# US 12,001,802 B2 · Training enrichment system for natural language processing > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,001,802 B2 - Document ID: US12001802B2 - Status: Granted - Kind: B2 - Application number: 17/337,835 - Filed: 2021-06-03 - Published: 2022-12-08 - Granted: 2024-06-04 - Earliest claimed: 2021-06-03 - Assignees: Sap SE - Applicants: Sap SE - Inventors: Tassilo Klein; Moin Nabi - Web page: https://patentgrep.com/patent/US12001802B2 ## CPC - G06F 40/30: Semantic analysis - G06F 16/90332: {Natural language query formulation or dialogue systems} - G06F 16/951: Indexing; Web crawling techniques - G06F 16/953: Querying, e.g. by the use of web search engines - G06F 40/20: Natural language analysis (semantic analysis of natural language G06F40/30) - G06N 20/00: Machine learning - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Disclosed herein are various embodiments for training and enriching a natural language processing system. An embodiment operates by identifying a natural language processor (NLP) trained on a first set of documents, wherein the NLP is trained to perform a set of functionality based on the first set of documents. An industry, set of words corresponding to the industry, and set of sentences including at least a subset of the set of words in which the NLP is to be configured to perform the set of functionality are identified. A set of sentences that exceed a similarity threshold are identified. The NLP is trained with the subset of the set of sentences that exceed the similarity threshold, wherein the trained NLP with the subset is configured to perform the set of functionality within the industry with a greater accuracy than NLP trained on only the first set of documents. ## Claims ### Claim 1 A method comprising: identifying a natural language processor (NLP) trained on a first set of documents, wherein the NLP is trained to perform a set of functionality based on the first set of documents; determining an industry in which the NLP is to be configured to perform the set of functionality; identifying a set of words corresponding to the industry; identifying a set of sentences including at least a subset of the set of words corresponding to the industry; scoring the set of sentences based on a similarity to one another; identifying a subset of the set of sentences that exceed a similarity threshold; and training the NLP with the subset of the set of sentences that exceed the similarity threshold, wherein the trained NLP with the subset is configured to perform the set of functionality within the industry with a greater accuracy than an NLP trained on only the first set of documents. ### Claim 2 (depends on 1) The method of claim 1, wherein the first set of documents are annotated with indications of correct and incorrect input and output. ### Claim 3 (depends on 2) The method of claim 2, wherein the subset of sentences are augmented indicating different variations of sentences with similar meanings. ### Claim 4 (depends on 1) The method of claim 1, wherein the identifying a set of sentences comprises: identifying a second set of documents, different from the first set of documents; and identifying, from the second set of documents, the set of sentences including at least a subset of the set of words corresponding to the industry. ### Claim 5 (depends on 4) The method of claim 4, wherein the second set of documents correspond to different webpages available on the Internet associated with the industry. ### Claim 6 (depends on 5) The method of claim 5, further comprising: crawling the different webpages publicly available on the Internet to identify the set of sentences. ### Claim 7 (depends on 1) The method of claim 1, wherein the scoring is based on BERTScore. ### Claim 8 A system, comprising: a memory; and at least one processor coupled to the memory and configured to perform instructions that cause the at least one processor to perform operations comprising: identifying a natural language processor (NLP) trained on a first set of documents, wherein the NLP is trained to perform a set of functionality based on the first set of documents; determining an industry in which the NLP is to be configured to perform the set of functionality; identifying a set of words corresponding to the industry; identifying a set of sentences including at least a subset of the set of words corresponding to the industry; scoring the set of sentences based on a similarity to one another; identifying a subset of the set of sentences that exceed a similarity threshold; and training the NLP with the subset of the set of sentences that exceed the similarity threshold, wherein the trained NLP with the subset is configured to perform the set of functionality within the industry with a greater accuracy than an NLP trained on only the first set of documents. ### Claim 9 (depends on 8) The system of claim 8, wherein the first set of documents are annotated with indications of correct and incorrect input and output. ### Claim 10 (depends on 9) The system of claim 9, wherein the subset of sentences are augmented indicating different variations of sentences with similar meanings. ### Claim 11 (depends on 8) The system of claim 8, wherein the identifying a set of sentences comprises: identifying a second set of documents, different from the first set of documents; and identifying, from the second set of documents, the set of sentences including at least a subset of the set of words corresponding to the industry. ### Claim 12 (depends on 11) The system of claim 11, wherein the second set of documents correspond to different webpages available on the Internet associated with the industry. ### Claim 13 (depends on 12) The system of claim 12, the operations further comprising: crawling the different webpages publicly available on the Internet to identify the set of sentences. ### Claim 14 (depends on 8) The system of claim 8, wherein the scoring is based on BERTScore. ### Claim 15 A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising: identifying a natural language processor (NLP) trained on a first set of documents, wherein the NLP is trained to perform a set of functionality based on the first set of documents; determining an industry in which the NLP is to be configured to perform the set of functionality; identifying a set of words corresponding to the industry; identifying a set of sentences including at least a subset of the set of words corresponding to the industry; scoring the set of sentences based on a similarity to one another; identifying a subset of the set of sentences that exceed a similarity threshold; and training the NLP with the subset of the set of sentences that exceed the similarity threshold, wherein the trained NLP with the subset is configured to perform the set of functionality within the industry with a greater accuracy than an NLP trained on only the first set of documents. ### Claim 16 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the first set of documents are annotated with indications of correct and incorrect input and output. ### Claim 17 (depends on 16) The non-transitory computer-readable medium of claim 16, wherein the subset of sentences are augmented indicating different variations of sentences with similar meanings. ### Claim 18 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the identifying a set of sentences comprises: identifying a second set of documents, different from the first set of documents; and identifying, from the second set of documents, the set of sentences including at least a subset of the set of words corresponding to the industry. ### Claim 19 (depends on 18) The non-transitory computer-readable medium of claim 18, wherein the second set of documents correspond to different webpages available on the Internet associated with the industry. ### Claim 20 (depends on 19) The non-transitory computer-readable medium of claim 19, the operations further comprising: crawling the different webpages publicly available on the Internet to identify the set of sentences.