Selection and configuration of an automated robotic process

1. A computer-implemented method for selection and configuration of an automated robotic process, the method comprising:

receiving a temporal biometric measurement of a worker performing a task;

receiving a spatial-temporal environmental input provided to the worker;

identifying a type of reasoning used when performing the task based, at least in part, on the temporal biometric measurement of the worker, the identifying the type of reasoning comprising:

identifying a set of spatial-temporal neocortical activity patterns of the worker; and

identifying an active area of a neocortex of the worker;

selecting a component of an AI solution to replicate the type of reasoning, the selecting the component of the AI solution is based, at least in part, on the identified active area of the neocortex, the types of reasoning comprising at least one of visual processing, inductive reasoning, audio processing, olfactory processing, muscle control, looking, listening, smelling, motion activity, listening to sound of equipment, or watching another negotiator; and

configuring the component of the AI solution based on the spatial-temporal environmental input,

wherein the temporal biometric measurement comprises a set of spatial-temporal imaging data of a brain of the worker.

2. The method of claim 1 , wherein:

the identified active area of the neocortex comprises a O1 neocortex region; and

the selected AI component is improved for visual processing.

3. The method of claim 2 , wherein the configuring the component of the AI solution further comprises identifying a visual input for the component based on the spatial-temporal environmental input.

4. The method of claim 1 , wherein:

the identified active area of the neocortex comprises a C3 neocortex region; and

the selected component is improved for at least one of data storage or retrieval.

5. The method of claim 1 , wherein the selected component comprises a block-chain based distributed ledger.

6. The method of claim 1 , further comprising identifying whether a serial or a parallel processing AI component is improved based, at least in part, on the spatial-temporal neocortical activity patterns.

7. The method of claim 1 , wherein the configuring the selected component of the AI solution further comprises identifying an ordered set of inputs to the component of the AI solution.

8. The method of claim 1 , wherein the configuring the selected component of the AI solution further comprises identifying efficiencies from combinations of the spatial-temporal environmental input.

9. The method of claim 1 , wherein the configuring the selected component of the AI solution further comprises:

identifying undesirable portions of the spatial-temporal environmental input that do not improve the selected component; and

configuring an input to a portion of the AI solution to limit undesirable input to the AI solution.

10. The method of claim 9 , wherein limiting undesirable input to the AI solution further comprises removing input noise.

11. The method of claim 1 , wherein the spatial-temporal environmental comprises at least one of: an auditory environment, a visual environment, an olfactory environment, or a device user interface.

12. The method of claim 1 , further comprising receiving a second temporal biometric measurement of the worker performing a task, the second temporal biometric measurement comprising at least one of: an image of the worker, a video feed of the worker, an audio feed from the worker, a movement of the worker, a heartbeat of the worker, a galvanic skin response of the worker, or eye movements of the worker.

13. The method of claim 1 , further comprising:

identifying a plurality of performed tasks from the biometric measurements; and

extracting a performance parameter from the biometric measurements,

wherein the configuring the selected component of the AI solution is based, at least in part, on the performance parameter.

14. The method of claim 12 , wherein the second temporal biometric measurement is provided in a training set for the component of the AI solution.

15. The method of claim 12 , further comprising:

receiving results data related to the task; and

correlating the second temporal biometric measurement with the received results data;

wherein the selecting the component of the AI solution is further based on, at least in part, at least one of the results data or the correlation.

16. The method of claim 1 , further comprising:

identifying a plurality of time intervals between each task of a plurality of performed tasks; and

configuring the selected component of the AI solution based on at least one of the time intervals.