IN THE UNITED STATES PATENT AND TRADEMARK OFFICE
`
`Attorney Docket No.: 41194-0002IP9
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`James J. Fallon
`In re Patent of:
`U.S. Patent No.: 9,054,728
`Issue Date:
`June 9, 2015
`Appl. Serial No.: 14/495,574
`Filing Date:
`September 24, 2014
`Title:
`DATA COMPRESSION SYSTEMS AND METHODS
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`Mail Stop Patent Board
`Patent Trial and Appeal Board
`U.S. Patent and Trademark Office
`P.O. Box 1450
`Alexandria, VA 22313-1450
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`Dated:
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`DECLARATION OF PROFESSOR JAMES A. STORER, Ph.D.
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`February 13, 2018
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`____________________
`By: ____________________________
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`James A. Storer James A. Storer
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`ECHOSTAR 1003
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`TABLE OF CONTENTS
`INTRODUCTION ............................................................................................. 1
`I.
`II. OVERVIEW OF CONCLUSIONS FORMED ................................................. 6
`III. BACKGROUND KNOWLEDGE ONE OF SKILL IN THE ART WOULD
`HAVE HAD PRIOR TO THE FILING OF THE ʼ728 PATENT ............................. 7
`IV. INTERPRETATIONS OF THE ʼ728 PATENT CLAIMS AT ISSUE ............. 9
`V. TECHNOLOGY OVERVIEW ........................................................................11
`VI. OVERVIEW OF THE PRIOR ART ...............................................................12
`A. Franaszek Overview ....................................................................................14
`B. Hsu Overview ..............................................................................................18
`C. Sebastian Overview .....................................................................................24
`D. Kawashima Overview .................................................................................25
`VII. LEGAL PRINCIPLES .....................................................................................27
`A. Anticipation .................................................................................................27
`B. Obviousness .................................................................................................28
`VIII. ANALYSIS OF FRANASZEK IN VIEW OF HSU AND SEBASTIAN
`(CLAIM 25) .............................................................................................................31
`A. Motivation to Combine Franaszek, Hsu, Sebastian ....................................31
`B. Claim 25 is Obvious Over Franaszek in view of Hsu and Sebastian ..........53
`[25.0]: “A computer implemented method comprising:” ................................53
`[25.1]: “analyzing, using a processor, data within a data block to identify one
`or more parameters or attributes of the data within the data block;” ..............60
`[25.2]: “determining, using the processor, whether to output the data block in
`a received form or in a compressed form; and” ...............................................64
`[25.3]: “outputting, using the processor, the data block in the received form or
`the compressed form based on the determination,” .........................................75
`[25.4]: “wherein the outputting the data block in the compressed form
`comprises determining whether to compress the data block with content
`dependent data compression based on the one or more parameters or attributes
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`Patent No.: 9,054,728
`Attorney Docket: 41194-0002IP9
`of the data within the data block or to compress the data block with a single
`data compression encoder; and” ......................................................................77
`[25.5]: “wherein the analyzing of the data within the data block to identify the
`one or more parameters or attributes of the data excludes analyzing based
`only on a descriptor that is indicative of the one or more parameters or
`attributes of the data within the data block.” ...................................................84
`IX. ANALYSIS OF FRANASZEK IN VIEW OF HSU, SEBASTIAN, AND
`KAWASHIMA (CLAIM 25) ...................................................................................86
`A. Motivation to Combine Franaszek, Hsu, Sebastian, Kawashima ...............86
`B. Claim 25 is Obvious Over Franaszek in view of Hsu, Sebastian, and
`Kawashima ...........................................................................................................90
`[25.0]: “A computer implemented method comprising:” ................................90
`[25.1]: “analyzing, using a processor, data within a data block to identify one
`or more parameters or attributes of the data within the data block;” ..............90
`[25.2]: “determining, using the processor, whether to output the data block in
`a received form or in a compressed form; and” ...............................................91
`[25.3]: “outputting, using the processor, the data block in the received form or
`the compressed form based on the determination,” .........................................95
`[25.4]: “wherein the outputting the data block in the compressed form
`comprises determining whether to compress the data block with content
`dependent data compression based on the one or more parameters or attributes
`of the data within the data block or to compress the data block with a single
`data compression encoder; and” ......................................................................97
`[25.5]: “wherein the analyzing of the data within the data block to identify the
`one or more parameters or attributes of the data excludes analyzing based
`only on a descriptor that is indicative of the one or more parameters or
`attributes of the data within the data block.” ...................................................98
`X. ADDITIONAL REMARKS ............................................................................98
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`I.
