Patent Template Extraction via Document Clustering
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Solution Overview
Problem
Patent assignees face challenges in maintaining consistent work product across their patent portfolio when changing or adding new outside patent counsel, as existing technologies lack efficient methods for extracting and replicating patent document templates from a corpus of documents.
Innovation Solution
The method involves obtaining a patent corpus, filtering it based on parameters, identifying document clusters with shared common text, and extracting patent document templates, which can include application, response, and appeal brief templates, without the need for full-text comparisons of each pair of documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full-text comparisons of each pair of patent documents are performed to extract templates, then template extraction accuracy is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the patent document into multiple sections (e.g., abstract, claims, specification, drawings) and processes each section separately to identify templates. This segmentation reduces the complexity of full-text comparison while maintaining template extraction accuracy by focusing on structurally significant portions of the document.
Solution Approach 2:
The patent extracts and removes commonly repeated text portions (templates) from the patent document corpus before performing comparisons. By pre-extracting these template elements, the system reduces the amount of text requiring full-text comparison, thereby decreasing processing time while preserving extraction accuracy.
2Manufacturing precision
If patent document templates are extracted manually by practitioners, then template quality and consistency are improved, but labor costs and processing time increase
Solution Approach 1:
The patent implements an automated system that extracts templates from patent documents without requiring manual practitioner intervention. The system uses computational algorithms to identify, extract, and validate templates autonomously, eliminating labor-intensive manual extraction while maintaining high template quality through programmed validation rules.
Solution Approach 2:
The patent replaces the manual mechanical process of template extraction by practitioners with an automated computational system. The system uses text analysis algorithms, pattern recognition, and data processing to perform template extraction, thereby increasing productivity while maintaining consistent template quality through systematic automated procedures.
3Adaptability or versatility
If new outside patent counsel are engaged to prepare patent applications, then legal expertise and resource availability are improved, but consistency of work product across the patent portfolio deteriorates
Solution Approach 1:
The patent changes the parameter of template standardization by establishing a unified template structure that all outside patent counsel must follow. The system extracts and enforces consistent template parameters (layout, formatting, section organization) across all patent documents prepared by different counsel, thereby maintaining work product consistency while allowing flexibility in legal expertise application.
Data Source
AI summary
Systems, methods, and storage media for extracting patent document templates from a patent corpus are disclosed. Exemplary implementations may: obtain a patent corpus; receive one or more parameters; determine one or more subsets of the patent corpus by filtering the patent corpus based on the one or more parameters; identify one or more document clusters within individual ones of the one or more subsets of the patent corpus; obtain a patent document template corresponding to the first document cluster; and/or perform other operations.