Multimodal Fragment Retrieval With Feedback-Driven Search Orchestration
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Solution Overview
Problem
Conventional language models lack the ability to reason, maintain memory, and generate contextually accurate, temporally relevant, and personalized responses to user queries, often producing irrelevant and inaccurate output.
Innovation Solution
A feedback-based multimodal fragment retrieval system that leverages an LM agent to infer user preferences from real-time feedback signals, generating multimodal search results that are contextually and temporally relevant, using a search orchestrator to orchestrate search execution plans and a post-processor to curate results based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional language models are used to process user queries, then the system is simple to operate, but the response accuracy and contextual relevance deteriorate
Solution Approach 1:
The system segments the search process into multiple independent components: query understanding module, search execution module, feedback processing module, and result generation module. Each module handles a specific aspect of the search process, improving overall accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent introduces intermediary components including a query understanding module that mediates between user input and search execution, and a feedback processing module that mediates between search results and final output. These intermediaries enhance contextual relevance and accuracy without requiring complete system redesign.
2Reliability
If comprehensive search execution plans are generated, then the relevance and completeness of search results improve, but the computational time and resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing user queries to extract intent, context, and key parameters before executing searches. Query understanding and feedback from previous interactions are prepared in advance, enabling faster and more relevant search execution without compromising result quality.
Solution Approach 2:
The patent implements periodic feedback collection and processing, where user feedback on search results is systematically gathered and used to refine subsequent search execution plans. This periodic refinement improves result relevance over time while maintaining efficient search execution through learned patterns.
3Adaptability or versatility
If user feedback is collected and processed in real-time, then the personalization and accuracy of results improve, but the system complexity and processing demands increase
Solution Approach 1:
The patent implements a feedback mechanism where user responses to search results are collected and processed to refine query understanding and improve future search execution. This feedback loop enables continuous personalization and adaptation while maintaining system manageability through structured feedback processing.
Solution Approach 2:
The system performs self-service by automatically analyzing user feedback and adjusting search strategies without requiring manual intervention. The query understanding module and search execution module adapt based on processed feedback, enabling automated personalization while controlling complexity through algorithmic rather than manual processes.
4Reliability
If redundant searches are performed to ensure completeness, then the thoroughness of results improves, but the computational efficiency deteriorates
Solution Approach 1:
The patent applies partial action by executing only the necessary searches required to answer the user's query based on query understanding and extracted parameters. Rather than performing all possible searches, the system selectively executes relevant searches, improving efficiency while maintaining result completeness through targeted query processing.
Data Source
AI summary
Systems, apparatuses, methods, and techniques are described for feedback-based multimodal fragment retrieval. An example method includes receiving user input data comprising a user search query. The example method also includes determining, based on the first user search query, a first and second set of search parameters. The example method also includes generating search execution plan. The example method also includes retrieving, based on executing the search execution plan with respect to a set of candidate retrieval sources, a first set of relevant multimodal fragments. The example method also includes generating a first multimodal search result object, wherein the first multimodal search result object comprises at least a subset of the first set of relevant multimodal fragments. The example method also includes providing the first multimodal search result object to at least a first user device associated with the first user.