SYSTEMS AND TECHNIQUES FOR IDENTIFYING AND EXPLOITING RELATIONSHIPS BETWEEN MEDIA CONSUMPTION AND HEALTH

• US 20160055420A1
• Filed: 08/20/2015
• Published: 02/25/2016
• Est. Priority Date: 08/20/2014
• Status: Abandoned Application

• Current Assignee: Bose Corporation
• Original Assignee: Puretech Management, Inc. (PureTech Health Plc)
• Inventors: Karanam, Ketki, Martucci, W. Edward, Kopikis, Alexis, Zohar, Daphne, Kuchekar, Nilesh N., Marsh, Abby B., Sian, Steven A., Krishnan, Vikram
• Application Number:
  US14/831,540
• Publication Number:
  US 20160055420A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• 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 5/4815 Sleep quality
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/744 Displaying an avatar, e.g. an animated cartoon character
• CPC Class Codes: A61M 2021/0016 by the smell sense
• CPC Class Codes: A61M 2021/0022 by the tactile sense, e.g. vibrations
• CPC Class Codes: A61M 2021/0027 by the hearing sense
• CPC Class Codes: A61M 2021/0044 by the sight sense
• CPC Class Codes: A61M 2021/005 images, e.g. video
• CPC Class Codes: A61M 21/00 Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
• CPC Class Codes: A61M 2205/3553 remote, e.g. between patient's home and doctor's office
• CPC Class Codes: A61M 2205/3584 using modem, internet or bluetooth
• CPC Class Codes: A61M 2205/505 Touch-screens; Virtual keyboard or keypads; Virtual buttons; Soft keys; Mouse touches
• CPC Class Codes: A61M 2205/52 with memories providing a history of measured variating parameters of apparatus or patient
• CPC Class Codes: A61M 2230/04 : Heartbeat characteristics, e.g. ECG, blood pressure modulation
• CPC Class Codes: A61M 2230/06 : Heartbeat rate only
• CPC Class Codes: A61M 2230/10 : Electroencephalographic signals
• CPC Class Codes: A61M 2230/201 : Glucose concentration
• CPC Class Codes: A61M 2230/30 : Blood pressure A61M2230/04 takes precedence
• CPC Class Codes: A61M 2230/42 : Rate
• CPC Class Codes: A61M 2230/50 : Temperature
• CPC Class Codes: A61M 2230/63 : Motion, e.g. physical activity
• CPC Class Codes: A61M 2230/65 : Impedance, e.g. conductivity, capacity
• CPC Class Codes: G06N 5/048 : Fuzzy inferencing
• CPC Class Codes: G10H 2210/041 : based on mfcc [mel -frequency spectral coefficients]
• CPC Class Codes: G10H 2220/371 : Vital parameter control, i.e. musical instrument control based on body signals, e.g. brainwaves, pulsation, temperature, perspiration; biometric information signals from body positions or movements G10H2220/321
• CPC Class Codes: G10H 2240/085 : Mood, i.e. generation, detection or selection of a particular emotional content or atmosphere in a musical piece
• CPC Class Codes: G10H 2240/131 : Library retrieval, i.e. searching a database or selecting a specific musical piece, segment, pattern, rule or parameter set
• CPC Class Codes: G10H 2240/251 : Mobile telephone transmission, i.e. transmitting, accessing or controlling music data wirelessly via a wireless or mobile telephone receiver, analog or digital, e.g. DECT GSM, UMTS smartphone, PDA or palmtop used for musical purposes G10H2230/015; mobile ringtone G10H2230/021
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 70/60 : relating to pathologies

Abstract

A predictive method may include determining the strength of a relationship between a user'"'"'s health state and the user'"'"'s consumption of media content having one or more features, based on health data and media consumption data corresponding to user consumption of media content items having the feature(s), and predicting an effect of consuming a media content item on the user'"'"'s health. The prediction may be based on a determination that the strength of the relationship between the health state and the consumption of media content having the one or more features exceeds a threshold strength. A diagnostic method may include determining whether a media consumption signature associated with a health condition matches media consumption data for a population, and diagnosing the population with the health condition based on a determination that the media consumption signature associated with the health condition matches the media consumption data for the population.

Claims

1. A predictive method comprising:

performing by one or more computers;

obtaining media consumption data regarding media content consumed by a user during one or more time periods, wherein the media content includes a plurality of media content items having one or more same features;

obtaining health data, wherein at least a portion of the health data relates to health states of the user during the one or more time periods;

synchronizing the media consumption data and the health data;

determining a strength of a relationship between a health state of the user and consumption by the user of media content having the one or more features, based at least in part on portions of the media consumption data corresponding to user consumption of the plurality of media content items having the one or more features and on the synchronized health data; and

predicting an effect of consuming a media content item on the user'"'"'s health, wherein the prediction is based at least in part on a determination that the strength of the relationship between the health state of the user and the consumption by the user of media content having the one or more features exceeds a threshold strength.

2. The method of claim 1, wherein obtaining the media consumption data comprises receiving the media consumption data from one or more sensors configured to detect media content and/or from one or more devices configured to present media content.

3. The method of claim 1, wherein the one or more features include one or more low-level audio features of an audio portion of the media content.

4. The method of claim 1, wherein the one or more low-level audio features include Mel-Frequency Cepstral Coefficients (MFCC), Audio Spectrum Envelope (ASE), Audio Spectrum Flatness (ASF), Linear Predictive Coding Coefficients, Zero Crossing Rate (ZCR), Audio Spectrum Centroid (ASC), Audio Spectrum Spread (ASS), spectral centroid, spectral rolloff, and/or spectral flux.

