Automated location and activity aware medical monitoring

• US 9,498,163 B2
• Filed: 09/09/2015
• Issued: 11/22/2016
• Est. Priority Date: 10/10/2013
• Status: Active Grant

• Current Assignee: Aura Home, Inc. (Pushd, Inc.)
• Original Assignee: Pushd, Inc.
• Inventors: Frieder, Ophir, Jensen, Eric, Chowdhury, Abdur, Cherry, Ben, Sanford, Matt
• Primary Examiner(s): Gillis, Brian J
• Assistant Examiner(s): Ballard, Matthew
• Application Number:
  US14/848,881
• Publication Number:
  US 20160066864A1
• Time in Patent Office: 440 Days
• US Class Current: 1/1
• 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 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/72 Signal processing specially adapted for physiological signals or for diagnostic purposes character recognition, recognising digital ink, document-oriented image-based pattern recognition G06V30/00; recognition or understanding of postal images G06V30/424
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7267 involving training the classification device
• 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: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/7455 characterised by tactile indication, e.g. vibration or electrical stimulation
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61B 5/7465 Arrangements for interactive communication between patient and care services, e.g. by using a telephone network telemetry of measured physiological signal A61B5/0002
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 21/552 involving long-term monitoring or reporting
• CPC Class Codes: G06F 2221/2111 Location-sensitive, e.g. geographical location, GPS
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 40/67 for remote operation
• 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 50/70 : for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 80/00 : ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
• CPC Class Codes: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass
• CPC Class Codes: H04L 51/08 : Annexed information, e.g. attachments
• CPC Class Codes: H04L 51/10 : Multimedia information
• CPC Class Codes: H04L 51/222 : using geographical location information, e.g. messages transmitted or received in proximity of a certain spot or area
• CPC Class Codes: H04L 51/52 : for supporting social networking services
• CPC Class Codes: H04L 51/58 : Message adaptation for wireless communication
• CPC Class Codes: H04L 67/535 : Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04M 1/72454 : according to context-related or environment-related conditions
• CPC Class Codes: H04M 1/72457 : according to geographic location
• CPC Class Codes: H04W 24/04 : Arrangements for maintaining operational condition
• CPC Class Codes: H04W 24/08 : Testing, supervising or monitoring using real traffic
• CPC Class Codes: H04W 4/02 : Services making use of location information
• CPC Class Codes: H04W 4/023 : using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
• CPC Class Codes: H04W 4/027 : using movement velocity, acceleration information
• CPC Class Codes: H04W 4/029 : Location-based management or tracking services
• CPC Class Codes: H04W 4/12 : Messaging; Mailboxes; Announcements
• CPC Class Codes: H04W 4/21 : for social networking applications
• CPC Class Codes: H04W 4/33 : for indoor environments, e.g. buildings
• CPC Class Codes: H04W 52/0229 : where the received signal is a wanted signal
• CPC Class Codes: H04W 52/0254 : detecting a user operation or a tactile contact or a motion of the device
• CPC Class Codes: H04W 64/00 : Locating users or terminals or network equipment for network management purposes, e.g. mobility management
• CPC Class Codes: H04W 64/006 : with additional information processing, e.g. for direction or speed determination
• CPC Class Codes: Y02D 30/70 : in wireless communication networks

Abstract

A method, system, and/or apparatus for automatically monitoring for possible mental or physical health concerns. The method or implementing software application uses or relies upon location information available on the mobile device from any source, such as cell phone usage and/or other device applications. The method and system automatically learns user activity patterns and detects significant deviations therefrom. The deviations are automatically analyzed for known correlations to mental or physical concerns, which can then be automatically communicated to a relevant friend, family member, and/or medical professional.

