Granted Patent B2

US 12,493,660 · App. 18/584,209 · Granted Dec 9, 2025

Inventor: Michael Mecca (Jersey City, NJ)

Assignee: PortPro Technologies, Inc.

G06F16/951G06Q10/1095

Abstract

Claims (70)

1 . An apparatus for communication with an intermodal terminal,

wherein the apparatus comprises:

at least a processor; and

a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:

receive a plurality of appointment datasets, wherein each appointment dataset of the plurality of appointment datasets comprises a plurality of data points describing a plurality of terminal events associated with at least an intermodal terminal;

receive entity transport data;

aggregate the plurality of appointment datasets by synthesizing the plurality of data points to form an integrated terminal event dataset;

populate a user interface data structure as a function of the aggregated plurality of appointment datasets, wherein the user interface data structure comprises a visual representation of the integrated terminal event dataset;

receive a user input from a user through the populated user interface data structure, wherein the user input comprises:

a selection of a terminal event from the plurality of terminal events; and

a plurality of corresponding event parameters;

generate a terminal event handler as a function of the user input, wherein the terminal event handler is configured to:

initiate a communication protocol with the respective intermodal terminal of the selected terminal event to convey the plurality of corresponding event parameters; and

modify the user interface data structure by updating the integrated terminal event dataset as a function of the plurality of corresponding event parameters, wherein modifying the user interface data structure further comprises: modifying a plurality of windows in the user interface;

utilizing a web crawler configured to scrape appointment confirmation data, real time updates related to appointments, and missed appointment data from the user interface;

update entity transport data as a function of one or more of the appointment confirmation data, real time updates related to the appointments, and the missed appointment data;

generate a plurality of recommended appointment times based on the user input, the updated entity transport data and the integrated terminal event dataset using a scheduling model by:

receiving training data including a plurality of data sets correlating appointment data to the entity transport data;

extracting constraints from the entity transport data;

determining intermodal terminal capacity as a function of the constraints;

updating the entity transport data as a function of real-time updates of the appointment data and the intermodal terminal capacity;

optimizing the scheduling model during a training phase to minimize a difference between a previously entered recommended appointment time and an updated recommended appointment time;

updating the training data as a function of user feedback and the optimization, wherein updating the training data further comprises:

receiving the user feedback from the user interface associated with a quality of the plurality of recommended appointment times previously entered by the processor; and

removing training data associated with a poorly rated output derived from the user feedback;

iteratively training the scheduling model using the updated training data; and

outputting, by the scheduling model, the updated recommended appointment time; and

transmit the at least an updated recommended appointment time to the user via the user interface.

2 . The apparatus of claim 1 , wherein the appointment dataset comprises information regarding to an appointment scheduling of an intermodal terminal.

3 . The apparatus of claim 1 , wherein the web crawler is further configured to scrape appointment data from a website of the intermodal terminal.

4 . The apparatus of claim 1 , wherein a user input comprises a user selection regarding an appointment time.

5 . The apparatus of claim 1 , wherein the at least a processor is further configured to transmit the user input utilizing an Application Programming Interface configured to programmatically interact with a website of the intermodal terminal by submission of the user input.

6 . The apparatus of claim 1 , wherein the user interface data structure comprises a dashboard categorizing the plurality of data points by an event status.

7 . The apparatus of claim 1 , wherein the user interface data structure comprises a watch list window configured to track and display entity transport data.

8 . The apparatus of claim 1 , wherein the scheduling model comprises an optimization algorithm configured to optimize an objective function related to minimizing a total appointment duration time based on constraints extracted from the entity transport data.

9 . The apparatus of claim 8 , wherein the scheduling model comprises a regression model configured to gather historical data to determine an appointment duration time to analyze in generating the plurality of recommend appointment times.

10 . A method for communication with an intermodal terminal, wherein the method comprises:

receiving, by at least a processor, a plurality of appointment datasets, wherein each appointment dataset of the plurality of appointment datasets comprises a plurality of data points describing a plurality of terminal events associated with at least an intermodal terminal;

receive entity transport data;

aggregating, by the at least a processor, the plurality of appointment datasets by synthesizing the plurality of data points to form an integrated terminal event dataset;

populating, by the at least a processor, a user interface data structure as a function of the aggregated plurality of appointment datasets, wherein the user interface data structure comprises a visual representation of the integrated terminal event dataset;

receiving, by the at least a processor, a user input from a user through the populated user interface data structure, wherein the user input comprises:

a selection of a terminal event from the plurality of terminal events; and

a plurality of corresponding event parameters;

generating, by the at least a processor, a terminal event handler as a function of the user input, wherein the terminal event handler is configured to:

initiate a communication protocol with the respective intermodal terminal of the selected terminal event to convey the plurality of corresponding event parameters; and

modifying, by the at least a processor, the user interface data structure by updating the integrated terminal event dataset as a function of the plurality of corresponding event parameters, wherein modifying the user interface data structure further comprises:

modifying a plurality of windows in the user interface;

utilizing a web crawler configured to scrape appointment confirmation data, real time updates related to appointments, and missed appointment data from the user interface;

updating, by the at least a processor, entity transport data as a function of one or more of the appointment confirmation data, real time updates related to the appointments, and the missed appointment data;

generating, by at least a processer, a plurality of recommended appointment times based on the user input, the updated entity transport data and the integrated terminal event dataset using a scheduling model by:

receiving training data including a plurality of data sets correlating appointment data to the entity transport data;

extracting constraints from the entity transport data;

determining intermodal terminal capacity as a function of the constraints;

updating the entity transport data as a function of real-time updates of the appointment data and the intermodal terminal capacity;

optimizing the scheduling model during a training phase to minimize a difference between a previously entered recommended appointment time and an updated recommended appointment time;

updating the training data as a function of user feedback and the optimization, wherein updating the training data further comprises:

receiving the user feedback from the user interface associated with a quality of the plurality of recommended appointment times previously entered by the processor; and

removing training data associated with a poorly rated output derived from the user feedback;

iteratively training the scheduling model using the updated training data; and

outputting, by the scheduling model, the updated recommended appointment time; and

transmitting, by the at least a processor, the at least an updated recommended appointment time to the user via the user interface.

11 . The method of claim 10 , wherein the appointment dataset comprises information associated with an appointment scheduling of an intermodal terminal.

12 . The method of claim 10 , wherein the web crawler is further configured to scrape appointment data from a website of the intermodal terminal.

13 . The method of claim 10 , wherein a user input comprises a user selection regarding an appointment time.

14 . The method of claim 10 , wherein the at least a processor is further configured to transmit the user input utilizing an Application Programming Interface configured to programmatically interact with a website of the intermodal terminal by submission of the user input.

15 . The method of claim 10 , wherein the user interface data structure comprises a dashboard categorizing the plurality of data points by an event status.

16 . The method of claim 10 , wherein the user interface data structure comprises a watch list window configured to track and display entity transport data.

17 . The method of claim 10 , wherein the scheduling model comprises an optimization algorithm configured to optimize an objective function related to minimizing a total appointment duration time based on constraints extracted from the entity transport data.

18 . The method of claim 17 , wherein the scheduling model comprises a regression model configured to gather historical data to determine an appointment duration time to analyze in generating the plurality of recommend appointment times.

Assignments (1)

Continuity (1)

References Cited (21)