Company Filing History:
Years Active: 2008-2010
Title: Innovations by Daniel-Alexander Billsus
Introduction
Daniel-Alexander Billsus, an inventive mind based in San Francisco, CA, has made notable contributions to the field of technology with his innovative patents. With two patents to his name, Billsus focuses on the enhancement of user experience through recommendation systems and content processing.
Latest Patents
His latest patents include "Recommendation aggregation for digest generation" and "Indexing for contextual revisitation and digest generation." The first patent outlines systems and methods for processing automatically generated recommendations. In various exemplary embodiments, it describes a method for producing a recommendation digest by generating a recommendation log for the user, which includes storing a variety of recommendations and analyzing the context that prompted them.
The second patent, "Indexing for contextual revisitation and digest generation," presents a medium, system, and method for processing content information. This system determines previously accessed content based on user actions on a document and the context of the information, thus providing users with relevant associated content.
Career Highlights
Daniel-Alexander Billsus is currently employed at Fuji Xerox Co., Ltd., a company recognized for its advancements in technology and document solutions. His work involves collaborating on projects that drive innovation and improve the functionality of systems catered to user needs.
Collaborations
Throughout his career, Billsus has worked alongside notable colleagues such as David Michael Hilbert and Jonathan James Trevor. Their combined expertise has fostered a creative environment that promotes groundbreaking ideas and solutions.
Conclusion
With a focus on implementing user-centric technologies, Daniel-Alexander Billsus continues to influence the tech landscape through his patents. His innovations demonstrate a deep understanding of user needs and contribute to the ongoing evolution of recommendation systems and content processing methodologies.