Joana De Noronha Ribeiro De Almeida

Title: Joana De Noronha Ribeiro De Almeida: Innovator in Subsurface Rock Property Prediction and Machine Teaching

Introduction

Joana De Noronha Ribeiro De Almeida is a distinguished inventor based in Lisbon, Portugal. He has made significant contributions to the fields of subsurface rock property prediction and machine teaching. With a total of three patents to his name, Joana's work showcases his innovative approach to complex problems.

Latest Patents

Joana's latest patents include groundbreaking technologies. One patent focuses on the "Storage and retrieval of subsurface rock physical property prediction models using seismic interpretation." This invention involves generating estimated subsurface rock physical property models based on seismic data, classifying them stratigraphically, and storing them in a database for efficient retrieval. The process includes determining the characteristics of seismic data and selecting models based on their similarity to new data.

Another notable patent is "Machine teaching complex concepts assisted by computer vision and knowledge reasoning." This invention allows for the annotation of images based on user input, breaking down images into parts, and establishing relationships among those parts. The relationships and classifications are stored in a knowledge base, enabling the annotation of similar images based on learned concepts.

Career Highlights

Throughout his career, Joana has worked with prominent companies, including International Business Machines Corporation (IBM) and Petrogal Brasil S.A. His experience in these organizations has contributed to his expertise in technology and innovation.

Collaborations

Joana has collaborated with notable individuals in his field, including Emilio Ashton Vital Brazil and Rodrigo Da Silva Ferreira. These collaborations have further enriched his work and expanded his impact in the industry.

Conclusion

Joana De Noronha Ribeiro De Almeida is a remarkable inventor whose work in subsurface rock property prediction and machine teaching has made significant strides in technology. His innovative patents and collaborations reflect his dedication to advancing these fields.