Cloud Storage Query Embeddings for Real-Time User Interest Anticipation
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Solution Overview
Problem
Users face challenges in locating relevant information for decision-making or actions within cloud-based content management platforms, as information is often dispersed across different sources, formats, and access permissions, leading to time-consuming searches and inefficient collaboration.
Innovation Solution
The implementation of a cloud-based content management platform that utilizes a generative machine learning model (MLM) to process documents for query embeddings, providing personalized prompts, real-time anticipation of user interest, and generating answers with citations to source documents, thereby streamlining information retrieval and collaboration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually search for information across disparate sources and documents, then they can access the information they need, but it takes a significant amount of time
Solution Approach 1:
The system performs preliminary actions by continuously processing documents, generating embeddings, and anticipating user needs before they explicitly search. The generative MLM proactively creates prompts and retrieves relevant information in advance, so when users need information, it is already prepared and accessible, significantly reducing search time and improving productivity.
Solution Approach 2:
The system provides self-service by automatically understanding user context, predicting information needs, and retrieving relevant documents without requiring manual user input for each search query. The generative MLM autonomously processes user behavior patterns and delivers personalized information streams, freeing users from repetitive manual searching.
2Loss of information
If information is stored in disparate sources with different formats and access permissions, then comprehensive information is available, but locating relevant information becomes difficult
Solution Approach 1:
The system merges disparate information sources by generating unified embeddings that capture semantic meaning across different document formats and locations. The generative MLM integrates information from various sources into coherent responses, presenting consolidated information to users regardless of its original dispersion across multiple sources with different access permissions.
Solution Approach 2:
The generative MLM acts as an intermediary between users and the dispersed information sources. It translates user queries into optimized search prompts, processes information across different formats and permission levels, and presents unified results, thereby mediating the complexity of retrieving information from disparate sources.
3Loss of information
If the system processes all documents to provide comprehensive information, then information completeness is improved, but computing resource consumption increases
Solution Approach 1:
The system applies local quality by processing only the portions of documents and information most relevant to current user needs and context. The generative MLM dynamically determines which documents and text segments require processing based on user behavior patterns and query semantics, rather than uniformly processing all available documents, thereby reducing computing resource consumption while maintaining information completeness for relevant queries.
Solution Approach 2:
The system changes processing parameters dynamically based on user context and query characteristics. The generative MLM adjusts the scope, depth, and methodology of document processing according to real-time user behavior patterns, selecting appropriate processing intensity levels that balance information completeness with computing resource consumption efficiency.
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.