The Pattern Recognition module is a critical component of the DataHive pipeline that identifies, analyzes, and extracts meaningful patterns from legal documents using advanced machine learning and natural language processing techniques.
class PatternAnalyzer:
def __init__(self):
self.nlp_processor = NLPProcessor()
self.pattern_matcher = PatternMatcher()
self.knowledge_graph = LegalKnowledgeGraph()
async def analyze_document(self, legal_text):
entities = self.nlp_processor.extract_entities(legal_text)
patterns = self.pattern_matcher.find_patterns(entities)
return self.knowledge_graph.update(patterns)- Court names and jurisdictions
- Legal citations and references
- Party names and roles
- Document types and classifications
- Precedent relationships
- Legal arguments and reasoning
- Regulatory requirements
- Compliance obligations
- Receives preprocessed documents from indexer
- Validates document structure
- Extracts relevant sections
- Prepares text for analysis
- Updates legal knowledge models
- Maps entity relationships
- Tracks pattern evolution
- Maintains citation networks
- Pattern verification against known models
- Cross-reference checking
- Consistency validation
- Accuracy assessment
- Pattern recognition accuracy
- Processing speed
- False positive rates
- Coverage statistics
- Pattern encryption
- Access control
- Audit logging
- Version control
- Privacy preservation
- Regulatory adherence
- Data minimization
- Retention policies