User Feedback Prioritization for Automated Process Enhancement

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Solution Overview

Problem

Existing systems fail to effectively implement changes based on user feedback, leading to a gap between user input and product/service enhancements.

Innovation Solution

An apparatus and method utilizing a processor and memory to prompt users, receive inputs, determine response modules, categorize response timings, generate importance scores, and identify modification targets through machine learning, enabling automated process enhancements.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If user feedback is collected through manual analysis, then understanding of user needs is achieved, but time consumption and resource requirements increase significantly

Engineering Contradiction:

Improveunderstanding accuracyVSAvoidanalysis time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical analysis with an automated machine learning system that processes user feedback. The system uses natural language processing and classification algorithms to automatically analyze feedback content, extract key insights, and generate actionable recommendations, eliminating the need for manual reading and interpretation while maintaining or improving analysis accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by automatically processing user feedback without requiring manual intervention. The machine learning model autonomously categorizes feedback, identifies patterns, and generates improvement recommendations, allowing the organization to continuously improve based on user input without dedicating manual resources to the analysis process.

Inventive Principle:

Principle #25Self-service

2Loss of information

If comprehensive user feedback analysis is performed manually, then detailed insights are obtained, but the complexity and cost of the system increase

Engineering Contradiction:

Improvefeedback information completenessVSAvoidsystem complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent replaces complex manual analysis processes with an automated machine learning system. The system handles information extraction, categorization, and pattern recognition through algorithmic processing, reducing the need for complex manual procedures while maintaining comprehensive information capture through automated natural language processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The machine learning model acts as an intermediary between raw user feedback and actionable insights. It automatically processes the feedback through multiple layers of analysis including sentiment analysis, topic modeling, and priority classification, transforming unstructured data into structured recommendations without requiring direct human intervention at each processing stage.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Adaptability or versatility

If manual processing of user feedback is used, then flexibility in handling diverse feedback types is maintained, but productivity and response speed decrease

Engineering Contradiction:

Improvefeedback handling flexibilityVSAvoidprocessing speed

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent implements a universal machine learning system that can handle multiple types of user feedback including text comments, ratings, and structured data through a single integrated platform. The system uses multi-class classification and natural language processing to adaptively process diverse feedback formats, maintaining flexibility while achieving high processing speeds through automated algorithmic operations.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system replaces manual flexible processing with automated intelligent processing. The machine learning model adapts to different feedback types through trained classification algorithms and natural language understanding, providing flexibility equivalent to or exceeding manual processing while achieving significantly higher throughput and response speeds through parallel computational processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20260065198A1Apparatus and methods for generating a process enhancement

Publication Date: 2026.03.05 UVA IP LLC

AI summary

An apparatus and method for generating a process enhancement, the apparatus comprising a memory and a processor configured to receive process data, receive user input, determine a plurality of response modules as a function of the user input, determine a modification target as function of the plurality of response modules, wherein determining the modification target includes calculating an importance score for each response module of the plurality of response modules, ranking each response module of the plurality of response modules as a function of the importance score and determining the modification target as a function of the ranking, identify at least a process modification as a function of the process data and the modification target and generate the process enhancement as a function of the at least a process modification.