Health Query Analysis Using LLMs for Actionable Metric Explanations
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Solution Overview
Problem
Users find it difficult and time-consuming to locate specific information related to their health data, activities, and nutrition, necessitating improved methods for processing and analyzing health data.
Innovation Solution
A computing system utilizing machine-learned models, particularly large language models (LLMs), processes user queries to determine topics, key metrics, and analytical techniques, generating natural language explanations and visualizations of health data to facilitate understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually review health data information, then they can understand their health metrics, but it is difficult and time-consuming to locate specific information
Solution Approach 1:
The patent introduces an intermediary system comprising a query processor and natural language generator that mediates between the user's information needs and the raw health data. The system translates user queries into analytical operations and generates natural language responses with visualizations, eliminating the need for users to manually search through data while maintaining ease of information access
Solution Approach 2:
The system enables self-service by automatically processing user queries and generating comprehensive health data analyses without requiring manual intervention. The query processor autonomously interprets user intent, performs appropriate analytical operations on health metrics, and delivers customized responses with visualizations, allowing users to efficiently locate specific information independently
2Loss of information
If the system provides comprehensive health data analysis, then user comprehension is enhanced, but the system complexity increases
Solution Approach 1:
The patent segments the complex health data analysis system into distinct functional modules: a query processor for interpreting user intent, an analysis engine for performing analytical operations on health metrics, and a natural language generator for creating comprehensible responses. This modular segmentation manages system complexity by organizing functions into independent, manageable components while delivering comprehensive health data analysis
Solution Approach 2:
The natural language generator acts as an intermediary that translates complex analytical results into user-comprehensible language with visualizations. This intermediary layer shields users from system complexity while ensuring complete information delivery, converting intricate health metric analyses into accessible insights without requiring users to understand the underlying complex processing
Data Source
AI summary
Methods, systems, devices, and non-transitory computer readable media for processing health data are provided. The disclosed technology can include receiving queries comprising health information. Based on inputting the queries into one or more machine-learned models, topics of the queries, key metrics of the health data, and analytical techniques based on the topics and the key metrics can be determined. Based on performing the analytical techniques on at least the health data comprising the key metrics, analytical results can be determined. Based on inputting the analytical results into the one or more machine-learned models, an analysis comprising explanations of the analytical results can be generated. Furthermore, visualizations based on the analysis can be generated.