Digital Assistant Continuous Learning System for Unknown Terminology
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Solution Overview
Problem
Digital assistants, such as chatbots, face limitations in understanding user terminology not stored in their static databases, leading to ineffective assistance for users who employ different or inaccurate terms.
Innovation Solution
A continuous learning system (CLS) that dynamically learns user terminology by requesting feedback and expanding its knowledge base through user input, utilizing natural language processing and machine learning to map unknown terms to existing entities, thereby providing customized responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static database is used to store terminology, then the digital assistant has a fixed knowledge base, but it cannot understand user terminology that is not stored in the database
Solution Approach 1:
The patent transforms the static database into a dynamic knowledge base that continuously evolves. The system automatically learns new terminology from user interactions and updates its knowledge base in real-time, enabling it to adapt to changing user language patterns without manual reconfiguration.
Solution Approach 2:
The system implements feedback mechanisms where user responses and interactions are continuously monitored and fed back into the learning process. This feedback loop enables the digital assistant to refine its understanding of terminology through iterative learning from actual usage patterns.
2Adaptability or versatility
If the digital assistant relies on pre-stored terminology, then the system is simple to operate, but it renders the assistant incapable of assisting users with unfamiliar terms
Solution Approach 1:
The system performs preliminary learning actions by continuously capturing and processing user terminology in advance. Before users encounter unfamiliar terms, the system is already equipped with learned patterns from previous interactions, enabling rapid recognition and appropriate responses without requiring real-time manual programming.
3Adaptability or versatility
If the digital assistant uses a fixed knowledge base, then the system is easy to maintain, but it limits the assistant's ability to provide customized responses
Solution Approach 1:
The digital assistant implements self-service learning capabilities, automatically improving its own knowledge base through user interactions without requiring external intervention. The system self-updates its terminology understanding by processing new user input and refining its responses autonomously.
Data Source
AI summary
Various embodiments for a continuous learning system are described herein. An embodiment operates by receiving a query from a user and identifying an unknown phrase in the query. User feedback regarding the unknown phrase is requested and received. A first pre-existing entity of a plurality of pre-existing entities that corresponds to the received user feedback is identified. A relationship between the first pre-existing entity and the unknown is added to the knowledgebase. The query is executed against the knowledgebase using the first pre-existing entity. A response, to the executed query, is provided to the user.