Chatbot Profile Ranking via Biometric Feedback
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Solution Overview
Problem
Existing chatbot systems lack the ability to effectively rank and select chatbot profiles based on individual user emotional responses, leading to suboptimal user interaction experiences.
Innovation Solution
A method and system that utilize biometric response data to score and rank chatbot profiles, updating scores based on user interactions, and selecting the most suitable chatbot profile for subsequent interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If chatbot systems use multiple chatbot profiles with different personas, then user interaction variety and engagement potential improve, but the system cannot determine which profile is most suitable for individual users
Solution Approach 1:
The system collects biometric response data from users during interactions with different chatbot profiles and uses this feedback to update scores and rankings. The feedback loop continuously refines the profile selection based on measured user emotional responses, allowing the system to adapt to individual user preferences over time.
Solution Approach 2:
The system changes the parameter of profile selection from static user choice to dynamic biometric-based ranking. By measuring physiological parameters such as heart rate variability, skin conductance, and facial expressions, the system objectively determines which chatbot profile elicits the most positive emotional response for each user.
2Measurement precision
If the system collects biometric data continuously to improve profile selection accuracy, then user engagement quality improves, but system complexity and data processing requirements increase
Solution Approach 1:
The system performs biometric data collection and profile ranking during an initial interaction phase before full deployment. By pre-collecting biometric data and establishing baseline scores and rankings upfront, the system reduces the need for continuous complex biometric monitoring during subsequent interactions, thereby lowering ongoing system complexity while maintaining measurement precision.
3Measurement precision
If the system maintains scores for all chatbot profiles based on biometric data, then profile selection accuracy improves, but computational resources and data storage requirements increase
Solution Approach 1:
The system extracts only the essential scoring information from biometric data and stores condensed score values for each chatbot profile rather than retaining all raw biometric measurements. By extracting and storing only the relevant aggregated metrics needed for ranking, the system maintains scoring accuracy while significantly reducing data storage requirements.
Data Source
AI summary
A computing system initializes a score for each chatbot profile of a plurality of chatbot profiles. The chatbot profiles correspond to different personas. For each chatbot profile, the computing system collects biometric response data for a user while the user has an interaction session with the chatbot profile. The computing system updates the score for the chatbot profile based on the biometric response data for the user collected while the user has the interaction session with the chatbot profile. The computing system ranks the chatbot profiles based on the scores and selects a chatbot profile from the plurality of chatbot profiles for a subsequent interaction session with the user based on the ranking of the chatbot profiles.