Empathic Chatbot Using User Trend Analysis for Trustworthy Responses
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Solution Overview
Problem
Current human-computer interaction systems, particularly chatbots, fail to provide genuine empathetic communication, leading to a breakdown in trust and the efficacy of therapy due to limitations in interpreting and interacting with users.
Innovation Solution
A computer-implemented method that evaluates user goals over time, determines trends, and generates empathic communications by accessing a recipe database to select and generate responses using explanation phrases provided by the user, creating a personalized database for empathic engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current NLP technologies are used to generate empathetic responses, then the system can attempt to mimic empathy, but the interactions are interpreted as non-genuine or uncanny, leading to breakdown of trust
Solution Approach 1:
The system performs preliminary actions by collecting and storing user feedback, explanations, and contextual information before generating empathetic responses. This preliminary data gathering enables the system to create genuinely tailored responses rather than generic empathetic statements, thereby building user trust while maintaining adaptability.
Solution Approach 2:
The system incorporates feedback mechanisms where users provide explanations and feedback about their experiences. This feedback is stored and used to generate subsequent empathetic communications, creating a loop where the system continuously improves its understanding of the user's needs and emotions, making responses more authentic and trustworthy.
2Adaptability or versatility
If the system collects and stores user feedback and explanations, then empathetic communications become more genuine and tailored, but the system complexity increases
Solution Approach 1:
The system segments the data collection and processing into distinct functional components: feedback collection module, data storage module, trend analysis module, and empathetic response generation module. This segmentation allows each component to handle specific tasks independently, reducing overall system complexity while maintaining high personalization capability through specialized processing functions.
Solution Approach 2:
The system employs a universal data structure and storage mechanism that handles multiple types of user inputs (feedback, explanations, contextual data) through a single integrated framework. This multi-functional approach allows the system to manage diverse data types without requiring separate complex processing pipelines for each data type, thereby reducing overall system complexity.
3Productivity
If the system uses classification-based NLP, then it can categorize input text, but it fails to capture the nuanced and contextual meaning behind user communications
Solution Approach 1:
The system transitions from static classification-based NLP to dynamic response generation that adapts to contextual nuances. By continuously analyzing user feedback, explanations, and interaction patterns, the system dynamically adjusts its interpretation and response strategy, enabling it to capture nuanced meanings while maintaining efficient response generation through learned patterns rather than rigid classifications.
Data Source
AI summary
A chatbot capable of empathic engagement with a user is disclosed. An identified trend in a user's mood or goals between a first time and a second time can be associated with open input (e.g., open text string input) from the user. As the user's mood or goals continue to be tracked, a subsequent trend can be identified that is the same as, similar to, different from, or opposite to the first identified trend. The user can then be automatically engaged based on the open input associated with the first identified trend. In an example, a user may input thoughts or reasons why they have been having a positively trending mood over a duration of time. The chatbot can then repeat or otherwise use those same thoughts or reasons to engage the user empathically when the chatbot detects that the user is experiencing a negatively trending mood.