Intent Discovery System Using ML Clusters for Real-Time Response
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Solution Overview
Problem
Current systems lack an effective method to identify user intents from inputs and provide actionable responses in real-time, leading to inefficiencies in processing customer requests and interactions.
Innovation Solution
A computer-implemented method that generates graphical representations of intent clusters using machine learning algorithms to detect and respond to user intents, updating in real-time based on historical data and customer interactions, allowing for real-time actionable responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to process customer requests, then system complexity is reduced, but response time and efficiency deteriorate
Solution Approach 1:
The patent segments customer requests into distinct intent categories (e.g., information seeking, transactional, support) and processes each segment through specialized handling paths. This segmentation enables parallel processing of different request types, improving overall response time while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The system performs preliminary classification of customer requests into intent clusters before detailed processing. By pre-grouping requests based on their fundamental purpose, the system prepares processed data structures in advance, reducing the computational burden during actual request handling and thereby improving response time.
2Measurement precision
If detailed analysis of customer inputs is performed, then intent identification accuracy is improved, but processing time increases
Solution Approach 1:
The patent implements a dynamic processing approach where the depth of analysis is adjusted based on the initial classification of the request. Common intents receive streamlined processing with predefined response templates, while ambiguous or complex intents trigger more detailed analysis. This dynamic adaptation maintains high accuracy for critical cases while reducing average processing time.
Solution Approach 2:
The system replaces exhaustive mechanical text analysis with machine learning-based intent classification models. These models have been pre-trained to recognize patterns and semantics, enabling accurate intent identification without requiring detailed step-by-step analysis of each input, thereby significantly reducing processing time while maintaining high accuracy.
3Productivity
If real-time updates based on historical data are implemented, then responsiveness to customer needs is improved, but computational resource consumption increases
Solution Approach 1:
The system performs preliminary processing of historical data to create pre-computed intent clusters and response templates during off-peak periods. This preliminary action stores processed patterns that can be quickly retrieved during real-time operations, enabling responsive customer service while minimizing computational resource consumption during peak usage periods.
Solution Approach 2:
The patent creates simplified copies or representations of complex historical interaction patterns in the form of intent clusters and response templates. These copies capture the essential characteristics of historical data without requiring the full computational overhead of the original data, enabling real-time updates with reduced resource consumption.
Data Source
AI summary
Disclosed embodiments provide a framework for intent discovery based on user input and execution of processes based on the discovered intents. An intent processing system provides, via an interface, a graphical representation of different intent clusters corresponding to different intents. An intent cluster includes a set of intent terms and/or phrases that can be used to submit a request or issue that is associated with an intent. As a user selects intent terms and/or phrases from an intent cluster via the interface, the intent processing system can identify actions that can be performed to address the user's request or issue.