Source: https://patents.google.com/patent/US20080016020A1/en
Timestamp: 2019-08-23 04:54:27
Document Index: 469908628

Matched Legal Cases: ['Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60']

US20080016020A1 - Knowledge discovery agent system and method - Google Patents
Knowledge discovery agent system and method Download PDF
US20080016020A1
US20080016020A1 US11/781,419 US78141907A US2008016020A1 US 20080016020 A1 US20080016020 A1 US 20080016020A1 US 78141907 A US78141907 A US 78141907A US 2008016020 A1 US2008016020 A1 US 2008016020A1
US11/781,419
US7882055B2 (en
2002-05-22 Priority to US38250302P priority Critical
2003-05-21 Priority to US10/443,653 priority patent/US7249117B2/en
2007-07-23 Application filed by Digital Reasoning Systems Inc filed Critical Digital Reasoning Systems Inc
2007-07-23 Priority to US11/781,419 priority patent/US7882055B2/en
2008-01-17 Publication of US20080016020A1 publication Critical patent/US20080016020A1/en
2011-02-01 Publication of US7882055B2 publication Critical patent/US7882055B2/en
2014-05-18 Assigned to SILICON VALLEY BANK reassignment SILICON VALLEY BANK SECURITY INTEREST Assignors: DIGITAL REASONING SYSTEMS, INC.
2014-09-08 Assigned to DIGITAL REASONING SYSTEMS, INC. reassignment DIGITAL REASONING SYSTEMS, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: ESTES, TIMOTHY W.
2017-06-14 Assigned to DIGITAL REASONING SYSTEMS, INC. reassignment DIGITAL REASONING SYSTEMS, INC. RELEASE BY SECURED PARTY (SEE DOCUMENT FOR DETAILS). Assignors: SILICON VALLEY BANK
This application is a continuation of, and claims the benefit of, U.S. application Ser. No. 10/443,653, entitled “Knowledge Discovery Agent System and Method,” filed May 21, 2003, by Timothy W. Estes, which claims benefit to U.S. Provisional Application No. 60/382,503, entitled “Knowledge Discovery Agent System and Method,” filed May 22, 2002 by Timothy W. Estes, and is entitled to the benefit of those filing dates for priority. The entire specification, drawings, appendices and attachments of U.S. application Ser. No. 10/443,653 and U.S. Provisional Application No. 60/382,503 are incorporated herein by specific reference.
In another embodiment, the invention comprises a system for processing information, which may be termed a dynamic conceptual network system or knowledge discovery agent system. The system comprises: a computer for running software programs in a distributed network, the computer including a processing means in communication with the distributed network; at least one storage means in communication with the processing means for storing programs and information; and at least one data agent in the distributed network for conducting a specific response to commands generated by the processing means. The computer, the at least one storage means, and the at least one data agent are configured to be operable for learning associations of natural language artifacts. Natural language artifacts include, but are not limited to, phrases, predicates, modifiers, and other syntactic forms, in unstructured data sources. The computer, storage means, and data agent are further operable for learning semantic and syntactic relationships in structured data sources, wherein the structured data sources comprise entities in conventional formats used by relational database systems. The learned associations are formed using grouping of one natural language artifact in an interaction window, which can be of a fixed or relative size, with another at least one natural language artifact, based on a criteria of shared features of one or more sets from the grouping. This interaction window, which is also referred to in this disclosure as a “semantic unit,” constitutes a range of measurement that approximates limited attentional perception by the processing agent or program and is the building block for measurements of conditional probability between one natural language artifact and another natural language artifact. The criteria for grouping are dynamically determined without the use of a priori classifications such as categories, topics, or classes, through satisfying conditional probability constraints between sets of learned associations.
The learned associations resulting from the learning associations of natural language artifacts are formed using grouping of one natural language artifact in an interaction window, which can be of either a fixed or relative size, with another at least one natural language artifact in the interaction window, based on a criteria of shared features of one or more sets from the grouping. The criteria are dynamically determined without the use of a priori classifications such as categories, topics, or classes, through satisfying conditional probability constraints between sets of learned associations.
