公开(公告)号：US20230196255A1

公开(公告)日：2023-06-22

申请号：US18109999

申请日：2023-02-15

Applicant: Pearson Education, Inc.

Inventor： Stephen CARROLL ,  David LOVEJOY ,  Simcha KNIF ,  Gennadiy KUKARTSEV

IPC: G06Q10/0639 ,  H04L12/18 ,  G06N3/02 ,  G06Q10/0635 ,  G06N20/00 ,  G06N20/20 ,  H04L67/306 ,  G06N20/10 ,  H04L9/40 ,  G06N3/08 ,  G06F3/0482 ,  H04L67/50 ,  G06F18/211 ,  G06F18/20 ,  G06F18/2431 ,  G06N5/01 ,  G06N7/01 ,  G06F3/048 ,  G06F21/55 ,  G06F21/56 ,  G06F21/57 ,  G06F3/0481 ,  G09B7/02 ,  H04L51/18 ,  G06Q50/20

CPC classification number: G06Q10/06398 ,  H04L12/1895 ,  G06N3/02 ,  G06Q10/0635 ,  G06N20/00 ,  G06N20/20 ,  H04L67/306 ,  G06N20/10 ,  H04L63/20 ,  H04L63/0227 ,  G06N3/08 ,  G06F3/0482 ,  H04L67/535 ,  G06F18/211 ,  G06F18/285 ,  G06F18/2431 ,  G06N5/01 ,  G06N7/01 ,  G06F3/048 ,  G06F21/552 ,  G06F21/554 ,  G06F21/56 ,  G06F21/577 ,  G06F3/0481 ,  G09B7/02 ,  H04L51/18 ,  H04L63/0428 ,  H04L63/10 ,  G06Q50/205 ,  H04L63/1416 ,  H04L63/1433 ,  H04L63/1441 ,  H04L41/0681

Abstract: Systems and methods for feature-based alert triggering are disclosed herein. The system can include memory including a model database containing a machine-learning algorithm. The system can include a user device that can receive inputs from a user; and at least one server. The at least one server can: receive electrical signals from the user device, the electrical signals corresponding to a plurality of user inputs provided to the user device; automatically generate input-based features from the received electrical signals; input the input-based features into the machine-learning algorithm; automatically and directly generate a risk prediction with the machine-learning algorithm from the input-based features; and generate and display an alert when the risk prediction exceeds a threshold value.

公开(公告)号：US20220198951A1

公开(公告)日：2022-06-23

申请号：US17130924

申请日：2020-12-22

Applicant: Pearson Education, Inc.

Inventor： Stephen CARROLL ,  Brian DAILEY ,  Emilia PANKOWSKA ,  Jennifer Arlene COLEMAN ,  Zachary Elewitz

IPC: G09B7/00 ,  G06Q10/10 ,  G06Q50/20 ,  G06F16/903 ,  H04L29/06

Abstract: A system including a computer server implementing a learning resource configured to monitor a user interaction with the learning resource, and encode, based on the user interactions, a user event. The system includes a computer server implementing an event processor. The event processor is configured to receive, from the computer server, the user event, parse the user event to determine the identifications of the user generating the user event, the assessment item, and the learning resource, and the indication of whether the user event is associated with a correct answer or an incorrect answer, and store, in an analytics storage database, a data record including the identification of the user generating the user event, the assessment item, the learning resource, and the indication of whether the user event is associated with a correct answer or an incorrect answer.

公开(公告)号：US20210152385A1

公开(公告)日：2021-05-20

申请号：US17162115

申请日：2021-01-29

Applicant: Pearson Education, Inc.

Inventor： Stephen CARROLL ,  David LOVEJOY ,  Simcha KNIF ,  Gennadiy KUKARTSEV

IPC: H04L12/18 ,  G06N3/02 ,  G06Q10/06 ,  G06K9/62 ,  G06N20/00 ,  G06F16/38 ,  G06F16/335 ,  G06N20/20 ,  H04L29/08 ,  G06N20/10 ,  H04L29/06 ,  G06N3/08 ,  G06F3/0482 ,  G06N5/00 ,  G06F16/20 ,  G06F3/048 ,  G06F21/55 ,  G06F21/56 ,  G06F21/57 ,  G06F3/0481 ,  G09B7/02 ,  H04L12/58 ,  G06Q50/20 ,  G06N7/00

Abstract: Systems and methods for feature-based alert triggering are disclosed herein. The system can include memory including a model database containing a machine-learning algorithm. The system can include a user device that can receive inputs from a user; and at least one server. The at least one server can: receive electrical signals from the user device, the electrical signals corresponding to a plurality of user inputs provided to the user device; automatically generate input-based features from the received electrical signals; input the input-based features into the machine-learning algorithm; automatically and directly generate a risk prediction with the machine-learning algorithm from the input-based features; and generate and display an alert when the risk prediction exceeds a threshold value.