Machine Learning Cosmetic Treatment Planning with Facial Mesh Simulation

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Solution Overview

Problem

Individuals seeking cosmetic medical treatments face challenges in accessing tailored information about available treatment options, risks, and costs due to the lack of easily accessible resources that consider their unique facial and body features and preferences.

Innovation Solution

A system and method utilizing a camera module, computer vision, and machine learning neural networks to analyze a user's face or body, determine treatment areas, simulate treatment outcomes, and provide personalized recommendations on medical products and procedures, including volume and type, through a user device application.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If consultations with multiple doctors are conducted to gather comprehensive treatment information, then the completeness of treatment information is improved, but the time and effort required increases significantly

Engineering Contradiction:

Improvecompleteness of treatment informationVSAvoidtime and effort for consultations

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system creates a digital copy of the patient's facial features using camera scanning and mesh model mapping. This digital replica allows multiple treatment scenarios to be simulated and compared without requiring multiple physical consultations, thereby preserving complete treatment information while significantly reducing time investment.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system performs preliminary treatment planning and simulation before the patient commits to actual treatment. By using machine learning to predict treatment outcomes and generate personalized recommendations in advance, patients can make informed decisions without undergoing multiple trial consultations.

Inventive Principle:

Principle #10Preliminary action

2Ease of operation

If generic treatment information is provided to all patients, then the ease of information delivery is improved, but the relevance to individual patient needs deteriorates

Engineering Contradiction:

Improveease of information deliveryVSAvoidrelevance to individual patient needs

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The system analyzes specific local features of each patient's face using computer vision and mesh modeling, identifying unique anatomical characteristics, skin conditions, and aesthetic concerns. This localized analysis enables the generation of personalized treatment recommendations that address individual patient needs while maintaining ease of automated delivery.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The machine learning model adjusts treatment parameters based on individual patient data, including facial geometry, skin type, age, and aesthetic goals. By dynamically changing treatment parameters for each patient rather than applying generic protocols, the system maintains both ease of automated delivery and high relevance to individual needs.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If detailed facial analysis is performed to ensure accurate treatment recommendations, then the precision of treatment planning is improved, but the complexity of the system increases

Engineering Contradiction:

Improveprecision of treatment planningVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system replaces manual facial analysis by doctors with automated computer vision and machine learning algorithms. This substitution maintains high measurement precision in facial feature detection and treatment planning while reducing operational complexity, as the automated system handles the complex analysis without requiring manual intervention.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs self-analysis of facial features using onboard camera modules and processing capabilities. By enabling the device to automatically scan, map, and analyze facial geometry without external equipment or manual measurement tools, the system achieves high precision while minimizing the complexity of required hardware and操作流程.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250329429A1Systems and methods for generating cosmetic medical treatment recommendations via machine learning

Publication Date: 2025.10.23 AI AESTHETICS LLC

AI summary

The method for generating medical treatment recommendations comprises: scanning a relevant part among the face, head, and body of a user; mapping the scanned relevant part onto a mesh model to ensure consistent positioning; positioning the scanned relevant part in the right angle using the mesh model; detecting a set of features associated with the scanned relevant part using the mesh model; determining a treatment area that is needed to be treated based on the detected features associated with the scanned relevant part; capturing an image of the relevant part; determining at least one treatment option for the treatment area based on the captured image; simulating a treatment outcome for the determined treatment option, producing a simulated image treated with the determined treatment option; and displaying recommendation for the determined treatment option with the simulated image and relevant information.