Case 4:23-cv-01147-ALM Document 53-3 Filed 10/29/24 Page 1 of 49 PageID #: 2282
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`Exhibit 3
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`Case 4:23-cv-01147-ALM Document 53-3 Filed 10/29/24 Page 2 of 49 PageID #: 2283
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`UNITED STATES DISTRICT COURT
`FOR THE EASTERN DISTRICT OF TEXAS
`SHERMAN DIVISION
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`R2 SOLUTIONS LLC,
`
`
`Plaintiff,
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`DATABRICKS, INC.,
`
`
`v.
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`
`
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`
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`Civil Action No. 4:23-cv-01147-ALM
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`Defendant.
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`DECLARATION OF DR. JON B. WEISSMAN
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`I, Jon B. Weissman, Ph.D., hereby declare as follows:
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`1.
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`My name is Jon Weissman. I am at least eighteen years of age. I have personal
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`knowledge of and am competent to testify as to the facts and opinions herein.
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`2.
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`I have been asked by Defendant Databricks, Inc. (“Databricks”) to provide my
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`expert opinions relating to certain terms and phrases used in the claims of U.S. Patent No.
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`8,190,610 (the “’610 patent”).
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`3.
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`This declaration sets forth my opinions on the disputed claim terms of the ’610
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`patent.
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`I.
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`BACKGROUND AND QUALIFICATIONS
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`4.
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`In the paragraphs below, I summarize my qualifications. My qualifications are
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`stated more fully in my curriculum vitae, which is attached to this declaration as Exhibit A.
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`5.
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`I am a Full Professor in the Department of Computer Science & Engineering at the
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`University of Minnesota, where I’ve been a professor since 1999. In addition to my professorship,
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`I lead the University of Minnesota’s Distributed Computing Systems Group. The Distributed
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`Computing Systems Group is focused on research into distributed and mobile systems, cloud
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`computing, and high-performance computing.
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`6.
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`In 1995, I received my Doctor of Philosophy degree (Ph.D.) in Computer Science
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`from the University of Virginia. For my Ph.D. thesis, I developed the first automated scheduling
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`system for parallel and distributed applications across heterogeneous local and wide-area
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`networks. In 1989, I received my Master of Science degree (M.Sc.) in Computer Science from the
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`University of Virginia. In 1984, I received my Bachelor of Science degree (B.Sc.) in Applied
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`Mathematics and Computer Science from Carnegie Mellon University.
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`7.
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`Before obtaining my Ph.D. degree, I worked as a software engineer for five years
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`in the early 1990s in the area of distributed systems. During this time, I was responsible for
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`designing, implementing, and maintaining distributed computing systems where multiple nodes or
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`computers worked together to achieve a common goal. In this capacity, I helped design and
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`implement a distributed AI framework for knowledge-based business applications. In another
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`project, I designed and implemented a parallel and distributed simulation framework for military
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`and air traffic control applications.
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`8.
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`In 1995, after earning my Ph.D., I returned to academia and began my career as a
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`professor. My research has been funded by NASA, the National Science Foundation, the
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`Department of Energy, and the Air Force, and has included the following projects related to
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`distributed computing and distributed data processing:
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`• Department of Energy (“DOE”), “Making Parallel Computing Easy”;
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`•
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` National Science Foundation,
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`“Collaborative Data Analysis
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`and
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`Visualization”;
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`• Department of Energy (“DOE”), “An Integrated Middleware Framework to
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`Enable Extreme Collaborative Science”;
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`• Army High Performance Computing and Research Center (“AHPCRC”),
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`“Metacomputing: Enabling Technologies and the Virtual Data Grid”;
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`• National Science Foundation (“NSF,” “Resource Management for Parallel and
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`Distributed Systems”;
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`• National Science Foundation (“NSF”), “A Framework for Adaptive Grid
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`Services” and
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`• Air Force Office of Scientific Research (“ASFOSR”), “Telecommunication
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`Networks for Mobile and Distributed Computing and Communications.”
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`9.