This invention presents a new knowledge language, Dynamic Molecular Language, that unites these AI approaches and takes them to a new level. Details of this new knowledge language and its implementation are explained in Appendix A to U.S. Provisional Application No. 60/382,503, entitled “Knowledge Discovery Agent System and Method,” filed May 22, 2002 by Timothy W. Estes, which is a part of this specification, is incorporated herein by reference and is made fully a part hereof. The software implementation of these details is set forth in the CD-ROM submitted with U.S. Provisional Application No. 60/382,503, entitled “Knowledge Discovery Agent System and Method,” filed May 22, 2002 by Timothy W. Estes, which is a part of this specification, is incorporated herein by reference and is made fully a part hereof.
Multi-agent systems are among the current research in this area. This technology has extended agent technologies to create community learning and collaboration and has its roots in advanced modeling software and Artificial Life. By assigning rules and behaviors to many small programs, Artificial Life scientists have created simulations of advanced, complex systems such as population growth of certain species in an environment where multiple predators exist. Some current implementations of multi-agent systems utilize a distributed structure that allows them to spread processing and storage over multiple systems, thereby solving problems faster and more efficiently. These distributed intelligent multi-agent systems represent the cutting edge of agent technology today. The present invention adds the advanced agent capacity of intentional agent self-modification to the field of intelligent agents. Such advanced agents are capable of rewriting their own code and changing their own actions as they run. Details of this advanced agent capability and its implementation are presented in Appendix A to U.S. Provisional Application No. 60/382,503, entitled “Knowledge Discovery Agent System and Method,” filed May 22, 2002 by Timothy W. Estes, which is a part of this specification, is incorporated herein by reference and is made fully a part hereof. The software implementation of these details is set forth in the CD-ROM submitted with U.S. Provisional Application No. 60/382,503, entitled “Knowledge Discovery Agent System and Method,” filed May 22, 2002 by Timothy W. Estes, which is a part of this specification, is incorporated herein by reference and is made fully a part hereof.
Further details of data bonding and its implementation are presented in Appendix A to U.S. Provisional Application No. 60/382,503, entitled “Knowledge Discovery Agent System and Method,” filed May 22, 2002 by Timothy W. Estes, which is a part of this specification, is incorporated herein by reference and is made fully a part hereof. The software implementation of these details is set forth in the CD-ROM submitted with U.S. Provisional Application No. 60/382,503, entitled “Knowledge Discovery Agent System and Method,” filed May 22, 2002 by Timothy W. Estes, which is a part of this specification, is incorporated herein by reference and is made fully a part hereof.
One exemplary implementation of the concepts discussed above and further disclosed in Appendix A and the CD-ROM, submitted with U.S. Provisional Application No. 60/382,503, entitled “Knowledge Discovery Agent System and Method,” filed May 22, 2002 by Timothy W. Estes, which is a part of this specification, is incorporated herein by reference and is made fully a part hereof, is a Personal Search Agent (PSA) which is a multi-phase effort designed to develop a set of search tools using Digital Reasoning and Data Bonding technologies developed by Unetworks. These new search tools learn the users preferences of a user and can infer his or her intent based on environmental factors and learned behaviors. This enhanced insight into the user's preferences, environment, and behavior, provide the user with a concise and better-fitted listing of responses to a given query.
