LLM-Based Insulin Pump Data Parsing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for managing diabetes, particularly for insulin delivery, face inefficiencies in collecting and categorizing user data, such as food consumption and activity levels, which are time-consuming and inaccurate due to limitations in traditional text recognition and keyword categorization.
Innovation Solution
Utilizing a large language model (LLM) to process naturally spoken user data, applying rules and constraints to parse this data into categories like food and activity, enabling automatic generation of insulin delivery parameters based on food and activity data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional text recognition algorithms and keyword categorization systems are used to collect user data, then the system structure remains simple, but the data collection efficiency and accuracy deteriorate
Solution Approach 1:
The patent replaces traditional mechanical text recognition algorithms and keyword categorization systems with a large language model-based natural language processing system. This substitution enables the system to understand and categorize user data with human-like comprehension, dramatically improving data collection efficiency and accuracy while maintaining manageable system complexity through cloud-based deployment.
2Productivity
If users manually enter user data into records, then data accuracy can be maintained, but the time consumption and operational burden increase significantly
Solution Approach 1:
The system enables self-service by automatically capturing, transcribing, and categorizing user data through natural language processing. Users simply speak or input data naturally, and the large language model automatically processes, categorizes, and stores the information without requiring manual data entry or user intervention in the categorization process, eliminating time loss while maintaining accuracy.
3Productivity
If automated analysis systems are implemented to process user data, then data processing speed improves, but the accuracy and understanding of contextual information deteriorate
Solution Approach 1:
The patent fundamentally changes the processing parameters from traditional keyword-based pattern matching to large language model-based semantic understanding. This parameter change enables the system to process data rapidly while simultaneously achieving high accuracy in categorization by comprehending contextual meaning, user intent, and nuanced information that traditional automated systems miss.
Data Source
AI summary
Systems and methods are provided including insulin pump systems for determining certain information about a user of a wearable insulin pump based on naturally spoken user data by applying the user data to a large language model (LLM) or other machine learning model to parse the user data into categories. For example, certain rules and/or constraints may be provided to the LLM and may cause the LLM or machine learning model to parse the user data into defined categories that may be used for updating a user record. Categories may include food consumption, activities, sleep, and/or pump operation information, for example. Additional data may be determined from the parsed data such as caloric information, exercise duration, sleep information and the like. The parsed categorized data and/or additional data may be used to adjust operation of the insulin delivery pump.