Battery Log Analysis for Electric Mover Fraud Detection

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Solution Overview

Problem

Existing electric mover rental services face challenges in detecting fraudulent use, particularly in joint usage scenarios, where stolen authentication information allows unauthorized users to operate electric vehicles, leading to potential overcharging and delayed detection.

Innovation Solution

An information processing method that acquires user IDs and log data from electric movers to estimate fraudulent use rates by analyzing time-series changes in battery discharge current, using learned models to identify unusual usage patterns and geographic features, enabling early detection of fraudulent activity.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If authentication information is used for joint use of electric movers, then service accessibility is improved, but vulnerability to fraudulent use increases

Engineering Contradiction:

Improvejoint use capabilityVSAvoidfraudulent use risk

Core Design Contradiction:

Adaptability or versatilityVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary analysis of usage patterns by collecting log data from sensors (accelerometer, gyroscope, microphone, camera) and comparing actual usage against pre-established legitimate usage patterns. This preliminary detection mechanism identifies fraudulent use before significant financial loss occurs, resolving the contradiction by maintaining joint use accessibility while proactively preventing fraud through pattern recognition and anomaly detection.

Inventive Principle:

Principle #10Preliminary action

2Device complexity

If fraudulent use detection is delayed until user reporting, then system complexity is reduced, but detection time increases

Engineering Contradiction:

Improvedetection system complexityVSAvoidfraud detection time

Core Design Contradiction:

Device complexityVSLoss of time

Solution Approach 1:

The system implements self-service fraud detection by automatically collecting sensor data from the electric mover (accelerometer, gyroscope, microphone, camera), processing this data through analysis units, and generating fraud probability assessments without requiring user intervention. The system serves itself by autonomously monitoring usage patterns and detecting anomalies, thereby reducing detection time while maintaining manageable complexity through automated workflows.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system establishes feedback loops where sensor data continuously flows to analysis units that compare actual usage against legitimate patterns, generating real-time fraud probability assessments. This feedback mechanism enables continuous monitoring and immediate detection of fraudulent behavior, significantly reducing detection time while keeping system complexity controlled through structured data processing pipelines.

Inventive Principle:

Principle #23Feedback

3Measurement precision

If detailed log data collection is implemented, then detection accuracy is improved, but data processing load increases

Engineering Contradiction:

Improvefraud detection accuracyVSAvoiddata processing energy

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system extracts only the essential and useful features from collected sensor data (accelerometer, gyroscope, microphone, camera) that are directly relevant to fraud detection. By filtering and selecting only critical data elements needed for pattern recognition, the system maintains high detection accuracy while significantly reducing the processing load and energy consumption associated with analyzing all raw sensor data.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20240403397A1Information processing method, information processing device, and non-transitory computer readable storage medium

Publication Date: 2024.12.05 PANASONIC INTELLECTUAL PROPERTY CORP OF AMERICA

AI summary

A server acquires a user ID proving a user to be an authenticated user for a vehicle and log data indicative of a use history of a storage battery associated with the user ID, estimates on the basis of the log data a fraudulent use rate of the vehicle by way of the user ID, and outputs a result of the estimation.