AI-DRIVEN PERSONAL ASSISTANT WITH ADAPTIVE RESPONSE GENERATION

1 . A system comprising:

a personal assistant electronic device that receives input data indicative of a query specifying a request from a user within an environment;

a context processing engine configured to establish a context for the query, the engine applying trained models to the input data to identify personal and environmental cues associated with the query; and

a response generator configured to output a response message based on the request, the query context and a response profile for the user, the response profile specifying one or more preferences for the user, each of the one or more preferences being associated with a manner in which the response generator responds to requests from the user, each of the one or more preferences being set by the response generator in response to feedback from the user to previous response messages.

2 . The system of claim 1 , wherein the context processing engine and the response generator execute on a processor of the personal assistant electronic device.

3 . The system of claim 1 , wherein the context processing engine and the response generator execute on a processor external to the personal assistant electronic device.

4 . The system of claim 1 , wherein the at least one input source of the personal assistant electronic device comprises a microphone and the input data indicative of the query comprises audio data.

5 . The system of claim 4 , wherein the at least one input source of the personal assistant electronic device further comprises a camera and the input data further comprises image data captured coincident with the audio data.

6 . The system of claim 1 , wherein the context processing engine is configured to apply the one or more trained models to the input data to determine environmental cues based on any of: (i) noise level, (ii) presence of people within close proximity to the user, (iii) whether the user is in the presence of one or more of a set of predefined users, (iv) location, (v) location acoustics, (vi) degree of privacy, and (vii) time of day.

7 . The system of claim 1 , wherein the context processing engine is configured to apply the one or more trained models to the input data to determine personal cues based on any of a user parameter, an emotion, a speech pattern of the user, pitch, cadence, tone of voice and stridency.

8 . The system of claim 7 , wherein the input data includes information received from social media, wherein the context processing engine determines one or more personal cues from the information received from social media.

9 . The system of claim 1 , further comprising a query handler connected to the response generator, the query handler configured to:

receive, from the response generator, the request and context information relevant to the request, the context information based on the query context; and

transmit, to the response generator, a response based on the request and the context information relevant to the request.

10 . The system of claim 1 , further comprising a query handler connected to the response generator, the query handler configured to:

receive, from the response generator, the request and context information relevant to the request, the context information based on the query context and the user preferences; and

transmit, to the response generator, a response based on the request and the context information relevant to the request.

11 . The system of claim 1 , wherein the response generator includes a personality mode and a query handler, the query handler configured to:

receive the request and context information relevant to the request, the context information based on the query context and the personality mode; and

generate a response based on the request and the context information relevant to the request.

12 . The system of claim 1 , wherein the response generator includes a language processing engine configured to convey the response message as audio.

13 . The system of claim 1 , wherein the response generator includes a speech recognition engine, wherein the speech recognition engine extracts the request from an audio recording.

14 . A method comprising:

receiving, by a personal assistant electronic device, input data indicative of a query specifying a request from a user within an environment;

determining, on a processor, a context for the query, wherein determining includes applying trained models to the input data to identify personal and environmental cues associated with the query; and

transmitting a response message to the user based on the request, the response message constructed based on the query context and on a response profile for the user, the response profile specifying one or more preferences for the user, each of the one or more preferences being associated with a manner in which the response generator responds to requests from the user, each of the one or more preferences being set by the response generator in response to feedback from the user to previous response messages.

15 . The method of claim 14 , wherein determining the context for the query includes obtaining one or more personal cues from social media.

16 . The method of claim 14 , wherein determining the context for the query includes obtaining personal cues from one or more of images and audio.

17 . The method of claim 14 , wherein the personal cues include one or more of user identifiers, user parameters, tone of voice, pitch, cadence and emotion.

18 . The method of claim 14 , wherein the environmental cues include one or more of location, noise level, size of group, and location acoustics.

19 . The method of claim 14 , wherein obtaining a response to the query includes accessing one or more of a calendaring application and a weather application.

20 . A computer-readable storage medium comprising instructions that, when executed, configure one or more processors to:

receive input data indicative of a query specifying a request from a user within an environment;

determine, on a processor, a context for the query, wherein determining includes applying trained models to the input data to identify personal and environmental cues associated with the query; and

transmit a response message to the user based on the request, the response message constructed based on the query context and on a response profile for the user, the response profile specifying one or more preferences for the user, each of the one or more preferences being associated with a manner in which the response generator responds to requests from the user, each of the one or more preferences being set by the response generator in response to feedback from the user to previous response messages.