Cloud Document Query Embeddings for Search Efficiency
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Solution Overview
Problem
Users face challenges in finding relevant information within disparate and permission-restricted documents stored across different locations and formats within a cloud-based content management platform, leading to inefficiencies in decision-making and collaboration due to time-consuming searches and unnecessary resource consumption.
Innovation Solution
A cloud-based content management platform employs a generative machine learning model (MLM) to process documents for query embeddings, providing personalized prompts, real-time user interest anticipation, and generative answers with citations to source documents, thereby gathering similar documents and generating relevant information without relying on user knowledge or extensive searching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually search for information in cloud storage documents, then they can access needed information, but the search process is time-consuming and inefficient
Solution Approach 1:
The system performs preliminary actions by processing documents in advance to generate query embeddings and store metadata about document content, structure, and relationships. This preparation enables rapid retrieval and generation of relevant information when users need it, eliminating the need for manual searching through large document collections.
Solution Approach 2:
The patent introduces an intermediary system consisting of a generative MLM and query embedding mechanism that mediates between users and document collections. This intermediary automatically processes queries, retrieves relevant documents, and generates synthesized answers, replacing the manual search process and significantly reducing time loss.
2Reliability
If the system processes all documents in cloud storage, then comprehensive information is available, but computing resources are consumed unnecessarily
Solution Approach 1:
The system extracts only the necessary information from the entire document collection by using query embeddings to identify and process only those documents relevant to current user queries. The generative MLM selectively retrieves and processes pertinent document portions rather than analyzing all stored documents, maintaining information completeness for relevant queries while minimizing computing resource consumption.
Solution Approach 2:
The patent changes the parameter of document processing from complete full-text analysis to selective embedding-based processing. By transforming documents into compressed query embeddings and only processing documents that match current queries, the system maintains comprehensive information availability while significantly reducing the computational energy required for document processing.
3Ease of operation
If the system transfers all documents to user devices, then users have full access to content, but network bandwidth and storage resources are wasted
Solution Approach 1:
Instead of transferring complete document copies to user devices, the system creates and transmits only lightweight query embeddings and metadata copies that contain essential information about document content and relevance. This allows users to access synthesized answers and document references without transferring the full document data, maintaining ease of operation while eliminating unnecessary network bandwidth consumption.
Data Source
AI summary
Systems and methods include pre-processing documents in cloud storage using query embeddings, providing personalized prompts to users based on documents in cloud storage, real-time anticipation of user interest in information contained in documents in cloud storage, and providing generative answers including citation to source documents in cloud storage. The system and methods generate generative machine learning model (MLM) prompts based on document portions of documents in a cloud-based content management platform. The systems and methods use the generative MLM to generate responses to prompts, and the responses include citations to the document portions used to generate the responses in order for users to verify the responses.