Automated Assistance Generation via Reward Estimation

Solution Overview

Problem

Modern interactive electronic devices require assistance for users with varying learning curves and backgrounds, making personalized and cost-effective support challenging, necessitating an automated assistance strategy.

Innovation Solution

A generating apparatus and device that utilize a processor and computer-readable medium to create a reward estimation model, decision-making model, and assistance strategy, selecting and presenting assistance based on user input history and behavior analysis, balancing exploration and exploitation of assistance forms.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If personal assistance is provided to help users learn electronic devices, then user support effectiveness is improved, but time consumption and cost increase

Engineering Contradiction:

Improveuser support effectivenessVSAvoidtime consumption

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables automated self-service assistance by analyzing user input history and behavior patterns to automatically generate personalized assistance strategies, eliminating the need for manual personal assistance while maintaining support effectiveness

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual personal assistance with an automated computational system that uses reward estimation models and decision-making algorithms to generate assistance strategies, substituting human effort with automated processing

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If personal assistance is provided to help users learn electronic devices, then user support effectiveness is improved, but cost increases

Engineering Contradiction:

Improveuser support effectivenessVSAvoidcost

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system enables automated self-service assistance by analyzing user input history and behavior patterns to automatically generate personalized assistance strategies, eliminating the need for manual personal assistance while maintaining support effectiveness

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual personal assistance with an automated computational system that uses reward estimation models and decision-making algorithms to generate assistance strategies, substituting human effort with automated processing

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If automated assistance is generated using reward estimation models and decision-making models, then assistance efficiency is improved, but device complexity increases

Engineering Contradiction:

Improveassistance efficiencyVSAvoidmodel complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system divides the assistance generation process into distinct modular components: reward estimation model, decision-making model, and assistance generation module, allowing each component to be developed and optimized independently while managing overall complexity

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system uses feedback from user responses to assistance and input history analysis to continuously refine and personalize assistance strategies, improving efficiency through adaptive learning while maintaining manageable complexity through iterative optimization

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS10878337B2Assistance generation

Publication Date: 2020.12.29 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

An assistance strategy may be generated with a generating apparatus including a processor, and one or more computer readable mediums collectively including instructions that, when executed by the processor, cause the processor to create a reward estimation model for estimating a reward for assisting at least one subject by analyzing a history of input by the subject, create a decision making model including a plurality of forms of assistance and estimated rewards for each form of assistance based on the reward estimation model and the history of input by the subject, and generate an assistance strategy based on the decision making model.