# US 11,443,216 B2 · Corpus gap probability modeling > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,443,216 B2 - Document ID: US11443216B2 - Status: Granted - Kind: B2 - Application number: 16/262,479 - Filed: 2019-01-30 - Published: 2020-07-30 - Granted: 2022-09-13 - Earliest claimed: 2019-01-30 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Michael McCawley; Adriana Valido; Robert E. Loredo - Web page: https://patentgrep.com/patent/US11443216B2 ## CPC - G06N 5/048: Fuzzy inferencing - G06F 16/3346: {using probabilistic model} - G06F 16/35: Clustering; Classification - G06F 16/36: Creation of semantic tools, e.g. ontology or thesauri - G06F 16/93: Document management systems - G06F 40/216: using statistical methods - G06F 40/247: Thesauruses; Synonyms - G06F 40/30: Semantic analysis - G06N 5/02: Knowledge representation; Symbolic representation - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N 20/00: Machine learning - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A system, computer program product, and method are provided to conduct gap probability mapping to predict presence and location of one or more gaps in a corpus. A probabilistic model is formed to represent inter-corpora associations of objects, which the model leverages to process query submissions. As queries are received and processed, the model creates an adjustment. Subject to evaluation, confidence of the adjustment is evaluated, and a response correlated to the confidence is returned. ## Claims ### Claim 1 A system comprising: a processing unit operatively coupled to memory; an artificial intelligence platform in communication with the processing unit and memory; and a knowledge engine operatively coupled to the processing unit, the knowledge engine to assess probability and generate a map based on the assessment, including: an organization manager configured to: for corpora having respective pluralities of objects, organize the objects into a plurality of taxonomies, including organize the respective plurality of objects of each corpus of the corpora into an associated taxonomy based on object characteristic data; and analyze the corpora according to the associated taxonomies, the analysis including formation of an inter-corpora ontology comprising one or more inter-corpora associations of the respective objects; a query manager operatively coupled to the organization manager, the query manager configured to: receive and process a query and associated query content, including leverage the inter-corpora ontology to identify, from the plurality of taxonomies, a taxonomy associated with the query content; submit the query to the corpus associated with the identified taxonomy; receive an initial response with an initial probability proportional to a frequency of the initial response in the corpus associated with the identified taxonomy; and apply the query content to the inter-corpora ontology, the application to form an adjusted probability and an adjusted response; and a director operatively coupled to the query manager, the director configured to assess confidence of accuracy of a response to the query based on evaluation of the initial probability value and the adjusted probability value; and the query manager to return a response, the response directly correlated to the assessed confidence conducted by the director, wherein the response includes the initial response or the adjusted response. ### Claim 2 (depends on 1) The system of claim 1, wherein the knowledge engine further comprises a natural language (NL) manager, the NL manager configured to identify the associated query content and discover context characteristic data. ### Claim 3 (depends on 1) The system of claim 1, wherein the knowledge engine further comprises a machine learning (ML) model, the ML model configured to dynamically build the inter-corpora ontology for object content, wherein the inter-corpora ontology identifies inherent relationships between the objects across the corpora. ### Claim 4 (depends on 3) The system of claim 3, wherein the knowledge engine is configured to dynamically optimize the ML model in response to amendment of one or more of the plurality of objects. ### Claim 5 (depends on 3) The system of claim 3, wherein the identified inherent relationships reveal inherent content relationships. ### Claim 6 A computer program product comprising: a computer readable storage medium having computer readable program code embodied therewith, the program code being executable by a processor to assess probability and generate a map based on the assessment, including program code executable by the processor to: for corpora having respective pluralities of objects, organize the objects into a plurality of taxonomies, including organize the respective plurality of objects of each corpus of the corpora into an associated taxonomy based on object characteristic data; analyze the corpora according to the associated taxonomies, the analysis including formation of an inter-corpora ontology comprising one or more inter-corpora associations of the respective objects; receive and process a query and associated query content, including leverage the inter-corpora ontology to identify, from the plurality of taxonomies, a taxonomy associated with the query content; submit the query to the corpus associated with the identified taxonomy; receive an initial response with an initial probability proportional to a frequency of the initial response in the corpus associated with the identified taxonomy; apply the query content to the inter-corpora ontology, the application to form an adjusted probability and an adjusted response; assess confidence of accuracy of a response to the query based on evaluation of the initial probability value and the adjusted probability value; and return a response, the response directly correlated to the assessed confidence, wherein the response includes the initial response or the adjusted response. ### Claim 7 (depends on 6) The computer program product of claim 6, further comprising program code executable by the processor to support natural language processing, including program code executable by the processor to identify the associated query content and discover context characteristic data. ### Claim 8 (depends on 6) The computer program product of claim 6, wherein the program product further comprises a machine learning (ML) model configured to dynamically build the inter-corpora ontology for object content, wherein the inter-corpora ontology identifies inherent relationships between the objects across the corpora. ### Claim 9 (depends on 8) The computer program product of claim 8, further comprising program code executable by the processor to dynamically optimize the ML model in response to amendment of one or more of the plurality of objects. ### Claim 10 (depends on 8) The computer program product of claim 8, wherein the identified-relationships reveal inherent content relationships. ### Claim 11 A method comprising: for corpora having respective pluralities of objects, organizing the objects into a plurality of taxonomies, including organize the respective plurality of objects of each corpus of the corpora into an associated taxonomy based on object characteristic data; analyzing the corpora according to the associated taxonomies, the analysis including forming an inter-corpora ontology comprising one or more inter-corpora associations of the respective objects; receiving and processing a query and associated query content, including leveraging the inter-corpora ontology to identify, from the plurality of taxonomies, a taxonomy associated with the query content; submitting the query to the corpus associated with the identified taxonomy; receiving an initial response with an initial probability proportional to a frequency of the initial response in the corpus associated with the identified taxonomy; applying the query content to the inter-corpora ontology, the application to form an adjusted probability and an adjusted response; assessing confidence of accuracy of a response to the query based on evaluation of the initial probability value and the adjusted probability value; and returning a response from the confidence assessment, the response directly correlated to the assessed confidence, wherein the response includes the initial response or the adjusted response. ### Claim 12 (depends on 11) The method of claim 11, further comprising supporting natural language processing, including identifying the associated query content and discovering context characteristic data. ### Claim 13 (depends on 11) The method of claim 11, further comprises utilizing a machine learning (ML) model and dynamically building the inter-corpora ontology for object content, wherein the inter-corpora ontology identifies inherent relationships between the objects across the corpora. ### Claim 14 (depends on 13) The method of claim 13, further comprising dynamically optimizing the ML model in response to amendment of one or more of the plurality of objects. ### Claim 15 (depends on 13) The method of claim 13, wherein the identified inherent relationships reveal inherent content relationships.