Synthetic Query Generation for Enterprise Search
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Solution Overview
Problem
Traditional search solutions rely on keyword-based document analysis, which is limited in understanding content at a granular level, making it difficult to find relevant data in large datasets and providing inadequate context to users.
Innovation Solution
An enterprise search service using machine learning to index and search text-based documents, enabling natural language querying, context understanding, and personalized answers by employing models for question answering, document ranking, and FAQ matching, with emphasis on confidence scoring for result relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If keyword-based document analysis is used, then search simplicity is maintained, but content understanding at granular level deteriorates
Solution Approach 1:
The patent replaces traditional keyword-based mechanical search with machine learning models that understand content semantically. The system uses transformers and other ML models to analyze document meaning, extract entities, and generate relevant queries automatically, transitioning from surface-level keyword matching to deep contextual understanding while maintaining user-friendly operation.
Solution Approach 2:
The patent introduces an intermediary layer of machine learning models between the user query and the document database. This intermediary includes query understanding models, document embedding models, and relevance ranking models that mediate the search process, enabling sophisticated content analysis without exposing complexity to the user.
2Device complexity
If traditional keyword search is used, then system complexity is low, but ability to find relevant data in large datasets deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-processing and indexing large datasets using machine learning models before actual search queries are made. Documents are embedded, indexed, and organized in advance, allowing the system to quickly retrieve and rank relevant information when queries arrive, significantly improving retrieval efficiency without requiring complex real-time processing.
Solution Approach 2:
The patent creates simplified representations (embeddings) of complex documents, effectively copying their essential meaning into a compressed format that can be efficiently searched and compared. This embedding approach allows the system to handle large datasets by working with condensed representations rather than raw text, maintaining speed while improving relevance.
3Ease of manufacture
If keyword-based approach is used, then implementation simplicity is maintained, but context understanding and personalized answers deteriorate
Solution Approach 1:
The patent changes the fundamental parameters of search from keyword matching to semantic understanding by transforming text into vector embeddings. This parameter transformation enables the system to capture context, relationships, and nuances in the data, preserving critical information that would be lost in traditional keyword-based approaches while maintaining implementation through standardized ML libraries.
Data Source
AI summary
Techniques for generation of synthetic queries from customer data for training of document querying machine learning (ML) models as a service are described. A service may receive one or more documents from a user, generate a set of question and answer pairs from the one or more documents from the user using a machine learning model trained to predict a question from an answer, and store the set of question and answer pairs generated from the one or more documents from the user. The question and answer pairs may be used to train another machine learning model, for example, a document ranking model, a passage ranking model, a question/answer model, or a frequently asked question (FAQ) model.