Continuous Glucose Monitoring Outcome Predictions With Engagement Data

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Solution Overview

Problem

Current diabetes management relies on sporadic blood glucose readings, which limit the data availability for effective treatment recommendations, leading to inadequate health and engagement outcome predictions.

Innovation Solution

A computer-implemented method using a continuous glucose monitoring device and machine learning models to predict future glucose and engagement levels by analyzing glucose data alongside medication intake, diet, physical activity, and education activity, determining a glycemia risk index, and training models to identify patterns for improved health and engagement outcomes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If sporadic blood glucose readings are used for diabetes management, then device complexity is reduced, but measurement precision and data availability for treatment recommendations deteriorate

Engineering Contradiction:

Improvemonitoring system complexityVSAvoidglucose level prediction accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent implements continuous glucose monitoring that continuously measures glucose levels over time, transforming sporadic discrete readings into a continuous data stream. This continuous monitoring provides ample data for effective treatment recommendations and accurate predictions of health outcomes, resolving the contradiction between simplicity and precision by maintaining continuous measurement action.

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The patent introduces machine learning models as intermediaries that process continuous glucose monitoring data along with engagement data to generate predictions. These intermediary computational systems transform raw continuous data into actionable insights, enabling precise treatment recommendations while keeping the user interface relatively simple.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If continuous glucose monitoring with machine learning models is implemented, then prediction accuracy is improved, but device complexity and data processing requirements increase

Engineering Contradiction:

Improvehealth outcome prediction accuracyVSAvoidsystem architecture complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the monitoring system into distinct functional modules: continuous glucose monitoring device, engagement data collection system, machine learning prediction engine, and user interface. This segmentation allows each component to be optimized independently, managing overall system complexity while maintaining high prediction accuracy through specialized processing in each segment.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent uses machine learning models as intermediary processing layers between raw glucose data and treatment recommendations. These intermediary models aggregate and analyze continuous data streams, transforming complex raw data into simplified prediction outputs that improve accuracy while managing computational complexity through layered processing.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Use of energy by moving object

If sporadic readings are used, then data processing load is reduced, but the ability to provide effective treatment recommendations deteriorates

Engineering Contradiction:

Improvedata processing energy consumptionVSAvoidtreatment recommendation quality

Core Design Contradiction:

Use of energy by moving objectVSLoss of information

Solution Approach 1:

The patent implements continuous glucose monitoring that continuously collects glucose level data over extended periods, providing ample information for effective treatment recommendations. This continuous data collection prevents information loss by ensuring sufficient data is available for analyzing treatment effectiveness and making informed clinical decisions.

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The patent extracts only the most relevant features from continuous glucose monitoring data and engagement data for input into machine learning models. This feature extraction process reduces data processing load by focusing computation on critical parameters while maintaining the quality of treatment recommendations through selective use of the most informative data elements.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20250218598A1Systems and methods for continuous glucose monitoring outcome predictions

Publication Date: 2025.07.03 WELLDOC INC

AI summary

Methods and devices include predicting future glucose and engagement levels for a user by receiving the user's glucose levels collected by a continuous glucose monitoring (CGM) device over a time period, receiving engagement data associated with the user, wherein the engagement data are associated with the user's medication intake, diet, physical activity, laboratory results, and education activity, determining a first glycemia risk index (GRI) value, determining, using a machine learning model and responsive to the user's glucose levels and the engagement data collected over the time period, one or more predictions for future glucose levels for the user including a prediction that a future GRI value is greater than or less than the first GRI value, and determining, using the machine learning model and responsive to the user's engagement data collected over the time period, one or more predictions for future engagement levels.