Automatic Movement and Activity Tracking

• US 20180128624A1
• Filed: 11/10/2016
• Published: 05/10/2018
• Est. Priority Date: 11/10/2016
• Status: Active Grant

• Current Assignee: SAP SE
• Original Assignee: SAP SE
• Inventors: Strauf, Florian, Fischer, Benjamin
• Granted Patent: US 10,309,787 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/103 Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes A61B5/08 takes precedence; measuring aids for tailors A41H1/00; measuring instruments specially adapted for dentistry A61C19/04
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G01C 21/20 Instruments for performing navigational calculations G01C21/24, G01C21/26 take precedence

Abstract

Computer-implemented systems and methods for classifying movement of an object are provided. Geolocation data for an object and timestamps associated with the geolocation data are processed to automatically identify a movement of the object. The movement is characterized by at least (i) timing data, and (ii) location data indicative of starting and ending locations of the object. One or more criteria for classifying the identified movement are accessed, where the one or more criteria are based on historical data for previous movements. An algorithm that evaluates the timing data and the location data of the identified movement against the one or more criteria is applied. The algorithm is configured to automatically assign a classification of a plurality of classifications to the identified movement based on the evaluation. A determination of whether to provide information on the identified movement to an output destination is made based on the assigned classification.

Claims

1. A computer-implemented method of classifying movement of an object, the method comprising:

receiving geolocation data indicative of an object'"'"'s location during a period of time;

processing the geolocation data and timestamps associated with the geolocation data with a processing system to automatically identify a movement of the object during the period of time, the movement being characterized by at least (i) timing data, and (ii) location data indicative of starting and ending locations of the object;

accessing one or more criteria for classifying the identified movement with the processing system, the one or more criteria being based on historical data for previous movements, the historical data comprising a classification for each of the respective previous movements;

applying an algorithm that evaluates the timing data and the location data of the identified movement against the one or more criteria with the processing system, the algorithm being configured to automatically assign a classification of a plurality of classifications to the identified movement based on the evaluation; and

determining whether to provide information on the identified movement to an output destination based on the assigned classification.

2. The computer-implemented method of claim 1, further comprising:

receiving an indication of whether the assigned classification is correct; and

applying a self-learning algorithm with the processing system to automatically adjust the one or more criteria based on the received indication.

3. The computer-implemented method of claim 2, wherein the received indication is based on an input received from a human user.

4. The computer-implemented method of claim 1, further comprising:

providing the information on the identified movement to the output destination based on the movement being assigned a first classification of the plurality of classifications, the information being provided to the output destination automatically and without user intervention; and

not providing the information to the output destination based on the movement being assigned a second classification of the plurality of classifications, the second classification being different than the first classification.

5. The computer-implemented method of claim 4, wherein the location data comprises latitude and longitude coordinates, the method further comprising:

applying a reverse-geocoding algorithm with the processing system to convert the latitude and longitude coordinates into human-readable addresses or toponyms,

wherein the information is provided to the output destination via a push mechanism or a pull mechanism and includes the human-readable addresses or toponyms and other information associated with the identified movement.

6. The computer-implemented method of claim 5, wherein the output destination is configured to fill one or more elements of a software form automatically and without user intervention based on the human-readable addresses or toponyms and other information.

7. The computer-implemented method of claim 1, wherein the timing data is indicative of a duration of the identified movement, a date associated with the identSified movement, and a time associated with the identified movement.

8. A computer-implemented system for classifying movement of an object, the system comprising:

a processing system; and

computer-readable memory in communication with the processing system encoded with instructions for commanding the processing system to execute steps comprising;

receiving geolocation data indicative of an object'"'"'s location during a period of time;

processing the geolocation data and timestamps associated with the geolocation data to automatically identify a movement of the object during the period of time, the movement being characterized by at least (i) timing data, and (ii) location data indicative of starting and ending locations of the object;

accessing one or more criteria for classifying the identified movement, the one or more criteria being based on historical data for previous movements, the historical data comprising a classification for each of the respective previous movements;

applying an algorithm that evaluates the timing data and the location data of the identified movement against the one or more criteria, the algorithm being configured to automatically assign a classification of a plurality of classifications to the identified movement based on the evaluation; and

determining whether to provide information on the identified movement to an output destination based on the assigned classification.