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`INTRODUCTION
`I, James A. Storer, of Waltham, MA, hereby declare the following:
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`I have been retained on behalf of EchoStar Corporation and Hughes
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`Network Systems, L.L.C. (“EchoStar/Hughes”). I understand that
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`EchoStar/Hughes is the Petitioner in an Inter Partes Review before the Patent Trial
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`and Appeal Board (“PTAB” or “Board”) of U.S. 9,054,728 (“the ʼ728 Patent”)
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`(ECHOSTAR1001). The ʼ728 Patent claims priority to U.S. Application No.
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`09/210,491 (“the ’491 Application”).
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`I am a Professor at Brandeis University in the Computer Science
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`Department. I am an expert in the field of computer algorithms, including data
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`communications and internet related computing, data compression, data and image
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`retrieval, storage and processing of large data sets, and image/video processing. I
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`have studied, researched, and practiced in the field of computer science for more
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`than 35 years, and have taught Computer Science at Brandeis since 1981.
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`
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`I received my Doctor of Philosophy (Ph.D.) degree in the field of Computer
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`Science from Princeton University in 1979. I received my Masters of Arts (M.A.)
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`degree in Computer Science from Princeton University and my Bachelor of Arts
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`(B.A.) degree in Mathematics and Computer Science from Cornell University.
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`Patent No.: 9,054,728
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`After receiving my Ph.D. degree, I worked in industry as a researcher at
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`AT&T Bell Laboratories from 1979 to 1981 before joining the faculty of Brandeis
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`University.
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`
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`I have been involved in computer science research since 1976. My research
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`has been funded by a variety of governmental agencies, including the National
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`Science Foundation (NSF), National Aeronautics and Space Administration
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`(NASA), and Defense Advanced Research Projects Agency (DARPA). In addition,
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`I have received government Small Business Innovation Research (SBIR) funding,
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`as well as numerous industrial grants.
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`I regularly teach courses in software and hardware technology for data
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`compression and communications (including text, images, video, and audio) at
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`both the undergraduate and graduate level, and in my capacity as co-chair of the
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`Annual Data Compression Conference, I regularly referee academic papers in these
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`areas. In addition, much of my consulting activity has been in the areas of software
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`and hardware for consumer electronic devices, including cell phones/PDAs
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`(including cellular technology), smartphones, digital cameras, digital video and
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`audio recorders, and personal computers (“PCs”), as well as devices for
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`communications over the Internet.
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`I am the author of two books: An Introduction to Data Structures and
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`Algorithms and Data Compression: Methods and Theory. Both books have been
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`Patent No.: 9,054,728
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`used as references for undergraduate level computer science courses in
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`universities. I am the editor or co-editor of four other books, including
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`Hyperspectral Data Compression and Image and Text Compression.
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`I have three issued U.S. patents that relate to computer software and
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`hardware (two for which I am sole inventor and one for which I am co-inventor). I
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`am the author or co-author of well over 100 articles and conference papers.
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`In 1991, I founded the Annual Institute of Electrical and Electronics
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`Engineers (IEEE) Data Compression Conference (DCC), the first major
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`international conference devoted entirely to data compression, and have served as
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`the conference chair since then. This conference continues to be the world’s
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`premier venue devoted to data compression research and development.
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`
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`I routinely serve as referee for papers submitted to journals such as, for
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`example, JACM, SICOMP, Theoretical CS, Computer Journal, J. Algorithms,
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`Signal Processing, JPDC, Acta Informatica, Algorithmicia, IPL, IPM, Theoretical
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`CS, J. Algorithms, Networks, IEEE J. Robotics & Automation, IEEE Trans.
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`Information Theory, IEEE Trans. Computers, IEEE Trans. Image Processing,
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`Proceedings of the IEEE, IBM J. of R&D, and J. Computer and System Sciences.
