Redmond, WA, United States of America

Olalekan Patrick Ogunmolu

This inventor holds 1 USPTO granted patent. Top assignee: Microsoft Technology Licensing, LLC. Active years: 2025.


% Patents Active = 100.0

Average Co-Inventor Count = 9.0

ph-index = 1


Company Filing History:


Years Active: 2025

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1 patent (USPTO):Explore Patents

Title: Innovations of Olalekan Patrick Ogunmolu

Introduction

Olalekan Patrick Ogunmolu is an accomplished inventor based in Redmond, WA (US). He has made significant contributions to the field of technology, particularly in the area of controllable latent space discovery. His innovative work has led to the development of a patent that addresses complex challenges in modeling and prediction.

Latest Patents

Ogunmolu holds a patent titled "Controllable latent space discovery using multi-step inverse model." This patent discusses devices, systems, and methods for determining a minimal controllable latent state and operating a model trained to implement this state. The method involves receiving observations from a sensor, encoding these observations into hidden state representations, and predicting actions based on combined representations. This innovative approach enhances the efficiency of predictive modeling.

Career Highlights

Ogunmolu is currently employed at Microsoft Technology Licensing, LLC, where he applies his expertise in technology and innovation. His work focuses on advancing the capabilities of machine learning and predictive analytics. With a patent portfolio that includes 1 patent, he has established himself as a key contributor in his field.

Collaborations

Some of his notable coworkers include Alexander Matthew Lamb and Riashat Islam. Their collaborative efforts contribute to the innovative environment at Microsoft Technology Licensing, LLC.

Conclusion

Olalekan Patrick Ogunmolu's contributions to technology through his patent and work at Microsoft Technology Licensing, LLC highlight his role as an influential inventor. His innovative approaches continue to shape the future of predictive modeling and machine learning.

Profile summary based on public USPTO records.
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