Paint Color Recommendation System Using Sparse Regression
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Solution Overview
Problem
Current methods fail to assist users in selecting paint colors that match their subjective perception, as color perception is subjective and varies among individuals, leading to dissatisfaction with paint color choices made at stores.
Innovation Solution
A system and method that maps environmental and cognitive context data to a standard color space, using sparse regression to recommend pigment mixtures that align with the user's desired color perception, enabling accurate paint color recommendations across multiple vendors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional paint color selection methods are used, then the selection process is simple, but the color perception accuracy deteriorates due to subjective variability
Solution Approach 1:
The patent introduces an intermediary system comprising a mobile device with camera, processing unit, and database that mediates between the user's subjective color perception and the objective paint selection. The system captures images of the environment, processes them through algorithms that account for human color perception characteristics, and recommends paint colors that objectively match the user's subjective experience, thereby resolving the contradiction between simple selection and accurate perception.
2Measurement precision
If environmental and cognitive context data are collected, then color recommendation accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-collecting and storing cognitive context data (user preferences, behavioral patterns) and environmental data (lighting conditions, room characteristics) in databases before the actual paint selection process. The processing unit has pre-loaded algorithms that account for human color perception characteristics. When a user requests paint recommendations, the system quickly retrieves and processes this pre-prepared data, achieving high accuracy without overwhelming processing complexity during the interaction.
3Manufacturing precision
If sparse regression mapping is used, then pigment recommendation precision is improved, but computational requirements increase
Solution Approach 1:
The patent extracts only the most relevant features from the comprehensive environmental and cognitive context data through sparse regression mapping. Instead of processing all available data, the system identifies and extracts the key determinants of color perception (such as dominant lighting conditions, critical room features, essential user preferences) and uses only these extracted features for pigment recommendation. This reduces computational energy consumption while maintaining high precision in paint color matching.
Data Source
AI summary
A method for paint color recommendation. The method obtains measures of an environment to be painted and trains a learned model to input data received from customers including data representing each customer's initial color paint and pigment selection, and one or more of: a customer perceptual, a customer context, and environment measure (P/C/E data) to generate a sparse matrix. One or more paint vendors may then use the generated sparse matrix to determine a color pigment recommendation from a pigments color space for a customer. From a user selected color/pigment, and using the learned model, the method maps the selection, together with the user's P/C/E data back to the color/pigments space. User feedback representing a degree of satisfaction that the recommended color pigment applied to the user environment has matched the user's initial color paint and color pigment selection is elicited.