecraft).
  • FIG. 56 illustrates a “Best Buys” Visualization—Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
  • FIG. 57 illustrates a semantic Visualization—Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc. (Coffee).
  • FIG. 58 illustrates a semantically appropriate Sample Image for “Classics” for smart hourglass, interstitial page, transition effects, background chrome, etc. (Car).
  • FIG. 59 illustrates a semantically appropriate “Recommendation” Visualization—Sample Image for the contextual/application elements of smart hourglass, interstitial page, transition effects, background chrome, etc. (Thumbs up).
  • FIG. 60 illustrates a semantic “Today” Visualization—Sample Image for the elements smart hourglass, interstitial page, transition effects, background chrome, etc.
  • FIG. 61 illustrates a semantic “Annotated Items” Visualization—Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
  • FIG. 62 illustrates a semantic “Annotations” Visualization—Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
  • FIG. 63 illustrates a semantic “Experts” Visualization—Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
  • FIG. 64 illustrates a semantic “Places” Visualization—Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
  • FIG. 65 illustrates a semantic “Blenders” Visualization—Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
  • FIGS. 66 through 84 illustrate semantic Visualizations for the following Information Object Types, respectively: Documents, Books, Magazines, Presentations, Resumes, Spreadsheets, Text, Web pages, White Papers, Email, Email Annotations, Email Distribution Lists, Events, Meetings, Multimedia, Online Courses, People, Customers, and Users.
  • FIG. 85 illustrates a semantic “Timeline” Visualization—Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
  • FIG. 1 is an Ontology Objects Table Data and Index Model according to an embodiment of the invention;
  • FIG. 2 is an Ontology Semantic Links Table Data and Index Model according to an embodiment of the invention;
  • FIGS. 3-6 are screenshots illustrating principles of at least one embodiment of the invention;
  • FIG. 7 is a Table Showing Semantic Search Qualifiers and Corresponding Predicates according to an embodiment of the invention;
  • FIG. 8 is a screenshot illustrating principles of at least one embodiment of the invention; and
  • FIGS. 9-12 are screenshots illustrating principles of at least one embodiment of the invention.
  • FIG. 1 is an Ontology Objects Table Data and Index Model according to an embodiment of the invention;
  • FIG. 2 is an Ontology Semantic Links Table Data and Index Model according to an embodiment of the invention;
  • FIGS. 3-6 are screenshots illustrating principles of at least one embodiment of the invention;
  • FIG. 7 is a Table Showing Semantic Search Qualifiers and Corresponding Predicates according to an embodiment of the invention;
  • FIG. 8 is a screenshot illustrating principles of at least one embodiment of the invention; and
  • FIGS. 9-12 are screenshots illustrating principles of at least one embodiment of the invention.
  • FIG. 1 is a block diagram of a method for implementing semantic advertisements in an internet browser.
  • FIG. 2 is a block diagram of a method for integrating HTTP metadata and RSS metadata in an information server.
  • FIG. 3 is a block diagram of a method for dynamically making input suggestions based upon prior user input.
  • FIG. 3 is a block diagram of a method for dynamically making input suggestions based upon prior user input.
  • FIG. 4 is a block diagram of a method for presenting time sensitive information to a user.
  • FIG. 5 is a block diagram for a method of presenting knowledge community statistics at a client user interface, in accordance with an embodiment of the invention.
  • FIG. 6 is a screen shot of a client user interface presenting statistics, in accordance with an embodiment of the invention.
  • FIG. 7 is a block diagram of a method for allowing users to remove duplicative presented information.
  • FIGS. 8A-8B illustrate a documents table data and index model, in accordance with an embodiment of the invention.
  • FIG. 9 is an objects table data and index model, in accordance with an embodiment of the invention.
  • FIG. 10 is a semantic links table data and index model, in accordance with an embodiment of the invention.
  • FIG. 11 is a composite index table model, in accordance with an embodiment of the invention.
  • FIG. 12 is a block diagram for a method of quickly indexing data contained in a metadata feed, in accordance with an embodiment of the invention.
  • FIG. 13 is a block diagram for a method of adjusting threshold values that are used to determine the most relevant objects in a given context, in accordance with an embodiment of the invention.
  • FIG. 14 is a method for indexing and retrieving semantically relevant documents, in accordance with an embodiment of the invention.
