Schedule Optimization Using Connectivity Networks
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Solution Overview
Problem
Employees traveling to new locations often lack information about local events and relevant individuals, leading to missed opportunities for collaboration and networking.
Innovation Solution
A computer-implemented system processes relationship data to generate a connectivity network, optimizing meeting schedules by linking users with relevant candidates based on interaction indicia and availability, thereby maximizing networking opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If employees travel to new locations without automated scheduling assistance, then they maintain control over their schedules, but they miss opportunities for collaboration and networking with relevant individuals
Solution Approach 1:
The system performs preliminary actions by pre-processing relationship data, event data, and availability information before the employee needs to schedule meetings. The optimization mechanism pre-calculates optimal meeting schedules based on connectivity networks and relevance metrics, so that when employees arrive at new locations, meeting suggestions are already prepared and ready for immediate review and acceptance, eliminating the need for time-consuming manual scheduling research
Solution Approach 2:
The system enables self-service by automatically generating meeting suggestions and optimizing schedules without requiring employee intervention. The optimization mechanism autonomously processes relationship data, evaluates connectivity networks, and populates schedules with relevant meeting opportunities based on predefined relevance criteria, allowing employees to passively benefit from automated collaboration opportunities while maintaining the ability to review and accept or decline suggestions
2Loss of information
If employees manually research and schedule meetings in new locations, then they can select specific individuals, but the process is time-consuming and opportunities are missed
Solution Approach 1:
The system merges multiple data sources including relationship data, event data, availability information, and connectivity networks into a unified scheduling framework. The optimization mechanism integrates these diverse information types to comprehensively identify relevant individuals and events at new locations, providing employees with consolidated meeting suggestions that incorporate all available information without requiring separate manual research efforts
Solution Approach 2:
The optimization mechanism serves as an intermediary between raw data (relationship information, event data, availability) and the employee's schedule. It processes and translates unstructured relationship data into structured meeting suggestions with relevance scores, acting as a mediator that converts complex multi-source information into actionable scheduling recommendations that employees can easily review and accept
3Productivity
If the system populates all open meeting spaces with suggested meetings, then networking opportunities are maximized, but the schedule becomes overly dense and less flexible
Solution Approach 1:
The system applies partial action by populating only the most relevant open meeting spaces with high-priority meeting suggestions based on connectivity network analysis and relevance metrics. Rather than forcing meetings into all available slots, the optimization mechanism selectively fills schedules with meetings that exceed minimum relevance thresholds, providing sufficient networking opportunities while preserving schedule flexibility and avoiding over-scheduling
Solution Approach 2:
The system implements dynamics by allowing the meeting schedule to remain flexible and adaptable. Meeting suggestions are generated dynamically based on real-time availability data and connectivity networks, and employees retain the ability to accept, decline, or modify suggestions. The optimization mechanism continuously re-evaluates scheduling options as availability changes, enabling the schedule to adapt to emerging opportunities and constraints rather than being rigidly fixed
Data Source
AI summary
Techniques to improve a schedule using optimization are described. Some described techniques improve the schedule using optimization upon a user's travel booking operation and/or in response to changes in the user's relationships. The techniques include an apparatus, a method, and a computer-readable medium configured to process relationship data associated with potential candidates for a set of meetings in a schedule, relationship data corresponding to interaction indicia with each potential candidate, generate, from the relationship data, a connectivity network comprising links with the potential candidates, each link of the links corresponding to a relevance value between a user and a specific potential candidate, and populate, via an optimization unit, open meeting spaces in the schedule with meeting data based upon the connectivity network and availability data of the potential candidates, the schedule being configured to substantially maximize relevancy of the set of meetings. Other embodiments are described and claimed.