Environment sharing

Patent metadata

Field	Value
Publication number	US-11483407-B1
Application number	US-202117509266-A
Country	US
Kind code	B1
Filing date	Oct 25, 2021
Priority date	Oct 25, 2021
Publication date	Oct 25, 2022
Grant date	Oct 25, 2022

Abstract

A method includes receiving, by a computing device, determining, by a computing device, a location of a user device; retrieving, by the computing device, public information from public sources using the location of the user device; providing, by the computing device, the public information to the user device; locating, by the computing device, environment devices within an environment of the user device using the location of the user device; collecting, by the computing device, data from the environment devices; determining, by the computing device, whether obstacles are present in the user environment using the collected data and the public information; and providing, by the computing device, alerts to the user device about the obstacles.

First claim

What is claimed is:

1. A method, comprising:
determining, by a computing device, a location of a user device;
retrieving, by the computing device, public information from public sources using the location of the user device;
providing, by the computing device, the public information to the user device;
generating, by the computing device, a plurality of target images from the public information using a machine learning model, each of the target images being a different type of obstacle;
locating, by the computing device, environment devices within an environment of the user device using the location of the user device;
collecting, by the computing device, data from the environment devices;
determining, by the computing device, whether obstacles are present in the user environment by comparing the collected data to the generated target images; and
providing, by the computing device, alerts to the user device about the obstacles present in the user environment in response to the collected data matching at least one of the target images.

2. The method of claim 1 , wherein the determining the location of the user device includes accessing GPS data from a GPS application of the user device and calendar data from a calendar application of the user device.

3. The method of claim 1 , wherein the locating the environment devices includes accessing GPS data of the environment devices.

4. The method of claim 1 , wherein the collecting the data from the environment devices includes accessing sensors, applications, and devices of the environment devices.

5. The method of claim 4 , wherein the collecting the data from the environment devices includes taking images using camera devices of the environment devices.

6. The method of claim 5 , wherein the determining whether the obstacles are present in the user environment includes comparing pixels in an image of the images to pixels in a target image.

7. The method of claim 1 , wherein the public sources include Wi-Fi networks, and the public information of the user device includes images, infraction rates, online map data including terrain changes, weather reports, city planning material including blueprints, building maps, geographic points of interests, zoning maps, building permits, construction sites, traffic patterns, accident reports, restaurants, and advertisements.

8. The method of claim 1 , wherein the public sources include a public database, and the public database includes a county level government public database, state government public databases, and federal government databases.

9. The method of claim 1 , further comprising training the machine learning model for the determining whether the obstacles are present in the user environment, and the machine learning model comprises generative adversarial networks (GANs).

10. The method of claim 1 , wherein the environment devices are selected from the group consisting of smartphones, smart vehicles, and smart cameras.

11. The method of claim 1 , wherein the providing the alerts to the user device includes providing vibration alerts, audible alarm alerts, voice messaging alerts, and text messaging alerts.

12. The method of claim 1 , wherein the computing device includes software provided as a service in a cloud environment.

13. A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:
determine a location of a user device;
retrieve public information from a public database using the location of the user device;
provide the public information to the user device;
generate a plurality of target images from the public information using a machine learning model, each of the target images being a different type of obstacle;
locate environment devices within an environment of the user device using the location of the user device;
collect data from the environment devices;
determine whether obstacles are present in the user environment by comparing image data of the collected data to the generated target images;
provide alerts to the user device about the obstacles present in the user environment in response to image data of the collected data matching at least one of the target images; and
provide feedback verifying that the obstacles are present in the user environment.

14. The computer program product of claim 13 , wherein the alerts include images of the obstacles.

15. The computer program product of claim 13 , wherein the alerts include descriptions of the obstacles.

16. The computer program product of claim 13 , wherein the alerts include images and descriptions of the obstacles.

17. A system comprising:
a processor, a computer readable memory, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:
determine a location of a user device;
retrieve public information from public networks using the location of the user device;
provide the public information to the user device;
generate a plurality of target images from the public information using a machine learning model, each of the target images being a different type of obstacle;
locate environment devices within an environment of the user device using the location of the user device;
collect data from the environment devices;
analyze pixels of image data of the collected data using the machine learning model;
determine whether obstacles are present in the user environment by comparing the pixels of the image data of the collected data to pixels of the generated target images; and
provide alerts to the user device about the obstacles present in the user environment in response to a predetermined threshold number of the pixels of the image data of the collected data matching the pixels of at least one of the generated target images,
wherein the alerts to the user device are provided within a predetermined range of the obstacles.

18. The system of claim 17 , wherein the environment devices are IoT devices.

19. The system of claim 17 , wherein the alerts include images and descriptions of the obstacles.

20. The system of claim 17 , wherein the alerts include vibration alerts, audible alarm alerts, voice messaging alerts, and text messaging alerts.

Assignees

• IBM

Inventors

• Villalobos Laurent Alonso Andres
• Fornash Sean Thomas
• Golcher Ugalde Ricardo
• Fuleihan Farid
• Vissuskus Christopher Kevin

Classifications

• H04L67/52 Primary

  specially adapted for the location of the user terminal · CPC title

• H04L67/12

  specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks · CPC title