Automated Task Template Generation from Real-Time Messages
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Solution Overview
Problem
Existing systems lack the ability to automatically identify and recommend tasks and projects to members in a task facilitation service, thereby increasing the member's cognitive load.
Innovation Solution
A computer-implemented method that uses real-time message analysis between members and representatives, combined with a trained machine learning algorithm, to automatically identify issues and recommend suitable task templates, which are then used to generate and perform tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual task identification and recommendation is used, then members can have personalized task suggestions, but the member's cognitive load increases and task processing efficiency decreases
Solution Approach 1:
The system automatically identifies issues from messages and recommends tasks without requiring manual input from members. The machine learning algorithm processes messages autonomously to generate task recommendations, allowing the system to serve itself rather than requiring continuous member intervention.
Solution Approach 2:
The patent replaces manual cognitive processing with automated machine learning algorithms. The ML model substitutes human analysts in identifying issues and recommending tasks, transforming a manual information processing system into an automated one that reduces cognitive load on members.
2Ease of operation
If automated task identification is implemented, then cognitive load is reduced, but system complexity increases
Solution Approach 1:
The patent introduces a machine learning algorithm as an intermediary between message input and task recommendation output. This intermediary layer automatically processes raw messages and transforms them into structured task recommendations, simplifying the user interface while managing system complexity through modular architecture.
Solution Approach 2:
The system segments the task management process into distinct modules: message reception, issue identification by ML algorithm, task template selection, and recommendation generation. This segmentation allows each component to be independently developed and maintained, managing overall system complexity through functional decomposition.
3Measurement precision
If real-time message analysis is performed, then task recommendations are more timely and relevant, but processing time and computational resources increase
Solution Approach 1:
The system pre-processes messages during the communication session itself, continuously analyzing incoming messages in real-time. By performing preliminary issue identification as messages are exchanged, the system prepares task recommendations in advance, reducing the time needed when actual task creation is required.
Solution Approach 2:
The machine learning algorithm continuously receives feedback from the communication session and adjusts its issue identification in real-time. The system monitors message patterns and updates task recommendations dynamically based on evolving conversation context, improving accuracy while maintaining efficient processing through adaptive learning.
Data Source
AI summary
Systems and methods for automatically providing templates for the creation of projects and tasks based on messages exchanged between members and assigned representatives are provided. A system receives, in real-time, a set of messages between a member and a representative as the set of messages are being exchanged. The system, based on these messages, automatically identifies issue. The system can further identify one or more templates for defining a task that is performable to address the issue. The system can present the one or more templates such that, when a template is selected and used to define a task, the task can be performed to address the issue.