Multi-Stage ML Hair Profiling via Knowledge Graph Segmentation
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Solution Overview
Problem
The hair health industry lacks sufficient granular data for diverse groups, leading to biased hair care recommendations and inaccurate health assessments, particularly for underrepresented communities, where nuanced hair and skin conditions are not adequately represented, resulting in harmful styling practices and misdiagnosis of health issues.
Innovation Solution
A multi-stage machine learning process that discretizes semantically defined attributes and care practices by creating a knowledge graph, using computer vision to identify user-specific hair attributes and correlating them with suitable hair care products, thereby providing personalized and accurate recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine learning models are applied to available hair care data, then processing speed is maintained at acceptable levels, but measurement precision deteriorates due to lack of data granularity and representation accuracy
Solution Approach 1:
The patent segments hair attributes into discrete categories (porosity, elasticity, texture, length, thickness) and divides care practices into specific components (washing, conditioning, styling). This segmentation enables precise measurement and representation of each attribute independently, resolving the contradiction between data quantity and measurement precision by organizing existing data into granular, actionable categories.
Solution Approach 2:
The patent introduces a new dimension of analysis by mapping hair attributes to specific care practice components and products. This dimensional expansion allows the system to represent hair care information more comprehensively, transforming 1D attribute lists into multi-dimensional relationships between attributes, practices, and products, thereby improving measurement precision without requiring additional data collection.
2Measurement precision
If comprehensive hair care data is collected for diverse groups, then measurement precision improves for underrepresented communities, but device complexity increases due to multi-stage processing requirements
Solution Approach 1:
The patent performs preliminary actions by pre-defining discrete attribute categories and care practice components before processing user data. This pre-structuring of the analysis framework allows the multi-stage machine learning process to operate more efficiently, reducing the computational complexity required to achieve comprehensive representation accuracy for diverse groups.
Solution Approach 2:
The patent introduces an intermediary knowledge graph structure that mediates between raw data and final recommendations. This knowledge graph serves as a structured intermediate representation that simplifies the multi-stage processing by organizing relationships between attributes, practices, and products in a standardized format, thereby reducing the complexity burden while maintaining representation accuracy.
3Adaptability or versatility
If personalized hair care recommendations are provided based on limited data, then adaptability to individual needs improves, but reliability deteriorates due to insufficient data foundation
Solution Approach 1:
The patent creates a universal framework that can handle multiple hair types and conditions within a single system. By defining discrete, comprehensive attribute categories and their relationships to care practices, the system achieves multi-functionality that allows it to provide reliable personalized recommendations across diverse user groups without requiring separate models for each hair type or condition.
Solution Approach 2:
The patent incorporates feedback mechanisms where user responses to recommendations and product results are fed back into the system to refine future recommendations. This feedback loop allows the system to improve reliability over time by learning from actual user outcomes, compensating for any initial limitations in the training data while maintaining adaptability to individual needs.
Data Source
AI summary
Techniques for generating recommendations using machine learning with respect to semantic concepts defined in a knowledge graph. A hair profile is determined for a user based on inputs related to the user. Determining the hair profile includes extracting attributes of the user from the inputs using natural language processing, computer vision, or both, and identifying respective nodes for the extracted attributes in the knowledge graph. The knowledge graph is created via machine learning using population data including hair-related data in order to identify relationships between semantic concepts represented by nodes of the knowledge graph. The nodes include discrete properties such as individual hair attributes, ingredients of products, or otherwise discrete characteristics of factors that may affect a user's hair or related health conditions. A generalized recommendation is generated based on the hair profile. A personalized recommendation may be generated based on the generalized recommendation and progress logged by the user.