  • FIG. 15 is a method for highlighting semantically relevant keywords in displayed documents resulting from semantic searches, in accordance with an embodiment of the invention.
  • FIG. 16 is an example of the highlighted document displayed as a result of the process in FIG. 15.
  • FIG. 17 is a block diagram showing methods for creating and managing multiple types of knowledge communities, in accordance with an embodiment of the invention.
  • FIG. 18 is a screen shot showing a possible implementation of the embodiment shown in FIG. 17 and described above.
  • FIG. 19 is a block diagram of a method for providing user feedback on the available knowledge communities, in accordance with an embodiment of the invention.
  • FIG. 20 is a screen shot showing a possible implementation of the embodiment shown in FIG. 19 and described above.
  • FIG. 21 illustrates a method of using semantic sounds to notify a user regarding the arrival of news in accordance with an embodiment of the invention.
  • FIG. 22 is a method of tracking and presenting multiple lists of categories to a client user as the categories evolve over time, in accordance with an embodiment of the invention.
  • FIG. 23 is a block diagram of a method of semantically indexing and retrieving non-text data, in accordance with an embodiment of the invention.
  • FIG. 24 is a block diagram of a method for providing ontology feedback in accordance with an embodiment of the invention.
  • FIG. 25 is a block diagram of a method for advanced semantic searching in accordance with an embodiment of the invention.
  • FIG. 26 is a block diagram of a method for handling floating text in an RSS feed.
  • FIG. 27 is an example of an RSS in FIG. 26 with a namespace qualified tag indicating the absence of a stored file in accordance with an embodiment of the invention.
  • FIG. 28 is a block diagram of a method for extracting a semantic query from an image, in accordance with an embodiment of the invention.
  • FIG. 29 is a block diagram for a method for improving ontology development in accordance with an embodiment of the invention.
  • FIG. 30 is a block diagram of a method for developing and maintaining ontologies, in accordance with an embodiment of the invention.
  • FIG. 31 is a block diagram for a method for semantic question answering in accordance with an embodiment of the invention.
  • FIG. 32 is a block diagram of a method of coupling natural language with semantic language queries in accordance with an embodiment of the invention.
  • FIG. 33 is a block diagram of a method for categorizing extracted concepts from a URI, in accordance with an embodiment of the invention.
  • FIG. 34 is a block diagram of a method for establishing context queries, in accordance with an embodiment of the invention.
  • FIG. 35 is a block diagram of a method for extracting concepts from disparate sources, in accordance with an embodiment of the invention.
  • FIG. 36 is a block diagram of a method for re-organizing independent website data according to semantic strength, in accordance with an embodiment of the invention.
  • FIG. 37 is a block diagram of a method for semantic analysis on the client, in accordance with an embodiment of the invention.
  • FIG. 38 is a block diagram for a method of generating information on experts, interest groups, or newsmakers, in accordance with an embodiment of the invention.
  • FIG. 39 is a method for adding new ontologies to a client semantic browser, in accordance with an embodiment of the invention.
  • FIG. 40 illustrates a method for using field and category specific searches to supplement keyword searches, in accordance with an embodiment of the invention.
  • FIG. 41 is a method for creating weighted indices and searching thereon, in accordance with an embodiment of the invention.
  • FIG. 1 illustrates defined knowledge filters/types, in accordance with an embodiment of the invention.
  • FIG. 2 is a sample illustration of a user-defined hierarchy for storing personal digital photos.
  • FIG. 3 illustrates sample fields of the Knowledge Domain Entry data structure returned by the KDS Web in accordance with an embodiment of the invention.
  • FIG. 4 illustrates the schema and/or sample fields of a KDS result, in accordance with an embodiment of the invention.
  • FIG. 5 illustrates the representation of a semantic network in the KIS, in accordance with an embodiment of the invention.
  • FIG. 6 illustrates the schema and/or sample fields of a category that gets added to the semantic network, in accordance with an embodiment of the invention.
  • FIG. 7 illustrates the end-to-end architecture of one embodiment of the invention.
  • FIG. 8 illustrates the representation of a semantic network in accordance with an embodiment of the invention.
  • FIG. 9 is a screenshot of a search conducted in accordance with an embodiment of the invention.
  • FIGS. 10 and/or 11 illustrate sample queries of one embodiment of the invention.
  • FIG. 12 is an illustrative example of a pagination pipeline architecture diagram in accordance with an embodiment of the invention.
