TECHNIQUES FOR APPLICATION PERSONALIZATION
Methods, systems, and devices for application personalization are described. The method may include receiving physiological data from a wearable device associated with a user and receiving data associated with previous user engagement by the user with user interface features of an application associated with the wearable device. The method may include determining a content layout of the user interface features within the application based on an output of a predictive model. The predictive model may use at least the received physiological data as input and be configured to increase future user engagement with the user interface features based on the received data associated with previous user engagement. In some cases, the method may include causing a graphical user interface of the user device to display the determined content layout of the user interface features.
1 . (canceled)
2 . A method, comprising:
receiving, via a wearable device associated with a user, physiological data;
receiving, via a user device associated with the user, data associated with previous user engagement by the user with user interface features of an application associated with the wearable device;
inputting, into a predictive model configured to increase future user engagement with the user interface features, the data associated with previous user engagement by the user with the user interface features;
identifying media content associated with the user interface features within the application associated with the wearable device based at least in part on an output of the predictive model; and
causing a graphical user interface of the user device to display the media content associated with the user interface features.
3 . The method of claim 2 , further comprising:
inputting, into the predictive model configured to increase future user engagement with the user interface features, the physiological data received from the wearable device.
4 . The method of claim 2 , further comprising:
determining an order to display the user interface features based at least in part on the physiological data, the data associated with previous user engagement, or both, wherein identifying the media content is based at least in part on determining the order.
5 . The method of claim 2 , further comprising:
determining a ranking of the user interface features based at least in part on the physiological data, the data associated with previous user engagement, or both, wherein identifying the media content is based at least in part on determining the ranking.
6 . The method of claim 2 , further comprising:
determining a size of the user interface features based at least in part on the physiological data, the data associated with previous user engagement, or both, wherein identifying the media content is based at least in part on determining the size.
7 . The method of claim 2 , further comprising:
determining that the physiological data satisfies one or more thresholds, wherein the media content comprises an indication that the physiological data satisfies the one or more thresholds.
8 . The method of claim 2 , further comprising:
identifying updated media content associated with the user interface features within the application based at least in part on the output of the predictive model and one or more indications of user engagement with the media content.
9 . The method of claim 2 , wherein identifying the media content associated with the user interface features within the application is based at least in part on the physiological data, the data associated with previous user engagement, or both.
10 . The method of claim 2 , wherein the media content comprises a recommended video associated with the physiological data, a recommended audio associated with the physiological data, a request to input symptoms associated with the physiological data, a pattern detected from the physiological data, a confirmation of the physiological data, a suggested tag associated with the physiological data, or a combination thereof.
11 . The method of claim 2 , wherein the application associated with the wearable device is configured to process and display the physiological data received from the wearable device to the user via the user device.
12 . An apparatus, comprising:
a processor;
memory coupled with the processor; and
instructions stored in the memory and executable by the processor to cause the apparatus to:
receive, via a wearable device associated with a user, physiological data;
receive, via a user device associated with the user, data associated with previous user engagement by the user with user interface features of an application associated with the wearable device;
input, into a predictive model configured to increase future user engagement with the user interface features, the data associated with previous user engagement by the user with the user interface features;
identify media content associated with the user interface features within the application associated with the wearable device based at least in part on an output of the predictive model; and
cause a graphical user interface of the user device to display the media content associated with the user interface features.
13 . The apparatus of claim 12 , wherein the instructions are further executable by the processor to cause the apparatus to:
input, into the predictive model configured to increase future user engagement with the user interface features, the physiological data received from the wearable device.
14 . The apparatus of claim 12 , wherein the instructions are further executable by the processor to cause the apparatus to:
determine an order to display the user interface features based at least in part on the physiological data, the data associated with previous user engagement, or both, wherein identifying the media content is based at least in part on determining the order.
15 . The apparatus of claim 12 , wherein the instructions are further executable by the processor to cause the apparatus to:
determine a ranking of the user interface features based at least in part on the physiological data, the data associated with previous user engagement, or both, wherein identifying the media content is based at least in part on determining the ranking.
16 . The apparatus of claim 12 , wherein the instructions are further executable by the processor to cause the apparatus to:
determine a size of the user interface features based at least in part on the physiological data, the data associated with previous user engagement, or both, wherein identifying the media content is based at least in part on determining the size.
17 . The apparatus of claim 12 , wherein the instructions are further executable by the processor to cause the apparatus to:
determine that the physiological data satisfies one or more thresholds, wherein the media content comprises an indication that the physiological data satisfies the one or more thresholds.
18 . The apparatus of claim 12 , wherein the instructions are further executable by the processor to cause the apparatus to:
identify updated media content associated with the user interface features within the application based at least in part on the output of the predictive model and one or more indications of user engagement with the media content.
19 . The apparatus of claim 2 , wherein identifying the media content associated with the user interface features within the application is based at least in part on the physiological data, the data associated with previous user engagement, or both.
20 . The apparatus of claim 2 , wherein the media content comprises a recommended video associated with the physiological data, a recommended audio associated with the physiological data, a request to input symptoms associated with the physiological data, a pattern detected from the physiological data, a confirmation of the physiological data, a suggested tag associated with the physiological data, or a combination thereof.
21 . A non-transitory computer-readable medium storing code, the code comprising instructions executable by a processor to:
receive, via a wearable device associated with a user, physiological data;
receive, via a user device associated with the user, data associated with previous user engagement by the user with user interface features of an application associated with the wearable device;
input, into a predictive model configured to increase future user engagement with the user interface features, the data associated with previous user engagement by the user with the user interface features;
identify media content associated with the user interface features within the application associated with the wearable device based at least in part on an output of the predictive model; and
cause a graphical user interface of the user device to display the media content associated with the user interface features.