TECHNIQUES FOR CONDUCTING EXPLORATION AND EXPLOITATION STRATEGY OF AN INPUT/OUTPUT DEVICE

1 . A method for conducting a strategy of a digital assistant, comprising:

identifying a plurality of potential plans for a user based on input data, wherein the plurality of potential plans includes an optimal plan having a highest expected reward value and at least one suboptimal plan having an expected reward value less than the highest expected reward value, wherein the input data includes historical data and a current state of the user;

extracting a first dataset, a second dataset, and a third dataset from the input data, wherein the first dataset provides a rejection history for the plurality of potential plans, wherein the second dataset indicates a receptiveness level of the user, wherein the third dataset includes confidence levels of expected reward values for each of the plurality of potential plans;

determining an exploration score based on the first dataset, the second dataset, the third dataset, and the input data;

determining a strategy based on the determined exploration score; and

causing the digital assistant to perform at least one of the plurality of potential plans based on the determined strategy.

2 . The method of claim 1 , wherein the strategy is an exploitation strategy when the exploration score is less than a predetermined threshold score, and wherein the exploitation strategy causes the digital assistant to perform the optimal plan.

3 . The method of claim 1 , wherein the strategy is an exploration strategy when the exploration score is equal or greater than a predetermined threshold score, and wherein the exploration strategy causes the digital assistant to perform the at least one suboptimal plan.

4 . The method of claim 3 , further comprising:

determining a specific suboptimal plan for the at least one suboptimal plan based on the first dataset, the second dataset, a third dataset, and the input data; and

causing the digital assistant to perform the determined specific suboptimal plan.

5 . The method of claim 1 , further comprising:

applying a machine learning model trained to determine the current state based on real-time data of the user and real-time data of an environment in a predetermined proximity to the user in real time.

6 . The method of claim 5 , wherein the real-time data of the user and a real-time data of an environment is captured by at least one sensor of an I/O device.

7 . The method of claim 1 , wherein the at least one of the plurality of potential plans are presented by at least one resource of an I/O device.

8 . The method of claim 1 , further comprising:

generating the expected reward values for each of the plurality of potential plans based on the current state and the historical data of the user, wherein the expected reward value is a numerical value to indicate probability of user to accept the respective performed potential plan.

9 . The method of claim 1 , further comprising:

generating feedback data, based on user data for the performed potential plan, wherein the user data includes at least one of: a user reply, a user reaction, and a sensory data of user;

storing the feedback data of the user for the performed potential plan; and

updating the input data.

10 . A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process, the process comprising:

identifying a plurality of potential plans for a user based on input data, wherein the plurality of potential plans includes an optimal plan having a highest expected reward value and at least one suboptimal plan having an expected reward value less than the highest expected reward value, wherein the input data includes historical data and a current state of the user;

extracting a first dataset, a second dataset, and a third dataset from the input data, wherein the first dataset provides a rejection history for the plurality of potential plans, wherein the second dataset indicates a receptiveness level of the user, wherein the third dataset includes confidence levels of expected reward values for each of the plurality of potential plans;

determining an exploration score based on the first dataset, the second dataset, the third dataset, and the input data;

determining a strategy based on the determined exploration score; and

causing the digital assistant to perform at least one of the plurality of potential plans based on the determined strategy.

11 . A system for conducting a strategy of a digital assistant, comprising:

a processing circuitry; and

a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:

identify a plurality of potential plans for a user based on input data, wherein the plurality of potential plans includes an optimal plan having a highest expected reward value and at least one suboptimal plan having an expected reward value less than the highest expected reward value, wherein the input data includes historical data and a current state of the user;

extract a first dataset, a second dataset, and a third dataset from the input data, wherein the first dataset provides a rejection history for the plurality of potential plans, wherein the second dataset indicates a receptiveness level of the user, wherein the third dataset includes confidence levels of expected reward values for each of the plurality of potential plans;

determine an exploration score based on the first dataset, the second dataset, the third dataset, and the input data;

determine a strategy based on the determined exploration score; and

cause the digital assistant to perform at least one of the plurality of potential plans based on the determined strategy.

12 . The system of claim 11 , wherein the strategy is an exploitation strategy when the exploration score is less than a predetermined threshold score, and wherein the exploitation strategy causes the digital assistant to perform the optimal plan.

13 . The system of claim 11 , wherein the strategy is an exploration strategy when the exploration score is equal or greater than a predetermined threshold score, and wherein the exploration strategy causes the digital assistant to perform the at least one suboptimal plan.

14 . The system of claim 13 , wherein the system is further configured to:

determine a specific suboptimal plan for the at least one suboptimal plan based on the first dataset, the second dataset, a third dataset, and the input data; and

cause the digital assistant to perform the determined specific suboptimal plan.

15 . The system of claim 11 , wherein the system is further configured to:

apply a machine learning model trained to determine the current state based on real-time data of the user and real-time data of an environment in a predetermined proximity to the user in real time.

16 . The system of claim 11 , wherein the real-time data of the user and a real-time data of an environment is captured by at least one sensor of an I/O device.

17 . The system of claim 11 , wherein the at least one of the plurality of potential plans are presented by at least one resource of an I/O device.

18 . The system of claim 15 , wherein the system is further configured to:

generate the expected reward values for each of the plurality of potential plans based on the current state and the historical data of the user, wherein the expected reward value is a numerical value to indicate probability of user to accept the respective performed potential plan.

19 . The system of claim 11 , wherein the system is further configured to:

generate feedback data, based on user data for the performed potential plan, wherein the user data includes at least one of: a user reply, a user reaction, and a sensory data of user;

store the feedback data of the user for the performed potential plan; and

update the input data.