Pleasanton, CA, United States of America

Guocai Shu


Average Co-Inventor Count = 2.7

ph-index = 1

Forward Citations = 4(Granted Patents)


Company Filing History:


Years Active: 2017-2024

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4 patents (USPTO):Explore Patents

Title: Guocai Shu: Innovator in Biological Cell Typing

Introduction

Guocai Shu is a prominent inventor based in Pleasanton, CA (US). He has made significant contributions to the field of biological cell typing, holding a total of 4 patents. His innovative work focuses on advanced imaging systems and machine learning algorithms to enhance the understanding of biological cells.

Latest Patents

One of Guocai Shu's latest patents is titled "Typing Biological Cells." This system utilizes a tunable Fabry-Perot etalon, an imaging sensor, and a processor. The imaging sensor captures images of biological cells through light transmitted via the etalon. The processor employs a machine learning algorithm to determine the type of each cell based on various image processing techniques.

Another notable patent is "Field Calibration for Near Real-Time Fabry Perot Spectral Measurements." This system includes a tunable Fabry-Perot etalon, a detector, and a processor. The detector measures light intensity, while the processor determines calibrated spectral measurements based on detected light intensities and calibration measurements using field material targets.

Career Highlights

Guocai Shu has worked with several companies, including Bayspec, Inc. and Trutag Technologies, Inc. His experience in these organizations has allowed him to develop and refine his innovative technologies in the field of biological imaging and analysis.

Collaborations

Some of his notable coworkers include Shu Zhang and William Yang. Their collaborative efforts have contributed to the advancement of technologies in their respective fields.

Conclusion

Guocai Shu's work in biological cell typing and imaging systems showcases his innovative spirit and dedication to advancing scientific knowledge. His patents reflect a commitment to leveraging technology for improved understanding and analysis of biological systems.

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