Methods and Systems for Adaptive Triggering of Data Collection

• US 20160003972A1
• Filed: 07/15/2014
• Published: 01/07/2016
• Est. Priority Date: 07/03/2014
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Williams, Brian Patrick, Angermann, Michael, Frassl, Martin, Robertson, Patrick Otto
• Granted Patent: US 9,971,320 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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;

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 one or more regions of the map for data collection during a future period of time; and

providing, based on the likelihood being greater than a threshold likelihood, a request for given sensor data from the one or more devices, wherein the given sensor data is associated with the future period of time, and wherein the given sensor data pertains to the physical features in the physical environment.

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 one or more regions of the map, wherein identifying the one or more regions is 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 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 map, a given time that corresponds to a previous update to the one or more regions of the map, wherein identifying the one or more regions is based on the given time being prior to a threshold time.

5. 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.

6. The method of claim 5, 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.

7. 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.

8. 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 one or more regions of the map.

9. 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;

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 one or more regions of the map for data collection during a future period of time; and

provide, based on the likelihood being greater than a threshold likelihood, a request for given sensor data from the one or more devices, wherein the given sensor data is associated with the future period of time, and wherein the given sensor data pertains to the physical features in the physical environment.

10. The computing system of claim 9, 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 one or more regions of the map, wherein identifying the one or more regions is based on the rate of change being greater than a threshold rate.

11. The computing system of claim 9, wherein identifying the one or more regions is 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.

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

determine, based on the map, a given time that corresponds to a previous update to the one or more regions of the map, wherein identifying the one or more regions is based on the given time being prior to a threshold time.

13. The computing system of claim 9, 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.

14. The computing system of claim 13, 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.

15. A method comprising:

determining, by a device that includes one or more processors, a plurality of datasets indicative of output from one or more sensors during a plurality of time-periods within a given period of time, wherein the output from the one or more sensors is indicative of features in an environment of the device;

receiving, from a computing system, a request for data collection by the device according to one or more criteria including a given feature of the features in the environment, a threshold rate of change of the given feature, a position of the device associated with the collected data, or a particular time-period associated with the collected data;

determining rankings for the plurality of datasets, wherein the rankings are based on an association between the plurality of datasets and the one or more criteria;

receiving configuration data indicative of a threshold number of datasets that are associated with the given period of time for provision by the device to the computing system;

identifying, based on the rankings, one or more datasets from within the plurality of datasets, wherein a number of the one or more datasets corresponds to the threshold number; and

providing, by the device, the one or more datasets for receipt by the computing system.

16. The method of claim 15, further comprising:

detecting, based on the output from the one or more sensors, a rate of change of the given feature being greater than the threshold rate indicated by the request, wherein the detection is associated with a given time when the output was received from the one or more sensors; and

identifying, based on the detection, a given dataset associated with a time-period that includes the given time, wherein identifying the one or more datasets includes identifying the given dataset.

17. The method of claim 15, further comprising:

receiving, by the device, a map of given features in the environment of the device; and

comparing, by the device, the given features of the map with the features indicated by the output from the one or more sensors, wherein identifying the one or more datasets is based also on the comparison.

18. The method of claim 17, wherein the map is included in a memory of the device, and wherein the given features correspond to previous output from the one or more sensors.

19. The method of claim 17, further comprising receiving the map from the computing system.

20. The method of claim 15, wherein the features in the environment include one or more of objects in the environment, motion of given objects in the environment, a magnetic field in the environment, an electromagnetic radiation pattern in the environment, wireless transmitters in the environment, signal strengths of the wireless transmitters in the environment, sounds in the environment, or an altitude of a given portion of the environment.