Device location based on machine learning classifications

Patent metadata

Field	Value
Publication number	US-11051129-B2
Application number	US-201916296153-A
Country	US
Kind code	B2
Filing date	Mar 7, 2019
Priority date	Aug 31, 2017
Publication date	Jun 29, 2021
Grant date	Jun 29, 2021

Abstract

A venue system of a client device can submit a location request to a server, which returns multiple venues that are near the client device. The client device can use one or more machine learning schemes (e.g., convolutional neural networks) to determine that the client device is located in one of specific venues of the possible venues. The venue system can further select imagery for presentation based on the venue selection. The presentation may be published as ephemeral message on a network platform.

First claim

What is claimed is:

1. A method comprising:
identifying, on a user device, a venue dataset comprising a plurality of venues, each venue of the plurality of venues associated with one or more tags that describe the venue;
identifying an image on the user device;
classifying the image using a machine learning scheme;
selecting a venue from the venue dataset based on the classified image matching a tag associated with the venue; and
storing the selected venue on the user device.

2. The method of claim 1 , wherein the plurality of venues are categorized on the user device into categories and subcategories, the one or more tags being metadata of the categories and subcategories.

3. The method of claim 2 , further comprising:
selecting one or more display elements based on the venue matching the tag; and
displaying, on the user device, a presentation comprising the one or more display elements.

4. The method of claim 3 , wherein the one or more display elements are categorized into the categories and subcategories.

5. The method of claim 4 , wherein the one or more display elements are associated with the selected venue in that the one or more display elements and the selected venue share at least one of a same category or subcategory.

6. The method of claim 3 , wherein the one or more display elements include an avatar of a user of the user device.

7. The method of claim 3 , wherein the one or more display elements include text data describing the selected venue.

8. The method of claim 1 , further comprising:
transmitting, by the user device, a network request for venues that are proximate to the user device:
receiving the venue dataset in response to the network request for venues; and
storing the venue dataset on the user device.

9. The method of claim 8 , further comprising:
generating, by the user device, location data describing a location of the user device, wherein the network request includes the location data.

10. The method of claim 9 , wherein the location data includes global positioning system (GPS) data generated by a GPS sensor on the user device.

11. The method of claim 9 , wherein the location data includes an Internet Protocol (IP) Network visible to a IP network sensor of the user device.

12. The method of claim 1 , further comprising:
generating the image using an image sensor on the user device.

13. The method of claim 1 , wherein the machine learning scheme is a convolutional neural network trained to classify image features in images.

14. The method of claim 13 , wherein the image features are physical items depicted in the images.

15. The method of claim 1 , wherein the venues are physical environments.

16. The method of claim 15 , wherein the physical environments include one or more of: an outdoor environment, an indoor environment, a restaurant, a beach, a park, a retail store, a concert stage, a transportation station, a school campus.

17. A system comprising:
one or more processors of a machine; and
a memory storing instructions that, when executed by the one or more processors, cause the machine to perform operations comprising:
identifying, on a user device, a venue dataset comprising a plurality of venues, each venue of the plurality of venues associated with one or more tags that describe the venue;
identifying an image;
classifying the image using a machine learning scheme;
selecting a venue from the venue dataset based on the classified image matching a tag associated with the venue; and
storing the selected venue.

18. The system of claim 17 , wherein the plurality of venues are categorized into categories and subcategories, the one or more tags being metadata of the categories and subcategories.

19. A machine-readable storage medium embodying instructions that, when executed by a machine, cause the machine to perform operations comprising:
identifying, on a user device, a venue dataset comprising a plurality of venues, each venue of the plurality of venues associated with one or more tags that describe the venue;
identifying an image;
classifying the image using a machine learning scheme;
selecting a venue from the venue dataset based on the classified image matching a tag associated with the venue; and
storing the selected venue.

20. The machine-readable storage medium of claim 19 , wherein the plurality of venues are categorized into categories and subcategories, the one or more tags being metadata of the categories and subcategories.

Assignees

• Snap Inc

Inventors

• Charlton Ebony James
• Hanumante Sumant
• Ren Zhou
• Sagar Dhritiman

Classifications

• G06V20/35

  Categorising the entire scene, e.g. birthday party or wedding scene · CPC title

• G06V20/70

  Labelling scene content, e.g. deriving syntactic or semantic representations · CPC title

• G06V10/82

  using neural networks · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title

• H04W4/029 Primary

  Location-based management or tracking services · CPC title