AI Grocery Cart Pre-filling with Hard and Soft Constraint Optimization

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Solution Overview

Problem

Existing online food distribution systems lack flexibility and accuracy in predicting and optimizing customer orders, relying heavily on customer input and failing to account for personal preferences, dietary restrictions, and inventory management, leading to inefficiencies and increased food waste.

Innovation Solution

Implementing an AI-driven system that predicts and optimizes recipe selection by using customer preferences, order history, and inventory data to generate and score recipe sets based on hard and soft constraints, ensuring compliance with customer preferences and inventory availability, and allowing for adjustments before fulfilling orders.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If the system requires customers to manually select grocery items, then customers have full control over their orders, but the system lacks accuracy in predicting customer needs and leads to increased food waste

Engineering Contradiction:

Improveorder accuracyVSAvoidcustomer convenience

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system enables self-service by allowing the AI to automatically predict and select grocery items for customers based on their order history and preferences, eliminating the need for manual item selection while maintaining customer control through review and modification capabilities

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system incorporates feedback mechanisms where customer modifications to AI-selected items are tracked and used to refine future predictions, improving order accuracy over time while maintaining ease of operation through iterative learning

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If the system provides limited recipe options based on basic customer input, then the system is simple to operate, but it lacks flexibility in accommodating customer preferences and dietary restrictions

Engineering Contradiction:

ImproveflexibilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments constraints into hard constraints (dietary restrictions, allergies) and soft constraints (preferences, trade-offs), allowing it to handle complex customer requirements systematically while maintaining operational simplicity through structured processing

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically adjusts recipe recommendations by changing parameters based on customer feedback and order history, enabling flexibility in accommodating preferences and restrictions without increasing perceived system complexity for the customer

Inventive Principle:

Principle #35Parameter changes

3Productivity

If the system does not account for inventory management in recipe selection, then recipe selection is faster and simpler, but it leads to increased food waste and reduced operational efficiency

Engineering Contradiction:

Improveoperational efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by pre-calculating recipe recommendations that account for inventory availability before customer ordering, enabling efficient operations by preparing optimized recipe sets in advance while managing complexity through automated preprocessing

Inventive Principle:

Principle #10Preliminary action

4Reliability

If the system generates multiple recipe sets with different constraints, then it can optimize for various customer preferences, but it increases computational complexity and processing time

Engineering Contradiction:

Improveconstraint complianceVSAvoidprocessing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system applies partial action by generating multiple recipe sets only when necessary to satisfy different constraint combinations, and uses scoring mechanisms to efficiently evaluate and select the best options, balancing reliability with acceptable processing time

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11587145B1System, method, and non-transitory computer-readable medium for artificial intelligence pre-filling grocery cart

Publication Date: 2023.02.21 HUNGRYROOT INC

AI summary

Systems and methods to predict and optimize automated recipe selection and item delivery for a customer. An item-selection server selects a plurality of predicted recipes based on customer preferences and order history. Hard and soft constraints are obtained to perform the optimization of the predicted recipes. The hard constraints define parameters that cannot be violated by a recipe or set of recipes, and soft constraints define parameters that expresses a tradeoff value associated with a recipe or set of recipes. Optimization includes selecting multiple sets of predicted recipes based on the hard constraints and scoring each recipe set based on the soft constraints, item inventory, and customer preferences. A specific set of recipes is selected for the order based on the scores, and the filling of the order is initiated for the customer with items associated with the selected set of recipes.