RECONFIGURATION OF EMBEDDED SERVICES ON DEVICES USING DEVICE FUNCTIONALITY INFORMATION

1 . A system, comprising:

an artificial intelligence (AI)-enabled device that comprises a plurality of embedded AI services on the AI-enabled device; and

a server that comprises neural circuitry, wherein the neural circuitry is configured to:

receive first usage information associated with the AI-enabled device and second usage information associated with the plurality of embedded AI services on the AI-enabled device;

generate an AI model based on the received first usage information and the received second usage information, wherein the AI model is a trained machine learning (ML) model on the server;

discover, from the plurality of embedded AI services, a first embedded AI service that requires a model response;

output the model response using the generated AI model,

wherein the model response comprises first functionality information and second functionality information associated with the AI-enabled device,

wherein the first functionality information comprises a new hardware-based functionality of a set of hardware-based functionalities of the AI-enabled device, and

wherein the second functionality information comprises a new application-based functionality of a set of application-based functionalities of the AI-enabled device; and

reconfigure the discovered first embedded AI service based on the model response.

2 . The system according to claim 1 , wherein the first usage information comprises device activity logs, physical port usage information, and network activity information.

3 . The system according to claim 1 , wherein the second usage information comprises operating system (OS) activity logs, application activity logs, user activity logs, and application usage pattern information.

4 . The system according to claim 1 , wherein the set of hardware-based functionalities of the AI-enabled device comprises an audio functionality, a video functionality, a touch screen functionality, an input/output (I/O) functionality, a gesture input functionality, a speaker functionality, a microphone functionality, and a High Definition Multimedia Interface (HDMI) functionality.

5 . The system according to claim 1 , wherein the set of application-based functionalities of the AI-enabled device comprises a media streaming functionality, a media storage functionality, an Audio/Video (A/V) codec functionality, and a local cloud caching functionality.

6 . The system according to claim 1 , wherein the neural circuitry is further configured to train an untrained AI model based on the first usage information and the second usage information associated with the AI-enabled device, wherein the training of the untrained AI model corresponds to the generation of the AI model.

7 . The system according to claim 1 , wherein the neural circuitry is further configured to update the AI model based on a real time or a near-real time change in the first usage information and the second usage information of the AI-enabled device.

8 . The system according to claim 1 , wherein the trained ML model is at least one of a trained deep learning model or a Bayesian model.

9 . The system according to claim 1 , wherein the neural circuitry is further configured to:

determine a set of current hardware-based functionalities and a set of current application-based functionalities in use by each embedded AI service of the plurality of AI embedded services; and

discover the first embedded AI service from the plurality of AI embedded services that requires a configuration setting for the new hardware-based functionality or the new application-based functionality based on the set of current hardware-based functionalities and the set of current application-based functionalities.

10 . The system according to claim 1 , further comprises a set of secondary devices in a vicinity of the AI-enabled device, wherein each secondary device of the set of secondary devices comprises a set of embedded AI services.

11 . The system according to claim 10 , further comprising control circuitry in the AI-enabled device, wherein the control circuitry is configured to generate a local network between the AI-enabled device and the set of secondary devices, and

wherein the generated local network is at least one of a wireless home network, a wireless local area network, or a wireless ad hoc network.

12 . The system according to claim 11 , wherein the neural circuitry is further configured to:

generate a first model response that indicates availability of the local network; and

update the plurality of embedded AI services on the AI-enabled device with the first model response that indicates the availability of the local network.

13 . The system according to claim 12 , wherein the control circuitry is further configured to share the model response with one or more of the plurality of secondary devices, via the local network.

14 . The system according to claim 13 , wherein the neural circuitry is further configured to reconfigure, via an application programming interface (API), the set of embedded AI services on one or more of the set of secondary devices, based on the shared model response.

15 . The system according to claim 1 , wherein the neural circuitry is further configured to validate the new hardware-based functionality or the new application-based functionality of the AI-enabled device by application of a self-diagnostic test scheme on the first functionality information and the second functionality information.

16 . The system according to claim 15 , wherein the neural circuitry is further configured to transmit the model response to the AI-enabled device, based on the validation of the new hardware-based functionality or the new application-based functionality.

17 . The system according to claim 16 , wherein the application of the self-diagnostic test scheme on the first functionality information and the second functionality information corresponds to the validation of presence of the new hardware-based functionality or the new application-based functionality of the AI-enabled device.

18 . A method, comprising:

in a system that comprises neural circuitry:

receiving, by the neural circuitry, first usage information associated with the AI-enabled device and second usage information associated with a plurality of embedded AI services on the AI-enabled device;

generating, by the neural circuitry, an AI model based on the received first usage information and the received second usage information, wherein the AI model is a trained machine learning (ML) model on a server;

discovering, by the neural circuitry, from the plurality of embedded AI services, a first embedded AI service that requires a model response;

outputting, by the neural circuitry, the model response using the generated AI model,

wherein the model response comprises first functionality information and second functionality information associated with the AI-enabled device,

wherein the first functionality information comprises a new hardware-based functionality of a set of hardware-based functionalities of the AI-enabled device, and

wherein the second functionality information comprises a new application-based functionality of a set of application-based functionalities of the AI-enabled device; and

reconfiguring, by the neural circuitry, the discovered first embedded AI service based on the model response.

19 . The method according to claim 18 , wherein the first usage information comprises device activity logs, physical port usage information, and network activity information.

20 . The method according to claim 18 , wherein the second usage information comprises operating system (OS) activity logs, application activity logs, user activity logs, and application usage pattern information.