9. The computer-implemented system of claim 8, wherein the steps further comprise:

receiving an indication of whether the assigned classification is correct; and

applying a self-learning algorithm to automatically adjust the one or more criteria based on the received indication.

10. The computer-implemented system of claim 9, wherein the received indication is based on an input received from a human user.

11. The computer-implemented system of claim 8, wherein the steps further comprise:

providing the information on the identified movement to the output destination based on the movement being assigned a first classification of the plurality of classifications, the information being provided to the output destination automatically and without user intervention; and

not providing the information to the output destination based on the movement being assigned a second classification of the plurality of classifications, the second classification being different than the first classification.

12. The computer-implemented system of claim 11, wherein the location data comprises latitude and longitude coordinates, the steps further comprising:

applying a reverse-geocoding algorithm to convert the latitude and longitude coordinates into human-readable addresses or toponyms,

wherein the information is provided to the output destination via a push mechanism or a pull mechanism and includes the human-readable addresses or toponyms and other information associated with the identified movement.

13. The computer-implemented system of claim 12, wherein the output destination is configured to fill one or more elements of a software form automatically and without user intervention based on the human-readable addresses or toponyms and other information.

14. The computer-implemented system of claim 8, wherein the timing data is indicative of a duration of the identified movement, a date associated with the identified movement, and a time associated with the identified movement.

15. A non-transitory computer-readable storage medium for classifying movement of an object, the computer-readable storage medium comprising computer executable instructions which, when executed, cause a processing system to execute steps including:

receiving geolocation data indicative of an object'"'"'s location during a period of time;

processing the geolocation data and timestamps associated with the geolocation data to automatically identify a movement of the object during the period of time, the movement being characterized by at least (i) timing data, and (ii) location data indicative of starting and ending locations of the object;

accessing one or more criteria for classifying the identified movement, the one or more criteria being based on historical data for previous movements, the historical data comprising a classification for each of the respective previous movements;

applying an algorithm that evaluates the timing data and the location data of the identified movement against the one or more criteria, the algorithm being configured to automatically assign a classification of a plurality of classifications to the identified movement based on the evaluation; and

determining whether to provide information on the identified movement to an output destination based on the assigned classification.

16. The non-transitory computer-readable storage medium of claim 15, wherein the steps further comprise:

receiving an indication of whether the assigned classification is correct; and

applying a self-learning algorithm to automatically adjust the one or more criteria based on the received indication.

17. The non-transitory computer-readable storage medium of claim 16, wherein the received indication is based on an input received from a human user.

18. The non-transitory computer-readable storage medium of claim 15, wherein the steps further comprise:

providing the information on the identified movement to the output destination based on the movement being assigned a first classification of the plurality of classifications, the information being provided to the output destination automatically and without user intervention; and

not providing the information to the output destination based on the movement being assigned a second classification of the plurality of classifications, the second classification being different than the first classification.

19. The non-transitory computer-readable storage medium of claim 18, wherein the location data comprises latitude and longitude coordinates, the steps further comprising:

applying a reverse-geocoding algorithm to convert the latitude and longitude coordinates into human-readable addresses or toponyms,

wherein the information is provided to the output destination via a push mechanism or a pull mechanism and includes the human-readable addresses or toponyms and other information associated with the identified movement.

20. The non-transitory computer-readable storage medium of claim 19, wherein the output destination is configured to fill one or more elements of a software form automatically and without user intervention based on the human-readable addresses or toponyms and other information.

21. The non-transitory computer-readable storage medium of claim 15, wherein the timing data is indicative of a duration of the identified movement, a date associated with the identified movement, and a time associated with the identified movement.