IP Library Granted Patent US 10,878,710
Granted Patent B1
US 10,878,710 · App. 14/845,057 · Granted Dec 29, 2020

Systems and methods for electronic platform and data processing for student success in higher education institutions

Inventors: Catherine Ingrid Shaw (Washington, DC); John Daniel Nelson, II (Washington, DC); Philip James Friesen (Ashburn, VA); Kathleen Susan Ash Chew (Manassas, VA); Robert Laurence Alcorn, IV (Arlington, VA); Sarah Zauner (Boulder, CO); Brittney Lim Davidson (Washington, DC); Christopher Lance Johnson (Washington, DC)
Assignee: EAB Global, Inc.
G09B7/00G09B5/00
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 10,878,710
App. No.
14/845,057
Granted
Dec 29, 2020
Kind
B1
Abstract

Systems and methods are disclosed herein for recommending an educational course to a user, and may comprise receiving data records associated with availability of a plurality of educational courses at one or more institutions; receiving educational course data and educational course focus data associated with the user; receiving prior user data records comprising prior user educational course data and prior user educational course focus data; determining index scores for each of the plurality of educational courses based upon a similarity between the educational course data and prior user educational course data, and based upon a similarity between the educational course focus data and prior user educational course focus data; and providing a recommended educational course from the plurality of educational courses to the user based upon the determined index scores.

Claims (78)

1. A computer-implemented method for generating an educational plan for a user, comprising:

using a processing server, automatically retrieving data records from an electronic student information system, the data records including a plurality of educational courses and a plurality of course requirements at one or more institutions;

receiving, at the processing server, educational course data and educational course focus data from an electronic profile survey via a user interface, the educational course data or the educational course focus data including at least one of a user academic history, a user educational focus, or a user schedule associated with the user in the electronic profile survey;

using the processing server, automatically retrieving prior user data vectors from an electronic archive, the prior user data vectors including prior user educational course data and prior user educational course focus data, the prior user educational course data or prior user educational course focus data including academic histories, educational focuses, academic schedules, or educational success rates associated with individuals other than the user;

using the processing server, automatically determining at least one similarity between the user and at least one individual other than the user by comparing a current user data vector to the prior user data vectors;

using the processing server, automatically generating and transmitting to the user a first tailored course recommendation by comparing at least one of the plurality of course requirements with the current user data vector and comparing at least one of the plurality of educational courses with the at least one similarity between the user and the at least one individual other than the user;

receiving, at the processing server, a selected educational course via the user interface, wherein the selected educational course is based on the first tailored course recommendation; and

using the processing server, automatically generating and transmitting to the user a second tailored course recommendation based on the selected educational course and the first tailored course recommendation.

2. The computer-implemented method of claim 1 , wherein automatically generating and transmitting to the user the second tailored course recommendation further comprises:

determining a first candidate educational course and a second candidate educational course based upon the comparison between the at least one of the plurality of course requirements and the current user data vector or the comparison between the at least one of the plurality of educational courses and the at least one similarity between the user and the at least one individual other than the user; and

including the second candidate educational course in the first tailored course recommendation based upon a determination that the second candidate educational course is closer to full capacity than the first candidate educational course.

3. The computer-implemented method of claim 1 , wherein automatically generating and transmitting to the user the first tailored course recommendation further comprises:

determining a batch of two or more educational courses based upon the comparison between the at least one of the plurality of course requirements and the current user data vector, the comparison between the at least one of the plurality of educational courses and the at least one similarity between the user and the at least one individual other than the user, and a compatibility of the two or more educational courses in the batch; and

including the batch of two or more educational courses in the first tailored course recommendation.

4. The computer-implemented method of claim 3 , wherein determining the compatibility of the two or more educational courses in the batch comprises determining that the educational courses are either taken coincident with each other or within a predetermined time period of each other in the prior use data vectors.

5. The computer-implemented method of claim 1 , wherein automatically generating and transmitting to the user the first tailored course recommendation further comprises:

including an educational course in the first tailored course recommendation based upon proximity between a location of the educational course and an address associated with the user.

6. The computer-implemented method of claim 1 , wherein automatically generating the second tailored course recommendation based on the selected educational course comprises:

updating the current user data vector based on the selected educational course;

using the processing server, automatically determining at least one updated similarity between the user and at least one of the individuals other than the user by comparing the updated current user data vector to at least one of the prior user data vectors; and

using the processing server, automatically generating the second tailored course recommendation by comparing at least one of the plurality of course requirements with the updated current user data vector and comparing at least one of the plurality of educational courses with the at least one updated similarity between the user and the at least one individual other than the user.

