Unified AI Model for Virtual Assistant Response Generation
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Solution Overview
Problem
Existing virtual assistant AI engines for local businesses struggle to accurately understand and interpret customer requests, access relevant systems of record, and coordinate with staff in real-time, especially when dealing with unique business workflows and limited training data.
Innovation Solution
A computerized method implementing a unified model that responds to customer messages by identifying suitable responses and notifications based on common or business-specific templates, using a multi-head attention-based binary classifier to determine the appropriateness of responses, and storing potential responses in a Document Store accessible through an Information Retrieval System.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional AI models are retrained for each business, then accuracy in understanding customer requests improves, but time consumption and computational resources increase significantly
Solution Approach 1:
The system pre-generates multiple candidate responses (R,B) pairs and organizes them in a Document Store with associated query variations before actual customer interactions occur. This preliminary preparation eliminates the need for time-consuming retraining when new businesses are onboarded, as the framework can directly utilize pre-organized response candidates.
Solution Approach 2:
Instead of retraining foundational AI models for each business, the system creates business-specific virtual assistants by copying and adapting the unified framework structure. The framework generates business-customized responses by selecting from pre-generated candidates rather than retraining the entire model, significantly reducing time and computational resources.
2Adaptability or versatility
If custom virtual assistants are created for each business, then adaptability to unique business workflows improves, but system complexity increases
Solution Approach 1:
The system employs a universal unified framework that serves multiple businesses simultaneously. The framework generates business-specific virtual assistants by configuring the same core system with different business parameters, templates, and pre-generated response candidates, eliminating the need for separate complex systems for each business.
Solution Approach 2:
The system segments the virtual assistant creation process into modular components: a unified framework, business-specific configurations, template selections, and pre-generated response candidates. This segmentation allows each component to be independently managed and reused across different businesses, reducing overall system complexity.
3Measurement precision
If comprehensive training data is collected for each business, then model accuracy improves, but data collection time and costs increase
Solution Approach 1:
The system pre-generates comprehensive response candidates and query variations in advance, organized in a Document Store. This preliminary action provides the model with sufficient training data structures before actual business operations begin, eliminating the need for time-consuming data collection during deployment.
Solution Approach 2:
The unified framework automatically generates business-specific configurations and response candidates without requiring external data collection efforts. The system serves itself by creating the necessary training structures through automated processes rather than manual data gathering.
4Reliability
If real-time coordination with staff is implemented, then customer service quality improves, but system complexity and processing time increase
Solution Approach 1:
The system extracts and separates the notification function from the main AI processing workflow. Business notifications are generated as distinct outputs from the unified framework, allowing real-time staff alerts to be coordinated independently without complicating the core customer interaction processing.
Data Source
AI summary
In one aspect, a computerized-method for implementing a unified model that responds to an incoming customer message or request, comprising: given a user input message, U: identifying a response, R, that is to be sent to the customer, identifying a business notification, B, that is to be sent to the staff at the business, basing the response, R, and the business notification, B, on a common template or a business-specific template or a canned response defined by the business; wherein a unique (R,B) pair comprises a potential response to the input user message, U, storing a plurality of (R,B) pairs in a Document Store that is accessible through an Information Retrieval System; alongside the plurality of (R,B) pairs, storing a set of examples and a set of variations of the customer query, Q, for which each (R,B) pair of the plurality of (R,B) pairs is the appropriate response; given a query, Q: providing a plurality of corresponding (Q,R,B) triples.