ESI Discovery Search Parameter Generation
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Solution Overview
Problem
Current methods for electronically stored information (ESI) discovery in litigation are inadequate due to challenges in searching natural language text, private codes, and vocabulary differences, leading to high false positives and false negatives, resulting in increased costs and legal risks.
Innovation Solution
A method and apparatus that perform electronic analysis of documents to generate automatic search parameters based on case knowledge, stored in a database, to improve the accuracy of search queries and reduce false positives and negatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search queries are used to search ESI, then the search process is simple and quick to execute, but the search accuracy is low resulting in high false positives and false negatives
Solution Approach 1:
The system performs preliminary actions by automatically generating search parameters and conducting searches before manual review. The automated generation of search parameters from case knowledge and iterative refinement process prepares the search results in advance, reducing both false positives and false negatives before documents reach human reviewers.
Solution Approach 2:
The system implements feedback mechanisms where search results are analyzed to generate refined search parameters for subsequent iterations. The automated process learns from previous search outcomes, adjusting and improving search parameters based on identified patterns and case knowledge, thereby continuously improving search accuracy.
2Reliability
If manual review of all returned documents is performed to ensure completeness, then false negatives are reduced, but the cost and time of review increase significantly
Solution Approach 1:
The system performs preliminary filtering and analysis of documents before they reach manual review. By automatically generating search parameters and conducting iterative searches, the system pre-processes the document set to identify and prioritize potentially relevant documents, reducing the volume requiring manual review while maintaining reliability.
Solution Approach 2:
The system performs self-service by automatically analyzing documents and generating search parameters without requiring manual intervention for every document. The automated process independently identifies patterns, refines search queries, and prioritizes documents, allowing human reviewers to focus only on the most promising candidates.
3Reliability
If iterative searches are conducted to reduce false negatives, then completeness improves, but the process becomes more time consuming
Solution Approach 1:
The system uses feedback from each iterative search to automatically refine and improve subsequent search parameters. By analyzing results and learning from patterns in the data, the system intelligently adjusts search strategies, reducing the number of iterations needed while maintaining high completeness.
Solution Approach 2:
The automated system performs iterative searches independently without requiring manual intervention for each iteration. The system self-manages the iterative process by automatically generating new search parameters, executing searches, and analyzing results, thereby improving productivity while maintaining reliability.
Data Source
AI summary
Method and apparatus for processing electronically stored information (ESI) for electronic discovery are described. In some examples, an electronic analysis of documents in the ESI indicated as being responsive to a search query of the ESI is performed. Results of the electronic analysis are stored in a database to provide a repository of case knowledge. Search parameters for at least one additional search query are generated automatically based on the case knowledge. The search parameters are provided as output.