{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-12001546","patent":{"patent_number":"US-12001546","title":"Systems and methods for causality-based multivariate time series anomaly detection","assignee":null,"inventors":[],"filing_date":"2021-10-29T00:00:00.000Z","publication_date":"2024-06-04T00:00:00.000Z","cpc_codes":["G06F","G06F","G06N","G06F"],"num_claims":20,"abstract":"Embodiments described herein provide a causality-based anomaly detection mechanism that formulates multivariate time series as instances that do not follow the regular causal mechanism. Specifically, the causality-based anomaly detection mechanism leverages the causal structure discovered from data so that the joint distribution of multivariate time series is factorized into simpler modules where each module corresponds to a local causal mechanism, reflected by the corresponding conditional distribution. Those local mechanisms are modular or autonomous and can then be handled separately. In light of this modularity property, the anomaly detection problem then naturally decomposed into a series of low-dimensional anomaly detection problems. Each sub-problem is concerned with a local mechanism."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Systems and methods for causality-based multivariate time series anomaly detection","description":"Embodiments described herein provide a causality-based anomaly detection mechanism that formulates multivariate time series as instances that do not follow the regular causal mechanism. Specifically, ","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-12001546","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-12001546","citation_suggestion":"Patentable. \"Systems and methods for causality-based multivariate time series anomaly detection\" (US-12001546). https://patentable.app/patents/US-12001546","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-12001546","json":"https://patentable.app/api/llm-context/US-12001546","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-31T09:44:44.390Z"}