Machine Learning Communication Scheduling System
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Solution Overview
Problem
Existing communication systems require manual scheduling for user interactions, which is time-consuming and assumes that the scheduled time remains ideal for communication.
Innovation Solution
A computer-implemented method using machine learning techniques to recommend and initiate electronic communication between users by analyzing communication parameters, objectives, and real-time data to determine favorable communication times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual scheduling is used for communication, then users can control communication times, but it is time-consuming and requires user intervention
Solution Approach 1:
The system automatically schedules communication sessions by monitoring user availability and initiating calls without manual intervention. The communication bot autonomously determines optimal times based on user profiles and schedules sessions, eliminating the need for users to manually coordinate schedules.
Solution Approach 2:
User profiles are pre-configured with availability information, communication preferences, and scheduling parameters before actual communication needs arise. This preliminary setup enables the system to automatically make scheduling decisions without requiring real-time user input or manual coordination.
2Adaptability or versatility
If scheduled communication times are set in advance, then users can plan ahead, but the scheduled time may no longer be ideal when communication is initiated
Solution Approach 1:
The scheduling system dynamically monitors user availability and adjusts communication timing in real-time. Rather than fixing schedules in advance, the system continuously tracks user status and initiates communications at optimal moments based on current availability, making the scheduling adaptive to changing conditions.
Solution Approach 2:
The system receives continuous feedback from user availability data, device status, and communication outcomes to refine and adjust scheduling decisions. This feedback loop enables the system to learn from past interactions and improve future scheduling accuracy, ensuring communications occur at truly optimal times.
3Extent of automation
If automated communication bots are used, then scheduling can be automated, but users still need to manually schedule the initial appointment
Solution Approach 1:
The communication bot performs complete autonomous scheduling without requiring users to initiate or manage the scheduling process. The system independently monitors availability, determines optimal communication times, and executes sessions automatically, freeing users from all scheduling tasks including initial appointment setting.
Solution Approach 2:
The communication bot acts as an intermediary between users, handling all scheduling coordination without requiring direct user-to-user negotiation. The bot autonomously manages the entire scheduling process, from monitoring availability to initiating and coordinating communication sessions, eliminating the need for users to manually schedule even initial appointments.
Data Source
AI summary
Disclosed are systems and methods for initiating communication between users of a user group based on machine learning techniques. The disclosed systems and methods provide a novel framework for automating communication scheduling and communication initiation based on user communication objectives and machine learning techniques. The disclosed framework operates by leveraging available user provided communication parameters, user provided objectives, and various real-time data associated with the users, and using the aforementioned data as inputs for machine learning models, in order to schedule communication between the users, automatically initiate communication between the users, or transmit communication notifications to the users.