Dynamic Event Scheduling via Machine Learning Message Analysis
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Solution Overview
Problem
Current event scheduling systems are labor-intensive and inefficient, particularly in large enterprises, as they require manual input and lack automated tools for standardization and quality control, leading to potential non-compliance with enterprise policies.
Innovation Solution
The implementation of a system that includes a machine learning engine, a message handler, and a scheduling engine to automate dynamic and interactive event scheduling, utilizing natural language processing to analyze messages, optimize event parameters, and enforce compliance with enterprise standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual typing and communication methods are used for event scheduling, then individuals can obtain event information, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system enables self-service scheduling by allowing individuals to automatically schedule events through natural language messages. The machine learning engine processes these messages autonomously, extracting event parameters and coordinating schedules without requiring manual typing or direct human-to-human coordination for each scheduling detail.
Solution Approach 2:
The patent replaces the mechanical system of manual typing and direct communication with an automated information processing system. The machine learning engine acts as an intelligent intermediary that automatically parses messages, extracts scheduling parameters, and coordinates events, substituting human manual operations with automated computational processes.
2Adaptability or versatility
If multiple individuals manually coordinate events across an enterprise, then event information can be exchanged, but the process becomes labor-intensive and difficult to standardize
Solution Approach 1:
The machine learning engine serves as an intelligent intermediary between individuals and the scheduling system. It receives natural language messages from users, automatically processes the information, and coordinates with the scheduling system to manage events. This intermediary layer simplifies the interaction model and enables standardized processing of diverse scheduling requests.
Solution Approach 2:
The system provides universal event management capabilities that can handle various types of events and coordination scenarios through a single standardized interface. The machine learning engine can process different message formats and extract relevant parameters universally, while the scheduling system manages diverse event types through unified templates and workflows.
3Reliability
If manual event tracking is performed, then attendance information can be collected, but quality controls and standards enforcement become difficult
Solution Approach 1:
The system implements automated feedback mechanisms where the machine learning engine continuously monitors event participation and sends follow-up messages to confirm attendance. This feedback loop ensures accurate tracking of event participation and automatically enforces quality standards by verifying that required events are attended and documented.
Solution Approach 2:
Attendees automatically confirm their participation through the messaging system, and the machine learning engine autonomously tracks and records attendance information. This self-service approach eliminates the need for manual tracking while ensuring accurate data collection and automatic enforcement of participation requirements.
Data Source
AI summary
The present disclosure provides a system having functionality that dynamic and interactive event scheduling. A message handler is provided to support automated message sequences, such as automated chat sessions and e-mail communications, in which the system exchanges messages with users in connection with scheduling or rescheduling events. A scheduling engine is provided to support creation and management of events, such as to create templates that may be used for event creation, as well as tracking attendance of events and other event-related information. A machine learning engine is provided to support analysis of messages exchanged between the system and users, where outputs of the machine learning engine may be used to identify optimal event parameters for events (e.g., optimal dates, times, locations, etc.)