Semantic Vector File Management for Legal Discovery
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Solution Overview
Problem
The electronic discovery process is hindered by the large volume of data that needs to be reviewed for relevance and privilege, with existing systems struggling to efficiently manage and search through diverse data formats under court-ordered deadlines.
Innovation Solution
An electronic file management system incorporating semantic vectors, which includes a processor, communication interface, and memory device, filters documents, applies semantic vector analysis, and uses a neural network to generate textual responses based on query vectors, thereby identifying relevant documents and reducing the need for manual review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional search methods are used to review documents for relevance and privilege, then the system can handle diverse data formats, but the large volume of data makes the review process time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical document review with an automated AI-based system that uses semantic vectors and neural networks to analyze documents. The system converts documents into semantic vectors and uses similarity search to automatically identify relevant documents, eliminating the need for manual review of each document while maintaining accuracy in relevance and privilege identification.
Solution Approach 2:
The system enables self-service document review by automatically performing relevance and privilege analysis without requiring human intervention. The AI model independently processes documents, applies filter rules, and generates search results, allowing users to simply input queries while the system handles the complex analysis tasks automatically.
2Reliability
If manual review of documents is performed to ensure accuracy, then relevance and privilege can be accurately identified, but the process is slow and cannot meet court-ordered deadlines
Solution Approach 1:
The patent replaces slow manual review with high-speed automated AI analysis. The system uses semantic vector embedding and neural networks to rapidly analyze document content and metadata, achieving both high accuracy in relevance and privilege identification and the speed required to meet court deadlines. The automated system processes documents much faster than human reviewers while maintaining consistent accuracy.
Solution Approach 2:
The system changes the parameters of document analysis from human-based subjective evaluation to machine-based objective semantic vector comparison. By transforming documents into mathematical vectors and using similarity metrics, the system achieves consistent, reproducible results that are both accurate and rapidly computable, enabling meeting of tight deadlines without sacrificing reliability.
3Adaptability or versatility
If comprehensive document search is performed to satisfy legal requirements, then all relevant documents can be found, but the complexity of managing diverse data formats increases the difficulty of the search process
Solution Approach 1:
The patent implements a universal document management system that can handle multiple data formats (emails, documents, spreadsheets, audio, video, social media, instant messages) through a single unified approach. The system uses a common semantic vectorization process that works across all document types, eliminating the need for separate processing mechanisms for each format while maintaining the ability to comprehensively search and analyze diverse data.
Solution Approach 2:
The system replaces complex manual management of diverse formats with automated AI processing. The neural network and semantic vector framework automatically adapt to different document types without requiring manual configuration or complex user intervention. The system handles format diversity internally through its AI models while presenting a simple, unified interface to users, reducing operational complexity while maintaining comprehensive capability.
Data Source
AI summary
Systems and methods receive a selection of and filter documents for an electronic document search. A semantic vector analysis module is applied to text of the filtered documents to build respective floating point vectors for each of the documents, and the respective floating point vectors are stored to a vector database. A textual query is received and vectorized to produce a query vector representing search criteria. The vector database is searched in accordance with the query vector to identify similar documents that satisfy a similarity measure. A ranked list of vectors that include highest similarity scores relative the query vector is generated and similar documents are identified. A neural network is used to process document data of the similar documents to generate a textual response to the textual query, and control signal(s) are transmitted to a user device to initiate displaying the generated textual response and question answer events.