Multimodal Decision Tree Node Selection for User Intent Accuracy
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Solution Overview
Problem
Existing decision tree systems struggle with efficiently processing user inputs in multiple forms, leading to increased processing costs and reduced accuracy in determining relevant questions or solutions due to the need for separate models for each input modality and limited information capture from single-form inputs.
Innovation Solution
A method and system that integrates multi-modal feature representations using a multi-modal encoder and graph neural network to determine a target node in a decision tree, enhancing traversal efficiency and relevance by fusing features from different input modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate models are used for each input modality, then each modality can be processed independently, but processing costs increase and accuracy decreases
Solution Approach 1:
The patent combines multiple separate modality processing models into a unified multi-modal encoder that processes text, image, and audio inputs simultaneously. This integration reduces the number of separate models needed, lowering processing costs while improving accuracy through fused multi-modal feature representations that capture complementary information from different input types.
Solution Approach 2:
The multi-modal encoder is designed as a universal processing component that handles multiple input modalities (text, image, audio) through a single architecture. This multi-functional design eliminates the need for separate specialized models for each modality, reducing overall system complexity and processing costs while maintaining high accuracy across all input types.
2Reliability
If single-form inputs are processed, then processing is simpler, but information capture is limited and accuracy decreases
Solution Approach 1:
The patent merges multiple input modalities (text, image, audio) into a unified processing framework where their features are combined and fused. This integration captures complementary information from different sources that would be lost if only single-form inputs were processed, thereby improving accuracy in determining user intentions while reducing information loss.
3Productivity
If traditional decision tree traversal is used, then the process is straightforward, but traversal efficiency is reduced
Solution Approach 1:
The patent replaces the traditional mechanical decision tree traversal process with a neural network-based graph neural network approach. Instead of systematically visiting nodes in a fixed sequence, the system uses learned representations and similarity computations to directly identify relevant paths and nodes, dramatically improving traversal efficiency while the modular architecture keeps system complexity manageable.
4Productivity
If multi-modal feature fusion is implemented, then traversal efficiency and relevance improve, but computational complexity increases
Solution Approach 1:
The patent segments the multi-modal processing into distinct modules: separate encoders for different modalities, a feature fusion layer, and a graph neural network component. This segmentation allows each module to handle specific computational tasks independently, improving traversal efficiency through specialized processing while managing overall computational complexity through modular architecture that enables efficient resource utilization.
Data Source
AI summary
Embodiments of the present disclosure relate to a method, a device, and a computer program product for determining a node of a decision tree. The method includes determining multiple features of multiple modals corresponding to input information. The method further includes generating a multi-modal feature representation by combining the multiple features of the multiple modals. The method further includes determining a target path in a decision tree that is associated with the multi-modal feature representation, the decision tree comprising multiple nodes. The method further includes determining, in the target path based on the multi-modal feature representation, a target node associated with the input information and used to indicate a question or an answer. This method enables the fusion of feature representations corresponding to input information of different modals to determine a multi-modal feature representation. In this way, it is possible to determine richer and more accurate user intentions.