Company Filing History:
Years Active: 2013
Title: The Innovative Mind of Bernie Tschirren
Introduction
Bernie Tschirren is a notable inventor based in Kirkland, WA (US). He has made significant contributions to the field of model visualization through his innovative patent. His work exemplifies the intersection of technology and creativity, showcasing how detailed information can be preserved in visual models.
Latest Patents
Bernie Tschirren holds a patent titled "Localized information-preserving levels in model visualization." This patent describes a method where local areas of a visualized modeling language diagram can be viewed at different levels of detail without losing critical information, such as model elements and their connectivity. The invention allows multiple elements to be associated with a group element, which visually represents the appearance of a group member element. Connectors between group member elements and non-member elements are suppressed, favoring replacement connectors between the group element and the non-member element(s). The integrity of incoming and outgoing connections to the group is maintained relative to the rest of the model. Additionally, ungrouping elements restores them to their original state, and grouping can be applied locally to one or more parts of the visual model.
Career Highlights
Bernie Tschirren is currently associated with Microsoft Technology Licensing, LLC, where he continues to innovate and contribute to the field. His work has garnered attention for its practical applications in enhancing model visualization.
Collaborations
Throughout his career, Bernie has collaborated with talented individuals such as Patrick Shyhuei Tseng and Durham Montgomery Goode. These collaborations have likely enriched his work and contributed to the development of innovative solutions in model visualization.
Conclusion
Bernie Tschirren's contributions to the field of model visualization through his patent demonstrate his innovative spirit and commitment to advancing technology. His work not only enhances the understanding of complex models but also paves the way for future innovations in the field.