Rio de Janeiro, Brazil

José Carlos Costa Da Silva Pinto


Average Co-Inventor Count = 8.0

ph-index = 1

Forward Citations = 2(Granted Patents)


Company Filing History:


Years Active: 2021

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1 patent (USPTO):Explore Patents

Title: Innovations of José Carlos Costa Da Silva Pinto

Introduction

José Carlos Costa Da Silva Pinto is a notable inventor based in Rio de Janeiro, Brazil. He has made significant contributions to the field of process monitoring through his innovative patent. His work focuses on the integration of large-scale time series data, which has important applications in various industries.

Latest Patents

José Carlos holds a patent for a process monitoring method that utilizes large-scale combinations of time series data. The patent describes methods and apparatus for monitoring processes by generating a model from time series data for a specific target time series. The method involves determining if the difference between measured and predicted values exceeds a predefined threshold, indicating a target prediction error. In response to this error, evaluations are performed to assess neighborhood coherence and differences in variable values. Notifications are provided when certain criteria are met, and the model is updated accordingly. This innovative approach enhances the accuracy and reliability of process monitoring systems. He has 1 patent to his name.

Career Highlights

José Carlos is currently associated with Emc IP Holding Company LLC, where he applies his expertise in process monitoring and data analysis. His work has contributed to advancements in the field, showcasing his commitment to innovation and technology.

Collaborations

Some of his notable coworkers include Mauricio Melo Camara and Angelo E M Ciarlini. Their collaboration has likely fostered a productive environment for innovation and development in their respective projects.

Conclusion

José Carlos Costa Da Silva Pinto is a distinguished inventor whose work in process monitoring has made a significant impact. His innovative methods for analyzing time series data demonstrate the importance of advancements in technology and their applications in real-world scenarios.

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