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`I have served as guest editor for a number of professional journals, including
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`Proceedings of the IEEE, Journal of Visual Communication and Image
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`Representation, and Information Processing and Management. I have served as a
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`program committee member for various conferences, including IEEE Data
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`Compression Conference, IEEE International Symposium on Information Theory,
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`Combinatorial Pattern Matching (CPM), International Conference on String
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`Processing and Information Retrieval (SPIRE), Conference on Information and
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`Knowledge Management (CIKM), Conference on Information Theory and
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`Statistical Learning (ITSL), Sequences and Combinatorial Algorithms on Words,
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`Dartmouth Institute for Advanced Graduate Studies Symposium (DAGS),
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`International Conference on Language and Automata Theory and Applications
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`(LATA), DIMACS Workshop on Data Compression in Networks and
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`Applications, Conference on Combinatorial Algorithms on Words.
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` A copy of my latest curriculum vitae (C.V.) is attached as Appendix A.
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` My compensation is in no way contingent on the results of these or any other
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`proceedings relating to the above-captioned patent.
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`I have been asked to opine on the subject of the validity of the claims of the
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`ʼ728 Patent.
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` As part of my independent analysis for this declaration, I have considered
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`the following: my own knowledge and experience, including my own work and
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`research experience in the fields of content rights management and content
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`distribution, my participation in professional organizations and conferences in
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`those fields; and my experience in working with others in the relevant technical
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`areas. In addition, I have analyzed the following materials (see also Appendix B):
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`(cid:120) The disclosure and claims of U.S. Patent No. 9,054,728 (“the ʼ728
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`Patent”; ECHOSTAR1001);
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`(cid:120) File History for U.S. Patent No. 9,054,728 (ECHOSTAR1002);
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`(cid:120) Automatic Synthesis of Compression Techniques for Heterogeneous
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`Files (“Hsu”; ECHOSTAR1004)
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`(cid:120) U.S. Patent No. 5,870,036 (“Franaszek”; ECHOSTAR1005)
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`(cid:120) U.S. Patent No. 6,253,264 (“Sebastian”; ECHOSTAR1006)
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`(cid:120) U.S. Patent No. 5,805,932 (“Kawashima”; ECHOSTAR1007)
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`(cid:120) U.S. Patent No. 3,394,352 (“Wernikoff”; ECHOSTAR1009)
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`(cid:120) FreeBSD General Commands Manual (“FreeBSD”; ECHOSTAR1010)
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`(cid:120) AT&T UNIX® PC UNIX System V User’s Manual (“UNIX System V
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`User’s Manual”; ECHOSTAR1011)
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`(cid:120) Extensions of the UNIX File Command and Magic File for File Type
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`Identification (“Underwood”; ECHOSTAR1012)
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`(cid:120) The Data Compression Book (“Nelson”; ECHOSTAR1013)
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`(cid:120) UNIX User’s Manual (ECHOSTAR1014)
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`(cid:120) UNIX Programmer’s Manual, Berkeley Software Distribution
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`(ECHOSTAR1015)
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`Patent No.: 9,054,728
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`(cid:120) Other background references, of which I had been previously aware, not
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`cited herein directly that a person of ordinary skill in the art at the time of
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`invention (“POSITA” or “one of ordinary skill in the art”) would have
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`recognized as being related to the subject matter of the ʼ728 Patent.
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` Although this Declaration refers to and cites to selected portions of the cited
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`references for the sake of brevity, it should be understood that one of ordinary skill
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`in the art would have viewed the references cited herein in their entirety and in
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`combination with other references cited herein or cited within the references
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`themselves. The references used in this Declaration, therefore, should be viewed as
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`being incorporated herein in their entirety.
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`I am not, and never have been, an employee of EchoStar/Hughes. I have
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`been engaged in the present matter to provide my independent analysis of the
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`issues raised in the petition for Inter Partes Review of the ʼ728 Patent. I received
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`no compensation for this declaration beyond my normal hourly compensation of
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`$785/hour based on my time actually spent studying the matter, and I will not
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`receive any added compensation based on the outcome of this Inter Partes Review
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`of the ʼ728 Patent.
