Company Filing History:
Years Active: 2021-2022
Title: Ted Tomlinson: Innovator in Address Extraction and Dynamic Sampling
Introduction
Ted Tomlinson is a notable inventor based in Oakland, CA. He has made significant contributions to the field of data processing and machine learning. With a total of 2 patents, Tomlinson's work focuses on innovative methods for identifying addresses within content and dynamic sampling based on talent pool size.
Latest Patents
One of Tomlinson's latest patents is titled "Identifying and extracting addresses within content." This invention provides a system for processing data by extracting text windows of varying lengths from content items associated with an entity. The system applies a machine learning model to these text windows to produce scores that indicate the likelihood of containing addresses. Ultimately, it identifies and stores the selected text window as the address for the entity.
Another significant patent is "Dynamic sampling based on talent pool size." This invention includes methods for determining a talent pool based on user queries, where the pool comprises members with various attributes. The method calculates a sampling size based on the talent pool's size and combines samples from each attribute to determine an aggregate distribution. This information is then displayed to the user, enhancing decision-making processes.
Career Highlights
Ted Tomlinson is currently employed at Microsoft Technology Licensing, LLC. His role involves leveraging his expertise in machine learning and data processing to develop innovative solutions that address complex challenges in technology.
Collaborations
Tomlinson has collaborated with notable colleagues, including Paul D. Bergeron and Junzhe Miao. Their combined efforts contribute to the advancement of technology and innovation within their field.
Conclusion
Ted Tomlinson is a distinguished inventor whose work in address extraction and dynamic sampling has made a significant impact in the tech industry. His contributions continue to shape the future of data processing and machine learning.