# US 10,945,680 B2 · Activity based medical monitoring

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- Number: US 10,945,680 B2
- Document ID: US10945680B2
- Status: Granted
- Kind: B2
- Application number: 17/038,064
- Filed: 2020-09-30
- Published: 2021-01-14
- Granted: 2021-03-16
- Earliest claimed: 2013-10-10
- Assignees: Aura Home, Inc.
- Applicants: Aura Home, Inc
- Inventors: Ophir Frieder; Eric Jensen; Abdur Chowdhury; Ben Cherry; Matt Sanford
- Web page: https://patentgrep.com/patent/US10945680B2

## CPC

- A61B 5/746: {Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms}
- A61B 5/1112: {Global tracking of patients, e.g. by using GPS}
- A61B 5/1118: {Determining activity level}
- A61B 5/1123: {Discriminating type of movement, e.g. walking or running (A61B5/1116, A61B5/112 take precedence)}
- A61B 5/4866: {Evaluating metabolism (using breath test A61B5/083)}
- A61B 5/6898: {Portable consumer electronic devices, e.g. music players, telephones, tablet computers}
- A61B 5/72: {Signal processing specially adapted for physiological signals or for diagnostic purposes}
- A61B 5/7246: {using correlation, e.g. template matching or determination of similarity}
- A61B 5/7267: {involving training the classification device}
- A61B 5/7282: {Event detection, e.g. detecting unique waveforms indicative of a medical condition (cough events A61B5/0823; seizures A61B5/4094; sleep apnoea A61B5/4818)}
- A61B 5/742: {using visual displays (displays for heart-related electrical signals, e.g. ECG, A61B5/339)}
- A61B 5/7455: {characterised by tactile indication, e.g. vibration or electrical stimulation}
- 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)}
- G06F 16/9535: Search customisation based on user profiles and personalisation
- G06F 21/552: {involving long-term monitoring or reporting}
- G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof
- G16H 40/63: for local operation
- G16H 40/67: for remote operation
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/30: for calculating health indices; for individual health risk assessment
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- G16H 80/00: ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
- G16Z 99/00: Subject matter not provided for in other main groups of this subclass
- H04L 51/10: Multimedia information
- H04L 51/222: using geographical location information, e.g. messages transmitted or received in proximity of a certain spot or area
- H04L 51/52: for supporting social networking services
- H04L 51/58: Message adaptation for wireless communication
- H04L 67/535: {Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34)}
- H04M 1/72457: according to geographic location
- H04W 4/02: Services making use of location information
- H04W 4/023: {using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds}
- H04W 4/027: {using movement velocity, acceleration information}
- H04W 4/029: Location-based management or tracking services
- H04W 4/12: Messaging; Mailboxes; Announcements
- H04W 4/21: for social networking applications
- H04W 4/33: for indoor environments, e.g. buildings
- H04W 52/0229: {where the received signal is a wanted signal}
- H04W 52/0254: {detecting a user operation or a tactile contact or a motion of the device}
- G06F 2221/2111: Location-sensitive, e.g. geographical location, GPS
- H04L 51/08: Annexed information, e.g. attachments
- H04M 1/72454: according to context-related or environment-related conditions
- H04W 24/04: Arrangements for maintaining operational condition
- H04W 24/08: Testing, {supervising or monitoring} using real traffic
- H04W 64/00: Locating users or terminals {or network equipment} for network management purposes, e.g. mobility management
- H04W 64/006: {with additional information processing, e.g. for direction or speed determination}
- Y02D 30/70: in wireless communication networks
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16Z: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)
- H04M: TELEPHONIC COMMUNICATION (circuits for controlling other apparatus via a telephone cable and not involving telephone switching apparatus G08)
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)
- Y02D: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE

## 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

### Claim 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 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 a communication pattern, a mobility pattern, a visitation pattern, a sleep pattern, an eating pattern and/or an exercise pattern, at a location and/or with one or more community members;
automatically determining a decrease in occurrences of the first user activity during a first predetermined time period;
automatically analyzing the decrease to identify a deviation significance, wherein the decrease and/or the deviation significance is automatically determined using an inference engine, and analyzing the decrease comprises correlating the decrease to the first user being at a current destination during the first predetermined time period, and analyzing the decrease comprises identifying a temporary change in the learned activity patterns as a function of the current destination not allowing for the first user activity at the at least one of the destinations;
automatically determining a second decrease in occurrences of the first user activity over a second predetermined time period after the first predetermined time period;
automatically analyzing the second decrease to identify a second deviation significance, wherein the second decrease and/or the second deviation significance is automatically determined using the inference engine;
automatically correlating the second 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 second deviation significance meeting a predetermined threshold, wherein the alerting includes a visual representation to the first electronic device and/or the second electronic device, and the second user is a medical professional, a close community member, and/or a community member engaged in a similar user activity.

