AI Image-Based Product Dosing Feedback for Oral Care
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Solution Overview
Problem
Consumers face challenges in accurately determining and adhering to proper product dosing for oral care products due to unclear instructions and difficulty in tracking dosage changes over time, leading to underdosing or overdosing, which can impact clinical efficacy and safety.
Innovation Solution
A digital imaging and AI-based system that analyzes product dosing by using a dosing learning model to compare actual product dosage in images with target dosages, providing real-time feedback through augmented reality overlays, and adjusting dosing accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If consumers rely on product instructions for dosing guidance, then they can obtain usage information, but the instructions are often unclear or illegible making it difficult to determine proper dosage
Solution Approach 1:
The patent introduces an intermediary system consisting of a mobile device with imaging capabilities and AI processing that mediates between the product and the consumer. The system captures images of the product, processes them through trained AI models to extract dosing information, and presents this information in a clear, actionable format. This intermediary resolves the contradiction by transforming unclear physical instructions into clear digital guidance without requiring consumers to directly read or interpret product labeling.
2Reliability
If consumers manually track dosage changes over time, then they can monitor product usage, but this requires significant effort and time making it difficult to maintain accurate tracking
Solution Approach 1:
The system implements self-service by automatically capturing images of the product at different time points and autonomously processing these images through AI models to determine dosage changes. The trained machine learning models automatically extract relevant information, compare it against reference data, and generate feedback without requiring consumer intervention. This automated self-service approach maintains high tracking reliability while eliminating the time burden from consumers.
Solution Approach 2:
The system incorporates continuous feedback loops where the mobile device captures images, the AI processing generates dosage analysis, and this feedback is presented to the consumer in real-time or near-real-time. The feedback mechanism includes visual indicators and notifications that guide consumers on proper dosage adjustments, creating a closed-loop system that maintains accuracy without requiring manual tracking efforts.
3Ease of operation
If product dosing instructions are made more detailed and clear, then consumers can understand proper usage better, but this increases the complexity of product labeling and instructions
Solution Approach 1:
The system segments the complex task of dosing determination into multiple manageable components: image capture, AI processing, data extraction, comparison with reference values, and feedback generation. By breaking down the complex information processing into discrete segments handled by the AI system, the patent eliminates the need for complex product labeling while maintaining ease of operation. Each segment is processed independently by the machine learning models, transforming a potentially overwhelming information task into a simple image capture and feedback reception process for consumers.
4Productivity
If consumers use more product than recommended, then they may perceive better results, but this leads to overdosing which can cause safety issues and reduced clinical efficacy
Solution Approach 1:
The system implements real-time feedback that monitors product usage and compares it against clinically determined optimal dosages. The AI processing continuously analyzes images to determine actual dosage levels and provides immediate feedback when usage deviates from recommended amounts. This feedback mechanism includes visual indicators and notifications that guide consumers to adjust their usage, ensuring they achieve optimal results without exceeding safe dosage limits. The feedback loop creates a self-correcting system that prevents both underdosing and overdosing.
Data Source
AI summary
Digital imaging and artificial intelligence (AI)-based systems and methods are described for analyzing pixel data of a product to determine product dosing. A product identifier of a product is detected, and a dosing application (app) receives a set of digital image(s) comprising pixel data depicting a dosage of the product and at least one of: (a) a product appliance configured to apply the product, or (b) a product implement configured to receive the product. An analysis is generated comprising a dosage comparison comparing the dosage of the product to a target dosage defining an expected dosage of the product at a first-time state. A feedback indication is output designed to address at least one feature identifiable within the pixel data comprising the dosage of the product.