Phenotype-Driven Digital Twin for Real-Time Obesity Interventions

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Solution Overview

Problem

Existing weight loss therapies fail to account for individual metabolic variations and dynamically changing physiological measurements, leading to ineffective outcomes and lack of adherence due to the absence of personalized and real-time feedback.

Innovation Solution

A phenotype-driven digital twin model that integrates biological processes and employs real-time data assimilation to simulate a user's metabolic system, using machine learning techniques for state estimation and prediction, enabling personalized optimization of pharmacological and digital therapeutic interventions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If standardized weight loss regimens are used, then implementation is simple and cost-effective, but effectiveness decreases due to individual metabolic variations

Engineering Contradiction:

ImproveEase of implementing weight loss programVSAvoidEffectiveness of weight loss therapy

Core Design Contradiction:

Ease of manufactureVSReliability

Solution Approach 1:

The patent implements dynamic treatment regimens that automatically adjust based on real-time physiological data from wearables and continuous glucose monitors. The system transitions from static, standardized protocols to dynamic, adaptive protocols that respond to individual metabolic states, thereby maintaining simplicity of implementation while significantly improving effectiveness through real-time personalization.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system enables self-service personalization where the treatment plan automatically adapts based on data collected from the user's own physiological measurements. The algorithm processes continuous data from wearables and adjusts recommendations without requiring manual intervention from healthcare providers, thus maintaining ease of implementation while achieving high effectiveness through individualized adaptation.

Inventive Principle:

Principle #25Self-service

2Reliability

If personalized treatment plans are created, then effectiveness improves by accounting for individual variations, but complexity increases due to need for multiple assessments

Engineering Contradiction:

ImproveEffectiveness of weight loss therapyVSAvoidComplexity of treatment program

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent employs a multi-functional platform that integrates data collection from various wearables, continuous glucose monitoring, dietary tracking, and treatment delivery into a single unified system. This universal platform handles multiple functions (data acquisition, processing, analysis, and personalized recommendation generation) through a common architecture, reducing overall system complexity while enabling comprehensive personalization.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system replaces complex manual assessment procedures with automated algorithmic processing of physiological data. Instead of requiring multiple manual assessments by healthcare providers, the system uses computational algorithms to continuously analyze wearable data and generate personalized treatment recommendations, thereby reducing operational complexity while maintaining high effectiveness.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If continuous monitoring and real-time feedback are implemented, then adherence improves through engagement, but use of energy and data requirements increase

Engineering Contradiction:

ImproveAdherence to treatment programVSAvoidEnergy consumption of monitoring system

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system implements partial monitoring strategies where continuous high-frequency data collection is applied only to critical physiological parameters (such as glucose levels and heart rate) while other parameters are monitored at lower frequencies or only under specific conditions. This selective approach maintains high adherence through sufficient engagement feedback while reducing overall energy consumption and data processing requirements compared to exhaustive continuous monitoring of all parameters.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20260057988A1Real-time delivery of dynamic, personalized digital therapies to users for addressing obesity conditions using phenotype-driven digital twin models

Publication Date: 2026.02.26 CLICK THERAPEUTICS INC

AI summary

Provided herein are systems and methods for presenting interventions to address obesity in users related to using digital twin model. A computing system may receive measurements of a metabolic system of a user. The computing system can then apply the measurements to a function to generate an output. The computing system can update, using the output from the function, at least one of a plurality of weights of a digital twin of the user. The computing system can generate using the digital twin, a metric indicative of an obesity condition in the metabolic system of the user. The computing system can identify an intervention for the obesity condition based on the metric and provide to a user device, an instruction identifying the intervention for the metabolic system of the user. The computing system can improve efficacy of the medication that the user is taking in concurrence to address their condition.