Claims

1. A method of determining medical conditions of users participating in a social networking service, the method executed by a computer system and comprising:

automatically monitoring destinations and user activities performed at the destinations of a first user via a first electronic device of the first user;

receiving user information comprising a first user activity performed at for at least one of the destinations upon a user arriving at the at least one destination;

automatically learning activity patterns for the first user activity by automatically associating the user information with the at least one of the destinations, automatically determining a user activity context for the first user activity, and automatically comparing a further context of each of a plurality of further user arrivals to the at least one of the destinations to the user activity context, wherein the activity patterns comprise an eating pattern or an exercise pattern, at a location or with one or more community members;

automatically updating context information associated with the at least one destination from the further context of each of the further user arrivals;

automatically identifying that the first user is participating in the first user activity upon future user arrivals at the at least one destination as a function of comparing the context information to a future context of each of the future user arrivals, without receiving any manually entered confirmation or manually entered additional user information;

automatically determining a decrease in occurrences of the first user activity over a predetermined time period without receiving the any manually entered confirmation or manually entered additional user information;

automatically analyzing the decrease to identify a deviation significance, wherein the decrease or the deviation significance is automatically determined using an inference engine, and analyzing the decrease comprises correlating the decrease to user locations and conditions of the current locations during the predetermined time period;

automatically correlating the deviation significance to a possible medical condition; and

automatically alerting the first user via the first electronic device or a second user via a second electronic device of the possible medical condition upon the deviation significance meeting a predetermined threshold, wherein the alerting includes a visual representation of the deviation significance, and the second user is a medical professional, close community member, or community member engaged in a similar user activity.

2. The method of claim 1, wherein automatically monitoring destinations and user activities of a first user via a first electronic device of the first user comprises:

automatically determining a corresponding context for each of the user activities at a first user visit to each of the destinations;

automatically tagging each of the destinations with a corresponding one of the user activities and corresponding context information, and storing the tagged location in a location database;

automatically updating the context information of the tagged location for each of a plurality of automatically determined further user visits at the destinations from the further context of each of the further user visits;

automatically associating the first user activity with corresponding destinations and the corresponding context information;

wherein each of the first context, the second context, the further context, and the context information comprises at least one of;

a time of day, a day of a week, a calendar date, a preceding user activity to the user visits, a weather condition, people accompanying the user, or community member activity bias information for the destination.

3. A method of determining medical conditions of users participating in a social networking service, the method executed by a computer system and comprising:

automatically monitoring destinations and user activities of a first user performed at the destinations via a first electronic device of the first user;

automatically learning activity patterns at the destinations for the first user from manually entered and manually confirmed user information and automatically determined context information associated with the user information;

automatically identifying that the first user is participating in a user activity upon future user arrivals at one of the destinations as a function of comparing the context information to a future context of each of the future user arrivals, without receiving any manually entered confirmation or manually entered additional user information;

automatically determining a deviation in the learned activity patterns without receiving the any manually entered information via the first electronic device;

automatically analyzing the deviation to identify a significance of the deviation;

automatically correlating the significance of the deviation to a possible medical condition; and

automatically alerting the first user via the first electronic device or a second user via a second electronic device of the possible medical condition upon the deviation significance meeting a predetermined threshold, wherein the alerting includes a visual representation of the deviation significance.

4. The method of claim 3, wherein the deviation is automatically detected using an inference engine.

5. The method of claim 3, wherein the activity pattern is an eating pattern at a location or with one or more community members.

6. The method of claim 3, wherein the activity pattern is an exercise pattern.

7. The method of claim 3, wherein the deviation comprises an increase or decrease in the user activity of the activity pattern.

8. The method of claim 3, wherein the second user is a health practitioner.

9. The method of claim 3, wherein the second user is a close proximity community member.

10. The method of claim 3, wherein the second user is a community member automatically identified as practicing a same user activity as the first user.

11. The method of claim 3, further comprising automatically sending a notification according to predetermined conditions.

12. The method of claim 3, wherein analyzing the deviation comprises correlating the deviation to at least one of:

a current location or a condition of the location.

13. The method of claim 3, further comprising automatically and continually tracking a location of an electronic device of each member of a social media community of the first user.