Formally, the primitives are inter-definable in the following ways: ( 1 ) ⁢ S ⁡ ( Ω ) = ∑ i = 1 n ⁢ S ⁡ ( ω i ) ( 3 ) ⁢ ∑ i = 1 n ⁢ ( φ i ) = ∑ i = 1 n ⁢ ∑ j = 1 n ⁢ [ α i ⁡ ( β j ) ] Decomposition ⁢ ⁢ of ⁢ ⁢ Desire ⁢ ⁢ into ⁢ ⁢ Desires Decomposition ⁢ ⁢ of ⁢ ⁢ Intentions ( 2 ) ⁢ ∑ i = 1 n ⁢ S ⁡ ( ω i ) = ∑ i = 1 n ⁢ ( φ i ) ( 4 ) ⁢ Dfn ⁢ ⁢ α := α ⁡ ( β x ) = β y Decomposition ⁢ ⁢ of ⁢ ⁢ Desires Definition ⁢ ⁢ of ⁢ ⁢ a ⁢ ⁢ method
The emergent network structure is a sorted array: ( [ Concept ] - ( Associative ⁢ ⁢ Weight 1 ) - [ ConceptLink 1 ] [ ⁢ `` ⁢ ] - ( Associative ⁢ ⁢ Weight 2 ) - [ ConceptLink 2 ] [ ⁢ `` ⁢ ] - ( Associative ⁢ ⁢ Weight n ) - [ ConceptLink n ] )
Each potential semantic progression is modeled as an event space that is a subspace of the global normalized concept space. As the DCN2 experiences additional documents, it begins to develop patterns of these progressions that are recorded and clustered to reduce the variance in distance in state space for the entire progression. This can be accomplished by discovering a recursive function that represents the maximum number of experienced states with the minimal ad hoc basis vector shifts i.e. prototype discovery. From a mathematical perspective, the eigenvalues (λ) of the state vector at time ti versus the state vector at time to represent the appropriate linear operator, and the prototype is a clustering about certain eigenvalues.
Agent Infrastructure Knowledge Discovery Agent System (KDAS) Core
The ALP is the module that processes all unstructured information and transforms it into schema that can be analyzed by the DCN2. It also contains the heuristics to transform part-of-speech information into Subject—Predicate—Object form. The ALP has 3rd Party dependencies on the Inxight LinguistX Platform SDK for document tokenization and part-of-speech tagging.
CMIS is essentially the KDAS system augmented with a chatbot system that is able to dialogue with the user in natural language. This requires extending the UA to connect to the chatbot engine and adding a Conversational Inference Module (CIM) to translate requests from the chatbot to the schema structure of the DCN2 and back. In addition, numerous data extractors are used to interface with proprietary medical systems, or the use of a 3rd Party data extractor may be required.
Commercial Systems Enterprise Knowledge Agent Systems (EKAS)
The following publications are hereby incorporated by reference in their entirety for all purposes, to the same extent as if each individual publication were specifically and individually indicated to be so incorporated by reference: Gelertner, David, “The Second Coming,” available online at http://www.edge.org/3rd_culture/gelernter/gelernter_p1.html); Sowa, John F., Conceptual Structures Information Processing in Mind and Machine, Addison-Wesley, Reading, Mass. (1984); Kohonen, Tuevo, Self-Organizing Maps, 3rd Edition. New York: Springer, 2001 (Springer series in information sciences, 30); Hulth, Nils and Peter Grenholm, “A Distributed Clustering Algorithm”, “Discovering Conceptual Dimensions in KDAS,” and “Scaling Conceptual Dimensions and Neural Network Weighting Algorithms” In Lund University Cognitive Studies, 74 (1998); Lenat, Douglas B. & R. V. Guha, Building Large Knowledge-Based Systems, Addison-Wesley, Reading, Mass. (1990); Sowa, John F., Knowledge Representation: Logical, Philosophical, and Computation Foundations, Brooks/Cole, Pacific Grove, Calif. (2000).
further wherein learned associations resulting from said learning associations of natural language artifacts are formed using grouping of one natural language artifact in an interaction window with another at least one natural language artifact in said interaction window, based on a criteria of shared features of one or more sets from said grouping constituting measurements from said data sources;
means for constructing hierarchies of association across a state space of term usage compatible for interpolation of mapping functions between sets of terms;
wherein said mapping functions include one or more of fuzzy-type, weighted-type, or other types of mapping functions.
means for generating a structure of mapping functions comprising sets of terms in particular semantic positions;
wherein said structure comprises a formal semantic structure of one or more of programming languages, modal logics, frame systems, and ontologies of objects and relationships.
