Privilege Log Classification Using Topic-Guided Document Labeling
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Solution Overview
Problem
Existing discovery processes face challenges in accurately and efficiently generating privilege logs for large volumes of documents due to the manual review limitations and inaccuracies in automated techniques, necessitating improved automated systems for identifying privileged documents.
Innovation Solution
A computer-implemented method utilizing unsupervised machine learning and classifier training models to identify topics and categories within a corpus of documents, followed by applying a set of rules to generate a privilege log, incorporating features like entity recognition and normalization to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of documents is performed to generate privilege logs, then accuracy of privilege identification is improved, but time consumption and processing speed deteriorate
Solution Approach 1:
The patent introduces an automated privilege log generation system that acts as an intermediary between the document corpus and the privilege identification process. The system uses machine learning models and natural language processing to automatically analyze documents, identify privileged content, and generate privilege logs, thereby eliminating the need for manual review while maintaining accuracy through algorithmic analysis and pattern recognition
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computational system. The system employs machine learning models, classifiers, and natural language processing algorithms to perform document analysis and privilege identification, substituting human manual labor with automated technological processes that can process large volumes of documents efficiently and accurately
2Productivity
If automated techniques are applied to identify privileged documents, then time consumption is reduced, but accuracy of privilege claims may deteriorate
Solution Approach 1:
The patent implements preliminary action by training machine learning models on annotated documents and establishing classification rules before the actual privilege log generation process. The system pre-processes the document corpus, identifies patterns and features associated with privileged documents, and prepares the automated system in advance to ensure accurate and timely privilege claim identification during the actual discovery process
Solution Approach 2:
The patent incorporates feedback mechanisms where the automated system continuously refines its classification accuracy by analyzing results and adjusting its models. The system uses feedback from the document analysis process to improve its identification of privileged documents, ensuring that accuracy is maintained or enhanced as the system processes more documents and learns from patterns in the data
Data Source
AI summary
Systems, methods, and computer readable media for intelligent generation of a privilege log are provided. These techniques may include accessing a corpus of documents and applying an unsupervised machine learning model thereto to identify a plurality of topics. Using the identified topics, the techniques include associating a plurality of categories with the identified topics and executing a classifier training model to train classifiers corresponding to the categories. These classifiers are then applied to the corpus of documents to label the documents. Subsequently, the techniques automatically generate a privilege log based upon the labels applied to the documents by the classifiers.