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`II. OVERVIEW OF CONCLUSIONS FORMED
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`Patent No.: 9,054,728
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` This Declaration explains the conclusions that I have formed based on my
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`independent analysis. To summarize, based upon my knowledge, experience, and
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`my review of the prior art publications listed above, it is my opinion that:
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`(cid:120)
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`Claim 25 of the ʼ728 Patent is obvious in view of Franaszek in view
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`of Hsu, Sebastian, and one of ordinary skill in the art’s knowledge.
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`(cid:120)
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`Claim 25 of the ʼ728 Patent is obvious in view of Franaszek in view
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`of Hsu, Sebastian, Kawashima, and one of ordinary skill in the art’s
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`knowledge.
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`III. BACKGROUND KNOWLEDGE ONE OF SKILL IN THE ART WOULD
`HAVE HAD PRIOR TO THE FILING OF THE ʼ728 PATENT
` The technology of the ʼ728 Patent at issue generally relates to compression
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`techniques for performing what the inventor calls “content independent data
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`compression and content dependent data compression,” terms that the inventor
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`used to describe old ideas, wherein “content dependent” compression is performed
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`with a compression method selected based on a “determined data type.”
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`ECHOSTAR1001, Abstract, 18:4-19:67. For example, a system implementing the
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`method of the ʼ728 Patent may perform characteristic analysis on an input block
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`without the use of descriptor; determine whether to output the data block in
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`received or compressed form; and, if the determination is to compress, output the
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`block with “content dependent” compressor or a content independent compressor.
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`Id.
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` This method is purported to be novel. Id. I respectfully disagree: based upon
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`my knowledge and experience, there existed numerous products, publications, and
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`patents that implemented or described the method claimed in the ʼ728 Patent, prior
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`to the earliest effective filing date of the ʼ728 Patent, which as described below is
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`assumed to be December 11, 1998 for purposes of my analysis here. Indeed, based
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`on my review of the prior art publications listed above, one of ordinary skill in the
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`art, at the time the ’491 Application was filed, would have recognized that the
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`subject matter described in the ʼ728 Patent was well-known in the prior art.
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`Further, to the extent there was any problem to be solved in the ’491 Application,
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`one of ordinary skill in the art at the time would have known that such a problem
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`had already been solved in prior art systems, patents, and other printed publications
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`appearing before the priority date of the ʼ728 Patent.
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` Based upon my experience in this area, one of ordinary skill in the art would
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`have had a bachelor’s degree in electrical engineering, computer engineering,
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`computer science, or the equivalent and 2-3 years of work experience with data
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`compression, storage, retrieval, processing, and transmission, or the equivalent.
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` Based on my experiences, I have a good understanding of the capabilities of
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`one of ordinary skill in the art, and I possess the capabilities of one of ordinary
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`skill in the art myself. Indeed, I have worked with, supervised, participated in
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`organizations with, and presented to a number of such persons over the course of
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`my career.
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`IV.
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`INTERPRETATIONS OF THE ʼ728 PATENT CLAIMS AT ISSUE
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`I understand that, for purposes of my analysis in this Inter Partes Review
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`proceeding, the terms appearing in the claims of the ʼ728 Patent should be
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`interpreted according to their “broadest reasonable construction in light of the
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`specification of the patent in which it appears.” 37 C.F.R. § 42.100(b). In that
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`regard, I understand that the best indicator of claim meaning is usage of the claim
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`language in the context of the patent specification as understood by one of ordinary
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`skill in the art. I further understand that the words of the claims should be given
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`their plain meaning unless that meaning is inconsistent with the patent
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`specification or the patent’s history of examination before the Patent Office. I also
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`understand that the words of the claims should be interpreted as they would by one
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`of ordinary skill in the art at the time the invention was made. I have used the
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`filing date of the ʼ491 Application as the point in time for claim interpretation
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`purposes, because it provides the earliest date to which the ʼ728 Patent face shows
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`continuation (in part). That date was December 11, 1998. Without exception,
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`however, the interpretations that I provide below would have also been correct if a
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`date between, for instance, the start of the 1990s and December 11, 1998, was
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`determined to be the date of invention.
