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US 12,608,372 B2Granted
System and method for automated analysis of legal documents within and across specific fields
AssigneeQomplx LLC
InventorsJason Crabtree, Andrew Sellers
Life of this patent
Estimating the term…
Abstract
Automated analysis of legal documents within and across different fields is disclosed. An extraction processor identifies and extracts knowledge from data contained in documents and transforms it into a common data form. An analysis processor develops local and global knowledge graphs containing the key entities, relationships and concepts encoded in the text.
Claims14
A computer system for automated analysis of legal documents within and across different fields employing a cyber decision platform, wherein the computer system is configured to execute software instructions stored on a nontransitory machine-readable storage media, the computer system comprising:
- one or more hardware processors configured for:
- receiving a data set containing documents including legal documents, technical documents, and domain-specific documents from multiple jurisdictions and practice areas;
- hierarchically classifying documents as legal or domain-specific to other fields including engineering, technology, cybersecurity and medicine, wherein legal documents are routed to legal-specific processing and non-legal documents are routed to domain-specific specialty models;
- automatically classifying the legal documents by legal domain, jurisdictional framework, and temporal context using machine learning classification algorithms;
- performing dynamic model selection of one or more models from a curated model catalogue of domain-specific natural language processing (NLP) models based on the classification, wherein the domain-specific NLP models are trained for specific specialties and jurisdictional requirements;
- extracting legal entities including laws, regulations, cases, judgments, contracts, legal concepts, parties, jurisdictions, organizations, people, events, locations, and dates, contractual clauses, and precedential relationships from the received legal documents using the selected domain-specific NLP models;
- generating jurisdiction-specific knowledge graphs that link legal concepts, case precedents, regulatory requirements, and contractual provisions with weighted relationship scores, wherein the knowledge graphs comprise:
- nodes representing legal entities, contractual terms, statutory provisions, and case precedents;
- edges representing legal relationships weighted by precedential strength and jurisdictional applicability;
- and
- temporal annotations tracking legal concept evolution and regulatory changes over time;
- performing real-time knowledge graph enrichment using current legal developments, regulatory changes, and litigation outcomes;
- and
- providing predictive legal risk assessments and outcome probabilities for the received legal documents by:
- querying the jurisdiction-specific knowledge graphs using the extracted legal entities and relationships from the received documents;
- analyzing relevant case precedents and regulatory requirements identified in the knowledge graphs;
- and
- generating document-specific risk predictions based on historical litigation data and current legal trends integrated within the knowledge graphs.
15 more elements
A computer-implemented method executed on a cyber decision platform for automated analysis of legal documents within and across different fields, the computer-implemented method comprising:
- receiving a data set containing documents including legal documents, technical documents, and domain-specific documents from multiple jurisdictions and practice areas;
- hierarchically classifying documents as legal or domain-specific to other fields including engineering, technology, cybersecurity, and medicine, wherein legal documents are routed to legal-specific processing and non-legal documents are routed to domain-specific specialty models;
- automatically classifying the legal documents by legal domain, jurisdictional framework, and temporal context using machine learning classification algorithms;
- performing dynamic model selection of one or more models from a curated model catalogue of domain-specific NLP models based on the classification, wherein the domain-specific NLP models are trained for specific legal specialties and jurisdictional requirements;
- extracting legal entities including laws, regulations, cases, judgments, contracts, legal concepts, parties, jurisdictions, organizations, people, events, locations, and dates, contractual clauses, and precedential relationships from the received legal documents using the selected domain-specific NLP models;
- generating jurisdiction-specific knowledge graphs that link legal concepts, case precedents, regulatory requirements, and contractual provisions with weighted relationship scores, wherein the knowledge graphs comprise:
- nodes representing legal entities, contractual terms, statutory provisions, and case precedents;
- edges representing legal relationships weighted by precedential strength and jurisdictional applicability;
- and
- temporal annotations tracking legal concept evolution and regulatory changes over time;
- performing real-time knowledge graph enrichment using current legal developments, regulatory changes, and litigation outcomes;
- and
- providing predictive legal risk assessments and outcome probabilities for the received legal documents by:
- querying the jurisdiction-specific knowledge graphs using the extracted legal entities and relationships from the received documents;
- analyzing relevant case precedents and regulatory requirements identified in the knowledge graphs;
- and
- generating document-specific risk predictions based on historical litigation data and current legal trends integrated within the knowledge graphs.
14 more elements
Dependencies are inferred from the claim text.