Food Profile ML Model for Inventory Waste Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data processing limitations prevent effective personalization of food recipes and product estimations based on user inventories, leading to overestimation and waste due to inaccurate quantity assessments and expiration date management.
Innovation Solution
A computer-implemented method using a machine learning model to generate a food profile based on user inventory, identify missing ingredients for recipes, and predict desirable quantities, thereby optimizing food purchases and reducing waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data processing methods are used to manage food inventory, then the system is simple and easy to implement, but the measurement precision of food quantities and expiration dates is inaccurate leading to overestimation and waste
Solution Approach 1:
The patent replaces traditional mechanical data processing methods with machine learning models that automatically analyze food inventory data, predict quantities, and determine expiration dates. This substitution enables high measurement precision without requiring complex manual processing systems, as the ML models handle the complexity internally while providing accurate predictions to users.
2Loss of substance
If real-time food inventory tracking is implemented, then food waste is reduced through accurate monitoring, but the device complexity and data processing requirements increase significantly
Solution Approach 1:
The system enables self-service inventory tracking where the machine learning model automatically monitors food items, predicts quantities, and alerts users about expiration dates without requiring complex manual intervention. The model processes data in the background and provides actionable insights, reducing food waste through automated real-time monitoring while keeping the user interface simple.
Solution Approach 2:
The patent implements feedback mechanisms where the machine learning model continuously learns from user interactions and inventory data, improving its predictions over time. This feedback loop enables accurate real-time tracking by adjusting predictions based on actual usage patterns, thereby reducing food waste while managing system complexity through adaptive learning rather than rigid complex rules.
3Adaptability or versatility
If personalized food recipe recommendations are provided based on user inventory, then food personalization is optimized, but the data processing requirements and computational complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and analyzing user inventory data upfront, creating personalized food profiles that enable quick recipe recommendations. The machine learning model prepares prediction models in advance based on stored inventory data, so when users request recommendations, the system can quickly match recipes without performing complex real-time calculations, thus optimizing personalization while managing computational complexity.
Data Source
AI summary
Techniques are described with respect to a system, method, and computer product for improving product demand. An associated method includes generating a food profile based on a food inventory of a user the food inventory available to a computing device in a computer-accessible form; and identifying at least one food recipe based on the food profile, the food recipe available in a computer-accessible form. The method further includes determining a plurality of food items associated with the food inventory; identifying one or more absent food items associated with the at least one food recipe from the plurality of food items, the one or more absent food items being absent from the food inventory; and communicating to the user that the one or more absent food items are absent from the food inventory.