Systems, devices, and/or processes for tracking behavioral and/or biological state

• US 10,373,466 B1
• Filed: 03/15/2018
• Issued: 08/06/2019
• Est. Priority Date: 03/15/2018
• Status: Active Grant

• Current Assignee: Arm Limited (SoftBank Group Corp.)
• Original Assignee: Arm Limited (SoftBank Group Corp.)
• Inventors: St Amant, Renee Marie
• Primary Examiner(s): Nguyen, Hung T
• Application Number:
  US15/922,644
• Time in Patent Office: 509 Days
• Field of Search: 34053911, 3405731
• CPC Class Codes: A61B 5/00 Measuring for diagnostic purposes radiation diagnosis A61B6/00; diagnosis by ultrasonic, sonic or infrasonic waves A61B8/00; Identification of persons
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/163 by tracking eye movement, gaze, or pupil change
• 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: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G08B 21/0423 detecting deviation from an expected pattern of behaviour or schedule
• CPC Class Codes: G08B 21/0453 worn on the body to detect health condition by physiological monitoring, e.g. electrocardiogram, temperature, breathing detecting, measuring or recording for diagnostic purposes A61B5/00
• CPC Class Codes: G08B 21/0476 Cameras to detect unsafe condition, e.g. video cameras
• CPC Class Codes: G08B 29/186 Fuzzy logic; neural networks
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Subject matter disclosed herein may relate to systems, devices, and/or processes for tracking signals and/or states representative of behavioral and/or biological state.

Claims

1. An apparatus for tracking behavioral content for a particular user, comprising:

at least one processor to track signals and/or states representative of the behavioral profile content for the particular user, the behavioral profile content to include a plurality of parameters representative of a current behavioral state of the particular user;

at least one memory to store the tracked signals and/or states representative of the behavioral content;

wherein the at least one processor to perform one or more machine learning operations to determine one or more relationships between the tracked signals and/or states representative of the behavioral profile content and bioavailability or balance, or a combination thereof, of one or more particular substances within the particular user'"'"'s body.

2. The apparatus of claim 1, wherein the signals and/or states representative of the behavioral profile content to further include a plurality of parameters representative of a current biological state of the particular user, wherein the at least one processor further to generate one or more recommendations for supplementation related to the one or more particular substances within the particular user'"'"'s body, the one or more recommendations to be directed to improvement of a subsequent state of the particular user, wherein the at least one processor to generate the one or more recommendations to be based at least in part on current behavioral profile content for the particular user or to be based at least in part on the one or more determined relationships, or a combination thereof.

3. The apparatus of claim 2, wherein the behavioral profile content to comprise one or more parameters representative of eye movement for the particular user.

4. The apparatus of claim 3, wherein to perform the one or more machine learning operations, the at least one processor to perform one or more training operations to be based at least in part on input to be obtained from one or more users, wherein the input to include content representative of intake by the particular user of supplementation related to the one or more particular substances within the particular user'"'"'s body.

5. The apparatus of claim 3, wherein the one or more particular substances within the particular user'"'"'s body to comprise gamma-aminobutyric acid (GABA) or glutamate, or a combination thereof.

6. The apparatus of claim 5, wherein the at least one processor to perform the one or more machine learning operations to determine the one or more relationships between the one or more parameters representative of the eye movement and a balance between the GABA and the glutamate within the particular user'"'"'s body.

7. The apparatus of claim 3, wherein the one or more particular substances within the particular user'"'"'s body to comprise 5-methyltetrahydrofolate (5-MTHF).

8. The apparatus of claim 7, wherein the at least one processor to perform the one or more machine learning operations to determine the one or more relationships between the one or more parameters representative of the eye movement and a bioavailability of the 5-MTHF within the particular user'"'"'s body.

9. The apparatus of claim 2, wherein the behavioral profile content to comprise one or more parameters representative of voice tonality, sentiment analysis, volume, frequency, pitch or timbre, or any combination thereof, for the particular user, and wherein the one or more particular substances within the particular user'"'"'s body to comprise 5-MTHF.

10. The apparatus of claim 1, wherein the at least one processor further to track signals and/or states representative of environmental noise associated with the particular user, and wherein the at least one processor to perform one or more additional machine learning operations to determine one or more relationships between the tracked environmental noise and a change in balance of one or more hormones including testosterone, estrogen or progesterone, or a combination thereof.

11. A method for tracking behavioral content for a particular user, comprising:

tracking, via at least one processor, signals and/or states representative of the behavioral profile content for the particular user, wherein the behavioral profile content includes a plurality of parameters representative of a current behavioral state of the particular user;

storing the tracked signals and/or states representative of the behavioral content in at least one memory;

determining, at least in part via the at least one processor performing one or more machine learning operations, one or more relationships between the stored and tracked signals and/or states representative of the behavioral profile content and bioavailability or balance, or a combination thereof, of one or more particular substances within the particular user'"'"'s body.

12. The method of claim 11, wherein the behavioral profile content further includes a plurality of parameters representative of a current biological state of the particular user, and further comprising generating, at least in part via the at least one processor, one or more recommendations for supplementation related to the one or more particular substances within the particular user'"'"'s body based at least in part on current behavioral profile content for the particular user or based at least in part on the one or more determined relationships, or a combination thereof, wherein the one or more recommendations are directed to improvement of a subsequent state of the particular user.

13. The method of claim 12, wherein the behavioral profile content comprises one or more parameters representative of eye movement for the particular user.

14. The method of claim 13, wherein the performing the one or more machine learning operations comprises performing one or more training operations based at least in part on input obtained from one or more users, wherein the input obtained from one or more users to include content representative of intake by the particular user of supplementation related to the one or more particular substances within the particular user'"'"'s body.

15. The method of claim 13, wherein the one or more particular substances within the particular user'"'"'s body comprises gamma-aminobutyric acid (GABA) or glutamate, or a combination thereof.

16. The method of claim 15, wherein the determining the one or more relationships between the tracked behavioral profile content and the bioavailability or balance, or a combination thereof, of the one or more particular substances within the particular user'"'"'s body comprises determining one or more relationships between the one or more parameters representative of the eye movement and a balance between the GABA and the glutamate within the particular user'"'"'s body.

17. The method of claim 13, wherein the one or more particular substances within the particular user'"'"'s body comprises 5-methyltetrahydrofolate.

18. The method of claim 17, wherein the determining the one or more relationships between the tracked signals and/or states representative of the behavioral profile content and the bioavailability or balance, or a combination thereof, of the one or more particular substances within the particular user'"'"'s body comprises determining one or more relationships between the one or more parameters representative of the eye movement and a bioavailability of the 5-methyltetrahydrofolate within the particular user'"'"'s body.

19. The method of claim 12, wherein the behavioral profile content comprises one or more parameters representative of voice tonality, sentiment analysis, volume, frequency, pitch or timbre, or any combination thereof, for the particular user, and wherein the one or more particular substances within the particular user'"'"'s body comprises 5-MTHF.

20. The method of claim 15, wherein the behavioral profile content includes one or more parameters indicative of one or more locations visited by the particular user, and wherein the determining the one or more relationships between the tracked signals and/or states representative of the behavioral profile content and the bioavailability or balance, or the combination thereof, of the one or more particular substances within the particular user'"'"'s body comprises identifying one or more locations at which a glutamate/GABA balance has been manipulated at least in part by identifying one or more changes in the glutamate/GABA balance and identifying one or more locations at which the changes occurred.