This inventor holds 1 USPTO granted patent. Top assignee: Microsoft Technology Licensing, LLC. Active years: 2026.
Company Filing History:
Years Active: 2026
Title: Akash Kodibail: Innovator in Transfer-Learning Technologies
Introduction
Akash Kodibail is a prominent inventor based in Bengaluru, India. He has made significant contributions to the field of artificial intelligence, particularly in the area of transfer-learning for structured data. His innovative approach has the potential to enhance the efficiency of machine learning models in various applications.
Latest Patents
Akash holds a patent titled "Transfer-learning for structured data with regard to journeys defined by sets of actions." This patent describes techniques capable of performing transfer-learning for structured data related to journeys defined by sets of actions. A first deep neural network (DNN) for a first journey is trained using structured data. The weights of nodes in the first DNN are transferred to nodes in a second DNN for a second journey using transfer-learning. An embedding layer replaces a final layer of the first DNN in the second DNN to provide an output with the same number of nodes as a pre-final layer of the first DNN. The weights of the nodes in the embedding layer are initialized based on the probability that a new feature of the second journey co-occurs with each feature in the structured data. A softmax function is applied on a final layer of the second DNN to indicate possible next actions of the second journey. Akash has 1 patent to his name.
Career Highlights
Akash is currently employed at Microsoft Technology Licensing, LLC, where he continues to develop innovative solutions in the field of technology. His work focuses on enhancing machine learning techniques and their applications in real-world scenarios.
Collaborations
One of Akash's notable coworkers is Kiran Rama. Together, they contribute to advancing the research and development of cutting-edge technologies.
Conclusion
Akash Kodibail is a talented inventor whose work in transfer-learning is paving the way for advancements in artificial intelligence. His contributions are significant in shaping the future of machine learning applications.