`
`I have published over 100 peer-reviewed technical articles, including some
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`awarded or nominated for Best Paper at highly competitive international conferences. Many of
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`my published papers relate to distributed computing and distributed data processing, including, for
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`example, the following:
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`• “Nebula: Distributed Edge Cloud for Data Intensive Computing,” Albert Jonathan,
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`Mathew Ryden, Kwangsung Oh, Abhishek Chandra, and Jon Weissman, IEEE
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`Transactions on Parallel and Distributed Systems;
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`• “TripS: Automated Multi-tiered Data Placement in a Geo-distributed Cloud
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`Environment,” Kwangsung Oh, Abhishek Chandra, and Jon Weissman, 10th ACM
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`International Systems and Storage Conference;
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`• “Passive Network Performance Estimation for Large-scale, Data-Intensive
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`Computing,” Jinoh Kim, Abhishek Chandra, and Jon B. Weissman, IEEE
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`Transactions on Parallel and Distributed Systems;
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`• “DDDAS/ITR: A Data Mining and Exploration Middleware for Grid and
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`Distributed Computing,” Jon B. Weissman, Vipin Kumar, Varun Chandola, Eric
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`Eilertson, Levent Ertoz, Gyorgy Simon, Seonho Kim, and Jinoh Kim, Workshop on
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`Dynamic Data Driven Application Systems – DDDAS;
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`• “Scheduling Parallel Applications in Distributed Networks,” Jon B. Weissman and
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`Xin Zhao, Journal of Cluster Computing;
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`• “Adaptive Resource Scheduling for Network Services,” Byoung-Dai Lee and Jon
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`B. Weissman, IEEE 3rd International Workshop on Grid Computing;
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`• “Adaptive Reputation-Based
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`Scheduling
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`on Unreliable Distributed
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`Infrastructures,” Jason D. Sonnek, Abhishek Chandra, and Jon B. Weissman, IEEE
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`Transactions on Parallel and Distributed Systems;
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`• “Integrated Scheduling: The Best of Both Worlds,” Jon B. Weissman, Darin
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`England, and Lakshman Abburi Rao, Journal of Parallel and Distributed
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`Computing;
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`• “Predicting the Cost and Benefit of Adapting Parallel Applications in Clusters,”
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`Jon B. Weissman, Journal of Parallel and Distributed Computing; and
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`• “Optimizing Remote File Access for Parallel and Distributed Network
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`Applications,” Jon B. Weissman, Mike Gingras, and Mahesh Marina, Journal of
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`Parallel and Distributed Computing.
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`10.
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`I am also the coauthor of the “Distributed and Multiprocessing Scheduling” chapter
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`of “The Computer Science and Engineering Handbook” (2nd edition 2004). The chapter discusses
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`CPU scheduling in parallel and distributed systems. I examine in the chapter techniques for
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`spreading tasks across several processors of a distributed system. Examples of scheduling criteria
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`in a distributed computing system discussed in the chapter include minimizing cost, minimizing
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`communication delay, giving priority to certain users’ processes, and needs for specialized
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`hardware devices.
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`11.
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`I have also coauthored publications related to MapReduce, including “Cross-Phase
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`Optimization in MapReduce,” in “Cloud Computing for Data-Intensive Applications,” Springer,
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`2014. This book chapter explores the application of MapReduce across widely distributed data
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`and distributed computation resources. The chapter “propose[s] new cross-phase optimization
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`techniques that enable independent MapReduce phases to influence one another” to address the
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`“problem” that “interaction of MapReduce phases becomes pronounced in the presence of
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`heterogeneous network behavior.” (Cross-Phase Optimization at Abstract.)
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`12.
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`I also coauthored the MapReduce paper, “Exploring MapReduce Efficiency with
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`Highly-Distributed Data,” published in June 2011 in the Proceedings of the Second International
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`Workshop on MapReduce and Its Applications. The paper has been downloaded over 1,200 times
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`from the ACM Digital Library and cited in over 100 other research papers, according to ACM
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`Digital Library and Google Scholar. The paper explains that conventional single-cluster
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`MapReduce architectures were not suitable when data and computing resources are widely
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`distributed. The paper examines three different architectural approaches to perform MapReduce
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`jobs on two platforms—PlanetLab and Amazon EC2—and explains that a local architecture
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`performs better in zero-aggregation conditions, while distributed architectures are preferred in
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`high-aggregation and equal-partition conditions.