14. The method of claim 13, wherein the electronic device of the each member of the social media community includes a location module in communication with the social networking service, the location module configured to identify location transmissions received by the electronic device, and further comprising automatically and repeatedly entering a sleep mode and then awakening from the sleep mode upon identification of a location transmission.

15. The method of claim 3, wherein automatically learning activity patterns comprises:

receiving user information about a destination upon a user arriving at the destination, wherein the user information comprises a user activity at the destination;

automatically associating the user information with the destination;

automatically determining a user activity context for the user activity of the received user information, and automatically comparing a further context of each of further user arrivals to the user activity context; and

automatically identifying the activity patterns from consistent user activity contexts patterns over a plurality of the further user arrivals.

16. The method of claim 15, wherein the user activity context information comprises at least one of:

time-dependent information, community information, or third-party information about the positional destination.

17. The method of claim 15, wherein the user activity context comprises at least one of:

time of day or presence of any community members.

18. The method of claim 15, wherein automatically correlating the significance of the deviation to a possible medical condition comprises automatically correlating health readings obtained by the first electronic device.

19. The method of claim 3, wherein automatically learning activity patterns comprises:

automatically determining a location of the first user;

automatically determining context information about the positional location without input by the first user;

automatically deducing as user information a location type and/or user activity of the location from the context information; and

automatically associating the user information to the context information.

20. The method of claim 19, further comprising automatically storing at least one of:

past location type or past user activity of the positional destination, wherein the user information is automatically deduced as a function of the stored at least one of past location type or past user activity of the positional destination.

21. A method of determining medical conditions of users participating in a social networking service, the method executed by a computer system and comprising:

automatically determining a first user visit of a first user at a destination via a first user electronic device, wherein the user activity is manually entered by the first user at the first user visit at the destination;

automatically determining a location of the destination via the first user electronic device;

receiving a user activity at the location through the first user electronic device;

automatically determining a first context for the first user visit and user activity;

automatically tagging the location with the user activity and context information from the first context, and storing the tagged location in a location database;

automatically determining a second user visit at the destination via the first user electronic device;

automatically delivering via the first user electronic device a request for manual confirmation of the user activity being performed for the second user arrival;

receiving the manual confirmation of the user activity for the second user visit to the destination;

automatically determining a second context for the second user visit at the destination;

automatically updating the context information of the tagged location from the second context;

for each of a plurality of automatically determined further user visits at the destination, automatically determining a potential for the user activity based upon a comparison of a corresponding further context automatically determined for the each of the further user visits to the context information;

automatically presenting the user activity to the first user through the first user electronic device for manual confirmation of the user activity through the user device at each of a plurality of further user visits at the location;

automatically updating the context information of the tagged location from the further context of each of the further user visits;

automatically learning a user activity pattern by automatically associating the user activity with the location and the context information after receiving the user activity for the plurality of further user visits at the location;

automatically identifying that the first user is participating in the learned user activity upon future user arrivals at the location as a function of comparing the context information to a future context of each of the future user arrivals, without receiving any manually entered confirmation or manually entered additional user information;

wherein each of the first context, the second context, the further context, and the context information comprises at least one of;

a time of day, a day of a week, a calendar date, a preceding user activity to the user visits, a weather condition, people accompanying the user, or community member activity bias information for the destination;

a server computer of the computer system automatically sharing via a communication network to a second user electronic device of a second user participating in the social networking service that the first user is participating in the learned user activity upon the automatically identifying that the first user is participating in the learned user activity at each of the future user arrivals at the location;

automatically determining a deviation in the learned user activity pattern;

automatically analyzing the deviation to identify a significance of the deviation;

automatically correlating the significance of the deviation to a possible medical condition; and

automatically alerting the first user via the first user electronic device or the second user via the second user electronic device of the possible medical condition upon the deviation significance meeting a predetermined threshold, wherein the alerting includes a visual representation of the deviation significance.