Real-time contextually aware artificial intelligence (AI) assistant system and a method for providing a contextualized response to a user using AI

1 . An artificial intelligence (AI) assistant system, comprising:

an audio input device configured to receive voice input from one or more users;

an audio output device configured to provide audio output;

a processor;

a wireless communication device;

a contextual memory unit configured to store conversational context data on a sliding window basis; and

a non-volatile system memory unit,

wherein the processor is configured to execute instructions to:

receive the voice input from the audio input device,

determine user identification information based on the voice input,

update the conversational context data within the contextual memory unit to include the determined user identification information and a tokenized representation of the voice input,

process the voice input using a transformer-based language model to generate a conversational response,

update the conversational context data within the contextual memory unit to include a tokenized representation of the generated conversational response, and

output the generated conversational response to the one or more users via the audio output device.

2 . The AI assistant system of claim 1 , wherein the contextual memory unit is configured to store the conversational context data for a predetermined time period, and wherein the processor is further configured to execute instructions to dynamically adjust the predetermined time period based on at least one of a user input, a system parameter, and a contextual factor.

3 . The AI assistant system of claim 1 , wherein the contextual memory unit is configured to store the conversational context data for a predetermined time period, and wherein the processor is further configured to execute instructions to dynamically adjust the predetermined time period based on at least one of user preferences, system performance, and contextual relevance.

4 . The AI assistant system of claim 1 , wherein the processor is further configured to execute instructions to dynamically adjust a context window size based on at least one parameter selected from a group consisting of available memory, processor speed, and estimated latency for processing user commands.

5 . The AI assistant system of claim 1 , further comprising a display device, wherein the processor is further configured to execute instructions to generate visual content based on the conversational context data and the generated conversational response, and output the generated visual content to the display device as part of the generated conversational response.

6 . The AI assistant system of claim 1 , further comprising a camera configured to capture visual input, wherein the processor is further configured to execute instructions to analyze the visual input captured by the camera, extract relevant visual information from the visual input, and update the conversational context data based on the extracted relevant visual information.

7 . The AI assistant system of claim 1 , wherein the processor is further configured to execute instructions to access an external knowledge base via the wireless communication device to retrieve relevant information based on the conversational context data, and utilize the retrieved relevant information in conjunction with the conversational context data stored in the contextual memory unit to generate the conversational response.

8 . The AI assistant system of claim 1 , wherein the processor is further configured to execute instructions to store user-specific information in a user profile database within the non-volatile system memory unit, retrieve the user-specific information from the user profile database based on the determined user identification information, and personalize the generated conversational response based on the retrieved user-specific information.

9 . The AI assistant system of claim 1 , wherein the processor is further configured to execute instructions to perform sentiment analysis on the voice input to determine an emotional state of the one or more users, and adapt the generated conversational response based on the determined emotional state.

10 . The AI assistant system of claim 1 , wherein the processor is further configured to execute instructions to solicit user feedback on the generated conversational response, process the solicited user feedback to generate processed feedback data, update the transformer-based language model based on the processed feedback data, and utilize an active learning algorithm to select conversational responses for which to solicit user feedback.

11 . The AI assistant system of claim 1 , wherein the processor is further configured to execute instructions to proactively generate a plurality of candidate conversational responses based on the conversational context data prior to receiving a subsequent user query or command, store the plurality of candidate conversational responses in memory, and select a conversational response from the stored plurality of candidate conversational responses based on the subsequent user query or command and the conversational context data.

12 . The AI assistant system of claim 1 , wherein the processor is further configured to execute instructions to receive a complex user request from the voice input, break down the complex user request into a plurality of manageable sub-tasks, coordinate the execution of the plurality of manageable sub-tasks; and generate a portion of the conversational response based on the execution of the plurality of manageable sub-tasks.