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`13.
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`During my academic appointments at the University of Minnesota, University of
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`Edinburgh National e-Science Center, and University of Texas San Antonio, I taught and continue
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`to routinely teach classes in operating systems and parallel and distributed systems. These classes
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`focus on parallel computing platforms, parallel applications, and parallel program scheduling.
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`These parallel computing platforms are used to implement various distributed processing
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`techniques, including, for example MapReduce.
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`14.
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`I have also served on the boards of several major journals, including IEEE
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`Transactions on Parallel and Distributed Systems and IEEE Transactions on Computers. I am the
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`steering committee chair of the ACM International Symposium on High Performance Parallel and
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`Distributed Systems, the premier annual conference for presenting the latest research on the design,
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`implementation, evaluation, and the use of parallel and distributed systems for high-end
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`computing. I have also served on the program committees of many conferences in the area of
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`distributed computing and distributed data processing, including many Institute of Electrical and
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`Electronic Engineers (“IEEE”) International conferences and workshops. Examples of the
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`program committees include Supercomputing Conference (SC), IEEE International Parallel &
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`Distributed Processing Symposium (IPDPS), and the International Conference on Parallel
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`Processing (ICPP). I also chaired/co-chaired conferences in the field, including for the
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`International Symposium on High-Performance Parallel and Distributed Computing (HDPC). As
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`part of my service to the scientific community, I have served on the editorial board of numerous
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`professional journals including, among others, the HPDC 1992-2012 special issue, Journal
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`Frontiers in High Performance Computing, and IEEE Transactions on Parallel and Distributed
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`Systems.
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`15.
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`In addition, throughout my professional career I have received many awards for my
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`technical contributions in the areas of parallel distributed systems. As some examples, I am the
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`“Best Paper” nominee for the 2015 IEEE International Conference on Cloud Engineering (IC2E),
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`and “Best Paper” winner for the 2009 IEEE Grid conference. I received the 1996 Career Award
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`from the National Science Foundation and the 1995 Supercomputing Award for “High-
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`Performance Computing with Legion,” Supercomputing Conference (SC).
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`16.
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`Over the past two decades, I have served as a technical consultant to many
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`companies and organizations in the fields of edge computing, cloud computing, grid computing,
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`and scheduling automation software. My work has spanned a wide range of industries.
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`Representative clients include prominent technology companies such as Cisco, Instrumental Inc.,
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`Beckman Coulter, Inc., Thompson Reuters, and Avaki Inc., which was later acquired by Oracle.
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`At Thompson Reuters I presented a tutorial on MapReduce.
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`II. MATERIALS CONSIDERED
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`17.
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`In forming the opinions set forth in this declaration, I have reviewed the ’610 patent,
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`its file history, statements R2 made in IPR2024-00659, and the extrinsic evidence identified by the
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`parties. Additionally, I have drawn on my many years of experience in the field of distributed
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`computing and distributed data processing.
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`III. COMPENSATION AND LACK OF FINANCIAL INTEREST IN THIS
`LITIGATION
`
`18.
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`I am being compensated for my time at my usual consulting rate of $800 per hour.
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`This compensation is not contingent upon my performance, the conclusions I reach in my analysis,
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`the outcome of this matter, or any issues involved in or related to this matter. I have no financial
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`interest in Databricks or this litigation.
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`IV.
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`LEVEL OF ORDINARY SKILL IN THE ART
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`19.
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`It is my understanding that a patent is to be interpreted based on how it would have
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`been read by a person of “ordinary skill in the art” (“POSITA”) at the time of the effective filing
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`date of the relevant application. It is my understanding that there are various factors which may
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`help establish the level of ordinary skill in the art, including: (1) the education level of those
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`working in the field; (2) the sophistication of the technology; (3) the types of problems
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`encountered in the art; (4) the prior art solutions to those problems; and (5) the speed in which
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`innovation are made in the field.
