ML Intent Prediction for Ranked Response Generation
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Solution Overview
Problem
Current customer service systems lack an intelligent assessment framework for predicting communication intents and providing timely, accurate responses, often resulting in repetitive and inefficient interactions, with a lack of proactive mechanisms for addressing compatibility issues and quality management.
Innovation Solution
A computer-based system employing a machine learning model to analyze communications, predict intents, and generate ranked response options, allowing entities to select appropriate responses based on confidence levels, with the option for artificial intelligence to provide additional language and templates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional customer service systems are used to respond to communications, then system simplicity is maintained, but response accuracy and intent prediction capability deteriorate
Solution Approach 1:
A machine learning model is introduced as an intermediary component between the communication input and response generation. The model receives communications, predicts intents with confidence levels, and generates ranked response options, thereby improving prediction accuracy without requiring complete system redesign.
Solution Approach 2:
The system is segmented into distinct functional modules: communication reception module, machine learning prediction module, response generation module, and user interface module. This allows the complex ML-based intent prediction to be isolated in one module while maintaining simpler traditional systems for other functions.
2Productivity
If manual response generation is used, then response customization is high, but productivity and response time deteriorate
Solution Approach 1:
The machine learning model performs preliminary actions by pre-analyzing communications and pre-generating multiple ranked response options before the user needs to respond. This prepares potential responses in advance, enabling faster response generation while maintaining user control over final selection.
Solution Approach 2:
The system enables semi-automated response generation where the ML model autonomously generates ranked response options based on predicted intents, and the user simply needs to review and select from these pre-prepared options, reducing the operational burden compared to complete manual generation.
3Reliability
If traditional communication handling is used, then system simplicity is maintained, but response quality and error reduction deteriorate
Solution Approach 1:
The system incorporates confidence level feedback from the machine learning model to indicate the reliability of predicted intents. High confidence predictions can be automatically processed with high reliability, while lower confidence predictions can be flagged for additional review, creating a feedback-based quality assurance mechanism.
Data Source
AI summary
One or more systems, computer-implemented methods and/or non-transitory computer-readable mediums are provided to facilitate a process to employ machine learning and selectable response options to respond to a communication from an identity. A system can comprise a processor, and a memory that stores computer executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise determining a communication associated with a user identity, and, employing a machine learning model generated based on machine learning applied to one or more historical communications determined to have communicated respective intents, predicting an intent of the communication, and generating a set of one or more ranked response options corresponding to the intent, wherein the one or more response options of the set are ranked based on respective confidence levels individually determined for the one or more response options relative to the predicted intent.