Conversational Intent Clustering for Accurate Real-Time Responses
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Solution Overview
Problem
Existing systems struggle to generate timely and pertinent conversational responses due to the sparseness of training data and individual user particularities, making it difficult to determine specific user intents accurately.
Innovation Solution
The system employs multiple machine learning models for unsupervised hierarchical clustering of intents into clusters, using models like BERT and ELMo for natural language processing, and selects a subset of intent clusters based on feature inputs to provide dynamic conversational responses, relying on user selection within clusters to reduce false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses machine learning models to determine specific user intents, then the accuracy of intent determination improves, but the complexity of the system increases due to multiple models and clustering mechanisms
Solution Approach 1:
The patent segments the intent determination process into multiple stages: first clustering intents into groups using unsupervised hierarchical clustering, then selecting representative intents from each cluster. This segmentation allows the system to handle complexity by breaking down the intent determination task into manageable steps, improving accuracy while managing system complexity through structured decomposition.
Solution Approach 2:
The patent introduces intent clusters as intermediary structures between raw user inputs and final intent determinations. These clusters act as mediators that group similar intents together, allowing the system to work with simplified cluster representations rather than directly handling the full complexity of individual intent classifications, thus reducing effective system complexity while maintaining precision.
2Measurement precision
If the system aggregates detailed information about user actions and circumstances, then the precision of intent determination improves, but the amount of data processing and computational resources required increases
Solution Approach 1:
The patent extracts and selects only the most relevant features from the aggregated user action and circumstance data to use in intent clustering. By identifying and extracting key discriminative features rather than processing all available data, the system achieves high precision intent determination while reducing computational resource consumption through feature selection and dimensionality reduction.
Solution Approach 2:
The patent applies partial action by processing only the necessary subset of aggregated information required for effective intent clustering. Rather than exhaustively analyzing all user action details and circumstances, the system selectively processes partial information that is sufficient for accurate intent determination, thereby reducing computational overhead while maintaining precision.
3Reliability
If the system provides multiple conversational response options, then the relevance and user satisfaction improve, but the time required to process and present responses increases
Solution Approach 1:
The patent performs preliminary clustering of intents into groups before generating conversational responses. This pre-processing organizes potential responses in advance, allowing the system to quickly retrieve and present relevant response options without time-consuming real-time analysis during user interaction, thus improving response relevance while minimizing time loss.
Solution Approach 2:
The patent implements dynamic response selection where the system adapts the number and type of response options based on contextual factors. The system dynamically adjusts conversational response strategies based on user profile, conversation history, and situation, presenting more detailed options when appropriate and simpler options when time is constrained, thereby balancing relevance with response generation time.
Data Source
AI summary
Methods and systems are described for generating dynamic conversational responses using two-tier machine learning models. The dynamic conversational responses may be generated in real time and reflect the likely goals and/or intents of a user. The two-tier machine learning model may include a first tier that determines an intent cluster based on a feature input, and a second tier that determines a specific intent from the cluster.