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`20.
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`It is my understanding that here, the earliest effective filing date for the ’610 patent
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`is October 5, 2006. I am familiar with the technological field at issue at that time.
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`21.
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`The ’610 patent generally relates to distributed data processing. In my opinion a
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`person of ordinary skill in the relevant art, or POSITA, at the time of the ’610 patent’s effective
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`filing date would have had at least a bachelor’s degree in computer science or a similar field, and
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`at least two years of industry or academic experience in a field related to performing data analytics
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`and/or related data processing tasks, including but not limited to, distributed computing systems
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`and distributed data processing. However, with more experience, less education may be needed,
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`and vice versa.
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`22.
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`I am familiar with the knowledge possessed by, and perceptions held by, one of
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`ordinary skill in the art at that time period for the ’610 patent, based on my experience, which
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`includes teaching undergraduate and graduate students at the University of Minnesota, the
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`University of Edinburgh, and the University of Texas San Antonio, and through my participation
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`in scientific community events in this time frame.
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`V.
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`LEGAL STANDARD
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`23.
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`Although I am an expert in the relevant technical field, I am not an attorney and I
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`do not intend to offer opinions on legal issues. The laws and principles of claim construction and
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`the material in this section have been provided to me by counsel, and my understanding is as
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`follows.
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`A.
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`24.
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`General Claim Construction Legal Standards
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`It is my understanding that the claims of a patent define the limits of the patentees’
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`exclusive rights. To determine the scope of the claimed invention, courts typically construe (or
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`define) claim terms, the meanings of which are disputed by the parties. It is my understanding that
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`claim terms should generally be given their ordinary and customary meaning as understood by one
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`of ordinary skill in the art at the time of the invention and after reading the patent and its
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`prosecution history.
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`25.
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`Claims must be construed, however, in light of and consistent, with the patent’s
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`intrinsic evidence. Intrinsic evidence includes the claims themselves, the written disclosure in the
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`patent’s specification, and the patent’s prosecution history, including the prior art that was
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`considered by the United States Patent and Trademark Office (“PTO”) as part of the patent’s
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`prosecution.
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`26.
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`The language of the claims helps guide the construction of claim terms. The context
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`in which a term is used in the claims can be highly instructive.
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`27.
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`The patent specification is the best guide to the meaning of a disputed claim term,
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`beyond the words of the claims themselves. Embodiments disclosed in the specification help teach
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`and enable those of skill in the art to make and use the invention and are helpful to understanding
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`the meaning of claim terms. Nevertheless, in most cases, the limitations of preferred embodiments
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`and examples appearing in the specification should not be read into the claims.
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`28.
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`In the specification, a patentee may also define his own terms, give a claim term a
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`different meaning than it would otherwise possess, or disclaim or disavow claim scope. A court
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`may generally presume that a claim term possesses its ordinary meaning. This presumption,
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`however, does not arise when the patentee acts as his own lexicographer by explicitly defining or
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`re-defining a claim term. This presumption of ordinary meaning can also be overcome by
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`statements, in the specification or prosecution history of the patent, of clear disclaimer or
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`disavowal of a particular claim scope.
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`29.
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`It is my understanding that the specification may also resolve any ambiguity where
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`the ordinary and customary meaning of a claim term lacks sufficient clarity to permit the scope of
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`the claim to be ascertained from the words of the claim alone.
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`30.
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`It is my understanding that the prosecution history is another important source of
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`evidence in the claim construction analysis. The prosecution history is the record of the
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`proceedings before the PTO, including communications between the patentee and the PTO. The
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`prosecution history can inform the meaning of the claim language by demonstrating how the
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`patentee and the PTO understood the invention and whether the patentee limited the invention in
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`the course of prosecution, making the claim scope narrower than it would otherwise be. It is my
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`understanding that a patentee may also define a term during the prosecution of the patent. The
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`patentee is precluded from recapturing through claim construction specific meanings or claim
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`scope clearly and unambiguously disclaimed or disavowed during the patent’s prosecution.
