Touchless Medical Device Operation Using LLM Semantic Embeddings
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Solution Overview
Problem
Existing touchless operation techniques for medical devices rely on rigid keyword searching, which fails to accurately interpret user commands with synonymous or non-identical phrasing, leading to inflexibility and errors in device operation.
Innovation Solution
Employing large language models (LLMs) to generate embeddings for user commands, allowing comparison with embeddings of available device operations, enabling accurate identification of intended operations through semantic similarity rather than keyword matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If keyword searching is used for touchless operation, then device operation can be controlled without contact, but the system becomes rigid and inflexible in interpreting user commands
Solution Approach 1:
The patent replaces the mechanical keyword-matching system with a semantic embedding-based interpretation system. Instead of relying on exact keyword matches, the system uses large language models to generate semantic embeddings that capture the meaning of user commands, enabling flexible interpretation of synonymous and paraphrased phrases while maintaining touchless operation.
Solution Approach 2:
The patent transforms the command interpretation from discrete keyword matching to continuous semantic space comparison. By converting both user commands and device operations into embedding vectors, the system can measure similarity through continuous parameter comparison rather than binary keyword presence, enabling nuanced interpretation of user intent.
2Ease of manufacture
If keyword searching is used for touchless operation, then the system remains simple to implement, but accuracy in identifying user intent deteriorates
Solution Approach 1:
The patent introduces semantic embeddings as an intermediary layer between user commands and device operations. This intermediary transforms raw text into a meaningful representation that bridges the gap between diverse user phrasing and standardized device commands, significantly improving interpretation accuracy while maintaining system manageability through the use of pre-trained language models.
3Measurement precision
If semantic similarity comparison is used instead of keyword matching, then interpretation accuracy improves, but computational complexity increases
Solution Approach 1:
The patent performs preliminary action by using pre-trained large language models to generate embedding vectors for device operations in advance. These pre-computed embeddings are stored and ready for comparison, eliminating the need to perform complex semantic analysis during real-time operation execution, thus reducing computational complexity while maintaining high accuracy.
Data Source
AI summary
Systems or techniques that facilitate touchless operation of medical devices via large language models are provided. In various embodiments, a system can access, via a microphone associated with a medical device, a first natural language sentence spoken by a user of the medical device, wherein the first natural language sentence requests that the medical device perform an equipment operation. In various aspects, the system can: extract, from an encoder portion of a large language model, an embedding corresponding to the first natural language sentence; identify the equipment operation, by comparing the embedding to a plurality of embeddings respectively corresponding to a plurality of available equipment operations of the medical device, wherein the equipment operation is identified as whichever of the plurality of available equipment operations whose embedding is most similar to the embedding of the first natural language sentence; and instruct the medical device to perform the equipment operation.