# US 10,482,088 B2 · Augmented exploration for big data and beyond > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,482,088 B2 - Document ID: US10482088B2 - Status: Granted - Kind: B2 - Application number: 15/584,874 - Filed: 2017-05-02 - Published: 2017-11-09 - Granted: 2019-11-19 - Earliest claimed: 2016-05-04 - Applicants: Eugene S. Santos; Eunice E. Santos; Evelyn W. Santos; Eugene Santos, Jr. - Inventors: Eugene S. Santos; Eunice E. Santos; Evelyn W. Santos; Eugene Santos, Jr. - Web page: https://patentgrep.com/patent/US10482088B2 ## CPC - G06F 16/24568: {Data stream processing; Continuous queries} - G06F 16/24573: {using data annotations, e.g. user-defined metadata} - G06F 16/254: {Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses} - G06F 16/27: Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor - G06F 16/28: Databases characterised by their database models, e.g. relational or object models - G06F 16/285: {Clustering or classification} - G06F 16/9024: {Graphs; Linked lists (G06F16/9027 takes precedence)} - G06F 16/9038: Presentation of query results - G06F 16/9027: {Trees} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract According to an aspect of an embodiment, a computer system including at least one computer is configured to generate, specification concept graphs of nodes spec1, spec2, . . . , specm including concepts node and relation nodes according to at least one of a plurality of digitized data from user input from a plurality of computerized data sources d1, d2, . . . , dl forming a first set of evidences U; generate concept graphs of nodes cα1, cα2, . . . , cαn including concept nodes and relation nodes for corresponding obtained plurality of IKs α1, α2, . . . , αn forming a second set of evidences U; select a subset of concept graphs of nodes cαi1, cαi2, . . . , cαih from cα1, cα2, . . . , cαn according to a computable measure of any one or combinations of consistency, inconsistency or priority threshold between cαj in cα1, cα2, . . . , cαn to specification concept graph speck in spec1, spec2, . . . , specm. Generate knowledge fragments for corresponding obtained subset of concept graphs cαi1, cαi2, . . . cαih. Create or add into at least one among a plurality of knowledge-bases (KBs) for the corresponding knowledge fragments obtained by creating objects in form ω=E→A. Determine relationship constraints κ in form of set relations among a plurality of subsets of evidences E. And execute any one or combination of knowledge completion, augmentation, and analysis. ## Claims ### Claim 1 A method, comprising: executing, by at least one processor, instructions stored in at least one memory coupled to the at least one processor to: generate specification concept graphs of nodes spec1,spec2, . . . ,specm including concept nodes of concepts and relation nodes according to at least one of a plurality of digitized data input from a plurality of computerized data sources d1, d2, . . . , dl forming a first set of evidences U to represent a first knowledge base (KB) among a plurality of KBs; generate concept graphs of nodes cα1, cα2, . . . , cαn including concept nodes and relation nodes for corresponding obtained plurality of information and knowledge (IKs) α1, α2, . . . , αn forming a second set of evidences U to represent a second knowledge base among the plurality of KBs; select a subset of concept graphs of nodes cαi1, cαi2, . . . , cαih from cα1, cα2, . . . , cαn according to a computable measure of consistency threshold between each caj in cα1, cα2, . . . , cαn to each specification concept graph speck in spec1,spec2, . . . , specm; generate knowledge fragment objects of concept fragments obtained for corresponding subset of concept graphs cαi1, cαi2, . . . , cαih, a knowledge fragment object among the knowledge fragment objects to store a mapping of values to first and second sets of evidences U, where A is a rule among rules A′ in at least the first and second KBs among the plurality of KBs, and E is a subset of the first and second sets of evidences U from the at least first and second KBs that supports the rule A, so that the rule A is supportable by the subset of evidences E, according to the concept fragments; generate a new KB, add into at least one KB among the plurality of KBs, and/or add into the first and/or second KBs for the concept fragments, to include augmenting information objects of augmenting information by, creating objects in form ω=E→A from the concept fragments; computing for each object co a validity (v) and a plausibility (p) based upon atomic propositions among the rules A′; obtaining relationship constraints κ in form of a plurality of set relations among a plurality of the subsets of evidences E for a plurality of the concept fragments; obtaining propositions κ for the plurality of fragment concepts in form of logical relations from among the rules A′ in the at least first and second KBs and/or from the atomic propositions; computing a validity (v) and a plausibility (p) for a combination of the relationship constraints κ and the propositions κ; and generating information tags to identify each object ω, each relationship constraint in κ, and each proposition in κ, to cause extending, by the augmenting information objects, at least a forecasting and/or an abduction based upon the concepts to a higher-order prosection and/or abduction deductively and/or inductively in conjunction with the generated information tags. ### Claim 2 (depends on 1) The method according to claim 1, wherein the executing of the instructions by the at least one processor further causes to: generate and rank new objects in the form ω=E→A based on validity and plausability using a computerized process of forecasting according to the spec1, spec2, . . . , specm. ### Claim 3 (depends on 1) The method according to claim 