Personalized Event Consolidation Bot for Calendar Conflict Resolution

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Solution Overview

Problem

Event management involves manually cross-referencing and resolving conflicts between multiple calendars, which is time-consuming and prone to errors due to human limitations.

Innovation Solution

A personalized event consolidation bot (PECB) uses machine learning models to classify event data structures as duplicative or conflicting, and merges or prioritizes them accordingly, rendering consolidated event data structures in electronic calendars.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual cross-referencing of multiple calendars is performed, then event conflicts can be identified, but the process is time-consuming and error-prone

Engineering Contradiction:

Improveaccuracy of conflict identificationVSAvoidtime required for manual cross-referencing

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs automatic self-service by using machine learning models to independently classify and consolidate events from multiple calendars without requiring manual human intervention. The bot autonomously identifies duplicative and conflicting events, merges them, and updates calendars, thereby eliminating time-consuming manual cross-referencing while maintaining high accuracy through automated classification algorithms.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical human cognitive process of manually cross-referencing calendars with an automated computational system. Machine learning models substitute human mental capabilities for detecting patterns, comparing events, and resolving conflicts, thereby eliminating errors associated with human limitations and dramatically reducing the time required for calendar consolidation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If multiple calendars are managed manually, then comprehensive event tracking is possible, but human mental capabilities are overwhelmed leading to errors

Engineering Contradiction:

Improveability to manage multiple calendar sourcesVSAvoidaccuracy of event information

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system autonomously manages multiple calendar sources by automatically classifying events as duplicative or conflicting using machine learning models. The bot independently processes events from diverse calendars, merges duplicative events, and resolves conflicts without human intervention, thereby maintaining high adaptability to handle multiple sources while ensuring reliable accuracy through automated classification algorithms.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The event consolidation bot serves multiple functions simultaneously: it consolidates duplicative events across different calendars, identifies and resolves conflicting events, and updates multiple calendar sources. This multi-functional approach enables comprehensive management of diverse calendar sources while maintaining accuracy through a unified machine learning-based classification system.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Productivity

If events are consolidated automatically using machine learning, then manual effort is reduced, but system complexity increases

Engineering Contradiction:

Improvespeed of event consolidationVSAvoidcomplexity of machine learning system

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The event consolidation bot acts as an intermediary layer between multiple calendar sources and the user. It employs machine learning models as mediators to automatically classify and consolidate events, thereby reducing manual effort and increasing productivity. The bot manages the complexity of the machine learning system internally while presenting a simplified interface to users, effectively bridging the gap between complex automated processing and simple user interaction.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250124409A1Personalized Event Consolidation Bot

Publication Date: 2025.04.17 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Mechanisms are provided to consolidate event data structures in an electronic calendar. An event data structure for an event is received and features are extracted from the event data structure and another having an overlapping temporal location in the electronic calendar. The event data structure is classified as to whether it is duplicative of the other event data structure and the event data structures are rendered based on the classification. This rendering may involve consolidating the event data structures into a consolidated event data structure if they are duplicative, and prioritizing the event data structures relative to each other if they are not duplicative.