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`31.
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`It is my understanding that courts can also consider extrinsic evidence when
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`construing claims. Extrinsic evidence is any evidence that is extrinsic to the patent itself and its
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`prosecution history. Examples of extrinsic evidence include technical dictionaries, treatises, and
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`expert testimony. It is my understanding that extrinsic evidence is less significant than the intrinsic
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`record in determining the meaning of claim language.
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`B.
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`32.
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`Legal Standards Governing Means-Plus-Function Terms
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`It is my understanding that some claim terms can be written in a means-plus-
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`function format. Construing such claim terms involves two steps. First, the Court must identify
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`the claimed function. Second, the Court must identify the structure, if any, disclosed in the
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`specification for performing that function. It is my understanding that in order to meet the
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`definiteness requirement of 35 U.S.C. § 1121, the specification must include a disclosure sufficient
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`1 It is my understanding that while means-plus-function limitations are now governed by § 112(f)
`rather than § 112, ¶ 6, the substantive requirements of that paragraph have not changed. It is my
`understanding that the amended statute applies only to patents and applications filed on or after
`September 16, 2012. Because the ’610 patent was filed before September 16, 2012, I will refer to
`the preamendment paragraph numbers.
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`for one skilled in the art to understand what structure disclosed in the specification performs the
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`recited function. To determine the structure that corresponds with the recited function, the
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`specification must clearly link or associate a structure with the particular function recited in the
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`claim.
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`33.
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`Generally, for claims directed towards computer-implemented inventions, the
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`structure disclosed in the specification must be more than a general-purpose computer or
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`microprocessor. This is because general purpose computers can be programmed to perform
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`different tasks in different ways and such a disclosure would effectively provide no limit on the
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`scope of the claims. Thus, the corresponding structure for a computer-implemented function is
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`not a computer but is a specific algorithm that allows a general-purpose computer or
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`microprocessor to perform the claimed function. An “algorithm” is a fixed step-by-step procedure
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`for accomplishing a given result. A patentee may express the procedural algorithm in any
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`understandable terms including as a mathematical formula, in prose, or as a flow chart. A patentee
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`is not required to produce a listing of source code or a highly detailed description of the algorithm
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`to be used to achieve the claimed function in order to satisfy 35 U.S.C. § 112, ¶ 6. The patentee is
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`required, however, to disclose in the patent specification the algorithm that transforms the general-
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`purpose microprocessor into a special purpose computer which is programmed to perform the
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`algorithm. I am informed that a patent claim is invalid as being indefinite if the specification fails
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`to disclose in sufficient detail an algorithm for programming the computer or microprocessor.
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`There is one limited exception to this general rule—a patent can meet the requirements of § 112,
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`¶ 6 by reciting only a general-purpose computer or microprocessor (with no corresponding
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`algorithm) if the claimed function can be achieved without any special programming.
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`34.
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`It is my understanding that although a claim element that does not contain the term
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`“means” is presumed not to be subject to 35 U.S.C. § 112, ¶ 6, this presumption is overcome where
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`the term does not connote a sufficiently definite structure to a person of ordinary skill in the art or
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`recites a function without reciting sufficient structure for performing that function. It is my further
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`understanding that certain terms have been explicitly recognized as “nonce” words or verbal
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`constructs that are not recognized as the name of structure and are simply a substitute for the term
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`“means for.” While claim language that includes adjectives further defining a generic term can
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`sometimes add sufficient structure to render the claim to be not means-plus-function under
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`35 U.S.C. § 112, ¶ 6, not just any description or qualification of functional language will suffice.
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`The proper inquiry is whether or not the claim limitation itself, when read in light of the
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`specification, connotes to a person of ordinarily skill in the art definite structure for performing
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`the claimed functions.
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`C.
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`35.
