Case 1:17-cv-02097-RBJ Document 135-1 Filed 11/07/18 USDC Colorado Page 2 of 111
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`IN THE UNITED STATES DISTRICT COURT
`FOR THE DISTRICT OF COLORADO
`
`REALTIME ADAPTIVE STREAMING, L.L.C.
`Plaintiff,
`
`v.
`
`SLING TV L.L.C.,
`SLING MEDIA L.L.C.,
`DISH TECHNOLOGIES L.L.C.,
`DISH NETWORK L.L.C., and
`ARRIS SOLUTIONS, INC.,
`Defendants.
`
`CIVIL ACTION NO. 1:17-CV-02097-RBJ
`
`LEAD CASE
`
`EXPERT DECLARATION OF DR. ALAN BOVIK
`
`

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`TABLE OF CONTENTS
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`
`
`I.
`
`ENGAGEMENT ..................................................................................................................1
`
`II.
`
`BACKGROUND AND QUALIFICATIONS .....................................................................1
`
`III.
`
`GOVERNING LEGAL PRINCIPLES ................................................................................5
`
`IV. MATERIALS REVIEWED FOR THIS DECLARATION .................................................7
`
`V.
`
`PERSON OF ORDINARY SKILL IN THE ART ...............................................................8
`
`VI.
`
`OVERVIEW OF THE ASSERTED PATENTS..................................................................9
`
`VII. DISPUTED TERM ..............................................................................................................9
`
`A.
`
`“asymmetric compressor(s)” (’535 Pat., Cl. 12, 15, 16, 24) / “asymmetric
`data compression” (’535 Pat., Cl. 1, 10) / “asymmetric compression
`algorithm” / “compression algorithms being asymmetric” (’610 Pat., Cl. 1,
`9) / “asymmetric” (’610 Pat., Cl. 6, 16) ...................................................................9
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`2
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`I, Alan Bovik hereby declare as follows
`
`I.
`
`ENGAGEMENT
`
`1.
`
`I have been retained by counsel for Defendants DISH Network L.L.C., DISH
`
`Technologies L.L.C., Sling TV L.L.C., and Sling Media L.L.C. I understand that the Asserted
`
`Patents are U.S. Patent No. 8,867,610 (“the ’610 Patent”) and U.S. Patent No. 8,934,535 (“the
`
`’535 Patent”).
`
`2.
`
`My consulting fee is $500 per hour in this matter regardless of the task that I am
`
`working on. My compensation is in no way tied to the outcome of this litigation or on the
`
`substance of my opinions.
`
`II.
`
`BACKGROUND AND QUALIFICATIONS
`
`3.
`
`I expect to testify regarding my background, qualifications, and experience
`
`relevant to the issues in this litigation. I hold a Ph.D. in Electrical and Computer Engineering
`
`from the University of Illinois, Urbana-Champaign (awarded in 1984). I also hold a Master’s
`
`degree in Electrical and Computer Engineering from the University of Illinois, Urbana-
`
`Champaign (awarded in 1982).
`
`4.
`
`I am a tenured full Professor and I hold the Cockrell Family Regents Endowed
`
`Chair at the University of Texas at Austin. My appointments are in the Department of Electrical
`
`and Computer Engineering, the Department of Computer Sciences, and the Department of
`
`Biomedical Engineering. I am also the Director of the Laboratory for Image and Video
`
`Engineering (“LIVE”).
`
`5.
`
`My research is in the general area of digital television, digital cameras, image and
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`video processing, computational neuroscience, and modeling of biological visual perception. I
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`have published over 800 technical articles in these areas and hold seven U.S. patents. I am also
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`the author of The Handbook of Image and Video Processing, Second Edition (Elsevier Academic
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`Press, 2005); Modern Image Quality Assessment (Morgan & Claypool, 2006); The Essential
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`Guide to Image Processing (Elsevier Academic Press, 2009); and The Essential Guide to Video
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`Processing (Elsevier Academic Press, 2009); and numerous other publications.
`
`6.
`
`I have been selected to receive the 2019 IEEE Fourier Award with citation: “For
`
`seminal contributions and high-impact innovations to the theory and application of perception-
`
`based image and video processing.” This Technical Field Award and medal is one of the highest
`
`honors accorded by the 423,000-member IEEE.
`
`7.