5. The method of claim 1, wherein the media consumption data includes preference data indicating one or more preferences of the user regarding the media content consumed by the user, and wherein the determination that the strength of the relationship between the health state of the user and the consumption by the user of the media content having the one or more features exceeds the threshold strength is based, at least in part, on the one or more preferences of the user.

6. The method of claim 1, wherein obtaining the health data comprises receiving the health data from one or more sensors configured to sense health parameters of the user.

7. The method of claim 1, further comprising:

obtaining context data, wherein at least a portion of the context data relates to contexts of the user during the one or more time periods; and

synchronizing the context data with the media consumption data and the health data,

wherein the determination that the strength of the relationship between the health state of the user and the consumption by the user of the media content having the one or more features exceeds the threshold strength is based, at least in part, on the context data.

8. The method of claim 1, wherein the media content consumed by the user during the one or more time periods does not include the media content item for which the prediction is made.

9. The method of claim 1, wherein the predicted effect of consuming the media content item comprises a predicted long-term effect of consuming the media content item.

10. The method of claim 1, further comprising attaching a health tag to the media content item as metadata of the media content item, wherein the health tag indicates the predicted effect of consuming the media content item on the user'"'"'s health.

11. The method of claim 1, further comprising:

predicting an effect of consuming the media content item on a population'"'"'s health, wherein the prediction of the effect on the population'"'"'s health is based at least in part on a determination that a strength of a relationship between the health state in the population and consumption of media content having the one or more features exceeds a threshold strength, and wherein the population includes other users; and

attaching a health tag to the media content item as metadata of the media content item, wherein the health tag indicates the predicted effect of consuming the media content item on the population'"'"'s health.

12. The method of claim 1, further comprising:

receiving data identifying a target health state of the user;

selecting one or more media content items, wherein a predicted effect of the user consuming the one or more media content items includes the user'"'"'s health attaining the target health state; and

recommending the one or more media content items for consumption by the user.

13. The method of claim 1, further comprising:

obtaining media biomarker data, wherein the media biomarker data includes a media consumption signature associated with a health condition;

determining whether the media consumption signature associated with the health condition matches the media consumption data for the user; and

diagnosing the user with the health condition based, at least in part, on a determination that the media consumption signature associated with the health condition matches the media consumption data for the user.

14. A diagnostic method comprising:

performing by one or more computers;

obtaining media biomarker data, wherein the media biomarker data includes a media consumption signature associated with a health condition;

obtaining media consumption data regarding media consumption of a population;

determining whether the media consumption signature associated with the health condition matches the media consumption data for the population;

diagnosing the population with the health condition based, at least in part, on a determination that the media consumption signature associated with the health condition matches the media consumption data for the population; and

communicating information associated with diagnosis of the health condition to a user.

15. The method of claim 14, wherein the population is a first population, wherein the media consumption data are first media consumption data, and wherein obtaining the media biomarker data comprises:

obtaining health data regarding health of a second population;

obtaining second media consumption data regarding media consumption of the second population, wherein the second media consumption data include the media consumption signature;

generating relationship data regarding a relationship between the media consumption signature and a portion of the health data corresponding to the health condition;

determining whether a strength of the relationship exceeds a threshold strength; and

generating the media biomarker data based, at least in part, on a determination that the strength of the relationship between the media consumption signature and the portion of the health data corresponding to the health condition exceeds the threshold strength.

16. The method of claim 14, wherein the relationship between the media consumption signature and the portion of the health data corresponding to the health condition comprises a correlation.

17. The method of claim 14, wherein the media consumption signature associated with the health condition comprises an amount, rate, pattern, range of amounts, range of rates, or plurality of patterns of consumption of media content.

18. The method of claim 14, wherein the media consumption signature associated with the health condition comprises an amount, rate, range of amounts, or range of rates of media content within a media content category.

19. The method of claim 14, wherein the media consumption signature associated with the health condition comprises an amount, rate, pattern, range of amounts, range of rates, or plurality of patterns of consumption of media content comprising a feature having a value within a particular range.

20. The method of claim 14, wherein the population consists of an individual person.

21. The method of claim 14, wherein the population comprises a plurality of people.

22. The method of claim 21, wherein the people have one or more characteristics in common.

23. The method of claim 14, wherein determining whether the media consumption signature associated with the health condition matches the media consumption data for the population comprises:

determining, based on the media consumption data, one or more media consumption signatures of the population; and

comparing the media consumption signature associated with the health condition to the one or more media consumption signatures of the population.

24. The method of claim 14, wherein the diagnosis is further based, at least in part, on health data regarding health of the population.

25. The method of claim 14, wherein communicating information associated with the diagnosis comprises causing the information to be displayed, causing the information to be presented audibly, and/or transmitting the information.

26. The method of claim 14, further comprising predicting, based at least in part on the determination that the media consumption signature associated with the health condition matches the media consumption data for the population, an effect on a member of the population of consuming a particular item of media content.

27. The method of claim 14, further comprising prescribing, based at least in part on the determination that the media consumption signature associated with the health condition matches the media consumption data for the population, a therapy for a member of the population.

28. The method of claim 27, wherein the prescribed therapy comprises consuming particular items of media content.

29. The method of claim 27, wherein the prescribed therapy further comprises administration of a drug or performance of a medical intervention in connection with the consumption of the particular items of media content.

30. The method of claim 14, further comprising attaching a health tag to a media content item as metadata of the media content item based, at least in part, on a determination that the media content item includes the media consumption signature associated with the health condition, wherein the health tag indicates that consumption of the media content item is associated with the health condition.