Representative Support Task Delegation Using Real-Time NLP
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Solution Overview
Problem
Existing systems lack an efficient framework for automating the processing and delegation of tasks based on real-time communications and member preferences, leading to increased cognitive load for individuals seeking task assistance.
Innovation Solution
A computer-implemented method that includes real-time analysis of member communications to identify and delegate tasks using Natural Language Processing (NLP) and sentiment analysis, automating the generation and monitoring of task proposals, and updating member profiles to improve task recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual task processing and delegation is used, then task completion can be achieved, but cognitive load for individuals increases and efficiency decreases
Solution Approach 1:
The system enables self-service by automatically analyzing communications, identifying tasks, and delegating them without human intervention. The NLP-based system processes messages, extracts task requirements, and autonomously assigns tasks to appropriate representatives, eliminating the need for manual task management and reducing cognitive load on users.
Solution Approach 2:
The patent replaces manual mechanical task processing with an automated NLP-based system. The system uses natural language processing algorithms to analyze communications, identify tasks, and delegate them automatically, substituting human cognitive effort with computational processing and significantly improving task processing efficiency.
2Measurement precision
If real-time analysis of communications is implemented, then task identification accuracy improves, but system complexity increases
Solution Approach 1:
The system introduces an intermediary NLP processing layer between communications and task identification. This intermediary automatically analyzes message content, extracts task requirements, and translates natural language communications into structured task definitions, improving identification accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The system changes parameters by transforming unstructured communication data into structured task parameters. The NLP system extracts key information from messages and converts it into standardized task parameters that can be processed by the delegation system, improving accuracy through systematic parameter transformation.
3Speed
If automated proposal generation is used, then task delegation speed increases, but proposal quality may decrease
Solution Approach 1:
The system implements feedback mechanisms where the NLP system continuously learns from communication patterns and task outcomes. By analyzing successful task delegations and communication styles, the system refines its proposal generation algorithms, maintaining high proposal quality while achieving rapid automated delegation through iterative improvement.
Data Source
AI summary
Systems and methods for implementing a representative support system in a task determination system are provided. The task determination system automatically receives in real-time a set of messages between a member and a representative as these messages are exchanged. The set of messages correspond to a set of proposals associated with a task. The task determination system automatically identifying a selection of a proposal from the set of proposals based on an analysis of the set of messages. Based on the selection, the task determination system generates a set of proposal tasks and processes communications associated with these proposal tasks to monitor performance of these proposal tasks for completion of the task.