Sentiment-Based User Profiles for AI Support Prioritization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing approaches for assessing user satisfaction and predicting user behavior in AI-driven interactions lack granularity and fail to leverage comprehensive user data, including non-work-environment sentiments, leading to suboptimal personalization and support processes.
Innovation Solution
A system that integrates sentiment analysis from both work-environment interactions and non-work-environment publications, such as social media, to generate a nuanced user profile, enabling AI tools to provide personalized and timely responses, and automate escalation mechanisms based on predefined negative sentiment thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sentiment analysis is performed only on work-environment interactions, then the analysis process is simple, but the user profile lacks granularity and comprehensiveness
Solution Approach 1:
The patent combines sentiment analysis from multiple data sources including work-environment interactions and non-work-environment publications into a unified sentiment-based user profile. This merging of diverse data sources enhances the precision and comprehensiveness of user sentiment assessment while managing integration complexity through systematic processing.
Solution Approach 2:
The system performs multiple functions using a unified approach: it analyzes sentiment from different sources (work interactions, social media publications), generates comprehensive user profiles, and feeds this information to AI models for various applications. This multi-functionality achieves high measurement precision across diverse contexts without proportionally increasing complexity.
2Measurement precision
If comprehensive user data from multiple sources is integrated, then user profile granularity is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs sentiment analysis on user data from multiple sources in advance to generate sentiment-based user profiles before they are needed for AI model processing. This preliminary action prepares comprehensive user profiles upfront, reducing processing time when the profiles are subsequently used for specific tasks or decisions.
Solution Approach 2:
The sentiment-based user profiles are dynamically updated as new interaction data and publications become available. This dynamic approach allows the system to maintain high user profile granularity without continuously reprocessing all data, as updates occur incrementally when new information is received.
3Adaptability or versatility
If sentiment analysis includes non-work-environment publications, then personalization capability is enhanced, but data privacy concerns increase
Solution Approach 1:
The system extracts and analyzes only sentiment-relevant information from non-work-environment publications while excluding personally identifiable information and sensitive data. This extraction approach enables enhanced personalization capability through sentiment insights while mitigating data privacy risks by removing harmful personal details from the analysis process.
Data Source
AI summary
A system can perform a first sentiment-based analysis based on interaction data representative of an interaction between the system and a user profile. The system can perform a second sentiment-based analysis based on publication data representative of a publication associated with the user profile. The system can generate a sentiment-based user profile for the user profile based on respective results of the first sentiment-based analysis and the second sentiment-based analysis. The system can input the sentiment-based user profile and impact data representative of an impact that the user profile has on an entity associated with the system to a trained artificial intelligence model, to produce an output that indicates a proposed action to take with respect to the user profile. The system can store an indication of the output.