Pearson Education, Inc.

Qwest Communications International Inc.

Kyle D Habermehl

Lee Becker

Scott Hellman

Peter Foltz

Alok Baikadi

Mark Rosenstein

Jill Budden

William M Murray

Stephen Hopkins

Andrew Gorman

Luis Manuel Oros

Walter Denny Way

Ryan Calme

Karen Lochbaum

Robert Sanders

Randall B Sparks

William D Hurley

Bradley S Reeves

Scott A Dooley

Dereck A Stertz

Sherman Woo

Samuel Downs

James L Lane

Marcia Derr

Systems and methods for automated custom training of a scoring model are disclosed herein. The method include: receiving a plurality of responses received from a plurality of students in response to providing of a prompt; identifying an evaluation model relevant to the provided prompt, which evaluation model can be a machine learning model trained to output a score relevant to at least portions of a response; generating a training indicator that provides a graphical depiction of the degree to which the identified evaluation model is trained; determining a training status of the model; receiving at least one evaluation input when the model is identified as insufficiently trained; updating training of the evaluation model based on the at least one received evaluation input; and controlling the training indicator to reflect the degree to which the evaluation model is trained subsequent to the updating of the training of the evaluation model.

Systems and methods for automated machine learning model training quality control

Systems and methods for interface-based automated custom authored prompt evaluation

Systems and methods for automated evaluation system routing are described herein. The system can include a memory, which can include a model database and a correlation database. The system can include a first user device and a second user device. The system can include at least one server. The at least one server can: receive a response communication from the user device; generate an initial evaluation value according to an AI model; determine a correlation between the initial evaluation value and evaluation range data; accept the initial evaluation value when the correlation exceeds a threshold value; and route the response communication to the second user device for generation of an elevated evaluation value when the correlation does not exceed the threshold value.

Systems and methods for real-time machine learning model training

Systems and methods for interface-based machine learning model output customization

Systems and methods for automated evaluation model customization

Real time development of auto scoring essay models for custom created prompts

Systems and methods for automated machine learning model training for a custom authored prompt

System and method for automated evaluation system routing

A collaboration model is provided for constructing evolutionary collaborative applications deployed in network environments, such as the World Wide Web, or wireless environments, which supports introduction of new object types into the deployed system. The collaboration model is arranged as a set of submodels. The highest-level control resides in a synchronization submodel. A merge submodel defines mechanisms for merging changes, detecting conflicts, and resolving conflicts whenever possible. A change submodel is operative to define allowable changes for shared objects. A replication model is arranged to provide replication functionality, and define how multiple versions of an object are presented. A distribution submodel provides distribution functionality, and a type submodel provides a programming language-independent representational scheme for the overall collaboration model. The synchronization submodel, the merge submodel, and the replication submodel are arranged to use constructs defined by the change submodel.

Collaborative model for software systems with synchronization submodel with merge feature, automatic conflict resolution and isolation of potential changes for reuse

A method and system for delivering audio and data files includes a data network and a voice network. A user station provided in communication with the data network receives data files. A telephone provided in communication with the voice network receives audio files. An application server in communication with the data network stores and executes applications programs which include audio features. An audio server in communication with the application server and the voice network is operative to dial a telephone, deliver a selected audio file to the telephone, or record and store an audio file.

Method and system for delivering audio and data files

Growing community of inventors

Niwot, CO, United States of America

Kyle D Habermehl