Context-Aware Chatbot Using User Profile Analysis
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Solution Overview
Problem
Chatbots often require users to provide repetitive and qualifying information to determine the context of their queries, leading to frustrating interactions, especially in complex environments where multiple bodies of knowledge need to be navigated.
Innovation Solution
A system and method that analyze user context by creating a user profile from query history, organizational data, and social media interactions to provide context to chatbots, allowing them to determine the relevant corpus of knowledge without asking qualifying questions, and incorporating a feedback loop for learning and error correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If chatbot requires users to provide repetitive qualifying information to determine context, then measurement precision of user intent is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting user data from multiple sources (query history, organizational data, social media interactions) and creating a user profile before the actual chat interaction occurs. This pre-processing of user context eliminates the need for repetitive qualifying questions during interaction, as the chatbot already has contextual information about the user's preferences, behavior patterns, and relevant background data.
2Adaptability or versatility
If chatbot creates detailed user profile from multiple data sources, then adaptability to user context is improved, but device complexity increases
Solution Approach 1:
The system segments the complex task of user context recognition into distinct functional modules: a data collection module that gathers information from multiple sources (query history, organizational data, social media), a user profile generation module that processes this data, and a chatbot module that utilizes the profile. This segmentation allows each component to handle specific aspects of the complexity independently, making the overall system more manageable while achieving high adaptability.
3Productivity
If chatbot collects and analyzes user data from multiple sources, then productivity of customer service is improved, but loss of user privacy information increases
Solution Approach 1:
The system incorporates a feedback mechanism where user responses and interactions continuously refine and update the user profile. This feedback loop allows the system to learn from user preferences and behaviors over time, improving service productivity by becoming increasingly accurate in understanding user needs. Simultaneously, the feedback mechanism gives users implicit control over their privacy, as their ongoing interactions determine what additional information is collected and how it is used.
Data Source
AI summary
Methods, systems and computer program products are described herein that provided context to user queries to a chat bot. In one embodiment, the method may include receiving a user query at a chat bot. The chat bot is communicating with a system providing conversation interaction with the user. The chat bot accessing a user profile being initiated by the user. The user can revoke the permission to the system for creating and/or using a user profile at any time. In some embodiments, the user profile includes a query history and profile information about the user that correlates keywords to context. The chat bot weighs query keywords for comparison with the keywords of the user profile that are correlated to context. The chat bot interacts with the user using the context correlated to the user in accordance with the weight of the query keywords matched to the keywords of the user profile that are correlated to the context.