AI Product Image Analysis for Personalized Oral Care Selection
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Solution Overview
Problem
Consumers face confusion in selecting oral care products due to a wide array of options and lack of knowledge about their specific oral care concerns and preferences, leading to inconsistent oral care routines.
Innovation Solution
A digital imaging and AI-based system analyzes product images and user parameters to provide personalized oral care product and routine recommendations, leveraging a product-based learning model, risk factor model, and recommender model to identify products and predict user-specific needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a wide array of oral care products is available, then product variety and options increase, but consumer confusion and difficulty in selecting the right product increase
Solution Approach 1:
The patent introduces an AI-based recommendation system as an intermediary between consumers and oral care products. The system analyzes consumer-specific parameters (oral conditions, preferences, demographics) and matches them with appropriate products from the wide array of available options, thereby resolving the contradiction by mediating the selection process without reducing product variety
Solution Approach 2:
The system enables consumers to independently determine their oral care needs through self-assessment questionnaires and automated product recommendations. Users input their own parameters and receive personalized recommendations without requiring assistance from dental professionals or extensive research, making the selection process easier while maintaining product variety
2Ease of operation
If consumers lack knowledge about oral care conditions and product attributes, then product selection becomes more difficult, but the complexity of consumer education and information provision increases
Solution Approach 1:
The patent replaces the traditional mechanical approach of consumer education (providing extensive information about oral care conditions and product attributes) with an AI-based system that automatically analyzes consumer inputs and generates personalized recommendations. The system handles the complexity of information processing internally while presenting simplified, actionable recommendations to users
Solution Approach 2:
The system transforms the complex multidimensional problem of oral care product selection into a parameter-based matching process. By defining specific consumer parameters (age, oral conditions, preferences) and product parameters (formulation, ingredients, benefits), the system simplifies the selection process while maintaining comprehensive product information
3Reliability
If personalized product recommendations are provided, then consumer satisfaction and oral care adherence improve, but the complexity of the recommendation system increases
Solution Approach 1:
The system performs preliminary actions by pre-defining oral care conditions, product attributes, and recommendation criteria before the consumer interaction. The AI model is pre-trained with extensive product data and consumer parameter definitions, enabling it to quickly generate personalized recommendations without requiring complex real-time analysis during user interaction
Solution Approach 2:
The system uses digital copies and representations of consumer oral conditions and product characteristics to perform analysis. Instead of physically analyzing oral health, the system processes digital inputs (user responses, images) and creates virtual product recommendations, reducing physical complexity while maintaining personalization
Data Source
AI summary
Digital imaging and artificial intelligence (AI)-based systems and methods are described for analyzing product images and making product recommendations. An imaging application (app) receives a set of digital image(s) comprising pixel data depicting a product. A product-based learning model is applied to the pixel data in order to predict one or more product identifiers corresponding to one or more products depicted within pixel data of a plurality of training images. One or more risk factors associated with the user are predicted based on applying a risk factor model to personal parameters of the user and the product identifier. One or more products and/or one or more routines are recommended, and output, for the user, based on applying a recommender 10 model to the product identifier, the personal parameters, the risk factors, one or more goals associated with the user, and, optionally, one or more preferences associated with the user.