AI Virtual Agent Trainer Framework
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Solution Overview
Problem
Existing virtual agents, such as chatbots and AI assistants, face challenges with low accuracy and maturity due to limited human resources, time constraints, and high computing demands for training, leading to inadequate test coverage and inauthentic responses.
Innovation Solution
An AI-based automated training framework that generates conversations, analyzes responses, and provides a maturity scorer and recommendations to improve the accuracy and authenticity of virtual agents by automating the training process, reducing memory demands, and increasing training efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If virtual agents are trained with more conversational scenarios to increase accuracy and authenticity, then training coverage and performance improve, but computing resources and time requirements increase
Solution Approach 1:
The training process is segmented into multiple stages: initial training with limited scenarios, then iterative retraining with newly generated synthetic scenarios. This divides the computationally intensive task of comprehensive training into manageable chunks, allowing the system to progressively improve accuracy without requiring all computing resources at once.
Solution Approach 2:
The system performs preliminary action by generating synthetic conversational scenarios in advance using AI-generated responses and feedback from initial testing. These pre-generated scenarios are then used for subsequent training iterations, reducing the need for real-time computing during actual training execution.
2Reliability
If virtual agents are trained with more conversational scenarios to increase accuracy and authenticity, then training coverage improves, but time consumption increases
Solution Approach 1:
The system implements continuous useful action through automated feedback loops where the virtual agent is continuously tested, evaluated for authenticity, and retrained with new scenarios without manual intervention. This continuous cycle maintains and improves authenticity over time while reducing overall time consumption through automation.
Solution Approach 2:
The system performs self-service by automatically generating synthetic scenarios, evaluating its own performance through testing, and initiating retraining cycles based on identified deficiencies. This autonomous operation eliminates the need for continuous human oversight and reduces time consumption associated with manual training management.
3Productivity
If automated training framework is implemented to reduce computing resources and increase efficiency, then training efficiency improves, but system complexity increases
Solution Approach 1:
The automated training framework implements multi-functionality by combining scenario generation, model training, evaluation, and feedback processing into a single integrated system. This universal system handles multiple training tasks simultaneously, improving overall efficiency while managing complexity through consolidation rather than separate dedicated systems for each function.
Data Source
AI summary
The present disclosure relates to a system, a method, and a product for an artificial intelligence based virtual agent trainer. The system includes a processor in communication with a memory storing instructions. When the processor executes the instructions, the instructions are configured to cause the processor to obtain input data and generate a preliminary set of utterances based on the input data, process the preliminary set of utterances to generate a set of utterance training data, generate a set of conversations based on the set of utterance training data, simulate the set of conversations on a virtual agent to obtain a conversation result, verify an intent and a response based on the conversation result, verify a use case flow and flow hops based on the conversation result, and generate recommendation information and maturity report based on verification results.