Scalp Image Analysis System for Automated Diagnosis
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Solution Overview
Problem
Existing methods for diagnosing and treating scalp conditions are time-consuming, error-prone, and often result in unsatisfactory outcomes due to the complexity of scalp and hair types across different demographics, leading to difficulties in identifying and addressing endogenous and exogenous factors affecting scalp health.
Innovation Solution
A digital imaging and learning system that analyzes pixel data of a scalp region using a trained artificial intelligence-based scalp learning model to generate user-specific scalp classifications, enabling accurate identification and recommendation of treatments based on features like white sebum, irritation, acne, and scalp plugs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional empirical methods are used for scalp condition diagnosis, then users can attempt to experiment with various products, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces manual empirical experimentation with an automated digital imaging and machine learning system. The system uses computer vision technology to capture scalp images and automatically analyzes them using trained machine learning models, substituting the mechanical process of trial-and-error product testing with an automated digital diagnosis system that provides accurate results without time-consuming manual experimentation
Solution Approach 2:
The patent implements preliminary classification of scalp conditions through digital image analysis before product recommendation. The machine learning model pre-analyzes scalp images to identify specific conditions (such as dandruff, seborrheic dermatitis, or product buildup) and categorizes them into predefined classes, enabling users to receive targeted product recommendations without undergoing lengthy trial periods of empirical testing
2Reliability
If users empirically experiment with various products to address scalp conditions, then they may find treatments, but they risk causing negative side effects impacting scalp health
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model continuously refines its classifications based on trained data from multiple scalp images. The system provides feedback-driven product recommendations tailored to the specific diagnosed condition, reducing the risk of harmful side effects by ensuring users receive scientifically-backed treatment suggestions rather than random empirical trials that could worsen scalp health
Solution Approach 2:
The patent introduces a digital imaging and machine learning system as an intermediary between the user and product selection. This intermediary objectively analyzes scalp conditions and mediates the selection process by recommending products specifically suited to the diagnosed condition, eliminating the harmful element of blind empirical experimentation that could lead to negative side effects
3Ease of operation
If prior art methods are used for scalp diagnosis, then simple product trials can be conducted, but the methods are error-prone and may yield negative results
Solution Approach 1:
The patent enables self-service scalp diagnosis through a user-friendly mobile application that allows individuals to capture their own scalp images and receive automated analysis. The system maintains ease of operation by allowing users to simply take photos of their scalp, while the underlying machine learning model performs the complex analysis work, combining simplicity of use with high diagnostic accuracy
Solution Approach 2:
The patent replaces error-prone manual product trial methods with an automated machine learning-based diagnosis system. The system uses computer vision and trained models to objectively analyze scalp images and identify conditions, substituting the unreliable mechanical process of guessing or random product testing with a scientifically-grounded automated analysis that maintains ease of use while dramatically improving diagnostic accuracy
Data Source
AI summary
Digital imaging and learning systems and methods are described for analyzing pixel data of a scalp region of a user's scalp to generate one or more user-specific scalp classifications. A digital image of a user is received at an imaging application (app) and comprises pixel data of at least a portion of a scalp region of the user's scalp. A scalp based learning model, trained with pixel data of a plurality of training images depicting scalp regions of scalps of respective individuals, analyzes the image to determine at least one image classification of the user's scalp region. The imaging app generates, based on the at least one image classification, a user-specific scalp classification designed to address at least one feature identifiable within the pixel data comprising the at least the portion of a scalp region of the user's scalp.