San Francisco, CA, United States of America

Michael Stillger


Average Co-Inventor Count = 11.0

ph-index = 1

Forward Citations = 3(Granted Patents)


Company Filing History:


Years Active: 2012

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1 patent (USPTO):Explore Patents

Title: Michael Stillger: Innovator in Enterprise Business Applications

Introduction

Michael Stillger is a notable inventor based in San Francisco, CA. He has made significant contributions to the field of enterprise business applications. His innovative work focuses on methods and systems that support multiple deployment models for these applications.

Latest Patents

Michael Stillger holds a patent for "Methods and systems for supporting multiple deployment models of enterprise business applications." This patent describes a method that includes designating pillars associated with an enterprise business application. Each pillar comprises application units that represent logical groupings for application units used in the enterprise business application. The method also determines which application units are dependent on more than one pillar. Based on this determination, deployment code is generated that is independent of the deployment model. At deployment time, a designation indicating the type of deployment model is received, allowing the enterprise business application to be deployed accordingly.

Career Highlights

Michael is currently employed at Oracle International Corporation, where he continues to innovate in the realm of enterprise solutions. His work has been instrumental in enhancing the flexibility and efficiency of business applications.

Collaborations

Michael has collaborated with notable colleagues such as Vivek Salgar and John Joseph Jakubik. Their combined expertise contributes to the advancement of technology in their field.

Conclusion

Michael Stillger's contributions to enterprise business applications through his innovative patent demonstrate his commitment to improving technology in this area. His work continues to influence the deployment of business applications across various models.

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