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`Legal Standards Governing Claim Indefiniteness
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`It is my understanding that there is a “definiteness requirement” that a patent claim
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`must distinctly claim the subject matter the inventor regards as his invention. It is my
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`understanding that the purpose of this requirement is to make sure that the scope of the claims is
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`clear enough so that the public knows what it can and cannot do without infringing the patent’s
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`claims. A claim limitation is indefinite if the claim, when read in light of the specification and the
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`prosecution history, fails to inform with reasonable certainty persons of ordinary skill in the art
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`about the scope of the invention. In other words, the claims, when read in light of the specification
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`and the prosecution history, must provide objective boundaries for those of skill in the art.
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`VI. GENERAL STATE OF THE ART
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`36.
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`The ’610 patent relates generally to distributed computing, and more specifically
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`to performing MapReduce operations in a distributed system. To provide context for my opinions
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`on the construction of certain terms in the ’610 patent, in this section I provide some background
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`information on distributed data processing, distributed computing systems, and MapReduce. All
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`of the concepts discussed in this section were well known before the earliest priority date for the
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`’610 patent, which is October 5, 2006. For example, distributed data processing and distributed
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`system textbooks used in undergraduate courses taught the concepts below, and I had taught these
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`concepts years before the earliest priority date.
`
`A.
`
`37.
`
`State of the Art of Relational Processing
`
`Relational processing was not new in 2006. Relational Database Management
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`Systems (RDBMS) employing Structured Query Language (SQL) performed relational processing
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`for years before the ’610 patent. (See Oracle Database SQL Reference (Ex. B-1) at 1-1 to 1-3.)
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`Relational processing involves processing tables of different schema, for example by executing
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`different functions that processes the different data in each of those tables, producing new tables
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`as a result of that processing, and merging or joining the tables, including over a distributed
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`network. These techniques have been described in numerous textbooks, publications, and journals
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`in the field of relational processing.
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`38. Well before the ’610 patent, documents describing Microsoft’s SQL Server 2005,
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`for example, demonstrated that multiple CPU machines used SQL commands to partition data,
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`process it in parallel, and then combine the results. (See Tripp Whitepaper (Ex. B-2) at
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`Databricks_R2_PA00005141 (“Why do you need Partitioning?”) (“Large-scale operations across
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`extremely large data sets—typically many million rows—can benefit by performing multiple
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`operations against individual subsets in parallel. . . . This allows SQL Server 2005 to more
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`effectively use multiple- CPU machines.”).) Partitioning is the database process that divides large
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`tables into multiple smaller parts. By partitioning a large table into smaller, individual tables,
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`queries that access only a fraction of the data can run faster because they have less data to scan.
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`The primary goal of partitioning is to aid in maintenance of large tables and to reduce the overall
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`response time by reading and loading less data for particular SQL operations. (Id. at
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`Databricks_R2_PA00005141, -5145.)
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`39.
`
`SQL Server 2005 could also process data sets with different schema like the
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`“Orders” and “OrderDetails” tables reproduced below:
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`
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`(Id. at Databricks_R2_PA00005145, Fig. 3.) In this example, the orders table could identify the
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`customer order numbers whereas order details may identify the items each customer purchased.
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`SQL Server 2005 was capable of processing and joining the data from these different tables,
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`including partitioning the data as shown in the image above.
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`40.
`
`The Tripp Whitepaper also provides an example of how data may be partitioned.
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`“The first step in partitioning tables . . . is to define the data on which the partition is ‘keyed.’”
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`(Id. at Databricks_R2_PA00005146.) The partitioning key is a set of one or more columns in the
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`table. (Id.) A partition function defines how the rows of a table are mapped to a set of partitions
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`based on the values of a certain column. The figure below shows one possible example of the
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`steps for creating a partitioned table:
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`(Id. at Databricks_R2_PA00005148, Fig. 11.)
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`41.
