Knowledge discovery agent system and method

A dynamic conceptual network system, comprising:

• a computer in a distributed network, said computer including a processor or microprocessor;
• and
• at least one data agent in said distributed network for conducting a specific response to commands generated by said processor or microprocessor;
• wherein said computer 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;
• further 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;
• further wherein said criteria of shared features are dynamically determined without the use of a priori classifications and using conditional probability constraints between sets of learned associations;
• and
• further wherein said grouping creates a network of conditional probabilities between all encountered natural language artifacts or a subset thereof, determined by consideration of conditional interaction probabilities based on a history of measurements from said data sources;
• and
• further wherein learned associations are represented in a specific format compatible for operations for mapping between a plurality of data structures and languages comprising one or more of arrays, vector spaces, first order predicate logic, Conceptual Graphs, SQL, and typed programming languages.

A dynamic conceptual network system, comprising:

• a computer in a distributed network, said computer including a processor or microprocessor;
• and
• at least one data agent in said distributed network for conducting a specific response to commands generated by said processor or microprocessor;
• wherein said computer 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;
• further 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;
• further wherein said criteria of shared features are dynamically determined without the use of a priori classifications and using conditional probability constraints between sets of learned associations;
• further wherein said grouping creates a network of conditional probabilities between all encountered natural language artifacts or a subset thereof, determined by consideration of conditional interaction probabilities based on a history of measurements from said data sources;
• and
• further wherein a structure of mapping functions is generated 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.

A dynamic conceptual network system, comprising:

• a computer in a distributed network, said computer including a processor or microprocessor;
• and
• at least one data agent in said distributed network for conducting a specific response to commands generated by said processor or microprocessor;
• wherein said computer 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;
• further 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;
• further wherein said criteria of shared features are dynamically determined without the use of a priori classifications and using conditional probability constraints between sets of learned associations;
• further wherein said grouping creates a network of conditional probabilities between all encountered natural language artifacts or a subset thereof, determined by consideration of conditional interaction probabilities based on a history of measurements from said data sources;
• and
• further wherein said at least one data agent is 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.

A method for processing information, comprising the steps of:

• (a) providing a computer in a distributed network, said computer including a processor or microprocessor;
• (b) providing at least one data agent in said distributed network for conducting a specific response to commands generated by said processor or microprocessor;
• (c) configuring said computer and said at least one data agent to be operable 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;
• or
• learning semantic and syntactic relationships in structured data sources, wherein said structured data sources comprise entities in conventional formats used by relational database systems;
• 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;
• further wherein said criteria of shared features are dynamically determined without the use of a priori classifications, using conditional probability constraints between sets of learned associations;
• and
• further wherein said grouping creates a network of conditional probabilities between all encountered natural language artifacts or a subset thereof, determined by consideration of conditional interaction probabilities based on a history of measurements from said data sources;
• and
• (d) 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, wherein said sets of terms have corresponding particular syntactic positions;
• (e) 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;
• (f) collecting data input generated by interaction with a human user;
• (g) learning from said input;
• (h) utilizing results of said learning to reorganize and alter mapping functions induced from analyzed input;
• and
• (i) aligning said mapping functions according to human natural usage information in said results of said learning, wherein said input data is in the form of structured information or unstructured information.