Facial Imaging Analytics Using Machine Learning Models
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Solution Overview
Problem
Current systems face challenges in accurately identifying and analyzing facial movements from imaging data, particularly in diagnosing facial defects, generating movement plans, and tracking defect correction progress.
Innovation Solution
A computer-implemented method that processes facial imaging data and user attribute data using a rules-based model or a machine learning model to generate user-specific analytics, including calorie burn and movement repetitions, and provides a graphical user interface for displaying these analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If facial imaging data is processed using traditional image analysis methods, then the system complexity is low, but the measurement precision of facial movements is insufficient
Solution Approach 1:
The patent replaces traditional mechanical/image processing methods with machine learning models and rules-based systems to analyze facial imaging data. The computing hardware processes facial images through trained machine learning models that automatically identify facial movements, defects, and progress without manual intervention, significantly improving measurement precision while managing system complexity through automated algorithms.
2Reliability
If comprehensive facial analysis is performed to diagnose defects and track progress, then the analytics quality improves, but the processing time increases
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models with extensive facial data before actual use. The models are prepared in advance to recognize various facial defects, movements, and progression patterns. During actual processing, these pre-trained models rapidly analyze new facial images, providing reliable defect diagnosis and progress tracking without requiring extensive processing time for each new case.
Solution Approach 2:
The system employs feedback mechanisms where the machine learning models continuously learn from processed facial data, improving their accuracy over time. The system provides feedback loops that refine defect identification and progress measurement, enhancing reliability while optimizing processing efficiency through learned patterns from previous analyses.
3Adaptability or versatility
If user-specific analytics are generated using multiple user attributes, then the personalization quality improves, but the data processing complexity increases
Solution Approach 1:
The patent applies local quality by tailoring the analysis and analytics to each specific user's attributes such as age, gender, and initial facial condition. The machine learning models adjust their processing and interpretation based on individual user characteristics, providing personalized defect diagnosis, movement analysis, and progress tracking. This localized approach enhances adaptability while the automated systems manage the complexity of processing multiple user-specific parameters.
Data Source
AI summary
In general, various aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for generating user-specific analytics from facial imaging data. In various aspects, a method is provided that comprises: receiving facial imaging data of a user; accessing user attribute data for the user; processing the facial imaging data and the user attribute data using at least one of a rules-based model or a machine learning model to generate a user-specific first data analytic for the user; generating a graphical user interface comprising an indication of the first data analytic; and providing the graphical user interface for display on a computing device.