DEVICE TELEMETRY FOR USER EXPERIENCE PREDICTIONS

1 . A method comprising:

receiving, by one or more processors, from a plurality of devices, sensor data associated with the plurality of devices;

predicting, by the one or more processors, for a set of users of the plurality of devices, one or more user sentiments toward the plurality of devices based on the received sensor data, wherein the one or more user sentiments comprise at least one of a positive sentiment and a negative sentiment toward the plurality of devices; and

in response to the prediction, performing at least one operation comprising:

providing an offer for an add-on product to at least one user of the set of users predicted to have the positive sentiment toward the plurality of devices; and

providing at least one of a corrective information, a suggestion, or an upgrade to at least one user of the set of users predicted to have the negative sentiment toward the plurality of devices.

2 . The method of claim 1 , further comprising:

matching, by the one or more processors, at least a portion of the user sentiments to one or more user identifications (IDs) of the set of users of the plurality of the devices; and

correlating, by the one or more processors, the matched one or more of the user IDs to one or more of the plurality of devices.

3 . The method of claim 2 , wherein predicting the one or more user sentiments toward the plurality of devices comprises performing data mining on social media information that correspond to the set of users to identify one of the positive sentiment and the negative sentiment toward the plurality of devices for the set of users.

4 . The method of claim 1 , wherein the one or more user sentiments further comprise one or more use cases with respect to the plurality of devices.

5 . The method of claim 4 , further comprising:

segregating, by the one or more processors, the one or more use cases with respect to the plurality of devices into light use cases and heavy use cases based on variations in one or more device specifications associated with the plurality of devices.

6 . The method of claim 1 , further comprising:

receiving, by the one or more processors, operational logs and operating status for the plurality of devices for a specific period of time, wherein predicting the one or more user sentiments toward the plurality of devices is further based on the received operational logs and the received operating status for the plurality of devices.

7 . The method of claim 1 , wherein the set of users includes users that experience similar operating regime or subject to similar use patterns of the plurality of devices.

8 . A non-transitory computer-readable medium that stores executable instructions that, in response to execution, cause one or more processors to perform or control performance of operations to:

obtain user identifications (IDs) and information from social media associated with a first set of users of a plurality of devices;

predict user sentiments toward the plurality of devices based on the obtained user IDs and the information from the social media, wherein the user sentiments comprise at least one of a positive sentiment and a negative sentiment toward the plurality of devices; and

in response to the prediction, perform or control the performance of at least one of the operations to:

send an offer for an add-on product to at least one user of the first set of users predicted to have the positive sentiment toward the plurality of devices, and

send at least one of a corrective information, a suggestion, or an upgrade to at least one user of the first set of users predicted to have the negative sentiment toward the plurality of devices.

9 . The non-transitory computer-readable medium of claim 8 , wherein the executable instructions comprise instructions that, in response to execution, cause the one or more processors to perform or control performance of at least one of the operations to:

match at least a portion of the user sentiments to one or more of the user IDs; and

associate the matched one or more of the user IDs to one or more of the plurality of devices.

10 . The non-transitory computer-readable medium of claim 8 , wherein the executable instructions comprise instructions that, in response to execution, cause the one or more processors to perform or control performance of at least one of the operations to:

group a second set of users of the plurality of devices into a plurality of clusters, wherein each of the plurality of clusters of users is associated with a respective user sentiment towards the plurality of devices.

12 . The non-transitory computer-readable medium of claim 10 , wherein each of the plurality of clusters includes users sharing similar experiences related to the plurality of devices.

13 . The non-transitory computer-readable medium of claim 10 , wherein the second set of users of the plurality of devices are grouped into the plurality of clusters based on operation and usage of the plurality of devices by the second set of users.

11 . The non-transitory computer-readable medium of claim 8 , wherein the executable instructions comprise instructions that, in response to execution, cause the one or more processors to perform or control performance of at least one of the operations to:

generate, based at least, in part, on the grouped second set of users, a first list of users from the second set of users having the positive sentiment towards the plurality of devices, a second list of users from the second set of users with a potential problem with the plurality of devices, and a third list of users from the second set of users with a particular user case with respect to the plurality of devices.

14 . An apparatus, comprising:

at least one processor;

at least one non-transitory computer readable medium coupled to the at least one processor and encoded with executable instructions that are executable by the at least one processor to:

receive, from a plurality of devices, sensor data associated with the plurality of devices;

predict, for a set of users, one or more user sentiments toward the plurality of devices based on the received sensor data, wherein the one or more user sentiments comprise at least one of a positive sentiment and a negative sentiment toward the plurality of devices; and

in response to the prediction:

provide an offer for an add-on product to at least one user of the set of users predicted to have the positive sentiment towards the plurality of devices, and

provide at least one of a corrective information, a suggestion, or an upgrade to at least one user of the set of users predicted to have the negative sentiment towards the plurality of devices.

15 . The apparatus of claim 14 , wherein the at least one non-transitory computer readable medium that is further executable by the at least one processor to:

receive, from the plurality of devices, user identifications (IDs) and social media information, associated with the set of users, wherein the prediction of the one or more user sentiments toward the plurality of devices is further based on the received user identifications (IDs) and social media information, associated with the set of users.

16 . The apparatus of claim 14 , wherein the at least one non-transitory computer readable medium that is further executable by the at least one processor to:

receive operational logs and operating status for the plurality of devices for a specific period of time, wherein the prediction of the one or more user sentiments, toward the plurality of devices, is further based on the received operational logs and the received operating status for the plurality of devices.

17 . The apparatus of claim 14 , wherein the one or more user sentiments further include one or more use cases with respect to the plurality of devices.

18 . The apparatus of claim 17 , wherein the at least one non-transitory computer readable medium that is further executable by the at least one processor to segregate the one or more use cases with respect to the plurality of devices into light use cases and heavy use cases based on variations in one or more device specifications associated with the plurality of devices.

19 . A method comprising:

receiving from a plurality of devices, device specification associated with each of the plurality of devices;

identifying, for a set of users of the plurality of devices, one or more use cases associated with the plurality of devices, based on the received device specification;

segregating the one or more use cases into light use cases and heavy use cases based on one or more variations in the received device specification;

predicting, for the set of users of the plurality of devices, one or more user sentiments toward the plurality of devices based, at least in part on, the segregation, wherein the one or more user sentiments comprise at least one of a positive sentiment and a negative sentiment toward the plurality of devices; and

in response to the prediction:

providing an offer for an add-on product to at least one user of the set of users predicted to have the positive sentiment toward the plurality of devices; and

providing at least one of a corrective information, a suggestion, or an upgrade to at least one user of the set of users predicted to have the negative sentiment toward the plurality of devices.

20 . The method of claim 19 , further comprising:

matching at least a portion of the user sentiments to one or more user identifications (IDs) of the set of users of the plurality of the devices; and

correlating the matched one or more of the user IDs to one or more of the plurality of devices.