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`Patent No.: 9,054,728
`Attorney Docket: 41194-0002IP9
` The ʼ728 Patent describes “data blocks” as ranging “in size from individual
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`bits through complete files or collections of multiple files.” ECHOSTAR1001,
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`6:58-67. The ʼ728 Patent reiterates that “a data block may represent any quantity
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`of data form a single bit through a multiplicity of files or packets and may vary
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`from block to block.” Id., 18:15-20. One of ordinary skill in the art would
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`understand that the broadest reasonable interpretation of “data block” in view of
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`the ʼ728 Patent to include “a single unit of data, which may range in size from
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`individual bits through complete files or collection of multiple files.”
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` The ʼ728 Patent uses “data block in [a/the] received form” in the context of
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`data “input into the data compression system,” wherein the data blocks in the
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`stream that are input into the data compression system are consequently “received”
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`by the data compression system. Id., 18:11-16. The ʼ728 Patent describes
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`outputting
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`“an uncompressed data stream in the event that . . . every encoder fails
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`to achieve a level of compression that exceeds a priori specified . . .
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`minimum compression ratio thresholds”
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`that indicate, for example, data expansion. Id., 7:3-10, 7:64-8:10. The ʼ728 Patent
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`describes the “received form” of the data block is the data block uncompressed,
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`where the output of the system is the data block without any processing applied.
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`Id., 7:3-10, 16:12-22. One of ordinary skill in the art would understand the
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`Patent No.: 9,054,728
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`broadest reasonable interpretation of a “data block in [a/the] received form” in
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`view of the ʼ728 Patent to mean “data block that was not further compressed.”
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`V. TECHNOLOGY OVERVIEW
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` The ʼ728 Patent is titled “Data Compression Systems and Methods.” It
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`describes techniques for performing “content independent data compression and
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`content dependent data compression,” wherein “content dependent” compression is
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`performed with a compression method selected based on a “determined data type,”
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`and a descriptor of the selected method is output with the compressed data block.
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`ECHOSTAR1001, Abstract, 18:4-19:67. The ʼ728 Patent specification admits
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`that, at the time of filing, techniques existed for “content dependent . . . data
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`compression and decompression” that select “a data compression method from a
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`preselected set of methods to compress the input data stream, with the intention of
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`producing the best available compression ratio for that particular data type.” Id.,
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`3:1-37, 5:5-8. The ʼ728 Patent contends, however, that it was “difficult and/or
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`impractical to predict which data encoding technique yields the highest
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`compression ratio” for data in which a “mix of data types” exist, and that there was
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`“a need for a data compression system and method that would address limitations
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`in conventional data compression techniques as described above.” Id., 3:39-55.
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`As a solution, the ʼ728 Patent offers a purported improvement of analyzing data
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`input “on a per block or multi-block basis,” selecting an “encoding technique
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`Patent No.: 9,054,728
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`applied to the [particular] data block” and outputting an “appropriate data
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`compression type descriptor” with the encoded data block, or outputting “the
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`original unencoded input data block” with “a null data compression type
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`descriptor.” Id., 18:4-19:67.
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` However, this purported improvement was not novel; it was taught by
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`several earlier publications, as described below.
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`VI. OVERVIEW OF THE PRIOR ART
` Prior to the filing date of the ʼ728 Patent, several prior art references devised
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`a compression method that performed “content dependent” and “content
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`independent” compression – just like the ʼ728 Patent. U.S. Patent No. 3,394,352
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`(“Wernikoff”), filed in 1965, discusses methods that
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`“minimize the number of symbols to be transmitted, and hence the
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`transmission time, in a communication system, by determining the
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`most efficient code for encoding sequences of message symbol and
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`transmitting the symbols in that code.” ECHOSTAR1009-Wernikoff,
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`1:13-17; FIG. 1A.
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`Patent No.: 9,054,728
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`
` U.S. Patent No. 5,870,036 (ECHOSTAR1005-Franaszek, “Franaszek”) was
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`filed over three years before the earliest proper priority date of the ʼ728 Patent, yet
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`Franaszek discloses a system that “using a plurality of data compression
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`mechanisms” selected based on the data type of the data block to be compressed.