7. The computer-implemented method of claim 1 , wherein the selected educational course is a first selected educational course, and further comprising:

receiving, at the processing server, a second selected educational course via the user interface, wherein the second selected educational course is based on the second tailored course recommendation, and wherein the second selected educational course has a dependency upon the first selected educational course;

receiving a move command, from the user via the user interface, to move the first selected educational course;

moving the first selected educational course, on the user interface, in a manner corresponding to the move command; and

automatically moving the second selected educational course, on the user interface, based upon the dependency upon the first selected educational course.

8. A system for generating an educational plan for a user, the system including:

at least one data storage device storing instructions for generating an educational plan; and

at least one processor configured to execute the instructions to perform operations comprising:

automatically retrieving data records from an electronic student information system, the data records including a plurality of educational courses and a plurality of course requirements at one or more institutions;

receiving educational course data and educational course focus data from an electronic profile survey via a user interface, the educational course data or the educational course focus data including at least one of a user academic history, a user educational focus, or a user schedule identified by the user in the electronic profile survey;

automatically retrieving prior user data vectors from an electronic archive, the prior user data vectors including prior user educational course data and prior user educational course focus data, the prior user educational course data or prior user educational course focus data including academic histories, educational focuses, academic schedules, or educational success rates associated with individuals other than the user;

automatically determining at least one similarity between the user and at least one individual other than the user by comparing a current user data vector to the prior user data vectors;

automatically generating and transmitting to the user a first tailored course recommendation by comparing at least one of the plurality of course requirements with the current user data vector and comparing at least one of the plurality of educational courses with the at least one similarity between the user and the at least one individual other than the user;

receiving a selected educational course via the user interface, wherein the selected educational course is based on the first tailored course recommendation; and

automatically generating and transmitting to the user a second tailored course recommendation based on the selected educational course and the first tailored course recommendation.

9. The system of claim 8 , wherein the operations further comprise:

determining a first candidate educational course and a second candidate educational course based upon the comparison between the at least one of the plurality of course requirements and the current user data vector or the comparison between the at least one of the plurality of educational courses and the at least one similarity between the user and the at least one individual other than the user; and

including the second candidate educational course in the first tailored course recommendation based upon a determination that the second candidate educational course is closer to full capacity than the first candidate educational course.

10. The system of claim 8 , wherein the operations further comprise:

determining a batch of two or more educational courses based upon their associated index scores and a compatibility of the two or more educational courses in the batch; and

including the batch of two or more educational courses in the first tailored course recommendation.

11. The system of claim 10 , wherein the operations further comprise determining that the educational courses are either taken coincident with each other or within a predetermined time period of each other in the prior use data vectors.

12. The system of claim 8 , wherein the operations further comprise:

including an educational course in the first tailored course recommendation based upon proximity between a location of the educational course and an address associated with the user.

13. The system of claim 8 , wherein the operations further comprise:

updating the current user data vector based on the selected educational course;

automatically determining at least one updated similarity between the user and at least one of the individuals other than the user by comparing the updated current user data vector to at least one of the prior user data vectors; and

automatically generating the second tailored course recommendation by comparing at least one of the plurality of course requirements with the updated current user data vector and comparing at least one of the plurality of educational courses with the at least one updated similarity between the user and the at least one individual other than the user.

14. The system of claim 8 , wherein the selected educational course is a first selected educational course, and wherein the operations further comprise:

receiving a second selected educational course via the user interface, wherein the second selected educational course is based on the second tailored course recommendation, and wherein the second selected educational course has a dependency upon the first selected educational course;

receiving a move command, from the user via the user interface, to move the first selected educational course;

moving the first selected educational course, on the user interface, in a manner corresponding to the move command; and

automatically moving the second selected educational course, on the user interface, based upon the dependency upon the first selected educational course.

15. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations for generating an educational plan for a user, the operations comprising:

automatically retrieving data records from an electronic student information system, the data records including a plurality of educational courses and a plurality of course requirements at one or more institutions;

receiving educational course data and educational course focus data from an electronic profile survey via a user interface, the educational course data or the educational course focus data including at least one of a user academic history, a user educational focus, or a user schedule identified by the user in the electronic profile survey;

automatically retrieving prior user data vectors from an electronic archive, the prior user data vectors including prior user educational course data and prior user educational course focus data, the prior user educational course data or prior user educational course focus data including academic histories, educational focuses, academic schedules, or educational success rates associated with individuals other than the user;

automatically determining at least one similarity between the user and at least one individual other than the user by comparing a current user data vector to the prior user data vectors;

automatically generating and transmitting to the user a first tailored course recommendation by comparing at least one of the plurality of course requirements with the current user data vector and comparing at least one of the plurality of educational courses with the at least one similarity between the user and the at least one individual other than the user;

receiving a selected educational course via the user interface, wherein the selected educational course is based on the first tailored course recommendation; and

automatically generating and transmitting to the user a second tailored course recommendation based on the selected educational course and the first tailored course recommendation.

