Discovery Avatar Training From Analyst Intuition for Data Retrieval
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Solution Overview
Problem
Traditional data search techniques, such as keyword searching and Boolean operators, are inadequate for efficiently organizing and discovering relevant data in large repositories due to mismatches in keyword usage and lack of intelligence, leading to incomplete or overly inclusive search results and the inability to represent human intuition effectively.
Innovation Solution
Development of computer-implemented discovery avatars that learn from human analysts' preferences and intuition, using machine learning to tokenize data, extract features, and create mathematical models for data clustering and scoring, allowing for iterative improvement and deployment across various data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional keyword searching is used, then search simplicity is maintained, but search accuracy and relevance deteriorate due to keyword mismatches and lack of intelligence
Solution Approach 1:
The patent replaces traditional mechanical keyword-matching search systems with an intelligent avatar-based system that uses machine learning and natural language processing. The avatar learns from human analyst behavior and intuition, then automatically performs semantic understanding and relevance scoring, substituting the simple but inaccurate keyword mechanism with a sophisticated intelligent agent that maintains ease of use while dramatically improving search accuracy.
Solution Approach 2:
The discovery avatar acts as an intermediary between the user's simple keyword input and the complex data repository. It translates basic search terms into intelligent queries, learns from analyst feedback, and mediates between user intent and data relevance, thereby bridging the gap between simple operation and accurate results.
2Device complexity
If keyword searching is used, then implementation complexity is low, but information completeness deteriorates due to documents being omitted from results
Solution Approach 1:
The system replaces simple keyword-matching mechanics with an intelligent avatar that performs semantic analysis, contextual understanding, and relevance ranking. This substitution captures documents that would otherwise be omitted due to keyword mismatches, significantly reducing information loss while the automated learning process manages the increased complexity.
Solution Approach 2:
The avatar performs preliminary learning from human analyst behavior and intuition before actual search operations. This preliminary training phase enables the system to anticipate user needs and understand contextual relevance, ensuring that no relevant documents are omitted during subsequent searches.
3Device complexity
If keyword searching is used, then system simplicity is maintained, but search efficiency deteriorates due to over-inclusive results that are too voluminous to review
Solution Approach 1:
The patent substitutes simple keyword-matching mechanics with an intelligent avatar system that performs semantic analysis and relevance ranking. The avatar learns from analyst feedback to accurately predict document relevance, automatically filtering out irrelevant results and presenting only the most pertinent documents, thereby dramatically improving search efficiency without requiring complex user configuration.
Solution Approach 2:
The system implements feedback loops where human analyst interactions with search results are continuously learned by the avatar. This feedback mechanism allows the system to refine its understanding of relevance over time, automatically adjusting to improve efficiency by presenting increasingly accurate results and reducing the volume of documents requiring human review.
4Device complexity
If traditional search methods are used, then deployment simplicity is maintained, but ability to represent human intuition deteriorates
Solution Approach 1:
The system replaces traditional mechanical search deployment with an intelligent avatar that is trained on human analyst behavior and intuition. The avatar learns to represent individual analyst expertise and intuitive judgment patterns, enabling the system to adapt to different users' mental models and search preferences while maintaining relatively simple deployment through automated training processes.
Data Source
AI summary
In embodiments of the present invention improved capabilities are described for developing, training, validating and deploying discovery avatars embodying mathematical models that may be used for document and data discovery and deployed within large data repositories. For example, an avatar may be constructed by machine learning processes, including by processing information related to what types of information analysts find useful in large data sets. Once constructed, an avatar may be deployed as an aid to human intuition in a wide range of analytical processes, such as related to national security, enterprise management (e.g., programs related to sales, marketing, product, promotions, placement, pricing and the like), dispute resolution (including litigation), forensic analysis, criminal, administrative, civil and private investigations, scientific investigations, research and development, and a wide range of others.