Conversation Context Switching with Neural Networks and Knowledge Graphs
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Solution Overview
Problem
Conventional conversation systems rely on rigid rule-based approaches that require programmer intervention for every possible natural language conversation scenario, limiting their flexibility and effectiveness in maintaining or switching context.
Innovation Solution
Utilizing an artificial neural network trained to determine context continuity or switching in natural language queries, allowing continuous learning and adaptation based on user feedback, and employing a knowledge graph to refine query updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If rule-based systems are used to determine context switching, then the system structure is simple and easy to implement, but the system lacks flexibility and requires programmer intervention for every possible scenario
Solution Approach 1:
The patent replaces the mechanical rule-based system with an artificial neural network that learns from data. Instead of programming explicit rules for context switching, the system uses a neural network model that processes natural language queries and automatically determines context continuity, eliminating the need for programmer intervention in every scenario while maintaining flexibility
Solution Approach 2:
The neural network system performs self-learning and self-adjustment based on training data and user feedback. The system automatically improves its context switching capabilities through continuous learning without requiring manual reprogramming, enabling it to handle diverse conversation scenarios autonomously
2Measurement precision
If the number of hidden layers in the neural network is increased, then the accuracy of context detection improves, but the training time and computational resources required increase
Solution Approach 1:
The patent optimizes the neural network architecture by adjusting parameters such as the number of hidden layers and neurons to achieve the desired accuracy while controlling training time. The system uses a balanced configuration that provides sufficient accuracy for context detection without excessive computational resources or training duration
Solution Approach 2:
The system incorporates user feedback mechanisms to continuously improve context detection accuracy. By learning from user corrections and feedback, the neural network refines its performance over time, reducing the need for overly complex initial architectures and allowing for more efficient training
Data Source
AI summary
Systems and methods are described to address shortcomings in a conventional conversation system via a novel technique utilizing artificial neural networks to train the conversation system whether or not to continue context. In some aspects, an interactive media guidance application determines a type of conversation continuity in a natural language conversation comprising first and second queries. The interactive media guidance application determines a first token in the first query and a second token in the second query. The interactive media guidance application identifies entity data for the first and second tokens. The interactive media guidance application retrieves, from a knowledge graph, graph connections between the entity data for the first and second tokens. The interactive media guidance application applies this data as inputs to an artificial neural network. The interactive media guidance application determines an output that indicates the type of conversation continuity between the first and second queries.