Systems and methods for using artificial intelligence to present geographically relevant user-specific recommendations based on user attentiveness

1. A system for using artificial intelligence to present geographically relevant user-specific recommendations based on vehicle operator attentiveness, the system comprising:

at least a transport communication device installed in a vehicle;

at least a driving condition sensor, in communication with the at least a transport communication device, the at least a driving condition sensor designed and configured to:

monitor driving conditions; and

generate at least a driving condition datum;

at least an operator sensor, in communication with the at least a transport communication device, the at least an operator sensor designed and configured to:

monitor an operator of the vehicle; and

generate at least an operator state datum;

an attention state module, operating on the at least a transport communication device, the attention state module designed and configured to generate an attentiveness level as a function of the at least a driving condition datum and the at least an operator state datum;

a location interface component, in communication with the at least a transport communication device, the location interface component designed and configured to determine geographical location; and

a recommender module, operating on the at least a transport communication device, the recommender module designed and configured to:

receive at least a local attraction datum;

generate an output message referencing the at least a local attraction datum as a function of the attentiveness level and the geographical location;

determine that the operator is attentive based on the attentiveness level exceeding a threshold level, wherein the threshold level is a percentage of an attentiveness level associated with a low probability of negative outcome; and

provide the output message to the operator based on the determination that the operator is attentive.

2. The system of claim 1 , wherein the at least a driving condition sensor further comprises at least a road-facing camera configured to capture a video feed of conditions external to the vehicle.

3. The system of claim 2 , wherein capturing the video feed further comprises:

detecting external objects; and

generating at least a driving condition datum as a function of detected external objects.

4. The system of claim 1 , wherein the at least an operator sensor further comprises at least an operator-facing camera configured to capture a video feed of the operator.

5. The system of claim 4 , wherein capturing the video feed of the operator further comprises:

detecting operator face contours; and

generating at least an operator state datum as a function of the detected operator face contours.

6. The system of claim 1 , wherein the at least an operator sensor is further configured to generate the at least an operator state datum by determining a direction of operator focus.

7. The system of claim 1 , wherein determination of attentiveness level further comprises:

making a risk determination as a function of the at least a driving condition datum; and

determining the attentiveness level based on the risk determination.

8. The system of claim 1 , wherein making the risk determination further comprises:

extracting a plurality of historical operator data and a plurality of correlated historical driving condition data;

generating a machine-learning model as a function of the plurality of historical operator data and the plurality of correlated historical driving condition data; and

making a risk determination using the machine-learning model and the at least a driving condition datum.

9. The system of claim 1 , wherein determining the attentiveness level further comprises:

determining a direction of operator focus; and

generating an attentiveness level using the direction of operator focus.

10. The system of claim 1 , wherein the recommender module is further configured to make a selection from the at least a local attraction datum by:

receiving a plurality of operator preference variables;

generating a loss function of the plurality of local attraction data using the plurality of operator preference variables; and

minimizing the loss function.

11. The system of claim 1 , wherein the recommender module further comprises a language processing module configured to provide the output message as a function of operator preferred language.

12. The system of claim 1 , wherein:

receiving the at least a local attraction datum includes receiving a plurality local attraction data; and

the recommender module is further configured to select at least a local attraction datum from the plurality of local attraction data.

13. A method of using artificial intelligence to present geographically relevant user-specific recommendations based on vehicle operator attentiveness, the method comprising:

communicating with a vehicle by at least a transport communication device;

monitoring, by the at least a transport communication device, using at least a driving condition sensor, driving conditions, wherein at least a driving condition datum is generated;

monitoring, by the at least a transport communication device, using at least an operator sensor, an operator of a vehicle, wherein at least an operator state datum is generated;

generating, by the at least a transport communication device, using an attention state module, an attentiveness level as a function of the at least a driving condition datum and the at least an operator state datum;

determining, by the at least a transport communication device, using a location interface component, geographical location; and

receiving, by the at least a transport communication device, using a recommender module, at least a local attraction datum, wherein at least an output message referencing the at least a local attraction datum is generated as a function of the attentiveness level and the geographical location, and the output message is provided to the operator based on a determination that the operator is attentive as a function of the attentiveness level exceeding a threshold level, wherein the threshold level is a percentage of an attentiveness level associated with a low probability of negative outcome.

14. The method of claim 13 , wherein monitoring driving conditions further comprises capturing a video feed of conditions external to the vehicle.

15. The method of claim 14 , wherein capturing the video feed further comprises:

detecting external objects; and

generating at least a driving condition datum as a function of detected external objects.

16. The method of claim 13 , wherein monitoring the operator of a vehicle further comprises capturing a video feed of the operator.

17. The method of claim 13 , wherein determining the attentiveness level further comprises:

making a risk determination as a function of the at least a driving condition datum; and

determining the attentiveness level based on the risk determination.

18. The method of claim 13 , wherein receiving at least a local attraction datum further comprises making a selection from the at least a local attraction datum by:

receiving a plurality of operator preference variables;

generating a loss function of the plurality of local attraction data using the plurality of operator preference variables; and

minimizing the loss function.

19. The method of claim 13 , wherein providing the output message to the operator further comprises generating the output message as a function of an operator preferred language.

20. The method of claim 13 , wherein:

receiving the at least a local attraction datum includes receiving a plurality of local attraction data; and

selecting at least a local attraction datum from the plurality of local attraction data.