Chatbot Context Expiration for Natural Language Disambiguation
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Solution Overview
Problem
Natural language interfaces, such as chatbots, face challenges in disambiguating user inputs due to the assumption of shared context between conversation participants, leading to inaccurate responses when context is not explicitly known.
Innovation Solution
A chatbot computing system that includes a natural language processor and a bot controller, where the natural language processor identifies concepts in textual inputs and feeds them back as context information for subsequent inputs, using a knowledge model and context filter/enhancement logic to manage context expiration and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If context information is maintained indefinitely for natural language processing, then response accuracy improves, but system complexity and computational overhead increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the lifespan and relevance weight of context information based on expiration indicators. Context items are assigned time-based or interaction-based expiration parameters, allowing the system to automatically degrade or remove context as it ages, thus maintaining accuracy for relevant context while preventing unbounded complexity growth.
Solution Approach 2:
The system implements dynamic context management where context relevance is not static but evolves over time through expiration mechanisms. The natural language processor dynamically adjusts which context items are active based on their expiration status, creating a living context window that adapts to conversation flow without requiring manual intervention or fixed结构设计.
2Measurement precision
If all context information is retained for disambiguation, then natural language understanding improves, but processing time increases
Solution Approach 1:
The patent extracts only the necessary context information for current processing by filtering out expired or irrelevant context items before each natural language processing operation. The system takes out only the active, unexpired context from the full context history, reducing the processing burden while maintaining understanding accuracy for relevant disambiguation.
Solution Approach 2:
The system performs preliminary filtering of context information by checking expiration indicators before each processing cycle. This preliminary action removes obviously expired context items in advance, so the main natural language processing doesn't have to evaluate and reject them, thereby reducing overall processing time while preserving understanding accuracy.
3Productivity
If context expiration is implemented to reduce memory usage, then system efficiency improves, but context accuracy may deteriorate
Solution Approach 1:
The patent implements periodic context refresh through expiration indicators that systematically update context relevance at defined intervals or after specific numbers of interactions. This periodic action ensures that context is maintained with appropriate accuracy for the intended conversation window while automatically expiring older context, balancing memory efficiency with contextual precision.
Solution Approach 2:
The system uses feedback mechanisms where the natural language processor evaluates whether expired context items should be retained or removed based on their continued relevance to current queries. This feedback loop allows the system to maintain context accuracy for important information while efficiently discarding obsolete context, optimizing the balance between precision and efficiency.
Data Source
AI summary
A chat bot computing system includes a bot controller and a natural language processor. The natural language processor receives a first textual input and identifies concepts represented by the first textual input. An indication of the concepts is output to the bot controller which generates a response to the first textual input. The concepts output by the natural language processor are also fed back into the input to the natural language processor, as context information, along with an expiration indicator when a second textual input is received. The natural language processor then identifies concepts represented in the second textual input, based on the second natural language, textual input and unexpired context information.