Multimodal Capability Hierarchy for Efficient AI Tool Matching
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Solution Overview
Problem
Existing information handling systems face inefficiencies in processing multimodal user-query inputs due to computationally demanding algorithms and challenges in mapping these inputs to the appropriate AI productivity tool-enablable software applications, leading to increased resource consumption and time complexity.
Innovation Solution
A hierarchical capabilities decision tree is implemented using a directed acyclic graph (DAG) to organize AI productivity tool-enablable software application capabilities, allowing for efficient tree-traversal algorithms that match multimodal user-query inputs through semantic similarity searches, reducing resource consumption by leveraging machine-readable program code instructions and ML models to generate and compare multimodal capability intent values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computationally demanding algorithms are used to process multimodal user-query inputs, then measurement precision of capability matching is improved, but use of energy and processing resources increases
Solution Approach 1:
The patent segments the capability matching process into multiple hierarchical levels (capability category level, capability group level, and capability level). At each level, the system performs similarity searches with progressively finer granularity. This segmentation allows the system to achieve high matching precision while reducing overall computational complexity by avoiding direct comparison at the finest level for all queries.
Solution Approach 2:
The patent implements preliminary action by pre-organizing capabilities into a hierarchical structure with capability categories, groups, and individual capabilities. Similarity searches are performed in advance at higher levels to identify candidate capabilities before conducting detailed matching. This preliminary organization and multi-level filtering reduces the computational burden of exact matching while maintaining precision.
2Adaptability or versatility
If comprehensive capability matching is performed across all AI productivity tool-enablable software applications, then adaptability to user queries is improved, but device complexity increases
Solution Approach 1:
The patent divides the comprehensive capability set into hierarchical segments (capability categories, groups, and individual capabilities). This segmentation allows the system to maintain comprehensive adaptability across all capabilities while managing complexity through structured organization. The hierarchical structure enables selective traversal based on query requirements, reducing the effective complexity for each specific matching task.
Solution Approach 2:
The patent introduces a hierarchical dimension to the capability matching system, organizing capabilities across multiple levels (category → group → capability). This dimensional organization transforms the flat, complex matching problem into a structured multi-level process, improving adaptability while making the system more manageable through hierarchical abstraction.
3Manufacturing precision
If similarity search is performed across a plurality of capabilities with hierarchical relationships, then manufacturing precision of capability selection is improved, but loss of time increases
Solution Approach 1:
The patent segments the similarity search process across hierarchical levels (capability categories, groups, and capabilities). By dividing the search into progressive stages, the system achieves high selection accuracy through multi-level filtering while reducing total processing time compared to performing exhaustive searches at the finest level directly.
Solution Approach 2:
The patent performs preliminary similarity searches at higher hierarchical levels to identify candidate capabilities before conducting detailed matching at lower levels. This preliminary filtering action reduces the search space for subsequent detailed matching, improving both accuracy and efficiency by avoiding unnecessary comparisons.
4Productivity
If tree-traversal algorithms are used to navigate hierarchical capabilities decision tree, then productivity of capability matching is improved, but device complexity increases
Solution Approach 1:
The patent segments the capability matching task into hierarchical traversal steps through the capabilities decision tree. The tree structure divides the matching process into manageable levels (capability categories, groups, and capabilities), improving productivity by enabling systematic navigation while organizing complexity into a structured format that is more manageable than flat exhaustive search.
Data Source
AI summary
A system and method for matching multimodal user-query input at an information handling system includes storing capabilities associated with a plurality of AI productivity tool-enablable software applications in a hierarchical capabilities decision tree with each node including natural language textual and non-textual modality descriptions of a capability and multimodal capability intent values generated from the same. Executing code instruction to receive a multimodal user-query input in any of text, audio, or image and generate a multimodal query input intent value for matching to a best match capability for a responsive action to be taken by one of the plurality of AI productivity tool-enablable software applications executing on the information handling system via a semantic similarity search comparing the multimodal query input intent value to the multimodal capability intent values in the hierarchical capabilities decision tree based on a highest cosine semantic similarity search score.