Smart Cart Content Selection for Item Removal Edge Cases

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Solution Overview

Problem

Smart cart systems fail to effectively account for edge cases in user interactions, limiting their functionality beyond detecting new item additions.

Innovation Solution

A smart cart system leverages sensor data and machine-learning models to detect item removals, identify the removed item, and present relevant content based on user interactions, using cameras, load sensors, and machine-learning models to enhance user experience.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If smart cart systems are configured for primary use cases (detecting new item additions), then the core functionality is simple and reliable, but the system fails to account for edge cases (item removals) and cannot provide comprehensive user interaction tracking

Engineering Contradiction:

Improvecapability to detect various user interactionsVSAvoidsystem configuration complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The smart cart system is configured to perform multiple functions: detecting both item additions and item removals using the same sensor and machine learning infrastructure. The load sensors and item identification models are universally applied to detect any item interaction (addition or removal) from the cart, eliminating the need for separate detection systems for different interaction types.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

Instead of only detecting item additions (the conventional approach), the system inverts the detection logic to also identify item removals. By monitoring weight changes and analyzing item identification data, the system detects when items are removed from the cart by identifying the absence of previously detected items, thereby capturing edge cases that were previously overlooked.

Inventive Principle:

Principle #13The other way round (Inversion)

2Loss of information

If the smart cart system uses multiple sensors and machine-learning models to detect item removals and provide personalized content, then user interaction tracking is comprehensive, but the computational processing requirements and system resource consumption increase

Engineering Contradiction:

Improvecompleteness of user interaction dataVSAvoidcomputational energy consumption

Core Design Contradiction:

Loss of informationVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary actions by continuously monitoring load sensor data and capturing images to build a timeline of item interactions. Item identification models are applied in real-time to detect when items are added or removed, and this pre-processed interaction data is stored for later content personalization, reducing the computational burden during content generation phases.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses an intermediary approach by introducing a content recommendation engine that acts as a mediator between item interaction detection and content delivery. This engine processes the interaction data, identifies patterns, and generates personalized content recommendations, thereby distributing computational tasks and optimizing resource utilization across different system components.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If the smart cart system identifies candidate items based on location sensor data and computes presentation scores using machine-learning models, then content selection is highly personalized, but the time required to process and select content increases

Engineering Contradiction:

Improveaccuracy of content selectionVSAvoidcontent processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system applies partial action by computing presentation scores for only the most relevant candidate items rather than all possible items. Location sensor data is used to identify items in proximity to the cart, and the machine learning model computes scores for this filtered subset of candidates, thereby reducing processing time while maintaining content selection accuracy for the most likely recommendations.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system dynamically adjusts content selection based on real-time context. As the user moves the cart through different store locations, the system updates candidate item lists and re-ranks them using location-aware machine learning models. This dynamic re-ranking process optimizes content selection accuracy adaptively without requiring complete re-processing of all items, thus balancing precision and processing time.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20260065253A1Computer-Enabled Cart System Leveraging Machine Learning Models for Content Selection Based on Sensor Data Describing User Interactions

Publication Date: 2026.03.05 MAPLEBEAR INC

AI summary

A smart cart system accounts for edge cases in user interactions by leveraging sensor data and machine-learning models of a smart cart system. For example, a smart cart system uses sensor data to detect when a user removes an item from the smart cart system and presents content to the user on a display of the smart cart system based on the removed item. The smart cart system captures images of the storage area and applies an item identification model to the images to identify the item removed from the storage area. The smart cart system identifies a set of candidate items based on location sensor data describing a location of the smart cart system when the item was removed and computes presentation scores for each of the set of candidate items based on item data for each item the removed item.