# US 2017/0286398 A1 · Method to resolve the meaning of a body of natural language text using artificial intelligence analysis in combination with semantic and contextual analysis > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2017/0286398 A1 - Document ID: US20170286398A1 - Status: Published application - Kind: A1 - Application number: 15/084,450 - Filed: 2016-03-29 - Published: 2017-10-05 - Earliest claimed: 2016-03-29 - Inventors: Geoffrey Hodgson Hunt; Michael Allen Bennett; James Noel Carboni; Georges Pierre Pantanelli - Web page: https://patentgrep.com/patent/US20170286398A1 ## CPC - G06F 40/30: Semantic analysis - G06N 3/04: Architecture, e.g. interconnection topology - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A method of language processing using a primary contextual and semantic analysis with reference to Rich dictionaries (created by combining dictionaries, thesauri, and language and jargon awareness databases) and with reference to connotation databases and contextual connotation databases to perform a full parsing of the text into parts of speech. If connotational or contextual ambiguities remain after this primary analysis is completed, a secondary artificial intelligence analysis module uses the primary analysis output as part of its input to modify some parameters and values within this artificial intelligence module. This module processes iteratively until any ambiguities are resolved. After primary and secondary analyses have taken place, a ranking matrix processor module processes all information acquired by the preceding modules to output a ranking matrix which encapsulates the meaning of the text in a form that may be readily used by machines or 3rd parties to react to the meaning of the text. Specialized Rich dictionaries can be created for use with this method to achieve specific goals, for cross-language translations, or to compare translations in different languages to detect inconsistencies. ## Claims ### Claim 1 A method to improve Information flow by using Algorithmic Semantic, Contextual and Artificial Intelligence. ### Claim 2 (depends on 1) The method of claim 1, wherein an Artificial Intelligence Neural Network is integrated into the system. ### Claim 3 (depends on 1) The method of claim 1, wherein a Natural Language Bayesian Network structure is created before using an Artificial Intelligence Neural Network. ### Claim 4 (depends on 1) The method of claim 1, wherein the system teaches an Artificial Intelligence Neural Network using information acquired from Semantic and Contextual Analysis. ### Claim 5 A method to generate a correlation matrix between text and its parsing to improve information flow. ### Claim 6 (depends on 5) The method of claim 5, wherein Semantic, Contextual and Artificial Intelligence analyses are used to create parsed text fragments. ### Claim 7 (depends on 5) The method of claim 5, wherein language-awareness is used to generate language-independent modules from parsed text fragments. ### Claim 8 (depends on 5) The method of claim 5, wherein texts are ranked on the fly and in real time using their connotation indices and their contextual connotation indices. ### Claim 9 (depends on 5) The method of claim 5, wherein parsing text matrices are ranked and compared on the fly and in real time. ### Claim 10 (depends on 5) The method of claim 5, wherein a user's or author's sentiment within a text is determined and identified on the fly and in real time. ### Claim 11 (depends on 5) The method of claim 5, wherein a user's or author's sentiment between two or more text parsing indices is identified and compared on the fly and in real time. ### Claim 12 A method to associate unique connotation indices and values with words in thesauri, dictionaries and texts. ### Claim 13 (depends on 12) The method of claim 12, wherein the association is between contextual connotation indices and their values and a text. ### Claim 14 (depends on 12) The method of claim 12, wherein the association is between unique connotation indices and each word connotation within a text. ### Claim 15 (depends on 12) The method of claim 12, comprising the association of opposite contextual connotation indices and values of an antonym with the contextual connotation indices of its synonym. ### Claim 16 (depends on 12) The method of claim 12, as applied to comparing and ranking connotation related indices and values by assigning relative scale values. ### Claim 17 (depends on 12) The method of claim 12, as applied to associating connotation scalable indices and values with verb tenses. ### Claim 18 (depends on 12) The method of claim 12, as applied to associating connotation indices and contextual connotation indices and values with a text. ### Claim 19 (depends on 12) The method of claim 12, as applied to creating parsing text matrices using word connotation and contextual indices and values.