Los Angeles, CA, United States of America

Aaron Schoenberger

This inventor holds 1 USPTO granted patent. Top assignee: Sta Group, Inc.. Active years: 2021.


% Patents Active = 100.0

Average Co-Inventor Count = 4.0

ph-index = 1

Forward Citations = 9(Granted Patents)


Company Filing History:


Years Active: 2021

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1 patent (USPTO):Explore Patents

Title: Aaron Schoenberger: Innovator in Social Media Evaluation

Introduction

Aaron Schoenberger is an accomplished inventor based in Los Angeles, CA. He has made significant contributions to the field of social media evaluation through his innovative patent. His work focuses on utilizing deep learning models to enhance the accuracy of content evaluation on social media networks.

Latest Patents

Schoenberger holds a patent titled "Evaluating content on social media networks." This patent describes a system and method for evaluating content on one or more social media networks. The process involves storing a deep learning model and receiving communications that are or will be shared on social networks. The deep learning model is applied to these communications to generate automated evaluations. User input is also collected, which includes evaluations from users that help train the deep learning model. This iterative process aims to improve the accuracy of automated evaluations over time. He has 1 patent to his name.

Career Highlights

Aaron Schoenberger is currently employed at Sta Group, Inc., where he continues to develop innovative solutions in the realm of social media. His expertise in deep learning and content evaluation has positioned him as a key player in his field.

Collaborations

Schoenberger collaborates with talented individuals such as Samuel Haaf and Vasant Kearney, contributing to a dynamic work environment that fosters innovation and creativity.

Conclusion

Aaron Schoenberger is a notable inventor whose work in social media evaluation is paving the way for advancements in automated content assessment. His contributions are significant in enhancing the accuracy of evaluations in the digital landscape.

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