Machine Learning Network Session Interaction for Adaptive Responses
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Solution Overview
Problem
Existing data management systems lack efficient and accurate methods for categorizing and responding to user experience feedback in network session interactions, leading to resource wastage and inefficiencies.
Innovation Solution
A machine learning-based system that receives user input, extracts attributes, determines adaptive response actions using ML models, and executes them, while categorizing inputs for efficient escalation and resource conservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data management methods are used for categorizing and responding to user feedback, then system complexity is low, but productivity and response efficiency are poor
Solution Approach 1:
The patent replaces manual data management processes with an automated machine learning-based system. The ML subsystem automatically categorizes user feedback, extracts attributes, determines response actions, and executes responses without human intervention, thereby dramatically improving productivity while accepting increased system complexity through automated intelligence.
Solution Approach 2:
The system implements self-service through autonomous ML models that independently process user feedback, categorize it, determine appropriate responses, and execute actions without requiring manual human operation. The ML subsystem serves itself by automatically learning from data and making decisions, eliminating the need for continuous human management.
2Measurement precision
If comprehensive data processing is performed on all user feedback, then measurement precision is high, but use of energy and computing resources increases
Solution Approach 1:
The patent extracts only the most relevant attributes from user feedback data using the ML subsystem. Instead of processing all data comprehensively, the system identifies and extracts key attributes necessary for accurate categorization and response determination, thereby maintaining high measurement precision while reducing computing resource usage by focusing only on essential information.
3Speed
If manual review and processing of user feedback is performed, then adaptability to complex situations is high, but loss of time and processing speed are poor
Solution Approach 1:
The patent implements preliminary action by pre-training ML models on extensive datasets before deployment. The models are预先 trained to recognize patterns, categorize feedback, and determine appropriate responses, enabling them to quickly adapt to new situations without requiring manual review. This preliminary preparation allows the system to achieve high processing speed while maintaining adaptability to complex user feedback scenarios.
Data Source
AI summary
Systems, computer program products, and methods are described herein for machine learning based network session interaction. The present disclosure is configured to receive, from an end-point device, a user input, wherein the user input comprises natural language data; extract a first set of attributes associated with the user input; determine, using a machine learning (ML) subsystem, a first set of adaptive response actions to the user input based on the first set of attributes; and execute the first set of adaptive response actions.