Improvement Tool Recommendation Engine for Behavior Modification

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Solution Overview

Problem

Current methods for improving user performance and modifying behavior are largely manual and lack follow-up reinforcement, making permanent changes difficult, and are focused on individual tasks without a holistic approach.

Innovation Solution

A system that automatically generates recommendations for improvement tools based on user data, including sentiment and communication channel data, using machine-learned or rules-based models to provide timely and integrated suggestions through various interfaces.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual behavior modification tools are provided for individual tasks, then user performance improvement is achieved, but the effects are temporary and lack permanent behavior change

Engineering Contradiction:

Improve permanence of behavior modificationVSAvoid automated follow-up and reinforcement

Core Design Contradiction:

ReliabilityVSExtent of automation

Solution Approach 1:

The system implements continuous feedback loops by monitoring user behavior data, sentiment data, and communication patterns. The model generates ongoing recommendations and tracks user progress, providing reinforcement that converts temporary performance improvements into permanent behavior changes through repeated positive feedback and adaptive guidance.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system enables users to receive personalized, automated behavior modification guidance without manual intervention. The machine learning model autonomously analyzes user data, generates tailored recommendations, and provides continuous support, allowing users to self-improve through automated reinforcement rather than requiring manual coaching for each interaction.

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If individual-task-associated behavior modification tools are used, then specific task performance improves, but holistic behavior modification is not achieved

Engineering Contradiction:

Improve holistic behavior modification capabilityVSAvoid integrated system architecture

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system employs a universal machine learning model that handles multiple behavior modification functions simultaneously. The same model architecture processes diverse data types (sentiment, communications, scheduling, task management) and generates comprehensive recommendations across different user contexts, achieving holistic behavior modification through a single multi-functional system rather than separate specialized tools.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system merges multiple data sources and functionality into a unified behavior modification platform. By integrating sentiment analysis, communication monitoring, scheduling data, and task management into one cohesive system, the solution achieves holistic behavior modification while managing complexity through centralized data processing and unified recommendation generation.

Inventive Principle:

Principle #5Merging (Combining)

3Productivity

If frequent improvement tool recommendations are provided, then user performance and behavior modification are enhanced, but manual intervention requirements increase

Engineering Contradiction:

Improve frequency of improvement recommendationsVSAvoid manual intervention level

Core Design Contradiction:

ProductivityVSExtent of automation

Solution Approach 1:

The system replaces manual analysis and recommendation generation with an automated machine learning model. The model continuously processes user data, sentiment information, and contextual factors to automatically generate frequent, personalized improvement recommendations without requiring human intervention for each recommendation, thereby increasing productivity while reducing manual workload.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system implements continuous automated monitoring and recommendation generation. The machine learning model operates continuously to analyze user behavior patterns, generate improvement recommendations, and provide ongoing guidance without interruption or manual restart, enabling frequent recommendations while maintaining minimal manual intervention through sustained automated operation.

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS20240362537A1Improvement tool recommendation engine

Publication Date: 2024.10.31 STATE FARM MUTAL AUTOMOBILE INSURANCE COMPANY

AI summary

Described herein are systems and techniques to automatically and with minimal human intervention facilitate determining recommendations for improvement tools to improve user performance and/or modify user behavior based on a variety of data. An input data structure containing sentiment data for a user as well as any one or more pieces of data associated with the user may be provided as input to an improvement tool recommendation model that may be executed to generate improvement tool recommendations for the user. The recommendations may be provided in dedicated interfaces and/or integrated into existing communications and/or communications channels.