Map generator for map-based device management

Patent metadata

Field	Value
Publication number	US-12563363-B2
Application number	US-202318141120-A
Country	US
Kind code	B2
Filing date	Apr 28, 2023
Priority date	Apr 28, 2023
Publication date	Feb 24, 2026
Grant date	Feb 24, 2026

Abstract

Disclosed are system, method and/or computer program products for generating a map for map-based management of a plurality of Internet of Things (IoT) devices. An embodiment obtains first data associated with a mobile device, including data indicative of a relative position of the mobile device with respect to one or more subsets of the plurality of IoT devices at different points in time, and/or second data from each of one or more of the plurality of IoT devices, including data indicative of a relative position of each of the one or more IoT devices with respect to a subset of other IoT devices in the plurality of IoT devices, generates, based on at least the first and/or second data, a map in which each of the IoT devices is assigned a corresponding location, and provides the map to an application that enables map-based management of the plurality of IoT devices.

First claim

What is claimed is:

1 . A computer-implemented method for map-based management of a plurality of Internet of Things (IoT) devices present on a premises, comprising:
obtaining, by at least one computer processor, map-building data associated with a mobile device, the map-building data including positional data that is indicative of a relative position of the mobile device with respect to one or more subsets of the plurality of IoT devices at different points in time; generating, based on at least the map-building data, a map in which each IoT device of the plurality of IoT devices is assigned a corresponding map location; generating a list of two or more of the plurality of IoT devices based on the relative position of the mobile device with respect to the corresponding map locations of the two or more of the plurality of IoT devices; and providing the map and the list to an application that enables the map-based management of the plurality of IoT devices.

2 . The computer-implemented method of claim 1 , wherein the positional data comprises, for a given point in time, one or more of:
received signal strength information for each IoT device in a subset of the plurality of IoT devices; time of flight information for each IoT device in the subset of the plurality of IoT devices; distance information for each IoT device in the subset of the plurality of IoT devices; bearing information for each IoT device in the subset of the plurality of IoT devices; an estimated position of the mobile device based on triangulation or trilateration with respect to the subset of the plurality of IoT devices; image data corresponding to the subset of the plurality of IoT devices; audio data corresponding to the subset of the plurality of IoT devices; LiDAR data corresponding to the subset of the plurality of IoT devices; or Wi-Fi sensing data corresponding to the subset of the plurality of IoT devices.

3 . The computer-implemented method of claim 1 , wherein the map-building data further includes, for a given point in time, one or more of:
a position of the mobile device as determined by a satellite-based positioning system; a position of the mobile device as determined by an indoor positioning system; or motion sensor data obtained from the mobile device.

4 . The computer-implemented method of claim 1 , wherein the map comprises one of a two-dimensional (2D) map or a three-dimensional (3D) map.

5 . The computer-implemented method of claim 1 , wherein the generating the map is performed by a machine-learning (ML) model, and wherein the computer-implemented method further comprises:
modifying a map location of at least one of the plurality of IoT devices based on user input; generating training data based at least on the modification; and using the training data to update the ML model.

6 . The computer-implemented method of claim 1 , further comprising:
by the application:
displaying at least a portion of the map.

7 . The computer-implemented method of claim 1 , further comprising:
determining a location of a user within the map, wherein the list is sorted by a proximity to the user based on the location of the user within the map and the locations of the two or more of the plurality of IoT devices within the map; and displaying, by the application, the list.

8 . The computer-implemented method of claim 1 , further comprising:
by the application:
determining a location and orientation of a user within the map;
based on the location and orientation of the user within the map, generating a simulated user view that includes representations of selected ones of the plurality of IoT devices; and
displaying the simulated view.

9 . The computer-implemented method of claim 1 , further comprising:
by the application:
determining a location of a user within the map;
identifying one or more IoT devices of the plurality of IoT devices having a location within the map that is within a predetermined distance to the location of the user within the map; and
in response to the identifying, actuating an operation of the identified one or more IoT devices.

10 . The computer-implemented method of claim 1 , wherein the generating the map comprises:
generating a floorplan of the premises; and incorporating the floorplan into the map.

11 . The computer-implemented method of claim 10 , wherein the floorplan comprises a plurality of rooms and wherein incorporating the floorplan into the map comprises:
selectively assigning different subsets of the plurality of IoT devices to different ones of the plurality of rooms.

12 . The computer-implemented method of claim 10 , wherein the generating the floorplan is performed by a machine learning (ML) model and wherein the computer-implemented method further comprises:
modifying a feature of the floorplan based on user input; generating training data based at least on the modification; and using the training data to update the ML model.

13 . A system, comprising:
one or more memories; and at least one processor each coupled to at least one of the memories and configured to perform operations comprising:
obtaining map-building data from each of one or more Internet of Things (IoT) devices of a plurality of IoT devices present on a premises, the map-building data including positional data that is indicative of a relative position of each of the one or more IoT devices with respect to a subset of other IoT devices in the plurality of IoT devices;
generating, based on at least the map-building data, a map in which each of the IoT devices in the plurality of IoT devices is assigned a corresponding map location;
generating a list of two or more of the plurality of IoT devices based on the relative position of a mobile device with respect to corresponding map locations of the two or more of the plurality of IoT devices, and
providing the map and the list to an application that enables map-based management of the plurality of IoT devices.

14 . The system of claim 13 , wherein the positional data comprises, for each of the one or more IoT devices of the plurality of IoT devices, one or more of:
received signal strength information for each IoT device in the subset of other IoT devices; time of flight information for each IoT device in the subset of other IoT devices; distance information for each IoT device in the subset of other IoT devices; bearing information for each IoT device in the subset of other IoT devices; an estimated position of IoT device based on triangulation or trilateration with respect to the subset of other IoT devices; image data corresponding to the subset of other IoT devices; audio data corresponding to the subset of other IoT devices; radar data corresponding to the subset of other IoT devices; LiDAR data corresponding to the subset of other IoT devices; or Wi-Fi sensing data corresponding to the subset of other IoT devices.

15 . The system of claim 13 , wherein the map-building data further includes, for each of the one or more IoT devices of the plurality of IoT devices, one or more of:
a position of the IoT device as determined by a satellite-based positioning system; a position of the IoT device as determined by an indoor positioning system; or motion sensor data obtained from the IoT device.

16 . The system of claim 13 , wherein the map comprises one of a two-dimensional (2D) map or a three-dimensional (3D) map.

17 . The system of claim 13 , wherein the generating the map is performed by a machine-learning (ML) model, and wherein

Assignees

• Roku Inc

Inventors

• Garner Gregory M
• Stern David
• Wood Anthony

Classifications

• H04W4/029

  Location-based management or tracking services · CPC title

• G06N20/00

  Machine learning · CPC title

• H04W4/021

  Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences · CPC title

• H04W4/023 Primary

  using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds · CPC title

• G01C21/3804

  Creation or updating of map data · CPC title