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`ECHOSTAR1005-Franaszek, Abstract, 5:46-6:50. Franaszek does so for precisely
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`the same reason as the ʼ728 Patent: to reduce data size by providing efficient data
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`compression. ECHOSTAR1001, 3:59-64; ECHOSTAR1005-Franaszek, 4:13-22.
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` A similar publication, W.H. Hsu et al., Automatic Synthesis of Compression
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`Techniques for Heterogeneous Files, Software Practice & Experience, Vol. 25, No.
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`10 pp. 1097-1116 (Oct. 1995) (ECHOSTAR1004-Hsu, “Hsu”) also discloses a
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`system that “uses statistical methods to determine the best algorithm to use” in
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`compressing “heterogeneous files, those files which contain multiple types of data
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`Patent No.: 9,054,728
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`such as text, images, binary, audio, or animation” to achieve “greater savings
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`achievable by compressing various segments of the file with different methods” to
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`provide “improved performance over using one program for all files.”
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`ECHOSTAR1004-Hsu, Summary, 1098.
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` Additionally, U.S. Patent No. 6,253,264 (ECHOSTAR1006-Sebastian,
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`“Sebastian”) also describes the “advantages of format-specific compression” that
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`serves “a wide range of data formats.” ECHOSTAR1006-Sebastian, Abstract.
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`Indeed, the system claimed by the ʼ728 Patent was discussed in substantial
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`art published prior to the alleged time of invention, as discussed in greater detail
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`below.
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`A. Franaszek Overview
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` Franaszek teaches a compression system that selects and applies
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`compression based on the data type of the received block. ECHOSTAR1005-
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`Franaszek, 3:25-45. For an individual data block, Franaszek first determines
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`whether the data type of the data block is known, based on the data type field 205,
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`which serves as an indicator of the block’s data type and is attached to the data
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`block. Id., 3:25-45, 5:46-54, FIG. 2.
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`Patent No.: 9,054,728
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` Based on the determination, Franaszek selects a list of compression
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`methods, each of which is to be tested on a sample of the data block. Id., 3:25-45,
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`5:8-18, 5:46-54, 6:1-11. If the data type of the block is known, Franaszek selects a
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`list of compression methods tailored to the data type of the data block to be tested
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`on a sample of the data block. If the data type of the block is not known (i.e. if the
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`data field 205 returns a null indicator), Franaszek selects a list of default
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`compression methods to be tested on a sample of the data block. Id., 3:25-45, 5:8-
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`18, 5:46-6:32.
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` Regardless of the determination, Franaszek takes a representative sample of
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`the data block, which could be any percentage, including the entire block, and
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`applies the selected list of compression methods to the sample. Id., 3:25-45, 5:19-
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`39, 5:46-6:32. The compressed sample is saved as an entry in the “compression
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`ratio test table CRTT” and once every compression method on the selected list has
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`been applied to the sample, the “smallest compressed length [CRTT(Q)] in the
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`table CRTT is found.” Id., 3:25-45, 5:19-39, 6:33-50. Franaszek compares this
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`length with a threshold to determine whether the length is “sufficiently small.” Id.,
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`3:25-45, 5:19-39, 6:33-50. If the length CRTT(Q) is not sufficiently small—in
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`other words, if the compression method that produced the smallest compressed
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`length, and therefore achieved the highest amount of compression—”the data block
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`B is not compressed,” and is “stored in uncompressed format.” Id., 3:25-45, 5:19-
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`39, 6:33-50. If CRTT(Q) is sufficiently small, the data block “B is compressed
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`using” the compression method M that produced CRTT(Q), and output with “an
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`identifier of the selected” compression method M. Id., 3:25-45, 5:19-39, 6:33-50,
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`8:21-31.
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` Franaszek’s system “increase[s] the number of data blocks that can be
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`stored” by applying compression to received data blocks based on the data type of
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`each block. Id., 4:12-22. For each data block with an available, or identifiable,
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`data type, the compression method list is “preselected for that data type” to
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`provide, for example, the most efficient data compression for the type of data
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`within the data block. Id., 5:46-54. Franaszek determines which of the
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`compression methods provides the best “compression ratio” by comparing the
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`“compressed length” to the length of “input block.” Id., 6:12-50.