13 . The AI assistant system of claim 1 , further comprising at least one agentic task processing unit (ATPU) configured to autonomously perform a task in a background, wherein the processor is further configured to execute instructions to: detect a command based on the conversational context data, in response to detecting the command, cause the at least one ATPU to initiate performance of the task, receive a result of the task from the at least one ATPU, generate a conversational response indicating a result of the task, and output the conversational response indicating the result of the task via the audio output device.

14 . The AI assistant system of claim 1 , wherein the processor is further configured to execute instructions to:

detect a command based on the conversational context data stored in the contextual memory unit;

in response to detecting the command, autonomously perform a multi-step task in a background,

wherein performing the multi-step task comprises:

decomposing the multi-step task into a plurality of subtasks;

assigning the plurality of subtasks to a plurality of agentic task processing units;

executing the plurality of subtasks across the plurality of agentic task processing units, wherein executing the plurality of subtasks comprises:

generating queries to retrieve data from at least one of the non-volatile system memory unit, the contextual memory unit, or an external data source accessed via the wireless communication device;

analyzing the retrieved data using at least one of natural language processing or machine learning models;

generating a plurality of results based on analyzing the retrieved data;

monitoring a progress of executing the plurality of subtasks;

aggregating the plurality of results from the plurality of agentic task processing units; and

generating a conversational response indicating a result of the multi-step task based on the aggregated plurality of results; and

output the generated conversational response via the audio output device.

15 . The AI assistant system of claim 1 , further comprising an emergency shut-off switch, wherein the processor is further configured to execute instructions to: detect activation of the emergency shut-off switch, and disable processing of voice input and generation of conversational responses in response to detecting the activation of the emergency shut-off switch.

16 . The AI assistant system of claim 1 , wherein the processor is further configured to execute instructions to: detect activation of an emergency event, and initiate a shutdown procedure in response to detecting the activation of the emergency event.

17 . The AI assistant system of claim 1 , wherein the processor is further configured to execute instructions to: assign a priority level to each user based on the determined user identification information, upon receiving voice input from multiple users, process the voice input based on the assigned priority levels.

18 . The AI assistant system of claim 1 , wherein the processor is further configured to execute instructions to: assign a priority level to each user based on the determined user identification information, upon receiving voice input from multiple users, determine a processing order for the received voice input based on the assigned priority levels, and process the voice input in the determined processing order, such that voice input from a user with a higher assigned priority level is processed before voice input from a user with a lower assigned priority level.

19 . The AI assistant system of claim 1 , wherein the processor is further configured to execute instructions to: assign a priority level to a user based on the determined user identification information, and process the voice input based on the assigned priority level.

20 . A method for providing a contextualized response to a user using artificial intelligence (AI), the method comprising:

receiving, by an audio input device, voice input from one or more users;

determining, by a processor, user identification information based on the voice input;

updating, by the processor, conversational context data within a contextual memory unit to include the determined user identification information and a tokenized representation of the voice input, wherein the contextual memory unit is configured to store the conversational context data on a sliding window basis;

processing, by the processor, the voice input using a transformer-based language model to generate a conversational response;

updating, by the processor, the conversational context data within the contextual memory unit to include a tokenized representation of the generated conversational response;

storing, by the processor, user-specific information in a non-volatile system memory unit; and

outputting, by an audio output device, the generated conversational response to the one or more users.

21 . The method of claim 20 , wherein the contextual memory unit is configured to store the conversational context data for a predetermined time period, and wherein the predetermined time period is dynamically adjusted, by the processor, based on at least one of a user input, a system parameter, and a contextual factor.

22 . The method of claim 20 , wherein the contextual memory unit is configured to store the conversational context data for a predetermined time period, and wherein the processor is further configured to execute instructions to dynamically adjust the predetermined time period based on at least one of user preferences, system performance, and contextual relevance.

23 . The method of claim 20 , further comprising dynamically adjusting, by the processor, a context window size based on at least one parameter selected from a group consisting of available memory, processor speed, and estimated latency for processing user commands.

24 . The method of claim 20 , further comprising generating, by the processor, visual content based on the conversational context data and the generated conversational response, and outputting, by a display device, the generated visual content as part of the generated conversational response.

