Use of Asset and Enterprise Data to Predict Asset Personality Attributes

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The present invention relates generally to the field of resource management, and more particularly to the use of operator (herein referred to as asset or operator interchangeably) data to predict an operator’s psychometric profile, such as the big five personality traits (i.e., those traits recognized by most psychologists as the five basic dimensions of personality) of openness, conscientiousness, extraversion, agreeableness, and neuroticism, and the use of the predicted profile in operator assignment and management.

One of the key roles of leaders, managers, supervisors, directors, etc. (herein collectively referred to as managers) is to ensure that the employees, contractors, operators, etc. (herein collectively referred to as assets or operators) that are placed on tasks and in groups match the needs of those tasks and groups as best as the manager can determine. A better match generally means better, more efficient productivity, and a positive outcome for the team and the company as a whole. Because of this, managers are very motivated to determine the correct operator be placed where the operator would do the most good, such as on a particular piece of machinery, equipment, apparatus, hardware, or inanimate asset (herein collectively referred to as inanimate asset). However, often it is difficult to determine which operator would be a good fit until the position has already been filled and the task is underway. Any ability to decrease this uncertainty, and to increase the chances that the chosen operator is a good fit and a good choice for the position, would be considered a positive for most of those in leadership roles.

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Tuhin Sharma
Senior Principal Data Scientist

My research interests include AI, NLP and Distributed Computing.