Stress and productivity insights based on computerized data

Patent metadata

Field	Value
Publication number	US-2018107962-A1
Application number	US-201615294539-A
Country	US
Kind code	A1
Filing date	Oct 14, 2016
Priority date	Oct 14, 2016
Publication date	Apr 19, 2018
Grant date	—

Abstract

Determining stress and productivity insights based on computerized data is described. Productivity data and stress-related behavior data associated with a user may be determined. The stress-related behavior data may be based on sensor data received from sensor(s) associated with a device corresponding to the user. The stress-related behavior data may be utilized to determine a first value indicative of stress associated with the user and the productivity data may be utilized to determine a second value indicative of productivity of the user. The productivity data, the stress-related behavior data, the first value, and/or the second value may be utilized to determine a recommendation. The recommendation may be intended to modify the first value and/or the second value. A user interface configured to communicate the recommendation to the user may be presented via the device.

First claim

What is claimed is:

1 . A system comprising:
one or more processors; and memory that stores instructions that are executable by the one or more processors to cause the system to perform operations comprising:
determining productivity data associated with a user;
receiving sensor data associated with the user;
determining, based at least in part on the sensor data, stress-related behavior data associated with the user;
inferring, based at least in part on the stress-related behavior data, a first value indicative of stress associated with the user;
inferring, based at least in part on the productivity data, a second value indicative of productivity of the user;
determining a recommendation to at least one of decrease the first value or increase the second value; and
causing an action to be performed such to effectuate the recommendation.

2 . A system as claim 1 recites, wherein the recommendation is associated with a health activity.

3 . A system as claim 1 recites, wherein the recommendation is associated with a change to an environment associated with the user.

4 . A system as claim 1 recites, wherein the recommendation is associated with a change to an activity that generates at least part of the productivity data.

5 . A system as claim 1 recites, wherein the recommendation is associated with a reduction in an amount of time that the user interacts with a display of a device associated with the user.

6 . A system as claim 1 recites, the operations further comprising:
causing a control corresponding to the action and the recommendation to be presented via a device associated with the user;
determining an actuation of the control; and
based at least in part on the actuation of the control, facilitating execution of the action.

7 . A system as claim 1 recites, the operations further comprising:
causing the action to be performed on behalf of the user; and
causing a notification to be presented via a device associated with the user, the notification indicating that the action has been performed.

8 . A computer-implemented method comprising:
determining productivity data associated with a user; determining stress-related behavior data associated with the user; inferring, based at least in part on the stress-related behavior data, a first value indicative of stress associated with the user; inferring, based at least in part on the productivity data, a second value indicative of productivity of the user; determining, based on at least one of the first value or the second value, a recommendation associated with at least one of a health activity or a change to an environment associated with the user; and causing a user interface configured to communicate the recommendation to the user to be presented via a device associated with the user.

9 . A computer-implemented method as claim 8 recites, wherein the productivity data comprises at least one of email data, event data, social data, browsing data, activity data, communication data, location data, or work data.

10 . A computer-implemented method as claim 8 recites, further comprising receiving sensor data from one or more sensors associated with the device, the sensor data being used to determine at least one of a heart rate of the user, a skin temperature of the user, a galvanic skin response of the user, or a distance traveled by the user.

11 . A computer-implemented method as claim 10 recites, further comprising determining the stress-related behavior data based at least in part on the sensor data.

12 . A computer-implemented method as claim 8 recites, wherein the stress-related behavior data comprises at least one of sleep data, fitness data, email data, event data, nutrition data, social data, browsing data, activity data, communication data, location data, environment data, electronic health record data, genomic data, work data, or demographic data.

13 . A computer-implemented method as claim 8 recites, wherein the device associated with the user comprises a wearable device.

14 . A computer-implemented method as claim 8 recites, wherein determining the recommendation is based at least in part on:
determining that the first value is above a threshold; and
determining, based on at least one of the productivity data or the stress-related behavior data, at least one of the health activity or the change to the environment associated with the user that may be modified to decrease the first value.

15 . A computer-implemented method as claim 8 recites, wherein determining the recommendation is based at least in part on:
determining that the second value is below a threshold; and
determining, based on at least one of the productivity data or the stress-related behavior data, at least one of the health activity or the change to the environment associated with the user that may be modified to increase the second value.

16 . A computer-implemented method as claim 8 recites, further comprising:
determining that the user accepts the recommendation;
determining updated productivity data;
determining updated stress-related behavior data;
determining, based at least in part on the updated stress-related behavior data, a third value indicative of stress associated with the user;
determining, based at least in part on the updated productivity data, a fourth value indicative of productivity of the user; and
comparing the first value with the third value and the second value with the fourth value to determine an effectiveness of the recommendation.

17 . A computer-implemented method as claim 16 recites, further comprising updating a model for determining recommendations based at least in part on the effectiveness of the recommendation.

18 . A device comprising:
one or more applications; one or more processors; and memory that stores one or more instructions that are executable by the one or more processors to cause the device to perform operations comprising:
receiving, from the one or more applications, application data associated with the device;
determining, based at least in part on the application data, productivity data associated with the user;
receiving, from one or more sensors, sensor data associated with a user;
determining, based at least in part on at least one of the application data or the sensor data, stress-related behavior data associated with the user;
determining, based at least in part on at least one of the productivity data or the stress-related behavior data, a recommendation associated with at least one of a health activity or a change to an environment associated with the user; and
presenting the recommendation to the user via a user interface configured to communicate at least one of stress associated with the user or predicted productivity of the user.

19 . A device as claim 18 recites, the operations further comprising presenting, with the recommendation and via the user interface, a control corresponding to an action associated with the recommendation, wherein actuation of the control enables execution of the action.

20 . A device as claim 18 recites, the operations further comprising controlling a power state of the system based at least in part on the recommendation.

Assignees

• Microsoft Technology Licensing Llc

Inventors

• Lundin Jessica
• White Ryen William
• Nathan Girish
• Ni Tachen

Classifications

• G06Q10/06398 Primary

  Performance of employee with respect to a job function · CPC title

• G06N20/00

  Machine learning · CPC title

• G06N5/04

  Inference or reasoning models · CPC title