`
`I received the 2017 Edwin H. Land Medal from the Optical Society of America in
`
`September 2017 with citation: For substantially shaping the direction and advancement of
`
`modern perceptual picture quality computation, and for energetically engaging industry to
`
`transform these ideas into global practice.
`
`8.
`
`I received a Primetime Emmy Award for Outstanding Achievement in
`
`Engineering Development, for the Academy of Television Arts and Sciences, in October 2015,
`
`for the widespread use of my video quality prediction and monitoring models and algorithms that
`
`are widely used throughout the global broadcast, cable, satellite and internet Television
`
`industries.
`
`9.
`
`Among other awards and honors, I have received the 2013 IEEE Signal
`
`Processing Society’s “Society Award,” which is the highest honor accorded by that technical
`
`society (“for fundamental contributions to digital image processing theory, technology,
`
`leadership and education”). In 2005, I received the Technical Achievement Award of the IEEE
`
`Signal Processing Society, which is the highest technical honor given by the Society, for “broad
`
`and lasting contributions to the field of digital image processing”; and in 2008 I received the
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`Education Award of the IEEE Signal Processing Society, which is the highest education honor
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`given by the Society, for “broad and lasting contributions to image processing, including popular
`
`and important image processing books, innovative on-line courseware, and for the creation of the
`
`leading research and educational journal and conference in the image processing field.”
`
`10. My technical articles have been widely recognized as well, including the 2009
`
`IEEE Signal Processing Society Best Journal Paper Award for the paper “Image quality
`
`assessment: From error visibility to structural similarity,” published in IEEE Transactions on
`
`Image Processing, volume 13, number 4, April 2004; this same paper received the 2017 IEEE
`
`Signal Processing Society Sustained Impact Paper Award as the most impactful paper published
`
`over a period of at least ten years; the 2013 Best Magazine Paper Award for the paper “Mean
`
`squared error: Love it or leave it?? A new look at signal fidelity measures,” published in IEEE
`
`Transactions on Image Processing, volume 26, number 1, January 2009; the IEEE Circuits and
`
`Systems Society Best Journal Paper Prize for the paper “Video quality assessment by reduced
`
`reference spatio-temporal entropic differencing,” published in the IEEE Transactions on Circuits
`
`and Systems for Video Technology, vol. 23, no. 4, pp. 684-694, April 2013; the 2017 IEEE
`
`Signal Processing Letters Best Paper Award for the paper A. Mittal, R. Soundararajan and A.C.
`
`Bovik, “Making a ‘completely blind’ image quality analyzer,” published in the IEEE Signal
`
`Processing Letters, vol. 21, no. 3, pp. 209-212, March 2013. This paper describes a unique
`
`“blind” (no-reference) video quality tool called NIQE that is being used to control the quality of
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`cloud-based streaming videos globally. Also, the 2018 EURASIP Best Paper Award of the
`
`European Association for Signal Processing for 2018, for the paper “Full-Reference Quality
`
`Assessment of Stereopairs Accounting for Rivalry,” Signal Processing: Image Communication,
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`vol. 28, no. 10, pp. 1143-1155, October 2013, and the Best Paper Award of the 2018 Picture
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`Coding Symposium for the paper, “Detecting Source Video Artifacts with Supervised Sparse
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`Filters.”
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`11.
`
`I received the Google Scholar Classic Paper citation twice in 2017, for the paper
`
`“Image information and visual quality,” published in the IEEE Transactions on Image
`
`Processing, vol. 15, no. 2, pp. 430-444, February 2006 (the main algorithm developed in the
`
`paper, called the Visual Information Fidelity (VIF) Index, is a core picture quality prediction
`
`engine used to quality-assess all encodes streamed globally by Netflix), and for “An evaluation
`
`of recent full reference image quality assessment algorithms,” published in the IEEE
`
`Transactions on Image Processing, vol. 15, no. 11, pp. 3440-3451, November 2006 (the picture
`
`quality database and human study described in the paper, the LIVE Image Quality Database, has
`
`been the standard development tool for picture quality research since its first introduction in
`
`2003). Google Scholar Classic Papers are very highly-cited papers that have stood the test of
`
`time, and are among the ten most-cited articles in their area of research over the ten years since
`
`their publication.
`
`12.
