Alamo, CA, United States of America

Susan Putvin


Average Co-Inventor Count = 1.0

ph-index = 1


Company Filing History:


Years Active: 2025

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1 patent (USPTO):Explore Patents

Title: Susan Putvin: Innovator in Artificial Intelligence Regression Testing

Introduction

Susan Putvin is a notable inventor based in Alamo, California. She has made significant contributions to the field of artificial intelligence, particularly in the area of regression testing. Her innovative approach has the potential to enhance the efficiency and accuracy of software testing processes.

Latest Patents

Susan Putvin holds a patent for "Systems and methods for regression testing using artificial intelligence." This patent describes embodiments that provide regression testing using artificial intelligence. The regression testing network model is designed for a first plurality of organizations utilizing a common codebase. It includes a finite state machine (FSM) model for each organization. The process involves receiving a first dataset that includes samples of the organization FSM models based on regression testing for previous releases of the common codebase. A training dataset is generated from this first dataset, which is then used to train the regression testing network model. Ultimately, a second plurality of organizations for regression testing is determined using the trained model.

Career Highlights

Susan is currently employed at Salesforce, Inc., where she applies her expertise in artificial intelligence and software testing. Her work at Salesforce has allowed her to collaborate with other talented professionals in the field, further enhancing her contributions to technology.

Collaborations

One of her notable coworkers is Govardana Sachithanandam Ramachandran, with whom she collaborates on various projects related to artificial intelligence and software development.

Conclusion

Susan Putvin's innovative work in artificial intelligence regression testing exemplifies her commitment to advancing technology. Her contributions are paving the way for more efficient software testing methodologies.

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