1, wherein the executing of the instructions by the at least one processor further causes to: generate and rank a plurality of the objects in the form ω=E→A based on validity and plausibility that support at least one object ω=E→A from a plurality of objects in the KBs corresponding to spec1, spec2, . . . , specm using a computerized process of abduction. ### Claim 4 (depends on 1) The method according to claim 1, wherein the executing of the instructions by the at least one processor further causes to: execute one or more processes according to spec1, spec2, . . . , specm of: compute a plurality of sequences of how a plurality of the objects in the form ω=E→A evolved and/or progressed using deductive and/or inductive reasoning corresponding to a plurality of IKs α1, α2, . . . , αn; compute a plurality of alternative outcomes on a plurality of the objects in the form ω=E→A using deductive projection forecasting and/or inductive projection forecasting; compute emergence on a plurality of the objects in the form ω=E→A; compute a plurality of new objects in the form ω=E→A that serve as unknown/missing links in the computerized reasoning processes for determining validity and plausibility; generates results of the one or more corresponding processes executed. ### Claim 5 (depends on 1) The method according to claim 1, wherein the executing of the instructions by the at least one processor further causes to: address current problems/questions based upon the spec1, spec2, . . . , specm as specified in support of the user. ### Claim 6 (depends on 1) The method according to claim 1, wherein the KBs include Bayesian Knowledge Bases (BKBs), Compound BKBs (CBKBs), Relational Databases (RDbs), Deductive Databases (DDbs), Augment Knowledge-Bases (AKBs). ### Claim 7 (depends on 1) The method according to claim 1, wherein the concept fragments include concept fragments obtained from authorized access to proprietary information. ### Claim 8 (depends on 1) The method according to claim 1, wherein the concepts are according to user input to control generation of the specification concept graphs. ### Claim 9 (depends on 1) The method according to claim 1, wherein the information tags include one or a combination of information to indicate an audit of generation of the knowledge fragments, or provide an explanation of the computing. ### Claim 10 An apparatus, comprising: at least one memory; and at least one processor coupled to the at least one memory to execute instructions stored in the at least one memory to cause the apparatus to: generate specification concept graphs of nodes spec1, spec2, . . . , specm including concept nodes of concepts and relation nodes according to at least one of a plurality of digitized data input from a plurality of computerized data sources d1, d2, . . . , dl forming a first set of evidences U to represent a first knowledge base (KB) among a plurality of KBs; generate specification concept graphs of nodes spec1,spec2, . . . ,specm including concept nodes of concepts and relation nodes according to at least one of a plurality of digitized data input from a plurality of computerized data sources d1, d2, . . . , dl forming a first set of evidences U to represent a first knowledge base (KB) among a plurality of KBs; select a subset of concept graphs of nodes cαi1, cαi2, . . . , cαih from cα1, cα2, . . . , cαn according to a computable measure of consistency threshold between each caj in cα1, cα2, . . . , cαn to each specification concept graph speck in spec1, spec2, . . . , specm; generate knowledge fragment objects of concept fragments obtained for corresponding subset of concept graphs cαi1, cαi2, . . . , cαih, a knowledge fragment object among the knowledge fragment objects to store a mapping of values to first and second sets of evidences U, where A is a rule among rules A′ in at least the first and second KBs among the plurality of KBs, and E is a subset of the first and second sets of evidences U from the at least first and second KBs that supports the rule A, so that the rule A is supportable by the subset of evidences E, according to the concept fragments; generate a new KB, add into at least one KB among the plurality of KBs, and/or add into the first and/or second KBs for the concept fragments, to include augmenting information objects of augmenting information by, creating objects in form ω=E→A from the concept fragments; computing for each object co a validity (v) and a plausibility (p) based upon atomic propositions among the rules A′; obtaining relationship constraints κ in form of a plurality of set relations among a plurality of the subsets of evidences E for a plurality of the concept fragments; obtaining propositions κ for the plurality of fragment concepts in form of logical relations from among the rules A′ in the at least first and second KBs and/or from the atomic propositions; computing a validity (v) and a plausibility (p) for a combination of the relationship constraints κ and the propositions κ; and generating information tags to identify each object ω, each relationship constraint in κ, and each proposition in κ, to cause extending, by the augmenting information objects, at least a forecasting and/or an abduction based upon the concepts to a higher-order projection and/or abduction deductively and/or inductively in conjunction with the generated information tags. ### Claim 11 (depends on 10) The apparatus according to claim 10, wherein the at least one processor is to receive the concepts according to user input to control generation of the specification concept graphs. ### Claim 12 (depends on 10) The apparatus according to claim 10, wherein the information tags include one or a combination of information to indicate an audit of generation of the knowledge fragments, or provide an explanation of the computing.