Virtual Assistant Response Generation Using Graph Database Retrieval
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Solution Overview
Problem
Existing virtual assistants provide generic responses and lack the ability to tailor their actions to specific user domains and behaviors, failing to leverage user expertise and context effectively.
Innovation Solution
The use of machine learning techniques to configure virtual assistants by converting natural language inputs into graphical inputs, retrieving relationship data from graph databases, and generating responses based on this data, allowing the assistants to learn and adapt to user trends and behavior over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtual assistants use generic response templates, then implementation is simple and fast, but they cannot provide personalized or context-aware responses
Solution Approach 1:
The patent implements a nested architecture where multiple machine learning models are embedded within each other. The NLU model is nested within the NLG model, which is nested within the overall virtual assistant system. This allows complex personalized responses to be generated through layered processing, where each nested model handles a specific aspect of the response generation task.
Solution Approach 2:
The patent introduces relationship graphs as intermediary structures that connect user data, context information, and response generation. These graphs serve as mediators that transform raw input data into structured representations that the ML models can process, enabling personalized responses without direct complex model-to-data mapping.
2Loss of information
If virtual assistants process and store extensive user data, then they can provide more personalized responses, but data management complexity and storage requirements increase
Solution Approach 1:
The patent merges multiple types of data (user profiles, conversation history, domain knowledge, behavioral patterns) into unified relationship graphs. This consolidation allows the system to retain comprehensive context information while managing data through a single integrated structure rather than separate databases for each data type.
Solution Approach 2:
The patent transforms traditional flat data storage into multi-dimensional relationship graphs where data points are connected through various relational dimensions. This dimensional transformation enables efficient querying and retrieval of context information by traversing relational paths rather than searching through flat data structures.
Data Source
AI summary
A method comprises receiving at least one natural language input, converting the at least one natural language input to a graphical input, retrieving relationship data from a graph database based at least in part on the graphical input, and generating at least one natural language response of a virtual assistant to the at least one natural language input based at least in part on the relationship data from the graph database. At least the generating is performed using one or more machine learning models.