  • FIG. 13 is a block diagram illustrating General Content Transformation Pipeline Architecture in accordance with an embodiment of the invention.
  • FIG. 14 shows a visual of semantic highlighting in accordance with an embodiment of the invention.
  • FIG. 15 is a screenshot showing additional KIS Features via KC Properties Dialog Box in accordance with an embodiment of the invention.
  • FIG. 16 shows a screenshot Showing UI for Browsing Ontologies (Category Folders) in a User Profile (or KC) in accordance with an embodiment of the invention.
  • FIG. 17 shows an illustration of the implementation of the feature, the well-known knowledge stack, and/or how this applies to this model in accordance with an embodiment of the invention.
  • FIG. 18 illustrates what many Web users goes through today while trying to browse the World Wide Web.
  • FIG. 19 shows the user-interface for installing and/or uninstalling Category Folder add-ins in accordance with an embodiment of the invention.
  • FIG. 20 illustrates display of statistics in accordance with an embodiment of the invention.
  • FIG. 21 illustrates a system in accordance with an embodiment of the invention.
  • FIG. 1 illustrates a pie chart of live search results by publisher;
  • FIG. 2 is a sample illustration of a bar chart of search results by publisher;
  • FIG. 3 is a screen shot of the Talent Matching functionality in accordance with an exemplary embodiment of the invention;
  • FIG. 4 is a screen shot of the Display Options window in accordance with an exemplary embodiment of the invention;
  • FIG. 5 is a screen shot of the Live Mode Options window in accordance with an exemplary embodiment of the invention;
  • FIG. 6 is a screen shot of the Security Settings window in accordance with an exemplary embodiment of the invention;
  • FIG. 7 is a screen shot of the Proxy Settings window in accordance with an exemplary embodiment of the invention;
  • FIG. 8 is a screen shot of the Knowledge Directories window in accordance with an exemplary embodiment of the invention;
  • FIG. 9 is a screen shot of the Knowledge Directories browser window in accordance with an exemplary embodiment of the invention;
  • FIG. 10 is a line chart illustrating long term decline in R & D Productivity v. NME Output per R & D dollar spent;
  • FIGS. 11 and 12 illustrates Data Growth Curves in Intractable v. Tractable conditions in accordance with an exemplary embodiment of the invention;
  • FIG. 13 illustrates the schema and/or sample fields categories and inputs that get added to the semantic network, in accordance with an embodiment of the invention;
  • FIG. 14 is a representation of a semantic network in accordance with an embodiment of the invention;
  • FIG. 15 is illustrates the end-to-end architecture of one embodiment of the invention;
  • FIG. 16 illustrates the representation of a semantic network in accordance with an embodiment of the invention;
  • FIG. 17 is a representation of below illustrates the Nervana Content Framework in accordance with an embodiment of the invention.
  • FIG. 18 illustrates the end-to-end process cycle of Nervana Discovery Spaces.
  • FIG. 19 illustrates Nervana's technology licensing partners.
  • FIG. 20 illustrates Nervana's Evidence-Based Medicine—a new business process for matching patient health records to diagnostic information.
  • FIGS. 21-22 illustrate Nervana's Semantic Discovery and Collaboration Platform in accordance with an embodiment of the invention.
  • FIGS. 23-24 illustrate product illustrations for Nervana Social Discovery in accordance with an embodiment of the invention.
  • FIG. 25 is an exemplary embodiment for Nervana Social Discovery in accordance with an embodiment of the invention.
  • FIG. 26 is a schematic diagram of Nervana's Ontology Automation Model Social Discovery in accordance with an embodiment of the invention.
  • FIG. 27 is an exemplary screenshot of Nervana's interface.
  • FIGS. 28-29 are schematic diagrams of Next-Generation Clinical Trial Services Proposal in accordance with an embodiment of the invention.
  • FIG. 30 is a schematic diagram of Next-Generation Research Collaboration Platform in accordance with an embodiment of the invention.
  • FIG. 31 is a table comparison of technology uniqueness.
  • FIG. 32 illustrates the schema and/or sample fields categories and inputs that get added to the semantic network, in accordance with an embodiment of the invention.
  • FIG. 33 is a schematic diagram of Business Process with Nervana Ad Engine in accordance with an embodiment of the invention.
