Adaptive Inference System for Multi-Modal User Intention
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Solution Overview
Problem
Current multi-modal inference systems face challenges in accurately inferring user intentions due to the complexity of integrating and analyzing various modalities, such as visual, voice, and text information, and lack of personalization based on user history and context.
Innovation Solution
An adaptive inference system that collects multi-modal information including visual, voice, and text data, uses recognition techniques like object, face, and emotion recognition, and incorporates user history and personal information to infer intentions, enabling more accurate and personalized inferences by integrating these insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-modal information (visual, voice, text) is collected and integrated for user intention inference, then inference accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex multi-modal inference process into distinct modules: a collection unit that gathers multi-modal information (visual, voice, text), a storage unit that maintains user history and context, and an inference unit that processes the data. This segmentation allows each module to handle specific tasks independently, improving overall inference accuracy while managing system complexity through modular architecture.
2Measurement precision
If user history information and personal information are integrated into the inference process, then personalization accuracy is improved, but information processing complexity increases
Solution Approach 1:
The system performs preliminary action by pre-collecting and storing user history information and personal information in a dedicated storage unit before the actual inference process. This allows the inference unit to access pre-processed user context data without performing complex real-time analysis, thereby improving personalization accuracy while reducing the computational burden during active inference operations.
3Measurement precision
If multiple recognition techniques (object recognition, face recognition, emotion recognition, voice recognition) are applied, then recognition precision is improved, but processing time increases
Solution Approach 1:
The system merges multiple recognition techniques (object recognition, face recognition, emotion recognition, voice recognition) into a unified inference framework. The collection unit simultaneously captures multi-modal information and the inference unit processes these diverse data types together, leveraging complementary information from different modalities to improve recognition precision while optimizing processing efficiency through integrated analysis.
Data Source
AI summary
This application relates to an adaptive inference system and an operation method therefor. In one aspect, the system includes a user terminal for collecting multi-modal information including at least visual information, voice information and text information. The system may also include an inference support device for receiving the multi-modal information from the user terminal, and inferring the intention of a user on the basis of pre-stored history information related to the user terminal, individualized information and the multi-modal information.