Machine Learning Model-Guided Training and Development

Number: 20240013673
Published: 2024-01-11
Filed: 2022-07-08
Assignee: Disney Enterprises, Inc.
Inventors: Wolfe; Anna T. et al.
CPC: G09B19/00; G06N20/00; G09B5/06; G09B5/065
Verdict: Set aside generic employee training ML, business
Source: Google Patents · FreePatentsOnline

Abstract

A system for creating accessibility enhanced content includes a computing platform having processing hardware and a system memory storing a software code and a machine learning (ML) model, the software code providing a graphical user interface (GUI). The processing hardware executes the software code to identify a user of the system, obtain a user profile of the user, obtain, from one or more application(s) utilized by the user, activity data relating to use of the application(s) by the user, and modify, using the user profile and the activity data, one or more node weights of the ML model to provide a tuned ML model. The processing hardware further executes the software code to infer, using the tuned ML model, at least one action for advancing a development of the user and output to the user, using the UI, a recommendation for performing the at least one action by the user.

Background

BACKGROUND

Conventional approaches to training and development in an organizational setting tend to be reactive rather than proactive for several reasons. For instance, it can be difficult and time consuming to identify core competencies that a large organization may need to develop in its members. Moreover, it is typically easier to offer introductory training or bulk learning resources at scale than to provide long-term individually curated guidance in identifying relevant learning and networking opportunities. In addition, for some organizations, particularly those in technology intensive industries, training needs often change rapidly along with industry changes, making it difficult to predict what specific skills are worth investing in. However, when opportunities for development fail to address the particular needs of individuals, or when individuals are unaware of opportunities that do address their interests and needs, it is less likely that those individuals will pursue them. In order for effective development to occur and progress, individuals need to be informed of training, networking, and mentoring options that are available them, as well as be able to participate in the identification of their own training pathways. Organizations are at risk of losing some of their most curious, ambitious, and potentially productive personnel if they do not provide them with a strategic, informative, and individualized approach to training and. development.

Claims

1. A system comprising: a computing platform including processing hardware and a system memory; a software code and a machine learning (ML) model stored in the system memory, the software code providing a user interface (UI); the processing hardware configured to execute the software code to: identify a user of the system; obtain a user profile of the user; obtain, from one or more applications utilized by the user, activity data relating to use of the one or more applications by the user; modify, using the user profile and the activity data, one or more node weights of the ML model to provide a tuned ML model; infer, using the tuned ML model, at least one action for advancing a development of the user; and output to the user, using the a recommendation for performing the at least one action by the user. || 11. A method for use by a system including a computing platform having processing hardware and a system memory storing software code and a machine learning (ML) model, the software code providing a user interface (UI), the method comprising: identifying, by the software code executed by the processing hardware, a user of the system; obtaining, by the software code executed by the processing hardware, a user profile of the user; obtaining, by the software code executed by the processing hardware, from one or more applications utilized by the user, activity data relating to use of the one or more applications by the user; modifying, by the software code executed by the processing hardware and using the user profile and the activity data, one or more node weights of the ML model to provide a tuned ML model; inferring, by the software code executed by the processing hardware and using the tuned ML model, at least one action for advancing a development of the user; and output to the user, by the software code executed by the processing hardware and using the UI, a recommendation for performing the at least one action by the user.