### Claim 2 (depends on 1)

The method of claim 1, further comprising automatically detecting a possible onset of depression from a combination of a reduction in physical activity, an increase in sleep duration, and an increased appetite over the predetermined time period.

### Claim 3 (depends on 1)

The method of claim 1, wherein the analyzing the decrease comprises correlating a geographic condition with the current destination, and identifying the temporary change in the learned activity patterns as based upon the geographic condition.

### Claim 4 (depends on 1)

The method of claim 1, wherein the analyzing the decrease change comprises determining a weather condition at the new user location.

### Claim 5 (depends on 1)

The method of claim 1, wherein the analyzing the decrease comprises:
automatically correlating the decrease to geographic conditions of user locations during the predetermined time period; and
automatically identifying the temporary change in the learned activity patterns as a function of an external factor of the geographic conditions, wherein the external factor impedes the first user activity within the predetermined time period.

### Claim 6 (depends on 5)

The method of claim 5, wherein the analyzing the decrease comprises determining a weather condition as the external factor at the user locations.

### Claim 7 (depends on 1)

The method of claim 1, further comprising automatically alerting the community member engaged in a similar user activity with a request to engage the first user in the first user activity.

### Claim 8 (depends on 1)

The method of claim 1, wherein the visual representation to the first electronic device comprises a graphical depiction of the change in occurrences of the first user activity during the predetermined time period from a second time period before the predetermined time period.

### Claim 9 (depends on 8)

The method of claim 8, wherein the graphical depiction comprises a pictorial representation of top activities for the predetermined time period determined by time spent in each of the monitored user activities.

### Claim 10 (depends on 1)

The method of claim 1, wherein the automatically correlating the deviation significance to a possible medical condition comprises assigning a confidence metric to the possible medical condition.

### Claim 11 (depends on 10)

The method of claim 10, further comprising automatically selecting the second user for alerting as a function of the confidence metric, wherein the close community member is alerted for a first confidence metric, and the medical professional is alerted for a second confidence metric, the second confidence metric being higher than the first confidence metric.

### Claim 12 (depends on 1)

The method of claim 1, wherein the alerting includes a comparison of physical activity, sleep duration, and eating duration between the predetermined time period and a second time period of predetermined duration before the predetermined time period.

### Claim 13 (depends on 1)

The method of claim 1, further comprising:
receiving a triggering condition for the first user from the first user or the second user;
automatically monitoring for the triggering condition; and
automatically forwarding an alert to the first user or the second user upon determining the triggering condition.

### Claim 14 (depends on 13)

The method of claim 13, further comprising:
receiving the triggering condition for the first user from the second user, wherein the triggering condition is the first user performing the first user activity;
automatically forwarding the alert to the second user upon determining the triggering condition for the first user.

### Claim 15

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 for the first user, wherein learned activity patterns include an exercise pattern and an eating pattern at a location or with one or more community members;
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, wherein the automatically correlating comprises assigning a confidence metric to the possible medical condition;
automatically selecting a second user for alerting as a function of the confidence metric, wherein a close community member is alerted for a first confidence metric, and a medical professional is alerted for a second confidence metric, the second confidence metric being higher than the first confidence metric; and
automatically alerting the second user via a second electronic device of the possible medical condition, wherein the alerting includes sending the confidence metric to the second user.

### Claim 16 (depends on 15)

The method of claim 15, further comprising automatically detecting a possible onset of depression from a combination of a reduction in physical activity, an increase in sleep duration, and an increased appetite over the predetermined time period.

### Claim 17 (depends on 15)

The method of claim 15, further comprising:
automatically and continually tracking a location of an electronic device of each member of a social media community of the first user; and
automatically alerting a community member engaged in a matching user activity to one user activity of the learned activity patterns with a request to engage the first user in the matching user activity.