# US 12,762,174 B2 · Systems and methods for automatic identification of anomalous data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,762,174 B2 - Document ID: US12762174B2 - Status: Granted - Kind: B2 - Application number: 18/774,446 - Filed: 2024-07-16 - Published: 2026-01-22 - Granted: 2026-10-13 - Earliest claimed: 2024-07-16 - Assignees: N-Power Medicine, Inc. - Applicants: N-Power Medicine, Inc. - Inventors: Nils Gustav Thomas Bengtsson; Mark Lee - Web page: https://patentgrep.com/patent/US12762174B2 ## CPC - G06F 16/353: into predefined classes - G06F 16/345: {Summarisation for human users} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract In some aspects, the disclosure is directed to methods and systems for automatic detection of outliers in unstructured and semi-structured data. In some implementations, unstructured or semi-structured data may be provided to a trained large language model (LLM), which may be used to summarize or extract important tokens or keywords from the data. The extracted tokens or keywords may be used to generate a vector in an n-dimensional space, and compared to other vectors generated from tokens or keywords extracted from other unstructured or semi-structured data. A cluster analyzer may identify clusters or groups of vectors within the n-dimensional space, and may identify outliers or vectors lying outside of the identified clusters or groups. ## Claims ### Claim 1 A method for automatic identification of anomalous data, comprising: receiving, by a computing system comprising one or more processors, a plurality of items of data; for each item of data of the plurality of items of data: generating, by the computing system using a trained language model, a keyword-based summary of the item of data, extracting, by the computing system using the summary generated by the trained language model, a plurality of keywords, and generating, by the computing system, a vector based on the extracted plurality of keywords; grouping, by the computing system, the vectors into one or more clusters in an n-dimensional space by determining a volume for each of the one or more clusters, assigning vectors to a cluster based on the vector being within the volume, and adjusting the volume of each cluster until a predetermined percentage of vectors are external to every cluster of the one or more clusters; identifying, by the computing system, at least one item of data corresponding to a vector external to every cluster of the one or more clusters; and providing, by the computing system, the identified at least one item of data as anomalous data. ### Claim 2 (depends on 1) The method of claim 1, wherein the plurality of items of data comprise unstructured data. ### Claim 3 (depends on 1) The method of claim 1, wherein the plurality of items of data lack identifiers of the plurality of keywords. ### Claim 4 (depends on 1) The method of claim 1, wherein generating the vector based on the extracted plurality of keywords comprises identifying a value corresponding to each keyword, each value corresponding to a dimension of the n-dimensional space. ### Claim 5 (depends on 1) The method of claim 1, wherein generating the vector based on the extracted plurality of keywords comprises calculating a value for each keyword based on a value for the keyword and a weight corresponding to the keyword. ### Claim 6 (depends on 1) The method of claim 1, wherein grouping the vectors into one or more clusters comprises determining a centroid for each of the one or more clusters, and assigning vectors to a cluster based on a distance between the vector and the centroid being less than a threshold. ### Claim 7 A method for automatic identification of anomalous data, comprising: receiving, by a computing system comprising one or more processors, a plurality of items of data; for each item of data of the plurality of items of data: extracting, by the computing system using a trained language model, a plurality of keywords, and generating, by the computing system, a vector based on the extracted plurality of keywords; grouping, by the computing system, the vectors into one or more clusters in an n-dimensional space by determining a centroid for each of the one or more clusters, and: (a) assigning vectors to a cluster based on a distance between the vector and the centroid being less than a first threshold, (b) determining whether a percentage of vectors not assigned to any cluster is less than a second threshold, and (c) repeating (a)-(b) while adjusting the first threshold until the percentage of vectors not assigned to any cluster is equal to or greater than the second threshold; identifying, by the computing system, at least one item of data corresponding to a vector external to every cluster of the one or more clusters; and providing, by the computing system, the identified at least one item of data as anomalous data. ### Claim 8 (depends on 7) The method of claim 7, wherein extracting the plurality of keywords from an item of data comprises generating a keyword-based summary of the item of data via the trained language model. ### Claim 9 A system for automatic identification of anomalous data, comprising: a computing system comprising one or more processors, the one or more processors configured to: receive a plurality of items of data; for each item of data of the plurality of items of data: extract, using a trained language model, a plurality of keywords, and generate, by the computing system, a vector based on the extracted plurality of keywords; group the vectors into one or more clusters in an n-dimensional space by determining a centroid for each of the one or more clusters, assigning vectors to a cluster based on a distance between the vector and the centroid being less than a threshold, and adjusting the threshold until a predetermined percentage of vectors are external to every cluster of the one or more clusters; identify at least one item of data corresponding to a vector external to every cluster of the one or more clusters; and provide the identified at least one item of data as anomalous data. ### Claim 10 (depends on 9) The system of claim 9, wherein the plurality of items of data comprise unstructured data. ### Claim 11 (depends on 9) The system of claim 9, wherein the plurality of items of data lack identifiers of the plurality of keywords. ### Claim 12 (depends on 9) The system of claim 9, wherein the one or more processors are further configured to extract the plurality of keywords from an item of data by generating a keyword-based summary of the item of data via the trained language model. ### Claim 13 (depends on 9) The system of claim 9, wherein the one or more processors are further configured to generate the vector based on the extracted plurality of keywords by identifying a value corresponding to each keyword, each value corresponding to a dimension of the n-dimensional space. ### Claim 14 (depends on 9) The system of claim 9, wherein the one or more processors are further configured to generate the vector based on the extracted plurality of keywords by calculating a value for each keyword based on a value for the keyword and a weight corresponding to the keyword. ### Claim 15 (depends on 9) The system of claim 9, wherein the one or more processors are further configured to determine a volume for each of the one or more clusters, and assign vectors to a cluster based on the vector being within the volume. ### Claim 16 (depends on 15) The system of claim 15, wherein the one or more processors are further configured to adjust the volume until a predetermined percentage of vectors are external to every cluster of the one or more clusters.