NLP Task Model Using Knowledge Base Modification
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Solution Overview
Problem
Natural language processing models often perform incorrect tasks that go against a user's intent due to the lack of understanding of the types of natural language inputs that can be executed, leading to unnecessary computational resource consumption and poor user experience.
Innovation Solution
A system that analyzes user input via natural language processing using a knowledge base of previous user interactions to identify and modify inputs, prompting users to adjust their language to achieve the intended computing tasks, thereby improving task execution and user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the NLP model processes natural language input directly without modification suggestions, then the system complexity is low, but the task execution accuracy deteriorates because the model performs incorrect tasks that go against user intent
Solution Approach 1:
The patent introduces an intermediary component that acts as a mediator between the user's natural language input and the NLP model. This intermediary analyzes the input, compares it with a knowledge base of previous successful interactions, and generates modification suggestions. This intermediary layer resolves the contradiction by improving task execution accuracy through additional processing while managing system complexity through modular design.
Solution Approach 2:
The system implements a feedback mechanism where the NLP model's initial task interpretation is evaluated against a knowledge base of proven successful interactions. Modification suggestions are generated based on this feedback loop, allowing the system to learn from past successes and improve future task execution accuracy without requiring complete system redesign.
2Ease of operation
If the NLP model executes tasks based on initial interpretation without user confirmation, then the processing speed is high, but the user experience deteriorates due to poor interaction when incorrect tasks are performed
Solution Approach 1:
The system applies partial action by not requiring full user confirmation for every task, but instead providing selective modification suggestions based on the confidence level and similarity to known successful patterns. This approach improves user experience by correcting obvious errors while maintaining processing speed for confident, straightforward interpretations.
3Adaptability or versatility
If the system uses a basic NLP model without knowledge base integration, then the computational resource consumption is low, but the ability to understand executable natural language inputs deteriorates
Solution Approach 1:
The system performs preliminary action by pre-processing and storing successful natural language interactions in a knowledge base before they are needed for new queries. When a user inputs natural language, the system quickly searches this pre-prepared knowledge base for matching patterns, enabling better understanding of executable inputs without requiring heavy real-time computational resources.
Data Source
AI summary
A present invention embodiment analyzes user input via natural language processing. A natural language utterance from a user is analyzed to determine one or more computing tasks. the natural language utterance is analyzed using a knowledge base to identify one or more modifications to the natural language utterance that are based on previous user modifications to a previous user utterance. An indication that the user accepted at least one modification of the one or more modifications is received, wherein the at least one modification modifies the one or more computing tasks. The modified one or more computing tasks are executed.