8. The system of claim 7, wherein said existing third-party information system is a medical information system selected from the group comprising physician offices systems, hospital systems, corporate systems, and health care providers network systems.
9. The system of claim 1, said at least one data agent further operable for interfacing with medical information systems selected from the group comprising physician offices systems, hospital systems, corporate systems, and health care provider network systems.
11. The system of claim 1, said at least one data agent further operable for clustering and visualizing data pertaining to defense intelligence.
providing at least one data agent in said distributed network for conducting a specific response to commands generated by said processing means; and
wherein learned associations resulting from said learning associations of natural language artifacts are formed using grouping of one natural language artifact in an interaction window with another at least one natural language artifact in said interaction window, based on a criteria of shared features of one or more sets from said grouping constituting measurements from said data sources;
constructing hierarchies of association across a state space of term usage compatible for interpolation of mapping functions between sets of terms;
generating a structure of mapping functions composed of sets of terms in particular semantic positions;
wherein said structure is a formal semantic structure of one or more of programming languages, modal logics, frame systems, and ontologies of objects and relationships.
20. The method of claim 19, wherein said existing third-party information system is a medical information system selected from the group comprising physician offices systems, hospital systems, corporate systems, and health care providers network systems.
24. A dynamic conceptual network system, comprising:
wherein said computer, said at least one storage means, and said at least one data agent are constructed and adapted for learning associations of natural language artifacts in unstructured data sources, wherein said artifacts include at least one of words, phrases, subjects, predicates, modifiers, and other syntactic forms.
25. The dynamic conceptual network system of claim 24, wherein said computer, said at least one storage means, and said at least one data agent are further constructed and adapted for learning semantic and syntactic relationships in structured data sources, and further wherein said structured data sources are in conventional formats used by relational database systems.
26. The dynamic conceptual network system of claim 24,
wherein the learned associations resulting from said learning associations of natural language artifacts are formed using grouping of one natural language artifact in an interaction window with another at least one natural language artifact in said interaction window, based on a criteria of shared features of one or more sets from said grouping constituting measurements from said data sources;
27. The dynamic conceptual network system of claim 25,
28. The dynamic conceptual network system of claim 27, further comprising means for representing learned associations in a specific format;
29. The dynamic conceptual network system of claim 27, further comprising means for constructing hierarchies of association across a state space of term usage compatible for interpolation of mapping functions between sets of terms;
30. The dynamic conceptual network system of claim 27, further comprising means for generating a structure of mapping functions composed of sets of terms in particular semantic positions;
31. The dynamic conceptual network system of claim 27, further comprising:
32. The dynamic conceptual network system of claim 27, further comprising:
33. The dynamic conceptual network system of claim 27, wherein said existing third-party information system is a medical information system selected from the group comprising physician offices systems, hospital systems, corporate systems, and health care providers network systems.
34. The dynamic conceptual network system of claim 27, further constructed and adapted for interfacing with medical information systems selected from the group comprising physician offices systems, hospital systems, corporate systems, and health care provider network systems.
35. The dynamic conceptual network system of claim 27, further comprising: at least one network data agent, constructed and adapted for connecting at least one preselected physician information system to at least one health care provider network, and further constructed and adapted for extracting data from said at least one preselected physician information system and providing said data to said at least one health care provider network.
36. The system of claim 27, further constructed and adapted to cluster and visualize data pertaining to defense intelligence and further comprising a human analyst of defense intelligence data, wherein said clustering and visualizing are provided to said human analyst as supplemental means for analyzing data.
37. A method for processing information, comprising the steps of:
US11/781,419 2002-05-22 2007-07-23 Knowledge discovery agent system and method Active 2023-06-20 US7882055B2 (en)
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US10/443,653 Continuation US7249117B2 (en) 2002-05-22 2003-05-21 Knowledge discovery agent system and method
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US11/781,419 Active 2023-06-20 US7882055B2 (en) 2002-05-22 2007-07-23 Knowledge discovery agent system and method
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