Context-Based Response Generation Using Machine Learning
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Solution Overview
Problem
Conversation systems face challenges in providing consistent and professional responses due to difficulties in discovering and selecting designated responses, which require multiple user interfaces and are not comprehensive, leading to increased costs for businesses.
Innovation Solution
A context-based response generation method using machine learning models to predict and identify designated responses from conversation logs, incorporating feedback for iterative improvement, and recommending responses through clustering and filtering techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple user interfaces are used to discover and select designated responses, then response selection capability is improved, but system complexity and operational difficulty increase
Solution Approach 1:
The patent extracts the response selection task from complex multi-interface systems and consolidates it into a single machine learning-based interface. The ML model directly predicts appropriate responses based on conversation context, eliminating the need for multiple user interfaces while maintaining response selection capability.
Solution Approach 2:
The system performs self-service by using machine learning models to automatically predict and select responses without requiring manual intervention through multiple interfaces. The model learns from conversation data and autonomously determines appropriate responses, reducing operational complexity.
2Reliability
If designated responses are manually curated and updated, then response quality is improved, but time and cost increase
Solution Approach 1:
The machine learning model performs self-service by automatically learning from conversation data and improving response quality over time without manual curating. The system continuously refines its predictions based on observed conversation patterns, eliminating the need for time-consuming manual updates while maintaining high response quality.
Solution Approach 2:
The system incorporates feedback mechanisms where conversation data is continuously analyzed and used to retrain or fine-tune the machine learning model. This feedback loop enables automatic improvement of response quality without manual intervention, reducing update time while maintaining reliability.
3Measurement precision
If comprehensive response coverage is achieved through manual curation, then response accuracy is improved, but system maintenance cost increases
Solution Approach 1:
The machine learning model achieves comprehensive response coverage through self-service learning from diverse conversation data. The model automatically adapts to new conversation patterns and contexts, maintaining high response accuracy without requiring expensive manual maintenance and updates.
Solution Approach 2:
The system dynamically changes its internal parameters and models based on incoming conversation data. This allows the system to adapt to evolving language patterns and contexts, maintaining comprehensive coverage and accuracy without static manual curation, thereby reducing maintenance costs.
Data Source
AI summary
Methods, systems, and computer program products for context-based response generation are provided herein. A method includes: obtaining conversation logs comprising agent responses matched to contexts and a set of designated responses that are not matched to the contexts; replacing at least a portion of the agent responses with the designated responses to form modified conversation logs; training a first model, using the modified conversation logs, to output a designated response in the set for a given context and a second model, using the historical conversation logs, to output one of the agent responses for a given context; identifying one or more new responses based at least in part on the output of the second machine learning model for a particular one of the contexts; and retraining the first machine learning model based at least in part on the one or more new responses.