Methods and systems for adaptive triggering of data collection

• US 9,971,320 B2
• Filed: 07/15/2014
• Issued: 05/15/2018
• Est. Priority Date: 07/03/2014
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google LLC (Alphabet Inc.)
• Inventors: Angermann, Michael, Frassl, Martin, Williams, Brian Patrick, Robertson, Patrick Otto
• Primary Examiner(s): Ngon, Ricky
• Application Number:
  US14/331,945
• Publication Number:
  US 20160003972A1
• Time in Patent Office: 1,400 Days
• Field of Search: 702 5
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 2219/2642 Domotique, domestic, home control, automation, smart house

Abstract

A computing system may receive a map of features in an environment. The computing system may identify one or more regions of the map for data collection. The computing system may receive sensor data from a plurality of devices. The sensor data may be associated with one or more periods of time when the sensor data was collected by the plurality of devices. The computing system may determine a likelihood of one or more devices being within a portion of the environment that corresponds to the one or more regions of the map for data collection during a future period of time. The computing device may provide a request for given sensor data from the one or more devices based on the likelihood. The given sensor data may be associated with the future period of time.

Claims

1. A method comprising:

receiving, by a computing system that includes one or more processors, a map of physical features in a physical environment;

determining, based on the map, a given time that corresponds to a previous update to the one or more regions of the map;

based on at least the given time being prior to a threshold time, identifying one or more regions of the map for data collection;

receiving sensor data from a plurality of devices, wherein the sensor data is associated with one or more periods of time when the sensor data was collected by the plurality of devices;

determining, based on the sensor data, a likelihood of one or more devices of the plurality of devices being within a portion of the physical environment that corresponds to the identified one or more regions of the map for data collection during a future period of time; and

based on the likelihood being greater than a threshold likelihood, and further based on the identified one or more regions, controlling the one or more devices to collect, during the future period of time, given sensor data in the portion of the physical environment that corresponds to the identified one or more regions, wherein the given sensor data pertains to physical features in the portion of the physical environment that corresponds to the identified one or more regions.

2. The method of claim 1, further comprising:

determining, based on the sensor data, a rate of change of one or more of the physical features within the portion of the physical environment that corresponds to the identified one or more regions of the map, wherein identifying the one or more regions is further based on the rate of change being greater than a threshold rate.

3. The method of claim 1, wherein identifying the one or more regions is further based on the one or more regions being associated with an amount of data pertaining to the physical features in the physical environment that is less than a threshold amount.

4. The method of claim 1, further comprising:

determining, based on the sensor data, a pattern of motion of the one or more devices, wherein determining the likelihood is based on the pattern.

5. The method of claim 4, further comprising:

determining, based on an anticipation of a reoccurrence of the pattern, the future period of time for collection of the given sensor data by the one or more devices, wherein providing the request is based on the determination of the future period of time.

6. The method of claim 1, wherein the physical features in the physical environment include one or more of:

objects in the physical environment, motion of given objects in the physical environment, a magnetic field in the physical environment, an electromagnetic radiation pattern in the physical environment, wireless transmitters in the physical environment, signal strengths of the wireless transmitters in the physical environment, sounds in the physical environment, or an altitude of a given portion of the physical environment.

7. The method of claim 1, further comprising:

receiving the given sensor data from the one or more devices; and

updating, based on the given sensor data, the identified one or more regions of the map.

8. A computing system comprising:

one or more processors; and

data storage configured to store instructions executable by the one or more processors to cause the computing system to;

receive a map of physical features in a physical environment;

determine, based on the map, a given time that corresponds to a previous update to the one or more regions of the map;

based on at least the given time being prior to a threshold time, identify one or more regions of the map for data collection;

receive sensor data from a plurality of devices, wherein the sensor data is associated with one or more periods of time when the sensor data was collected by the plurality of devices;

determine, based on the sensor data, a likelihood of one or more devices of the plurality of devices being within a portion of the physical environment that corresponds to the identified one or more regions of the map for data collection during a future period of time; and

based on the likelihood being greater than a threshold likelihood, and further based on the identified one or more regions, control the one or more devices to collect, during the future period of time, given sensor data in the portion of the physical environment that corresponds to the identified one or more regions, wherein the given sensor data pertains to physical features in the portion of the physical environment that corresponds to the identified one or more regions.

9. The computing system of claim 8, wherein the instructions executable by the one or more processors further cause the computing system to:

determine, based on the sensor data, a rate of change of one or more of the physical features within the portion of the physical environment that corresponds to the identified one or more regions of the map, wherein identifying the one or more regions is further based on the rate of change being greater than a threshold rate.

10. The computing system of claim 8, wherein identifying the one or more regions is further based on the one or more regions being associated with an amount of data pertaining to the physical features in the physical environment that is less than a threshold amount.

11. The computing system of claim 8, wherein the instructions executable by the one or more processors further cause the computing system to:

determine, based on the sensor data, a pattern of motion of the one or more devices, wherein determining the likelihood is based on the pattern.

12. The computing system of claim 11, wherein the instructions executable by the one or more processors further cause the computing system to:

determine, based on an anticipation of a reoccurrence of the pattern, the future period of time for collection of the given sensor data by the one or more devices, wherein providing the request is based on the determination of the future period of time.

13. The computing system of claim 8, wherein the instructions executable by the one or more processors further cause the computing system to:

receive the given sensor data from the one or more devices; and

update, based on the given sensor data, the identified one or more regions of the map.