Multimodal Query Architecture for Medical Procedure Awareness
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Solution Overview
Problem
Conventional systems fail to provide sufficient awareness of medical procedures in operating rooms, limiting efficiency and effectiveness due to the complexity of the environment and actions involved.
Innovation Solution
A multi-modal query and response architecture that processes data of different formats, including video, audio, text, and metadata, using machine learning models to analyze and generate responses to queries about medical procedures, enhancing procedural awareness and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional systems are used to monitor medical procedures, then device complexity is reduced, but procedural awareness and analysis capability are insufficient
Solution Approach 1:
The patent combines multiple data modalities (video, audio, text, sensor data) into a unified multi-modal processing system. Different data sources are integrated and processed together through a common architecture that uses large language models and neural networks to achieve comprehensive procedural awareness, resolving the contradiction by merging information sources rather than using separate conventional systems
Solution Approach 2:
The patent introduces a multi-modal processing system with large language models as an intermediary between raw medical procedure data and actionable insights. This intermediary layer processes and synthesizes information from multiple sources, transforming complex multi-modal data into meaningful procedural awareness and analysis capabilities
2Loss of information
If multi-modal data processing is implemented, then procedural insights are improved, but processing time and computational resources increase
Solution Approach 1:
The patent employs pre-trained large language models and neural networks that have been previously trained on extensive medical data. These pre-trained models can process multi-modal data more efficiently during actual procedure analysis, as the heavy computational lifting has already been done during the pre-training phase, reducing real-time processing requirements
3Productivity
If conventional single-modal analysis is used, then system simplicity is maintained, but analytical capability and efficiency are limited
Solution Approach 1:
The patent creates a universal multi-modal processing architecture that can handle multiple data types (video, audio, text, sensor data) through a single integrated system. The large language models and neural networks are designed to process diverse modalities uniformly, enabling the system to perform multiple analytical functions simultaneously and improve procedural efficiency across different surgical contexts
Data Source
AI summary
Aspects of this technical solution can receive a text prompt from a user, generate, based at least in part on a plurality of sets of data associated with at least one medical procedure, an output corresponding to the text prompt, where each of the plurality of sets of data has a different modality, where the plurality of sets of data comprises depth data, and provide the output for display.