Smart Cart Error Prediction for Maintenance Priority Assignment

Solution Overview

Problem

Smart cart systems experience device errors that degrade performance or render them inoperable, necessitating human intervention for maintenance, which can be inefficient and disruptive.

Innovation Solution

A cart management system generates an error priority assignment based on device error predictions using a machine-learning model, optimizing maintenance schedules to minimize overall device error likelihood across a set of smart carts.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If human intervention is used for maintenance, then device errors can be addressed, but maintenance efficiency is low and disruption is high

Engineering Contradiction:

Improvedevice error addressingVSAvoidmaintenance efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs preliminary actions by predicting device errors before they occur and automatically scheduling maintenance tasks in advance. The machine learning model analyzes sensor data to forecast potential failures, and the system proactively assigns maintenance tasks to service technicians, eliminating the need for reactive human intervention and improving maintenance efficiency.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system enables self-service through automated error prediction and maintenance scheduling. The machine learning model continuously monitors device data and autonomously generates maintenance assignments without requiring constant human oversight. Service technicians receive automated notifications with maintenance instructions, allowing them to perform repairs efficiently with minimal coordination overhead.

Inventive Principle:

Principle #25Self-service

2Reliability

If reactive maintenance is performed, then device errors are addressed after occurrence, but system downtime increases

Engineering Contradiction:

Improvedevice error correctionVSAvoidsystem downtime

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by predicting device errors before they occur and automatically scheduling maintenance tasks in advance. The machine learning model analyzes sensor data to forecast potential failures, and the system proactively assigns maintenance tasks to service technicians, eliminating the need for reactive human intervention and improving maintenance efficiency.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements continuous feedback loops where sensor data from smart cart devices is continuously monitored and fed into the machine learning model. The model processes this feedback to update its predictions and adjust maintenance schedules dynamically, ensuring that maintenance activities are timed optimally to minimize downtime while maintaining high reliability.

Inventive Principle:

Principle #23Feedback

3Ease of manufacture

If maintenance is scheduled without optimization, then simplicity is maintained, but overall device error likelihood increases

Engineering Contradiction:

Improvemaintenance scheduling simplicityVSAvoidoverall device error likelihood

Core Design Contradiction:

Ease of manufactureVSReliability

Solution Approach 1:

The system enables self-service through automated error prediction and maintenance scheduling. The machine learning model continuously monitors device data and autonomously generates maintenance assignments without requiring constant human oversight. Service technicians receive automated notifications with maintenance instructions, allowing them to perform repairs efficiently with minimal coordination overhead.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system applies parameter changes by dynamically adjusting maintenance schedules based on real-time device conditions and predicted error probabilities. The machine learning model analyzes multiple parameters including sensor data, device usage patterns, and historical failure rates to optimize maintenance timing. This allows the system to maintain simplicity in scheduling while improving reliability through data-driven optimization.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20260079804A1Device error priority assignment generation for smart cart systems

Publication Date: 2026.03.19 MAPLEBEAR INC

AI summary

A cart management system generates an error priority assignment for smart cart systems based on device error predictions for those smart cart systems. An error priority assignment is an assignment of the relative priority of servicing or providing maintenance to a set of smart cart systems. To generate the error priority assignment, the cart management system applies an error detection model to cart data received from the set of smart cart systems. The cart data has measurements captured by sensors coupled to the smart cart systems, and the error detection model uses the cart data to generate device error predictions. Each of these predictions represents a likelihood that a smart cart system will experience a device error within some time period. The cart management system uses the device error predictions to generate the error priority assignment and selects which smart cart system to service based on the error priority assignment.