TECHNIQUES FOR USING A HYBRID MODEL FOR GENERATING TAGS AND INSIGHTS

1 . A method for identifying taggable events using a wearable device, comprising:

receiving geographical location data associated with a user throughout a time interval;

receiving physiological data associated with the user from a wearable device;

correlating the physiological data with one or more candidate taggable events of a plurality of candidate taggable events defined within an application associated with the wearable device, the one or more candidate taggable events associated with one or more confidence values that indicate a confidence level that the corresponding candidate taggable events occurred within the time interval;

selectively modifying the one or more confidence values associated with the one or more candidate taggable events based at least in part on the geographical location data to generate one or more modified confidence values;

identifying a taggable event of the one or more candidate taggable events within the time interval based at least in part on a modified confidence value associated with the taggable event satisfying a threshold confidence value; and

causing a graphical user interface of a user device running the application to display an indication of the identified taggable event.

2 . The method of claim 1 , further comprising:

identifying a relationship between the identified taggable event and the physiological data acquired during the time interval, additional physiological data acquired during a different time interval, or both; and

causing the graphical user interface of the user device to display a message associated with the relationship.

3 . The method of claim 1 , further comprising:

inputting the physiological data and the geographical location data into a machine learning model, wherein correlating the physiological data with the one or more candidate taggable events, selectively modifying the one or more confidence values, identifying the taggable event, or any combination thereof, is based at least in part on inputting the physiological data and the geographical location data into a machine learning model.

4 . The method of claim 1 , further comprising:

identifying historical taggable event data associated with the user, the historical taggable event data comprising a plurality of historical taggable events identified for the user and historical geographical location data corresponding to the plurality of historical taggable events; and

identifying that the geographical location data of the user is associated with the historical geographical location data, wherein selectively modifying the one or more confidence values is based at least in part on the historical taggable event data and identifying that the geographical location data of the user is associated with the historical geographical location data.

5 . The method of claim 1 , further comprising:

identifying historical taggable event data associated with the user, the historical taggable event data comprising a plurality of historical taggable events and a time of day in which the plurality of historical taggable events were identified; and

identifying that the time interval during which the physiological data was acquired is within the time of day, wherein selectively modifying the one or more confidence values is based at least in part on the historical taggable event data and identifying that the time interval is within the time of day.

6 . The method of claim 1 , further comprising:

receiving, via the graphical user interface and based at least in part on displaying the indication of the taggable event, a confirmation of the taggable event, a modification of the taggable event, or both.

7 . The method of claim 1 , wherein the physiological data comprises at least motion data, the method further comprising:

identifying a plurality of motion segments within the time interval based at least in part on the motion data; and

identifying a gesture the user engaged in based at least in part on matching a motion segment of the plurality of motion segments to a gesture profile of a set of gesture profiles defined within the application, wherein selectively modifying the one or more confidence values, identifying the taggable event, or both, is based at least in part on identifying the gesture.

8 . The method of claim 7 , further comprising:

identifying a relationship between the identified gesture and the physiological data acquired during the time interval, additional physiological data acquired during a different time interval, or both, wherein correlating the physiological data with one or more candidate taggable events, selectively modifying the one or more confidence values, identifying the taggable event, or any combination thereof, is based at least in part on identifying the relationship.

9 . The method of claim 1 , wherein the geographical location data is associated with a semantic location, the method further comprising:

determining that one or more additional users are located at the semantic location during at least a portion of the time interval, wherein selectively modifying the one or more confidence values, identifying the taggable event, or both, is based at least in part on determining that one or more additional users are located at the semantic location during at least a portion of the time interval.

10 . The method of claim 1 , further comprising:

selectively adjusting a Readiness Score associated with the user, an Activity Score associated with the user, a Sleep Score associated with the user, or any combination thereof, based at least in part on the identified taggable event.

11 . The method of claim 1 , wherein the geographical location data comprises geographical positioning data acquired via the user device.

12 . The method of claim 1 , wherein the geographical location data is received via a calendar application executable by the user device.

13 . The method of claim 1 , wherein the taggable event comprises a workout, food consumption, caffeine consumption, alcohol consumption, or any combination thereof.

14 . An apparatus for identifying taggable events using a wearable device, 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 geographical location data associated with a user throughout a time interval;

receive physiological data associated with the user from a wearable device;

correlate the physiological data with one or more candidate taggable events of a plurality of candidate taggable events defined within an application associated with the wearable device, the one or more candidate taggable events associated with one or more confidence values that indicate a confidence level that the corresponding candidate taggable events occurred within the time interval;

selectively modify the one or more confidence values associated with the one or more candidate taggable events based at least in part on the geographical location data to generate one or more modified confidence values;

identify a taggable event of the one or more candidate taggable events within the time interval based at least in part on a modified confidence value associated with the taggable event satisfying a threshold confidence value; and

cause a graphical user interface of a user device running the application to display an indication of the identified taggable event.

15 . The apparatus of claim 14 , wherein the instructions are further executable by the processor to cause the apparatus to:

identify a relationship between the identified taggable event and the physiological data acquired during the time interval, additional physiological data acquired during a different time interval, or both; and

cause the graphical user interface of the user device to display a message associated with the relationship.

16 . The apparatus of claim 14 , wherein the instructions are further executable by the processor to cause the apparatus to:

input the physiological data and the geographical location data into a machine learning model, wherein correlating the physiological data with the one or more candidate taggable events, selectively modifying the one or more confidence values, identifying the taggable event, or any combination thereof, is based at least in part on inputting the physiological data and the geographical location data into a machine learning model.

17 . The apparatus of claim 14 , wherein the instructions are further executable by the processor to cause the apparatus to:

identify historical taggable event data associated with the user, the historical taggable event data comprising a plurality of historical taggable events identified for the user and historical geographical location data corresponding to the plurality of historical taggable events; and

identify that the geographical location data of the user is associated with the historical geographical location data, wherein selectively modifying the one or more confidence values is based at least in part on the historical taggable event data and identifying that the geographical location data of the user is associated with the historical geographical location data.

18 . The apparatus of claim 14 , wherein the instructions are further executable by the processor to cause the apparatus to:

identify historical taggable event data associated with the user, the historical taggable event data comprising a plurality of historical taggable events and a time of day in which the plurality of historical taggable events were identified; and

identify that the time interval during which the physiological data was acquired is within the time of day, wherein selectively modifying the one or more confidence values is based at least in part on the historical taggable event data and identifying that the time interval is within the time of day.

19 . The apparatus of claim 14 , wherein the instructions are further executable by the processor to cause the apparatus to:

receive, via the graphical user interface and based at least in part on displaying the indication of the taggable event, a confirmation of the taggable event, a modification of the taggable event, or both.

20 . A non-transitory computer-readable medium storing code for identifying taggable events using a wearable device, the code comprising instructions executable by a processor to:

receive geographical location data associated with a user throughout a time interval;

receive physiological data associated with the user from a wearable device;

correlate the physiological data with one or more candidate taggable events of a plurality of candidate taggable events defined within an application associated with the wearable device, the one or more candidate taggable events associated with one or more confidence values that indicate a confidence level that the corresponding candidate taggable events occurred within the time interval;

selectively modify the one or more confidence values associated with the one or more candidate taggable events based at least in part on the geographical location data to generate one or more modified confidence values;

identify a taggable event of the one or more candidate taggable events within the time interval based at least in part on a modified confidence value associated with the taggable event satisfying a threshold confidence value; and

cause a graphical user interface of a user device running the application to display an indication of the identified taggable event.