Centurion Patentors as of November 5, 2024

Adrien David Gaidon

Dr. Adrien Gaidon is an experienced Machine Learning researcher, engineer, manager, executive, and investor. He is a Partner at Calibrate Ventures, an Adjunct Professor of Computer Science at Stanford, and a technical advisor and former head of ML at TRI.

Since 2007, Dr. Gaidon has worked on Principle-centric ML: learning algorithms that effectively combine concise prior knowledge and large-scale data. In particular, Dr. Gaidon has a deep expertise in Embodied Intelligence, including Computer Vision, Autonomous Driving, and Robotics.

Besides scientific expertise, Dr. Gaidon has experience in building and leading world-class teams of ML researchers and engineers who work together to invent and develop new solutions to hard real-world problems like safe and automated driving.

Patent №: 12136251 (November 5, 2024) – Guided domain randomization via differentiable dataset rendering. In one embodiment, the method includes receiving an input image with an object and background, decomposing it into feature data, augmenting each feature with a 2.5D differentiable renderer to create augmented images, and compiling these into a training dataset for a downstream task network.

Erik Richard Stauffer

Wireless Communication System Scientist.

Patent №: 12136967 (November 5, 2024) – User-equipment-coordination set for a wireless network.

A base station designates a set of UEs for joint data transmission and reception to a target UE, selecting one UE as the coordinator. The base station sends a downlink signal to all UEs in the set; each UE samples the signal and forwards it to the coordinator, which combines and processes the samples to decode the data.

Philippe Bordes

Principal Scientist at InterDigital, Inc.

Dr. Bordes received the M.Sc. degree in engineering from the Ecole Nationale Supérieure d’Ingénieurs Electriciens de Grenoble, France, in 1989, and the Ph.D. from the University of Rennes in 2016. From 1990 to 1997, he was a Researcher with the Electronic Laboratory of Rennes (Thomson-CSF).

In 1997, he joined the Corporate Research of Thomson Multimedia and Next Technicolor as a Project Leader.

Philippe is currently with InterDigital. He has been involved in MPEG, DVB, and BDA standardization activities. His research interests include video coding, signal processing, and deep learning.

Patent №: 12137228 (November 5, 2024) – Estimating weighted-prediction parameters. There is provided a method for estimating weighted prediction parameters intended to be used for predicting an image block.

Zhaocheng Wang

Zhaocheng Wang received his B.S., M.S. and Ph.D. degrees from Tsinghua University in 1991, 1993 and 1996, respectively.

From 1996 to 1997, he was a Post Doctoral Fellow at Nanyang Technological University, Singapore. From 1997 to 1999, he was a Research Engineer/Senior Engineer with OKI Techno Centre Pte. Ltd., Singapore.

From 1999 to 2009, Dr. Wang was a Senior Engineer/Principal Engineer with Sony Deutschland GmbH, Germany.

Since 2009, Zhaocheng has been a Full Professor at the Department of Electronic Engineering, at Tsinghua University. He is a Fellow of IEEE and a Fellow of IET.

Patent №: 12136976 (November 5, 2024) – Electronic device, wireless communication method, and computer-readable storage medium. A device and method in a wireless communication system are disclosed. The device includes:

1. A first generating unit to create an initial pre-coding matrix based on channel information between two communication devices.
2. A second generating unit to generate a refined pre-coding matrix using the initial matrix and additional channel information.
3. A pre-coding unit to encode a data signal using both matrices.

This approach reduces interference between devices, lowers operational complexity, and optimizes overall system performance.

Patent №: 12136972 (November 5, 2024) – Wireless communication system, and device and method in wireless communication system. A wireless communication device and method are provided, comprising units to generate and refine pre-coding matrices based on channel information, followed by a unit to pre-code a data signal using these matrices. This approach reduces interference, simplifies operations, and improves overall system performance.