IP Library › Granted Patent US 12,567,096
Granted Patent B2
US 12,567,096 · App. 17/992,694 · Granted Mar 3, 2026

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

Inventors: Roberto Sicconi (Purdys, NY); Malgorzata Stys (Purdys, NY)
G06Q30/0631G07C5/02
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Quick Facts
Patent No.
US 12,567,096
App. No.
17/992,694
Granted
Mar 3, 2026
Kind
B2
Abstract

A system for using artificial intelligence to present geographically relevant user-specific recommendations based on vehicle operator attentiveness includes at least a transport communication device installed in a vehicle, 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, designed and configured to monitor a vehicle operator and generate at least an operator state datum, an 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, designed and configured to determine geographical location, and a recommender module, designed and configured to receive at least a local attraction datum, generate an associated output message as a function of the attentiveness level and geographical location, and provide the output message to the operator.

Claims (48)

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 driving condition sensor, the at least a driving condition sensor designed and configured to:

monitor driving conditions; and

generate at least a driving condition datum wherein the at least a driving condition sensor comprises at least a road-facing camera configured to capture a video feed of conditions external to a vehicle wherein capturing the video feed further comprises:

detecting external objects by comparing a plurality of video feed frames and determining that a number of pixels exceeding a threshold has changed with respect to vehicle dynamics parameters, including at least positioning in a lane, wherein a frequency of a sample rate of the plurality of video feed frames is selected using a machine leaning model; and

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

at least an operator sensor, the at least an operator sensor designed and configured to:

monitor an operator of the vehicle; and

generate at least an operator state datum; and

at least a transport communication device installed in the vehicle and communicatively connected to the at least a driving condition sensor and the at least an operator sensor, the at least a transport communication device configured to:

determine an attentiveness level as a function of the at least a driving condition datum and the at least an operator state datum;

identify an upcoming loss in network connectivity;

generate an output message as a function of the attentiveness level and the upcoming loss in network connectivity;

determine that the operator is attentive based on the attentiveness level exceeding a threshold level; and

provide the output message to the operator based on the determination that the operator is attentive using a language processing module configured to generate an audial output as a function of the output message based on user information related to operator skills to determine content and a pace of dialog of the audial output.

2 . 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.

3 . The system of claim 2 , 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.

4 . 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.

5 . The system of claim 1 , wherein the output message includes a warning about predicted interruption of service ahead.

6 . The system of claim 5 , wherein the output message includes an amount of look-ahead timing to the user.

7 . The system of claim 1 , wherein identifying the upcoming loss of communications further comprises identifying the upcoming loss of communications as a function of a plurality of historical connection data.

8 . 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.

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

receiving, by at least a transportation communication device:

at least a driving condition datum from at least a driving condition sensor wherein the at least a driving condition sensor comprises at least a road-facing camera configured to capture a video feed of conditions external to a vehicle wherein capturing the video feed further comprises:

detecting external objects by comparing a plurality of video feed frames and determining that a number of pixels exceeding a threshold has changed with respect to vehicle dynamics parameters, including at least positioning in a lane, wherein a frequency of a sample rate of the plurality of video feed frames is selected using a machine leaning model; and

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

at least an operator state datum from at least an operator sensor;

determining, by the at least a transportation communication device, an attentiveness level as a function of the at least a driving condition datum and the at least an operator state datum;

identifying, by the at least a transportation communication device, an upcoming loss in network connectivity;

generating, by the at least a transportation communication device, an output message r as a function of the attentiveness level and the upcoming loss in network connectivity;

determining, by the at least a transportation communication device, that the operator is attentive based on the attentiveness level exceeding a threshold level; and

providing, by the at least a transportation communication device, the output message to the operator based on the determination that the operator is attentive using a language processing module configured to generate an audial output as a function of the output message based on user information related to operator skills to determine content and a pace of dialog of the audial output.

10 . The method of claim 9 , wherein the at least an operator sensor further comprises at least an operator-facing camera configured to capture a video feed of the operator.

11 . The method of claim 10 , 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.

12 . The method of claim 9 , 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.

13 . The method of claim 9 , wherein the output message includes a warning about predicted interruption of service ahead.

14 . The method of claim 13 , wherein the output message includes an amount of look-ahead timing to the user.

15 . The method of claim 9 , wherein identifying the upcoming loss of communications further comprises identifying the upcoming loss of communications as a function of a plurality of historical connection data.

16 . The method of claim 9 , wherein determining the attentiveness level further comprises:

determining a direction of operator focus; and

generating an attentiveness level using the direction of operator focus.

Continuity (2)
Continuation In Part 16589241 · Oct 1, 2019
Related Publication 20230112797A1 · Apr 13, 2023
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