Method and apparatus for a predictive tracking device

• US 8,768,864 B2
• Filed: 08/02/2011
• Issued: 07/01/2014
• Est. Priority Date: 08/02/2011
• Status: Active Grant

• Current Assignee: Alcatel-Lucent SA (Nokia Corporation)
• Original Assignee: Alcatel-Lucent SA (Nokia Corporation)
• Inventors: Kane-Esrig, Yana
• Primary Examiner(s): Hill, Stanley K
• Application Number:
  US13/196,033
• Publication Number:
  US 20130036080A1
• Time in Patent Office: 1,064 Days
• Field of Search: None
• US Class Current: 706/12
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A predictive tracking method and apparatus utilizing objective and subjective data in order to predict user states is provided herein. For example, some such embodiments may allow a user to track their mood or health symptoms in relation to retrieved data regarding their environmental in order to reveal patterns that can help forecast and proactively manage mood or health symptoms.

Claims

1. A method, comprising:

at a processor and a digital data storage connected thereto, collecting a first training set of values of a user state for a first sequence of times and a second training set of values of an environmental state for a temporally overlapping second sequence of times;

determining a future time and an identified location based on a calendar entry; and

retrieving at least one forecasted value of the environmental state based on the future point in time and the identified location; and

determining, by the processor in cooperation with the digital data storage, a value of the user state at the future time based on the at least one forecasted value of the environmental state and the collected first and second training sets.

2. The method of claim 1, wherein the act of determining is based on an association between the values of the two training sets.

3. The method of claim 2, wherein the determining comprises:

associating at least one of the collected values of user state with one or more of the collected values of environmental state based on a correlation between an entry time and an identified user location of the collected value of user state and a time and a location of the one or more collected values of environmental state.

4. The method of claim 1, wherein the retrieved at least one forecasted value of the environmental state includes at least one of:

a pollutant measurement;

a barometric pressure reading;

a temperature reading;

a sun index reading; and

a calendar entry.

5. The method of claim 1, wherein the retrieved at least one forecasted value of the environmental state includes a noise measurement.

6. The method of claim 1, wherein a plurality of the collected values of the environmental state includes a communication session parameter and the communication session parameter includes at least one of:

a telephone number;

an email address;

a text message recipient; and

communication content.

7. The method of claim 1, further comprising reporting, by the processor in cooperation with the digital data storage, a user report, the user report comprising the value of the user state near the future time.

8. The method of claim 7, wherein the reporting comprises at least one of:

sending an email;

displaying an alert on a user interface; and

delivering an audible to a user interface.

9. The method of claim 7, wherein the user report further comprises at least one of:

an alert indicating a predicted adverse value of user state; and

a remediation recommendation based on the at least one forecasted value of the environmental state at a future time.

10. The method of claim 1, further comprising:

retrieving, by the processor in cooperation with the digital data storage, at least one tracking parameter;

retrieving, by the processor in cooperation with the digital data storage, at least some of the second training set of values based on the at least one tracking parameter; and

receiving at least some of the first training set of values from a user interface.

11. The method of claim 10, wherein at least one tracking parameter specifies a triggering event that triggers an automated collection of environmental conditions.

12. The method of claim 10, further comprising receiving a location identifier representing the identified location from a GPS, wherein the retrieving of at least one of the values in the second training set of values is further based on the identified location.

13. The method of claim 10, wherein the retrieving of at least one of the values in the first training set of values comprises translating a gesture on a touch screen.

14. The method of claim 10, wherein the retrieving of at least one of the values in the first training set of values comprises analyzing a voice input to determine a user state.

15. The method of claim 10, wherein the retrieving of at least one of the values in the first training set of values comprises receiving an indicator from a mood sensor.

16. The method of claim 1, wherein the retrieving at least one forecasted value of the environmental state at a future time comprises:

determining a future point in time; and

retrieving the at least one forecasted value of the environmental state at a future time based on the future point in time.

17. An apparatus, comprising:

a processor and a digital data storage connected thereto, the processor and the digital data storage configured to;

collect a first training set of values of a user state for a first sequence of times and a second training set of values of an environmental state for a temporally overlapping second sequence of times;

determine a future time and an identified location based on a calendar entry; and

retrieve at least one forecasted value of the environmental state based on the future point in time and the identified location; and

determine a value of the user state at the future time based on the at least one forecasted value of the environmental state and the collected first and second training sets.

18. The apparatus of claim 17, wherein the apparatus is a mobile computing device.

19. The apparatus of claim 17, wherein the apparatus is a networked host.

20. The apparatus of claim 17, further comprising:

a user interface configured to receive at least some of the first training set of values; and

the processor and digital data storage being further configured to;

retrieve at least one tracking parameter; and

retrieve at least some of the second training set of values based on the at least one tracking parameter.

21. The apparatus of claim 20, further comprising a GPS configured to determine an identified location of the apparatus and wherein the system is further configured to associate at least one of the collected values of user state with one or more of the collected values of environmental state based on a correlation between an entry time and the identified location of the collected value of user state and a time and a location of the one or more collected values of environmental state.

22. The apparatus of claim 20, wherein the user interface comprises a mood sensor.

23. The apparatus of claim 20, wherein the digital data storage comprises a calendar.

24. A non-transitory digital data storage medium encoding a machine-executable program of instructions to perform a method, the method comprising:

collecting a first training set of values of a user state for a first sequence of times and a second training set of values of an environmental state for a temporally overlapping second sequence of times;

determining a future time and an identified location based on a calendar entry; and

retrieving at least one forecasted value of the environmental state based on the future point in time and the identified location; and

determining a value of the user at the future time based on the at least one forecasted value of the environmental state and the collected first and second training sets.

25. The digital data storage medium encoding a machine-executable program of instructions of claim 24, wherein the determining comprises:

associating at least one of the collected values of user state with one or more of the collected values of environmental state based on a correlation between an entry time and an identified user location of the collected value of user state and a time and a location of the one or more collected values of environmental state.

26. The digital data storage medium encoding a machine-executable program of instructions of claim 24, further comprising reporting a user report, the user report comprising at least one of:

an alert indicating a predicted adverse value of user state; and

a remediation recommendation based on the at least one forecasted value of the environmental state at a future time.

27. The digital data storage medium encoding a machine-executable program of instructions of claim 24, further comprising:

retrieving, by the processor in cooperation with the digital data storage, at least one tracking parameter;

retrieving, by the processor in cooperation with the digital data storage, at least some of the second training set of values based on the at least one tracking parameter; and

receiving at least some of the first training set of values from a user interface.

28. The digital data storage medium encoding a machine-executable program of instructions of claim 27, wherein at least one tracking parameter specifies a triggering event that triggers an automated collection of environmental conditions.

29. A method for determining a user state at a future time, the method comprising:

at a processor and a digital data storage connected thereto, collecting a first training set of values of a user state for a first sequence of times and a second training set of values of an environmental state for a temporally overlapping second sequence of times;

retrieving, by the processor in cooperation with the digital data storage, at least one forecasted value of the environmental state associated with the future time; and

determining, by the processor in cooperation with the digital data storage, a value of the user state at the future time based on the at least one forecasted value of the environmental state and the collected first and second training sets;

wherein the at least one forecasted value of the environmental state includes a noise measurement.