SPACE UTILIZATION MEASUREMENT AND MODELING USING ARTIFICIAL INTELLIGENCE

• US 20200134418A1
• Filed: 10/26/2018
• Published: 04/30/2020
• Est. Priority Date: 10/26/2018
• Status: Active Grant

• Current Assignee: Millerknoll Incorporated
• Original Assignee: Herman Miller Incorporated (Millerknoll Incorporated)
• Inventors: Harrington, Patrick, Cammenga, Alex K.
• Granted Patent: US 11,468,284 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/909 using geographical or spatial information, e.g. location spatiotemporally dependent retrieval from the web G06F16/9537
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06F 18/254 of classification results, e.g. of results related to same input data
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G06N 3/042 Knowledge-based neural networks; Logical representations of neural networks
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: H04W 4/021 Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• CPC Class Codes: H04W 4/025 using location based information parameters
• CPC Class Codes: H04W 4/33 for indoor environments, e.g. buildings

Abstract

Methods and systems for training a multivariate model predicting utilization of a space. One method includes receiving, over a period of time, signals from each of a plurality of mobile devices located in the space and generating, based on the signals received from each of the plurality of mobile devices, a plurality of location data points for each of the plurality of mobile devices, each of the plurality of location data points for a mobile device including a timestamp and a position within the space of the mobile device. The method also includes accessing metadata of the space, and using, with an electronic processor, the plurality location data points for each of the plurality of mobile devices and the metadata of the space to train machine learning engine. In addition, the method includes predicting a utilization of the space using the machine learning engine.

Claims

1. A method of training a multivariate model predicting utilization of a space, the method comprising:

receiving, over a period of time, signals from each of a plurality of mobile devices located in the space;

generating, based on the signals received from each of the plurality of mobile devices, a plurality of location data points for each of the plurality of mobile devices, each of the plurality of location data points for a mobile device including a timestamp and a position within the space of the mobile device;

accessing metadata of the space;

using, with an electronic processor, the plurality location data points for each of the plurality of mobile devices and the metadata of the space to train machine learning engine; and

predicting a utilization of the space using the machine learning engine.

2. The method of claim 1, wherein receiving the signals from each of the plurality of mobile devices includes receiving passive signals from each of the plurality of mobile devices.

3. The method of claim 1, wherein each of the plurality of location data points for the mobile device includes an identifier of the mobile device.

4. The method of claim 1, wherein the position within the space included in each of the plurality of location data points includes a latitude value and a longitude value.

5. The method of claim 4, wherein the position within the space included in each of the plurality of location data points includes an altitude value.

6. The method of claim 1, wherein generating the plurality of location data points for each of the plurality of mobile devices includes generating a geohash of the position within the space included in each of the plurality of data points.

7. The method of claim 6, further comprising appending, for each of the plurality of location data points for the mobile device, appending the timestamp to the geohash generated of the position within the space.

8. The method of claim 7, further comprising detecting a collision of a subset of the plurality of mobile devices by comparing the geohashes generated for each of the plurality of mobile devices.

9. The method of claim 8, wherein comparing the geohashes generated for each of the plurality of mobile devices includes comparing a prefix of the geohashes and the timestamp appended to each of the geohases.

10. The method of claim 1, further comprising

determining, based on the plurality of location data points for at least one of the plurality of mobile devices, an amount of time the at least one of the plurality of mobile devices is positioned within an area within the space associated with a user category;

and

in response to the amount of time exceeding a threshold, assigning the at least one of the plurality of mobile devices to the user category.

11. The method of claim 10, wherein the user category includes at least one selected from a group consisting of a department, a team, and an assigned workspace.

12. The method of claim 1, wherein accessing the metadata of the space includes accessing a position of landmarks included in the space.

13. The method of claim 1, wherein accessing the metadata of the space includes accessing a configuration of the space including defined areas included in the space, a position of landmarks included in the space, a size of the landmarks included in the space, and esthetics of the space.

14. The method of claim 1, wherein the machine learning engine includes a mixture of experts neural network.

15. The method of claim 1, wherein predicting utilization of the space using the machine learning engine includes predicting utilization of the space based on at least one selected from a group consisting of a time, a category of users, and a change to the metadata of the space.

16. The method of claim 1, further comprising outputting the predicted utilization of the space as a heat map overlaid on a floorplan of the space.

17. A system for training a multivariate model predicting utilization of a space, the system comprising:

at least one electronic processor configured to

receive, over a period of time, signals from each of a plurality of mobile devices located in the space,

generate, based on the signals received from each of the plurality of mobile devices, a plurality of location data points for each of the plurality of mobile devices, each of the plurality of location data points for a mobile device including a timestamp and a position within the space of the mobile device,

determine, based on the plurality of location data points for at least one of the plurality of mobile devices, an amount of time the at least one of the plurality of mobile devices is positioned within an area within the space associated with a user category,

in response to the amount of time exceeding a threshold, assigning the at least one of the plurality of mobile devices to the user category,

access metadata of the space,

use the plurality location data points for each of the plurality of mobile devices and the metadata of the space to train machine learning engine, and

predict a utilization of the space using the machine learning engine.

18. The system of claim 17, wherein the user category includes at least one selected from a group consisting of a department, a team, and an assigned workspace.

19. Non-transitory, computer-readable medium including instructions that, when executed by at least one electronic processor, perform a set of functions, the set of functions comprising:

receiving, over a period of time, signals from each of a plurality of mobile devices located in the space;

generating, based on the signals received from each of the plurality of mobile devices, a plurality of location data points for each of the plurality of mobile devices, each of the plurality of location data points for a mobile device including a timestamp and a position within the space of the mobile device;

accessing metadata of the space;

using the plurality location data points for each of the plurality of mobile devices and the metadata of the space to train machine learning engine; and

predicting a utilization of the space using the machine learning engine.

20. The non-transitory, computer-readable medium of claim 19, wherein the machine learning engine includes a mixture of experts neural network representation of a conditional probability density function.