  • FIG. 34 is a schematic diagram of Nervana Ad Engine Workflow in accordance with an embodiment of the invention.
  • FIG. 35 is a schematic diagram of Nervana Site Ranking Attributes in accordance with an embodiment of the invention.
  • FIG. 36 is a schematic diagram of Nervana Content Sources in accordance with an embodiment of the invention; and
  • FIG. 37 is a schematic diagram of Nervana Talent Engine AI Model Components in accordance with an embodiment of the invention.
  • DOCUMENTS INCORPORATED BY REFERENCE
  • The Appendix attached hereto and referenced herein is incorporated by reference. This Appendix includes exemplar code illustrating a preferred embodiment of the present invention.
  • CONTENTS OF DETAILED DESCRIPTION OF THE INVENTION A. DEFINITIONS B. OVERVIEW
      • 1. INVENTION CONTEXT
      • 2. VALUE PROPOSITIONS
      • 3. TODAY'S “INFORMATION” WEB VS. THE INFORMATION NERVOUS SYSTEM OF THE PRESENT INVENTION
    C. SYSTEM ARCHITECTURE AND TECHNOLOGY CONSIDERATIONS
      • 1. SYSTEM OVERVIEW
      • 2. SYSTEM ARCHITECTURE
      • 3. TECHNOLOGY STACKS
      • 4. SYSTEM HETEROGENEITY
      • 5. SECURITY
      • 6. EFFICIENCY CONSIDERATIONS
    D. SYSTEM COMPONENTS AND OPERATION
      • 1. AGENCIES AND AGENTS
        • a. Agencies
        • b. Agents
      • 2. KNOWLEDGE INTEGRATION SERVER
        • a. Semantic Network
        • b. Semantic Data Gatherer
        • c. Semantic Network Consistency Checker
        • d. Inference Engine
        • e. Semantic Query Processor
        • f. Natural Language Parser
        • g. Email Knowledge Agent
        • h. Knowledge Domain Manager
        • i. Other Components
      • 3. KNOWLEDGE BASE SERVER
      • 4. INFORMATION AGENT (SEMANTIC BROWSER PLATFORM)
        • a. Overview
        • b. Client Configuration
        • c. Client Framework Specification
        • d. Client Framework
        • e. Semantic Query Document
        • f. Semantic Environment
        • g. Semantic Environment Manager
        • h. Environment Browser (Semantic Browser or Information Agent™)
        • i. Additional Application Features
      • 5. PROVIDING CONTEXT IN THE PRESENT INVENTION
        • a. Context Templates
        • b. Context Skins
        • c. Skin Templates
        • d. Default Predicates
        • e. Context Predicates
        • f. Context Attributes
        • g. Context Palettes
        • h. Intrinsic Alerts
        • i. Smart Recommendations
      • 6. PROPERTY BENEFITS OF THE PRESENT INVENTION
    E. SCENARIOS
      • 1. EXAMPLES OF SEMANTIC QUERIES UTILIZING THE PRESENT INVENTION
      • 2. BUSINESS PROBLEMS
      • 3. SITUATIONS
    DETAILED DESCRIPTION OF THE INVENTION A. Definitions
  • ActionScript. Scripting language of Macromedia Flash. This two-way communication assists users in creating interactive movies. See also http://www.macromedia.com/support/flash/action_scripts/actionscript_tutorial/.
  • Agency. A named instance of a Knowledge Integration Server (KIS) that is the semantic equivalent of a website.
  • Agency Directory. A directory that stores metadata information for Agencies and allows clients to add, remove, search, and browse Agencies stored within. Agencies can be published on directories like LDAP or the Microsoft Active Directory. Agencies can also be published on a proprietary directory built specifically for Agencies.
  • Agent. A semantic filter query that returns XML information for a particular semantic object type (e.g., documents, email, people, etc.), context (e.g., Headlines, Conversations, etc.) or Blender.
      • Blender™ or Compound Agent™. Trademarked name for an Agent that contains other Agents and allows the user (in the case of client-side blenders) or the Agency administrator (in the case of server-side blenders) to create queries that generate results that are the union or intersection of the results of their contained Agents. In the case of client-side blenders, the results can be generated using different views (showing each Agent in the blender in a different frame, showing all the objects of a particular object type across the contained Agents, etc.)
