# US 10,120,863 B2 · System, method, and recording medium for regular rule learning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,120,863 B2 - Document ID: US10120863B2 - Status: Granted - Kind: B2 - Application number: 15/791,791 - Filed: 2017-10-24 - Published: 2018-02-15 - Granted: 2018-11-06 - Earliest claimed: 2016-03-31 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Octavian Popescu; Vadim Sheinin - Web page: https://patentgrep.com/patent/US10120863B2 ## CPC - G06F 40/30: Semantic analysis - G06F 40/211: Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars - G06F 40/284: Lexical analysis, e.g. tokenisation or collocates - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A regular rule learning system, including an analyzing circuit configured to analyze a corpus of sentences to find semantic relationships between sentence constituents that are responsible for specific senses of words in that sentence by describing the semantic relationships and grammatical relations that are actuated in the sentence. ## Claims ### Claim 1 A regular rule learning system, comprising: a processer: and a memory, the memory storing instructions to cause the processor to execute: an analyzing circuit configured to analyze a corpus of sentences to find semantic relationships between sentence constituents that are responsible for specific senses of words in that sentence by describing the semantic relationships and grammatical relations that are actuated in the sentence to create a set of regular rules, a regular rule does not depend on a history or applying any other rules in the set of regular rules. ### Claim 2 (depends on 1) The system of claim 1, wherein the analyzing further analyzes the corpus of sentences stored in a database to discover lexical features of the sentences and conjunctively create the set of regular rules based on the discovered lexical features and syntactical features of the sentences. ### Claim 3 (depends on 2) The system of claim 2, wherein the syntactical features of the corpus of sentences are given. ### Claim 4 (depends on 2) The system of claim 2, wherein the lexical features are unknown prior to the analyzing circuit discovering the lexical features. ### Claim 5 (depends on 2) The system of claim 2, wherein the lexical features are calculated from the corpus of sentences stored in the database prior to the syntactical features being given to the corpus of sentences. ### Claim 6 A regular rule learning method, comprising: analyzing a corpus of sentences to find semantic relationships between sentence constituents that are responsible for specific senses of words in that sentence by describing the semantic relationships and grammatical relations that are actuated in the sentence, wherein the syntactical features of the corpus of sentences are given to create a set of regular rules, a regular rule does not depend on a history or applying any other rules in the set of regular rules. ### Claim 7 (depends on 6) The method of claim 6, wherein the analyzing further analyzes the corpus of sentences stored in a database to discover lexical features of the sentences and conjunctively create the set of regular rules based on the discovered lexical features and syntactical features of the sentences. ### Claim 8 (depends on 7) The method of claim 7, wherein the lexical features are unknown prior to the analyzing discovering the lexical features. ### Claim 9 (depends on 7) The method of claim 7, wherein the lexical features are calculated from the corpus of sentences stored in the database prior to the syntactical features being given to the corpus of sentences. ### Claim 10 A non-transitory computer-readable recording medium recording a regular rule learning program, the program causing a computer to perform: analyzing a corpus of sentences to find semantic relationships between sentence constituents that are responsible for specific senses of words in that sentence by describing the semantic relationships and grammatical relations that are actuated in the sentence, wherein the syntactical features of the corpus of sentences are given to create a set of regular rules, a regular rule does not depend on a history or applying any other rules in the set of regular rules. ### Claim 11 (depends on 10) The non-transitory computer-readable recording medium of claim 10, wherein the analyzing further analyzes the corpus of sentences stored in a database to discover lexical features of the sentences and conjunctively create the set of regular rules based on the discovered lexical features and syntactical features of the sentences. ### Claim 12 (depends on 10) The non-transitory computer-readable recording medium of claim 10, wherein the lexical features are unknown prior to the analyzing discovering the lexical features. ### Claim 13 (depends on 10) The non-transitory computer-readable recording medium of claim 10, wherein the lexical features are calculated from the corpus of sentences stored in the database prior to the syntactical features being given to the corpus of sentences. ### Claim 14 (depends on 1) The system of claim 1, wherein the set of regular rules predicts a meaning of a user input in the corpus of sentences without referring to a database and independently of any other rule. ### Claim 15 (depends on 6) The method of claim 6, wherein the set of regular rules predicts a meaning of a user input in the corpus of sentences without referring to a database and independently of any other rule. ### Claim 16 (depends on 10) The non-transitory computer-readable recording medium of claim 10, wherein the set of regular rules predicts a meaning of a user input in the corpus of sentences without referring to a database and independently of any other rule.