Medical monitoring with location and activity tracking

• US 10,039,504 B2
• Filed: 10/12/2016
• Issued: 08/07/2018
• 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): Keller, Michael A
• Application Number:
  US15/291,819
• Publication Number:
  US 20170027529A1
• Time in Patent Office: 664 Days
• 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;

automatically determining positional locations of the first user, wherein automatically determining positional locations of a user comprises;

providing a location module on the first electronic device configured to receive location transmissions;

placing the location module into a sleep mode;

awakening the location module upon receipt of a first location transmission;

determining a first location with the location module

incrementing a timer count configured to monitor a duration of time, wherein whenever a time exceeds a predetermined time allocation, the location module is placed into the sleep mode;

incrementing a stationary counter configured to monitor a duration of time since a previous detected movement of the mobile user;

receiving a second location transmission of a second location;

comparing the second location to the first location;

calculating a rate of travel when the second location differs from the first location, wherein if the rate of travel exceeds a predetermined threshold the location module is placed into the sleep mode;

receiving a third location transmission of a third location;

comparing the third location to at least one of the first location or the second location;

further incrementing the stationary counter when the second or third location is the same as the first location;

determining an arrival event upon reaching a predetermined arrival threshold of the stationary counter;

storing data of all locations; and

placing the location module into the sleep mode;

automatically awakening the location module at each of the destinations upon receipt of a corresponding location transmission;

automatically determining as user information a location type and user activity at each of the destinations;

automatically learning activity patterns from the user information at the destinations for the first user;

automatically determining a deviation in the learned activity patterns;

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 at least one predetermined threshold, wherein the alerting includes a visual communication of the deviation significance.

2. The method of claim 1, wherein automatically monitoring destinations and user activities of the first user via the 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 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. The method of claim 1, wherein the deviation is automatically detected using an inference engine.

4. The method of claim 1, wherein the activity patterns include an eating pattern at one or more of the destinations or with one or more community members.

5. The method of claim 1, wherein the activity patterns include an exercise pattern.

6. The method of claim 1, wherein the deviation comprises an increase or decrease in a user activity of the activity patterns.

7. The method of claim 1, wherein the second user is a health practitioner.

8. The method of claim 1, wherein the second user is a close proximity community member.

9. The method of claim 1, wherein the second user is a community member automatically identified as practicing a same user activity as the first user.

10. The method of claim 1, wherein analyzing the deviation comprises correlating the deviation to a current location or a condition of the location.

11. The method of claim 1, further comprising automatically and continually tracking a location of an electronic device of each member of a social media community of the first user, 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 automatically 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.

12. The method of claim 1, wherein the 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, wherein the user activity context information comprises time-dependent information, community member information, or third-party information about the destination; and

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

13. The method of claim 1, wherein the automatically correlating the significance of the deviation to a possible medical condition comprises automatically correlating health readings automatically obtained by the first electronic device.

14. The method of claim 1, wherein automatically learning activity patterns comprises:

automatically determining a positional location of the first user;

automatically storing past location type or past user activity of the positional location;

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

automatically deducing as the user information a location type or user activity of the location from the context information, wherein the user information is automatically deduced as a function of the stored past location type or user activity of the positional location; and

automatically associating the user information to the context information.