      • Breaking News Agent™. Trademarked name for a Smart Agent that users specially tag as being indicative of time-criticality. Users can tag any Smart Agent as a Breaking News Agent. This attribute is then stored in users' Semantic Environment. A Breaking News Agent preferably shows an alert if there is breaking news related to any information being displayed.
      • Default Agent™. Trademarked name for standardized, non-user modifiable Agents presented to the user.
      • Domain Agent™. Trademarked name for an Agent that belongs to a semantic domain. It is initialized with an Agent query that includes reference to the “categories” table.
      • Dumb Agent™. Trademarked name for an Agent that does not have an Agency and which refers to local information (on a local hard drive), on a network share or on a Web link or URL. Dumb Agents are used to essentially load information items (e.g., documents) from a non-smart sandbox (e.g., the file-system or the Internet) to a smart sandbox (the Information Nervous System via the Information Agent (semantic browser)).
      • Email Agent™ (or Email Knowledge Agent™). Trademarked names for a Public Agent used to publish or annotate information and share knowledge on an Agency.
      • Favorite Agent™. Trademarked name for Agents that users indicate they like and access often.
      • Public Agent™. Trademarked name for Agents that are created and managed by the system administrator.
      • Private or Local Agents™. Trademarked names for Agents that are created and managed by users.
      • Search Agent™. Trademarked name for a Smart Agent that is created by searching the semantic environment with keywords or by searching an existing Smart Agent, in order to invoke an additional, text-based query filter on the Smart Agent.
      • Simple or Standard Agent™. Trademarked names for Standalone Agents that encapsulate structured, non-semantic queries (e.g., from the local file system or data source).
      • Smart Agent™. Trademarked name for a standalone Agent that encapsulates structured, semantic queries that refers to an Agency via its XML Web Service.
      • Special Agent™. Trademarked name for a Smart Agent that is created based on a Context Template.
  • Agent Discovery. The property of the information medium of the present invention that allows users to easily and automatically discover new server-side Agents or client-side Agents created by others (friends or colleagues). Also see “Discoverability.”
  • Annotations. Notes, comments, or explanations that are used to add personal context to an information object. In the preferred embodiment, annotations are email messages that are linked to the object they qualify, and which can have attachments (just like regular email messages). In addition, annotations are first class information objects in the system and as such can be annotated themselves, thereby resulting in threaded annotations or a tree of annotations with the initial object as the root.
  • Application Programming Interface (API). Defines how software programmers utilize a particular computer feature. APIs exist for windowing systems, file systems, database systems, networking systems, and other systems.
  • Calendar Access Protocol (CAP). Internet protocol that permits users to digitally access a calendar store based on the iCalendar standard.
  • Compound Agent Manager™. Trademarked name for an Agency component that programmatically allows the user to create and delete Compound Agents and to manage them by adding and deleting Agents.
  • Context. Information surrounding a particular item that provides meaning and otherwise assists the information consumer in interpreting the item as well as finding other relevant information related to the item.
  • Context Results Pane. A Results Pane that displays results for context-based queries. These include results for Context Palettes, Smart Lenses, Deep Information, etc. See “Results Pane.”
  • Context-Sensitivity. The property of an information medium that enables it to intelligently and dynamically perceive the context of all the information it presents and to present additional, relevant information given that context. A context-sensitive system or medium understands the semantics of the information it presents and provide appropriate behaviors (proactive and reactive based on the user's actions) in order to present information in its proper context (both intrinsically and relationally).
  • Context Template™. Trademarked name for scenario-driven information query templates that map to specific and familiar semantic models for information access and retrieval. For example, a “Headlines” template in the preferred embodiment has parameters that are consistent with the delivery of “Headlines” (where freshness and the likelihood of a high interest level are the primary axes for retrieval). An “Upcoming Events” template has parameters that are consistent with the delivery of “Upcoming Events.” And so on. Essentially, Context Templates can be analogized to personal, digital semantic information retrieval “channels” that deliver information to the user by employing a well-known semantic template.
  • Deep Information™. Trademarked name for a feature of the present invention that enables the Information Agent to display intrinsic, contextual information relating to an information object. The contextual information that includes information that is mined from the Semantic Network of the Agency from whence the object came.
  • Discoverability. The ability of the information medium of the present invention to intelligently and proactively make information known or visible to the user without the user having to explicitly look for the information.
  • Domain Agent Wizard™. Trademarked name for a system component and its user interface for allowi