Dynamic scheduling tool for office appointments management

• US 20060053044A1
• Filed: 03/17/2005
• Published: 03/09/2006
• Est. Priority Date: 09/07/2004
• Status: Abandoned Application

• Current Assignee: Alphaglobal It Incorporated
• Original Assignee: Alphaglobal It Incorporated
• Inventors: Melek, William W., Kurian, Joseph C.
• Application Number:
  US11/082,681
• Publication Number:
  US 20060053044A1
• Time in Patent Office: Days
• US Class Current: 705/9
• CPC Class Codes: G06Q 10/063116 Schedule adjustment for a person or group
• CPC Class Codes: G06Q 10/06314 Calendaring for a resource
• CPC Class Codes: G06Q 10/109 Time management, e.g. calendars, reminders, meetings or time accounting
• CPC Class Codes: G06Q 10/1095 Meeting or appointment
• CPC Class Codes: G06Q 10/1097 Task assignment

Abstract

Method and system for dynamically scheduling and managing appointments of professional services using artificial intelligence. The appointment schedule can be shifted, updated and rearranged in real time based on a plurality of inputs.

Claims

1. A method of scheduling a professional service using artificial intelligence, the method comprising the steps of:

storing historical data;

training a neural network using the historical data to learn a schedule allocation;

receiving a current request for the professional service;

generating one or more than one input signals based on the current request;

applying the input signals to the neural network having the learned schedule allocation;

reserving an appointment in a schedule; and

outputting the appointment.

2. The method according to claim 1, wherein the training step comprises generating a plurality of weights for the neural network from the historical data.

3. The method according to claim 1, wherein the input signal comprises a lead-lag indicator, the lead-lag indicator derived from the real time of the professional service, and completed tasks in the schedule.

4. The method according to claim 1, wherein the professional service is a medical service.

5. The method according to claim 1, wherein the professional service is a service requiring a plurality of resources.

6. The method according to claim 1, wherein the professional service is a service provided by a plurality of professionals.

7. The method according to claim 1, wherein the input signal comprises a type of medical visit.

8. The method according to claim 1, wherein the current request is for rescheduling an existing appointment.

9. The method according to claim 1, wherein the appointment is a blocked time.

10. The method according to claim 1, wherein the input signal comprises average time needed by a professional to complete a type of the appointment.

11. The method according to claim 1, wherein the input signal comprises average appointment time needed for a person receiving the professional service.

12. The method according to claim 1, wherein the input signal comprises scheduled appointment time.

13. The method according to claim 1, wherein the input signal comprises next appointment time.

14. The method according to claim 1, wherein the appointment is a planed periods of multiple appointments to a single start time.

15. The method according to claim 1, wherein appointments of same type is grouped together.

16. An apparatus for scheduling a professional service using artificial intelligence, the apparatus comprising:

a memory for storing historical data;

a neural network having a plurality of nodes, each node connected to one or more than one neighboring nodes, said neural network trained by the historical data to modify the weights, said neural network being responsive to a plurality of input signals, including one or more than one input signals generated from a current request for the professional service;

said neural network generates an appointment in a schedule; and

means for displaying the schedule.

17. The apparatus according to claim 16, wherein a background discontinuity is displayed in the schedule, said background discontinuity indicating the real time of the apparatus.

18. The apparatus according to claim 16, wherein the professional service is a medical service.

19. The apparatus according to claim 16, wherein the professional service is a service provided by a plurality of professionals.

20. The apparatus according to claim 16, wherein the current request is for rescheduling an existing appointment.

21. The apparatus according to claim 16, wherein the input signal comprises average time needed by a professional to complete a type of the appointment.

22. The apparatus according to claim 16, wherein the input signal comprises average appointment time needed for a person receiving the professional service.

23. The apparatus according to claim 16, wherein the input signal comprises scheduled appointment time.

24. A computer readable medium storing instructions or statements for use in the execution in a computer of a method of scheduling a professional service using artificial intelligence, the method comprising the steps of:

storing historical data;

training a neural network using the historical data to learn a schedule allocation;

receiving a current request for the professional service;

generating one or more than one input signals based on the current request;

applying the input signals to the neural network having the learned schedule allocation;

reserving an appointment in a schedule; and

outputting the appointment.

25. The computer readable medium according to claim 24, wherein the training step comprises generating a plurality of weights for the neural network from the historical data.

26. The computer readable medium according to claim 24, wherein the input signal comprises a lead-lag indicator, the lead-lag indicator derived from the real time of the professional service, and completed tasks in the schedule.

27. The computer readable medium according to claim 24, wherein the professional service is a medical service.

28. The computer readable medium according to claim 24, wherein the professional service is a service provided by a plurality of professionals.

29. The computer readable medium according to claim 24, wherein the current request is for rescheduling an existing appointment.

30. The computer readable medium according to claim 24, wherein the appointment is a blocked time.

31. The computer readable medium according to claim 24, wherein the input signal comprises average time needed by a professional to complete a type of the appointment.

32. The computer readable medium according to claim 24, wherein the input signal comprises average appointment time needed for a person receiving the professional service.

33. The computer readable medium according to claim 24, wherein the input signal comprises scheduled appointment time.

34. A computer program product comprising:

a memory having microcontroller-readable code embedded therein for scheduling a professional service using artificial intelligence, comprising;

code means training a neural network using the historical data to learn a schedule allocation;

code means for receiving a current request for the professional service;

code means for generating one or more than one input signals based on the current request;

code means for applying the input signals to the neural network having the learned schedule allocation;

code means for generating an appointment in a schedule; and

code means for outputting the appointment.