18064230. Building Bots from Raw Logs and Computing Coverage of Business Logic Graph simplified abstract (Google LLC)
Contents
- 1 Building Bots from Raw Logs and Computing Coverage of Business Logic Graph
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Building Bots from Raw Logs and Computing Coverage of Business Logic Graph - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Dynamic Training Data Generation
- 1.13 Original Abstract Submitted
Building Bots from Raw Logs and Computing Coverage of Business Logic Graph
Organization Name
Inventor(s)
Henry Scott Dlhopolsky of Zurich (CH)
Vladimir Vuskovic of Zollikerberg (CH)
Building Bots from Raw Logs and Computing Coverage of Business Logic Graph - A simplified explanation of the abstract
This abstract first appeared for US patent application 18064230 titled 'Building Bots from Raw Logs and Computing Coverage of Business Logic Graph
Simplified Explanation
The patent application describes a method for generating training data for a model based on conversations between a customer and an agent. The method involves selecting responses from a logic model to train a machine learning model.
- The method receives a transcript of a conversation between a customer and an agent.
- It selects a response from a logic model based on the agent's input.
- If the selected response is similar enough to the agent's input, it is used to train a machine learning model.
Key Features and Innovation
- Dynamic generation of training data based on real conversations.
- Utilization of a logic model to select responses for training.
- Incorporation of similarity scoring to determine the effectiveness of selected responses.
Potential Applications
This technology can be applied in:
- Customer service training programs.
- Chatbot development.
- Call center optimization.
Problems Solved
- Efficient generation of training data for machine learning models.
- Improved accuracy in response selection based on real conversations.
- Enhanced performance of AI systems in customer-agent interactions.
Benefits
- Streamlined training process for AI models.
- Enhanced customer service experiences.
- Increased efficiency in agent training programs.
Commercial Applications
- "Dynamic Training Data Generation for AI Customer Service Models: Market Implications"
Prior Art
There may be prior art related to machine learning models trained on conversation transcripts and logic models for response selection.
Frequently Updated Research
No specific information on frequently updated research related to this technology.
Questions about Dynamic Training Data Generation
How does this method improve the accuracy of AI models in customer service applications?
This method enhances accuracy by selecting responses based on real conversations, improving the relevance of training data.
What are the potential challenges in implementing this technology in different industries?
The challenges may include adapting the logic model to specific industry jargon and conversation nuances.
Original Abstract Submitted
A method for dynamically generating training data for a model includes receiving a transcript corresponding to a conversation between a customer and an agent, the transcript comprising a customer input and an agent input. The method includes receiving a logic model including a plurality of responses, each response of the plurality of responses representing a potential reply to the customer input. The method further includes selecting, based on the agent input, a response from the plurality of responses of the logic model. The method includes determining that a similarity score between the selected response and the agent input satisfies a similarity threshold, and, based on determining that the similarity score between the selected response and the agent input satisfies the similarity threshold, training a machine learning model using the customer input and the selected response.