25 . The method of claim 20 , further comprising capturing visual input by a camera, analyzing the visual input captured by the camera, extracting relevant visual information from the visual input, and updating the conversational context data based on the extracted relevant visual information.

26 . The method of claim 20 , further comprising accessing an external knowledge base using a wireless communication device to retrieve relevant information based on the conversational context data, and combining the retrieved relevant information with the conversational context data stored in the contextual memory unit to generate the conversational response.

27 . The method of claim 20 , further comprising retrieving the user-specific information from a user profile database within the non-volatile system memory unit based on the determined user identification information, and personalizing the generated conversational response based on the retrieved user-specific information.

28 . The method of claim 20 , further comprising performing sentiment analysis on the voice input to determine an emotional state of the one or more users, and adapting the generated conversational response based on the determined emotional state.

29 . The method of claim 20 , further comprising soliciting user feedback on the generated conversational response, processing the solicited user feedback to generate processed feedback data, updating the transformer-based language model based on the processed feedback data, and using an active learning algorithm to select conversational responses for which to solicit user feedback.

30 . The method of claim 20 , further comprising proactively generating a plurality of candidate conversational responses based on the conversational context data prior to receiving a subsequent user query or command, storing the plurality of candidate conversational responses in memory, and selecting a conversational response from the stored plurality of candidate conversational responses based on the subsequent user query or command and the conversational context data.

31 . The method of claim 20 , further comprising receiving a complex user request from the voice input, breaking down the complex user request into a plurality of sub-tasks, coordinating execution of the plurality of sub-tasks, and generating a portion of the conversational response based on the execution of the plurality of sub-tasks.

32 . The method of claim 20 , further comprising detecting a command based on the conversational context data, in response to detecting the command, causing at least one agentic task processing unit (ATPU) to autonomously perform a task in a background, receiving a result of the task from the at least one ATPU, generating a conversational response indicating the result of the task, and outputting the conversational response indicating the result of the task via the audio output device.

33 . The method of claim 20 , further comprising:

detecting a command based on the conversational context data stored in the contextual memory unit;

in response to detecting the command, autonomously performing a multi-step task in a background, wherein performing the multi-step task comprises:

decomposing the multi-step task into a plurality of subtasks;

assigning the plurality of subtasks to a plurality of agentic task processing units;

executing the plurality of subtasks across the plurality of agentic task processing units, wherein executing the plurality of subtasks comprises:

generating queries to retrieve data from at least one of the non-volatile system memory unit, the contextual memory unit, or an external data source accessed via a wireless communication device;

analyzing the retrieved data using at least one of natural language processing or machine learning models; and

generating a plurality of results based on analyzing the retrieved data;

monitoring a progress of executing the plurality of subtasks;

aggregating the plurality of results from the plurality of agentic task processing units; and

generating a conversational response indicating a result of the multi-step task based on the aggregated plurality of results; and

outputting the generated conversational response via the audio output device.

34 . The method of claim 20 , further comprising detecting activation of an emergency shut-off switch, and initiating a shutdown procedure in response to detecting the activation of the emergency shut-off switch.

35 . The method of claim 20 , further comprising detecting activation of an emergency event, and disabling processing of at least one of the voice input and generation of conversational responses in response to detecting the activation of the emergency event.

36 . The method of claim 20 , further comprising assigning a priority level to each user based on the determined user identification information, and processing the voice input based on the assigned priority levels in response to receiving voice input from multiple users.

37 . The method of claim 20 , further comprising assigning a priority level to each user based on the determined user identification information, upon receiving voice input from multiple users, determining a processing order for the voice input based on the assigned priority levels, and processing the voice input in the determined processing order, such that voice input from a user with a higher assigned priority level is processed before voice input from a user with a lower assigned priority level.

38 . The method of claim 20 , further comprising assigning a priority level to a user based on the determined user identification information, and processing the voice input based on the assigned priority level.