ML Occupancy Forecasting Platform for Dynamic Resource Allocation
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Solution Overview
Problem
Enterprise organizations face challenges in identifying optimal resource allocations in real-time under occupancy modifications, leading to reduced efficiencies and safety risks in physical spaces.
Innovation Solution
A computing platform that uses machine learning to analyze enterprise data, internal policies, and user input to determine valid occupancy permissions, dynamically managing resources and updating security information to enforce these permissions, thereby optimizing resource allocation and ensuring safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual resource allocation methods are used, then employees have flexibility in planning their work, but optimal resource allocation cannot be achieved in real-time under occupancy modifications
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between occupancy modifications and resource allocation decisions. The model processes occupancy data, enterprise data, and user input to generate optimized resource allocation recommendations, enabling real-time adaptive planning without requiring complex manual coordination systems.
Solution Approach 2:
The system enables employees to self-report their availability and task information through user interfaces. The machine learning model then automatically processes this self-service data along with occupancy modifications to generate resource allocation plans, reducing the need for manual intervention while maintaining employee flexibility.
2Reliability
If occupancy modifications are implemented without real-time resource allocation, then safety risks are reduced, but enterprise efficiencies are reduced
Solution Approach 1:
The system implements continuous feedback loops where occupancy modifications are monitored in real-time, and the machine learning model continuously generates updated resource allocation recommendations based on current occupancy status, enterprise data, and user input. This ensures safety constraints are maintained while optimizing enterprise efficiency dynamically.
Solution Approach 2:
The resource allocation system is designed to be dynamic and adaptive, automatically adjusting allocations in real-time based on occupancy modifications. The machine learning model reprocesses data whenever occupancy changes occur, enabling the system to maintain safety assurance while responding flexibly to changing conditions to preserve enterprise efficiency.
3Loss of time
If reactive resource planning is used, then response to occupancy changes is simple, but availability and workload cannot be evaluated in advance
Solution Approach 1:
The system collects and processes user input information including availability and task information in advance of occupancy modifications. The machine learning model evaluates this preliminary data to predict resource allocation needs before occupancy changes occur, enabling proactive planning that reduces planning time loss while maintaining accurate availability evaluation.
Data Source
AI summary
Aspects of the disclosure relate to using machine learning for resource planning. A computing platform may detect an occupancy modification event for a physical space. Based on detecting the occupancy modification event, the computing platform may send commands directing display of a data collection prompt to end user devices, which may prompt for work to be performed by users of the end user devices in the physical space during a first day. Using natural language processing, the computing platform may analyze user input information and other occupancy data to determine whether or not the users of the end user devices have permission to occupy the physical space during the first day. The computing platform may cause the end user devices to display a resource management interface indicating whether or not the users of the end user devices have valid permission to physically occupy the physical space during the first day.