NLP Software Optimization From Negative User Conversation Signals
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Solution Overview
Problem
Current software optimization solutions fail to identify optimization opportunities effectively using natural language processing and understanding of user-to-user business-related conversations within an entity, leading to an inability to enhance software products' usage, performance, or functionality.
Innovation Solution
A computer-implemented method utilizing natural language processing and understanding to analyze user-to-user conversations, identify negative sentiments, and recommend software product optimizations or customizations based on keyword analysis and inventory availability, while ensuring compliance and consent from users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language processing is applied to analyze user conversations, then software product optimization identification is improved, but system complexity increases
Solution Approach 1:
A natural language processing system acts as an intermediary between user conversations and software product optimization identification. The system processes and analyzes conversation data, extracting meaningful insights about software usage issues and optimization opportunities without requiring direct complex analysis of raw conversation data.
Solution Approach 2:
Manual analysis of user conversations for software optimization is replaced with automated natural language processing systems. The mechanical process of reading and interpreting conversations is substituted with computational language analysis, significantly improving efficiency and scalability while maintaining or enhancing identification accuracy.
2Loss of information
If user-to-user conversations are monitored and analyzed, then software optimization insights are improved, but user privacy concerns increase
Solution Approach 1:
The system extracts only the specific information needed for software optimization from user conversations, separating relevant software-related feedback from personal or sensitive data. This extraction approach maintains optimization insights while minimizing privacy intrusion by focusing solely on software usage patterns and issues.
Solution Approach 2:
Privacy protection measures are implemented before analysis occurs. The system pre-configures filtering mechanisms to automatically exclude or anonymize sensitive personal information from conversation analysis, preventing privacy violations before they can occur during the optimization insight generation process.
Data Source
AI summary
Software product optimization is provided. Expressed sentiment in a set of opted in user-to-user business-related conversations among a group of users corresponding to an entity is associated with a software product in response to determining that the software product is identified by mentioned keywords in the set of opted in user-to-user business-related conversations based on a keyword corpus. It is determined whether the expressed sentiment associated with the software product is negative sentiment. A related software product listed in a software product catalog of the entity is identified as an optimization to the software product in response to determining that the expressed sentiment associated with the software product is negative sentiment. A recommendation to implement the related software product as the optimization to the software product is generated. The recommendation to implement the related software product as the optimization to the software product is sent to the group of users.