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`The first step may be to determine whether an object, like a table, should be
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`partitioned. Generally, Tripp recommends partitioning large tables, because partitioning adds
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`administrative overhead that outweighs its benefits for small tables. The next step may be to
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`determine a partitioning key and the number of partitions for the data. After that, one or more
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`filegroups can be created to store and separate the data. Filegroups “place a partitioned table on
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`multiple files for better IO balancing.” (Id. at Databricks_R2_PA00005149.) Then, a partition
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`function and a partition scheme may be created. As Tripp explains, “[o]nce you have created a
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`partition function you must associate it with a partition scheme to direct the partitions to specific
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`filegroups.” (Id. at Databricks_R2_PA00005151.) After both the partition function and partition
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`scheme are defined, a partition table may be defined to take advantage of them. “The table defines
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`which “scheme” should be used and the scheme defines the function.” (Id.)
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`B.
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`42.
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`History of MapReduce
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`As the ’610 patent explains, MapReduce refers to a well-known and preexisting
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`programming methodology for processing “parallel computations over distributed (typically, very
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`large) data sets.” (’610 patent, 1:6-27.) In 2004, Jeffrey Dean and Sanjay Ghemawat published a
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`paper while working at Google that described MapReduce as a programming methodology for
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`processing big data sets in a parallel, distributed computing environment. (Dean & Ghemawat
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`(Ex. B-3) (“Dean”).) The paper was presented at the 6th Symposium on Operating Systems Design
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`and Implementation (OSDI). (Id.)
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`43.
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`As the name suggests, the MapReduce methodology includes two steps: a map
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`function and a reduce function. (Ex. B-3 at 2.) The map function “written by the user, takes an
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`input pair and produces a set of intermediate key/value pairs” that “group[] together all
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`intermediate values associated with the same intermediate key.” (Id.) Dean provides examples of
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`key/value pairs, including a word count example where the word is the key and each instance of
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`the word in a document is the value. (Id.)
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`44.
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`The reduce function “also written by the user, accepts an intermediate key I and a
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`set of values for that key” and “merges together these values.” (Id.) The intermediate values are
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`supplied to the user’s reduce function via an iterator. (Id.) An iterator simply reads in the data
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`and calls the reduce function for each item. Dean’s “implementation of MapReduce runs on a
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`large cluster of commodity machines,” and “often on several thousand machines.” (Id. at 1, 7.)
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`45.
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`According to Dean, one of Google’s “most significant uses of MapReduce” by 2004
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`was using it to index webpages for Google’s web search service. (Id. at 10-11 (Section 6.1).) The
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`indexing process ran “as a sequence of five to ten MapReduce operations,” and considered “as
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`input a large set of documents that have been retrieved by [Google’s] crawling system,” where
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`“[t]he raw contents for these documents are more than 20 terabytes of data.” (Id. at 11.) Dean
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`places no limitation on the type of input data and contemplates processing “large collection[s] of
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`documents,” including websites and logs from the Internet. (See id. at 2, 10-11.) Dean also
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`discloses a MapReduce library that provides “support for reading input data in several different
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`formats. For example, ‘text’ mode input treats each line as a key/value pair . . . [e]ach input type
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`implementation knows how to split itself into meaningful ranges for processing as separate map
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`tasks (e.g., text mode’s range splitting ensures that range splits occur only at line boundaries).”
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`(Id. at 6-7 (Section 4.4).)
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`VII. THE ’610 PATENT
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`46.
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`I provide below a brief summary of the ’610 patent. The ’610 patent is directed to
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`an “enhanced” form of MapReduce. (’610 patent at Abstract, 1:31-44.) But the patent concedes
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`that the actual change is not an enhancement of MapReduce, but a different treatment of inputs:
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`“[t]he inventors have realized that, by treating an input data set as a plurality of grouped sets of
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`key/value pairs, the utility of the MapReduce programming methodology may be enhanced.” (Id.
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`at 1:66-2:8, 1:31-41, Abstract.) In other words, the patent specifies a plurality of “data groups”
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`with different schema that are processed by MapReduce.
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`47.
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`The claimed idea of the ’610 patent can be understood with reference to Fig. 5,
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`below.
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`Case 4:23-cv-0