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` Franaszek discusses use of the Lempel-Ziv compression method, which, as
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`one of the most widely used families of lossless compression methods of the time,
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`one of ordinary skill in the art at the time of the alleged invention was very familiar
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`with. Franaszek describes the general Lempel-Ziv family and basic theory behind
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`the Lempel-Ziv method. ECHOSTAR1005-Franaszek, 1:40-2:60. By way of
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`background, LZ1 type methods are often referred to as LZ77 type methods, in view
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`of the 1977 paper of Lempel and Ziv. LZ2 type methods often referred to as LZ78
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`type methods, in view of the 1978 paper of Lempel and Ziv.
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` Franaszek specifically identifies the LZ1 type methods:
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`“The other three methods indicated in the figure are arithmetic coding
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`600, run-length coding 601, and LZ1 using one of the fixed set of
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`static dictionaries 602.” ECHOSTAR1005-Franaszek, 7:15-7:19,
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`7:55-65, FIG. 2 (emphasis added).
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`Franaszek also identifies the LZ2, or LZ78, type methods at 2:20-2:36,
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`incorporating by reference the original LZ78 paper. ECHOSTAR1005-Franaszek,
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`2:20-2:36. Indeed, Franaszek also refers to Mark Nelson’s discussion of the
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`Lempel-Ziv method in The Data Compression Book (ECHOSTAR1013), which
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`detailed presentations of LZ78 and, specifically, LZW implementations. Id.
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`B. Hsu Overview
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` Hsu discloses a compression system that “determine[s] the best algorithm to
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`use” prior to compressing “heterogeneous files, those files which contain multiple
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`types of data such as text, images, binary, audio, or animation,” ECHOSTAR1004-
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`Hsu, Summary. Hsu teaches an adaptive compression technique that, prior to
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`compressing, analyzes the data within a data block, selects an appropriate
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`compression algorithm for compressing data within a data block, and then applies
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`the selected compression algorithm to the block. Id., 1097-1098. Hsu therefore
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`compresses data via compression methods tailored to the data to be compressed, to
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`achieve “improved performance over using one program for all files,” irrespective
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`of data type, and maximize the extent to which the data size is reduced. Id.,
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`Summary, 1098.
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` Hsu improves upon applying a blanket compression algorithm
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`unconditionally, irrespective of the characteristics or type of data to be
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`compressed. Indeed “no algorithm is effective in compressing all files.” Id. For
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`example,
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`“dynamic Huffman coding works best on data files with a high
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`variance in the frequency of individual characters[,] . . . achieves
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`mediocre performance on natural language text files, and performs
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`poorly in general on high-redundancy binary data. On the other hand,
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`run length encoding works well on high-redundancy binary data, but
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`performs very poorly on text files. Textual substitution works best
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`when multiple-character strings tend to be repeated, as in English text,
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`but this performance degrades as the average length of these strings
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`decreases.” Id., 1097-1098.
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`Thus, rather than selecting one compression algorithm to use on all data blocks,
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`Hsu teaches “determin[ing] the type and compressibility of each block,” and then,
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`based on the determined type, applying an appropriate compression algorithm to
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`the block. Id., 1098-1102, 1104, 1106-1108, 1111-1112, 1115. Creating a
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`“specifically tailored program for each file” “gives improved performance over
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`using one program for all files.” Id., 1097, 1098. This approach solves the
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`inefficiencies that result from applying a single compression algorithm to all data
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`blocks, wherein the compression algorithm might run poorly on many of the data
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`blocks.
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` Hsu uses the “statistical properties of the input data” to devise a
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`“compression plan” for the file on a block-by-block basis, and the result is a
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`system that “achieves consistent average performance throughout the file . . .” Id.,
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`1098. Hsu analyzes and recognizes data type and compressibility of a received data
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`block, based on the “block type and the largest” of “three quantitative metrics.”
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`ECHOSTAR1004-Hsu, 1098-1104, 1111-1112, 1115. Specifically, Hsu uses an
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`identified block type that “describes the nature of a segment of input data” and
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`“redundancy metrics” that are “used to determine the compressibility of a block of
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`data” to select a compression algorithm fro