Dynamic Neural Network Node Creation for Virtual Assistant Context
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Solution Overview
Problem
AI virtual assistants face challenges in deciphering human users' unique communication styles, including incomplete sentences and co-reference patterns, which hinders their ability to provide appropriate responses.
Innovation Solution
A method where a virtual assistant computer uses a neural network to determine the context of user-generated text input by comparing confidence scores to existing nodes, creating new nodes if scores are below a threshold, and assigning context to existing nodes if scores are above, allowing for dynamic and accurate response generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a virtual assistant uses a fixed classification system to identify user intent, then it can provide consistent responses for common patterns, but it fails to adapt to unique communication styles and incomplete sentences
Solution Approach 1:
The patent implements a dynamic neural network where nodes are created, modified, or deleted based on incoming user inputs and confidence score thresholds. This allows the classification system to evolve and adapt to new communication patterns while maintaining structure for common patterns, resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The system pre-establishes a neural network structure with initial nodes and confidence thresholds before operation. This preliminary structure provides consistent handling of common patterns, while the dynamic modification capability enables later adaptation to unique styles without starting from scratch.
2Reliability
If the virtual assistant creates new nodes for every unique input pattern, then it can capture all communication styles, but it increases network complexity and processing overhead
Solution Approach 1:
The patent introduces confidence score thresholds as a critical parameter that controls node creation. By adjusting this parameter, the system balances between capturing unique patterns (higher reliability) and limiting network growth (lower complexity). Inputs below the threshold trigger new node creation, while those above reuse existing nodes.
Solution Approach 2:
Instead of creating nodes for every possible input variation, the system applies partial action by only creating nodes when confidence scores fall below a threshold. This selective approach captures essential unique patterns while avoiding excessive network complexity from minor variations.
3Ease of operation
If the virtual assistant uses incomplete sentences and co-reference patterns, then it reflects natural human communication, but it becomes difficult to decipher and respond appropriately
Solution Approach 1:
The neural network acts as an intermediary layer between raw user inputs (including incomplete sentences and co-references) and the response generation system. It transforms ambiguous natural language into structured context representations, making interpretation manageable while preserving natural communication styles.
Solution Approach 2:
The system uses feedback from confidence score calculations to dynamically adjust the neural network structure. When incomplete sentences or co-reference patterns are encountered, the feedback mechanism determines whether to create new nodes to capture these patterns, improving interpretation capability over time.
Data Source
AI summary
Pairing a user response and associated context with a neural network associated with a virtual assistant computer during a dynamic text conversation with an end user. The virtual assistant computer receives a detected user generated text input; determines context of the detected user generated text input; compares the context of the detected user generated text input by comparing a confidence score representing context of the user generated input to a classification associated with each of a plurality of existing nodes of a neural network. For confidence scores below a threshold relative to the classification associated with each of the existing nodes of the neural network, the virtual assistant computer creates a new node within the neural network and assigns the context of the user generated text to the new node.