16. The non-transitory computer-readable medium of claim 15 , the operations further comprising:

determining a first candidate educational course and a second candidate educational course based upon the comparison between the at least one of the plurality of course requirements and the current user data vector or the comparison between the at least one of the plurality of educational courses and the at least one similarity between the user and the at least one individual other than the user; and

including the second candidate educational course in the first tailored course recommendation based upon a determination that the second candidate educational course is closer to full capacity than the first candidate educational course.

17. The non-transitory computer-readable medium of claim 15 , the operations further comprising:

determining a batch of two or more educational courses based upon their associated index scores and a compatibility of the two or more educational courses in the batch; and

including the batch of two or more educational courses in the first tailored course recommendation.

18. The non-transitory computer-readable medium of claim 17 , wherein the operations for determining the compatibility of the two or more educational courses in the batch further comprise determining that the educational courses are either taken coincident with each other or within a predetermined time period of each other in the prior use data vectors.

19. The non-transitory computer-readable medium of claim 15 , the operations further comprising:

updating the current user data vector based on the selected educational course;

automatically determining at least one updated similarity between the user and at least one of the individuals other than the user by comparing the updated current user data vector to at least one of the prior user data vectors; and

automatically generating the second tailored course recommendation by comparing at least one of the plurality of course requirements with the updated current user data vector and comparing at least one of the plurality of educational courses with the at least one updated similarity between the user and the at least one individual other than the user.

20. The non-transitory computer-readable medium of claim 15 , wherein the selected educational course is a first selected educational course, and the operations further comprising:

receiving a second selected educational course via the user interface, wherein the second selected educational course is based on the second tailored course recommendation, and wherein the second selected educational course has a dependency upon the first selected educational course;

receiving a move command, from the user via the user interface, to move the first selected educational course;

moving the first selected educational course, on the user interface, in a manner corresponding to the move command; and

automatically moving the second selected educational course, on the user interface, based upon the dependency upon the first selected educational course.

Assignments (9)
RELEASE OF SECOND LIEN SECURITY INTEREST IN PATENTS (REEL/FRAME 057335/0908) Recorded Jun 20, 2024
From: UBS AG, STAMFORD BRANCH
To: EAB GLOBAL, INC.; ROYALL & COMPANY, LLC
Reel/Frame 067799/0153 →
RELEASE OF SECURITY INTEREST IN PATENTS Recorded Aug 27, 2021
From: CORTLAND CAPITAL MARKET SERVICES LLC, AS COLLATERAL AGENT
To: EAB GLOBAL, INC.; ROYALL & COMPANY, LLC (F/K/A ROYALL & COMPANY)
Reel/Frame 057336/0052 →
RELEASE OF SECURITY INTEREST IN PATENTS Recorded Aug 27, 2021
From: MACQUARIE CAPITAL FUNDING LLC, AS COLLATERAL AGENT
To: EAB GLOBAL, INC.; ROYALL & COMPANY, LLC (F/K/A ROYALL & COMPANY)
Reel/Frame 057336/0012 →
FIRST LIEN PATENT SECURITY AGREEMENT Recorded Aug 26, 2021
From: EAB GLOBAL, INC.; ROYALL & COMPANY, LLC (F/K/A ROYALL & COMPANY)
To: MACQUARIE CAPITAL FUNDING LLC, AS COLLATERAL AGENT
Reel/Frame 057350/0322 →
SECOND LIEN PATENT SECURITY AGREEMENT Recorded Aug 26, 2021
From: EAB GLOBAL, INC.; ROYALL & COMPANY, LLC (F/K/A ROYALL & COMPANY)
To: UBS AG, STAMFORD BRANCH, AS COLLATERAL AGENT
Reel/Frame 057335/0908 →
FIRST LIEN PATENT SECURITY AGREEMENT Recorded Nov 21, 2017
From: ROYALL & COMPANY; EAB GLOBAL, INC.
To: MACQUARIE CAPITAL FUNDING LLC
Reel/Frame 044751/0041 →
SECOND LIEN PATENT SECURITY AGREEMENT Recorded Nov 21, 2017
From: ROYALL & COMPANY; EAB GLOBAL, INC.
To: CORTLAND CAPITAL MARKET SERVICES LLC
Reel/Frame 044751/0058 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 17, 2017
From: THE ADVISORY BOARD COMPANY
To: EAB GLOBAL, INC. (F/K/A AVATAR PURCHASER, INC.)
Reel/Frame 044165/0698 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 15, 2016
From: SHAW, CATHERINE INGRID; NELSON, JOHN DANIEL, II; FRIESEN, PHILIP JAMES; ASH CHEW, KATHLEEN SUSAN; ALCORN, ROBERT LAURENCE, IV; ZAUNER, SARAH; DAVIDSON, BRITTNEY LIM; JOHNSON, CHRISTOPHER LANCE
To: THE ADVISORY BOARD COMPANY
Reel/Frame 039028/0252 →