AI Exception Handling in Consumer Electronics

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Solution Overview

Problem

Consumer electronic devices often encounter unhandled exceptions, leading to errors or malfunctions that require user intervention or service center involvement, which is time-consuming and inconvenient.

Innovation Solution

An AI service-based system using a neural network model to detect and resolve unhandled exceptions in consumer electronic devices by determining the cause of the issue and generating instructions for resolution, allowing for automatic correction without user input.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional exception handling routines are used in CE devices, then common errors can be resolved, but unhandled exceptions require service center intervention which is time-consuming

Engineering Contradiction:

Improveexception handling capabilityVSAvoidtime for service center intervention

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent implements self-service by enabling the CE device to automatically detect, diagnose, and resolve unhandled exceptions through integrated AI/ML models. The device autonomously collects error data, determines exception causes, generates resolution instructions, and applies fixes without requiring service center intervention, thereby eliminating time loss while maintaining high reliability.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical service center intervention process with an automated AI/ML-based exception handling system. Instead of manual diagnosis and repair procedures, the system uses machine learning models to automatically analyze exception data, identify root causes, and implement resolutions, substituting human-driven mechanical processes with intelligent automation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Extent of automation

If AI service-based exception handling is implemented, then autonomous resolution of unhandled exceptions is achieved, but device complexity increases

Engineering Contradiction:

Improveautonomous exception handlingVSAvoidsystem architecture complexity

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The patent applies universality by designing a multi-functional exception handling module that integrates data collection, AI/ML model execution, exception diagnosis, and resolution implementation within a single unified system. This modular approach enables autonomous handling of diverse exception types without proportionally increasing overall device complexity, as the same infrastructure serves multiple functions.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent introduces an intermediary exception handling module that acts as a mediator between the device's operational systems and the AI/ML processing infrastructure. This intermediary layer manages the complexity by providing a standardized interface for exception data collection, model invocation, and resolution application, thereby shielding the rest of the device architecture from the complexities of AI/ML integration.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Ease of operation

If conventional exception handling is used, then device structure remains simple, but users must rely on service centers for unhandled exceptions

Engineering Contradiction:

Improveuser convenienceVSAvoidexception handling system

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

Inventive Principle:

Principle #25Self-service

4Productivity

If AI service-based handling is implemented, then immediate exception resolution is achieved, but more processing resources are required

Engineering Contradiction:

Improveexception resolution speedVSAvoidprocessing resource consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The patent applies partial action by implementing selective exception handling where the AI/ML models are invoked only when conventional exception handling routines fail to resolve an exception. The system first attempts standard handling procedures, and only activates the resource-intensive AI/ML processing for unhandled cases, thereby achieving fast resolution for critical exceptions while minimizing overall processing resource consumption.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12020169B2Artificial intelligence (AI) service-based handling of exceptions in consumer electronic devices

Publication Date: 2024.06.25 SONY GROUP CORP

AI summary

A server for artificial intelligence (AI) service-based handling of exceptions in consumer electronic (CE) devices is provided. The server stores a neural network model that provides an AI service to handle exceptions in a plurality of CE devices. The server detects a new exception in an in-device system function of an in-device system of a first CE device and determines a cause of trigger of the new exception in the in-device system function of the in-device system based on the AI service and a plurality of different parameters of the first CE device. The server generates a first instruction to configure the in-device system specific to the new exception based on the AI service and the cause of trigger of the new exception. The server controls the in-device system in accordance with the first instruction based on the AI service such that the in-device system function is restored.