Method and system for intelligently managing facilities

Patent metadata

Field	Value
Publication number	US-12020186-B2
Application number	US-202217707779-A
Country	US
Kind code	B2
Filing date	Mar 29, 2022
Priority date	Dec 14, 2021
Publication date	Jun 25, 2024
Grant date	Jun 25, 2024

Abstract

A system and method for managing one or more physical spaces includes receiving data relating to location of a plurality of users, determining based on the location, an occupancy rate of the plurality of users for at least one of the one or more physical spaces, identifying based on the occupancy rate and features provided at the one or more physical spaces, one or more user preferred features, providing the occupancy rate and the user preferred features to a trained machine-learning (ML) model for determining optimal uses for the one or more physical spaces in a future time period, receiving as an output from the trained ML model suggested plans for use or management of the one or more physical spaces in the future time period, and providing the suggested plans for display in a user interface (UI) screen.

First claim

What is claimed is:

1. A data processing system comprising:
a processor; and
a memory in communication with the processor, the memory comprising executable instructions that, when executed by the processor, cause the data processing system to perform functions of:
receiving data relating to location of a plurality of users;
determining based on the location, an occupancy rate for one or more physical spaces;
identifying based on the occupancy rate and features provided at the one or more physical spaces, one or more user preferred features;
determining, via a trained machine-learning (ML) model, optimal uses for the one or more physical spaces in a future time period, the trained ML model receiving the occupancy rate and the user preferred features as inputs and providing as an output one or more suggested plans for use or management of the one or more physical spaces in the future time period; and
providing the one or more suggested plans for display in a user interface (UI) screen,
wherein a training dataset used to train the trained ML model is updated and the updated training dataset is used to provide updated training to the trained ML model.

2. The data processing system of claim 1 , wherein the location is inferred based on at least one of user data, contextual data and facility data.

3. The data processing system of claim 1 , wherein the user preferred features include at least one of WiFi, refreshments, parking options, and transportation options.

4. The data processing system of claim 1 , wherein the memory comprises executable instructions that, when executed by processor, further cause the data processing system to perform functions of providing for displaying the occupancy rate of at least one of the one or more physical spaces on a UI screen associated with reserving the at least one of the one or more physical spaces.

5. The data processing system of claim 1 , wherein the one or more suggested plans include at least one of divesting of at least one of the one or more physical spaces which has an occupancy rate below a first threshold, acquiring additional physical spaces in a vicinity of the one or more physical spaces which has an occupancy rate above a second threshold, and obtaining at least one of the one or more employee user preferred features for at least one of the one or more physical spaces that does not have the at least one of the one or more user preferred features.

6. The data processing system of claim 1 , wherein the memory comprises executable instructions that, when executed by processor, further cause the data processing system to perform functions of providing a survey to one or more users for rating the one or more physical spaces.

7. The data processing system of claim 1 , wherein the memory comprises executable instructions that, when executed by processor, further cause the data processing system to perform functions of:
providing a first selectable UI element for selecting one of the one or more physical spaces;
providing a second selectable UI element for selecting a desired time period; and
providing a third selectable UI element for reserving a space at the selected one of the one or more physical spaces for the desired time period.

8. A method for managing one or more physical spaces comprising:
receiving data relating to location of a plurality of users;
determining based on the location, an occupancy rate for one or more physical spaces;
identifying based on the occupancy rate and features provided at the one or more physical spaces, one or more user preferred features;
determining, via a trained machine-learning (ML) model, optimal uses for the one or more physical spaces in a future time period, the trained ML model receiving the occupancy rate and the user preferred features as inputs and providing as an output one or more suggested plans for use or management of the one or more physical spaces in the future time period; and
providing the one or more suggested plans for display in a user interface (UI) screen,
wherein a training dataset used to train the trained ML model is updated and the updated training dataset is used to provide updated training to the trained ML model.

9. The method of claim 8 , wherein the location is inferred based on at least one of user data, contextual data and facility data.

10. The method of claim 8 , wherein the user preferred features include at least one of WiFi, refreshments, parking options, and transportation options.

11. The method of claim 8 , further comprising providing for displaying the occupancy rate of at least one of the one or more physical spaces on a UI screen associated with reserving the at least one of the one or more physical spaces.

12. The method of claim 8 , wherein the one or more suggested plans include at least one of divesting of at least one of the one or more physical spaces which has an occupancy rate below a first threshold, acquiring additional physical spaces in a vicinity of the one or more physical spaces which has an occupancy rate above a second threshold, and obtaining at least one of the one or more user preferred features for at least one of the one or more physical spaces that does not have the at least one of the one or more employee user preferred features.

13. The method of claim 8 , further comprising providing a survey to one or more users for rating the one or more physical spaces.

14. The method of claim 8 , further comprising:
providing a first selectable UI element for selecting one of the one or more physical spaces;
providing a second selectable UI element for selecting a desired time period; and
providing a third selectable UI element for reserving a space at the selected one of the one or more physical spaces for the desired time period.

15. A non-transitory computer readable medium on which are stored instructions that, when executed, cause a programmable device to perform functions of:
receiving data relating to location of a plurality of users;
determining based on the location, an occupancy rate for one or more physical spaces;
identifying based on the occupancy rate and features provided at the one or more physical spaces, one or more user preferred features;
determining, via a trained machine-learning (ML) model, optimal uses for the one or more physical spaces in a future time period, the trained ML model receiving the occupancy rate and the user preferred features as inputs and providing as an output one or more suggested plans for use or management of the one or more physical spaces in the future time period; and
providing the one or more suggested plans for display in a user interface (UI) screen,
wherein a training dataset used to train the trained ML model is updated and the updated training dataset is used to provide updated training to the trained ML model.

16. The non-transitory computer readable medium of claim 15 , wherein the location is inferred based on at least one of user data, contextual data and facility data.

17. The non-transitory computer readable medium of claim 15 , wherein the one or more suggested plans include at least one of divesting of at least one of the one or more physical spaces which has an occupancy rate below a first threshold, acquiring additional physical spaces in a vicinity of the one or more physical spaces which has an occupancy rate above a second threshold, and obtaining at least one of the one or more user preferred features for at least one of the one or more physical spaces that does not have the at least one of the one or more user preferred features.

Assignees

• Microsoft Technology Licensing Llc

Inventors

• Kenyon Heidi
• Chong Choo Yei
• Slater Megan Elizabeth
• Dollar Christopher Michael
• Copfer Wende Ellen
• Kannepalli Venkata Sreekanth
• Erfani Shabnam
• Huang Amy Lieh-An
• Shah Neha Parikh
• Doran Edward Michael

Classifications

• G06Q10/1093

  Calendar-based scheduling for persons or groups · CPC title

• G06Q10/06

  Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling · CPC title

• G06Q10/00

  Administration; Management · CPC title

• G06N20/00

  Machine learning · CPC title

• G06N5/04

  Inference or reasoning models · CPC title