`
`I have also been honored by other technical organizations, including the Society
`
`for Photo-optical and Instrumentation Engineers (SPIE), from which I received the Technology
`
`Achievement Award (2013) “For Broad and Lasting Contributions to the Field of Perception-
`
`Based Image Processing,” and the Society for Imaging Science and Technology, which accorded
`
`me Honorary Membership, which is the highest recognition by that Society given to a single
`
`individual, “for his impact in shaping the direction and advancement of the field of perceptual
`
`image processing.” I was also elected as a Fellow of the Institute of Electrical and Electronics
`
`Engineers (IEEE) “for contributions to nonlinear image processing” in 1995, a Fellow of the
`
`Optical Society of America (OSA) for “fundamental research contributions to and technical
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`leadership in digital image and video processing” in 2006, and as a Fellow of SPIE for
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`“pioneering technical, leadership, and educational contributions to the field of image processing”
`
`in 2007.
`
`13.
`
`Among other relevant research, I have worked with the National Aeronautics and
`
`Space Administration (“NASA”) to develop high compression image sequence coding and
`
`animated vision technology, on various military projects for the Air Force Office of Scientific
`
`Research, Phillips Air Force Base, the Army Research Office, and the Department of Defense.
`
`These projects have focused on developing local spatio-temporal analysis in vision systems,
`
`scalable processing of multi-sensor and multi-spectral imagery, image processing and data
`
`compression tools for satellite imaging, AM-FM analysis of images and video, the scientific
`
`foundations of image representation and analysis, computer vision systems for automatic target
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`recognition and automatic recognition of human activities, vehicle structure recovery from a
`
`moving air platform, passive optical modeling, and detection of speculated masses and
`
`architectural distortions in digitized mammograms. My research has also recently been funded
`
`by Netflix, Qualcomm, Facebook, Texas Instruments, Intel, Cisco, and the National Institute of
`
`Standards and Technology (NIST) for research on image and video quality assessment. I have
`
`also received numerous grants from the National Science Foundation for research on image and
`
`video processing and on computational vision.
`
`14.
`
`Additional details about my employment history, fields of expertise, and
`
`publications are further described in my curriculum vitae, which is attached as Exhibit A to this
`
`declaration.
`
`III. GOVERNING LEGAL PRINCIPLES
`
`15.
`
`I have been informed by counsel that claim construction begins with a focus on
`
`the words of the claims themselves, as they would have been understood by a person of ordinary
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`skill in the art. I have also been informed by counsel that, absent some reason to the contrary,
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`claim terms are typically given their ordinary and accustomed meaning as would be understood
`
`by a person of ordinary skill in the art. I have further been informed by counsel that intrinsic
`
`evidence of a patent, including the claim language, the specification, and the prosecution history,
`
`may be used for guidance in claim construction.
`
`16.
`
`I have also been informed by counsel that the Court may also look to extrinsic
`
`evidence for a variety of purposes, including for providing background on the technology at
`
`issue, explaining how an invention works, ensuring that the Court’s understanding of the
`
`technical aspects of the patent is consistent with that of a person skill in the art, or establishing
`
`that a particular term in the patent or the prior art has a particular meaning in the pertinent field. I
`
`have been informed by counsel that some examples of extrinsic evidence are things such as
`
`inventor testimony, dictionaries, treatises, and expert opinions/testimony.
`
`17.
`
`I have been informed that many factors should be considered to determine the
`
`skill level of a person of ordinary skill in the art for each patent, such as (1) the types of
`
`problems encountered in the art, the solutions to those problems, (2) the sophistication of the
`
`technology in question and the pace of the innovation in the field, and (3) the education level of
`
`active workers in the field, and (4) the educational level of the inventor.
`
`18.
`
`These legal principles have provided me with the framework for my analysis, and,
`
`where applicable, I have relied upon and followed these principles in my analysis.
`
`19.
`
`I understand that 35 U.S.C. § 112, ¶ 2 requires that the “claims particularly point[]
`
`out and distinctly claim[] the subject matter which the applicant regards as his invention.” When
`
`a claim or claim term does not meet this requirement, it is indefinite. I further understand that a
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`term is indefinite if, read in light of the patent’s specification and prosecution history, it fails to
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`inform, with reasonable certainty, those skilled in the art about the scope of the invention. I
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`further understand that indefiniteness is to be measured as of the time of filing of the patent
`
`application.
`
`20.
`
`It is also my understanding that terms of degree are not automatically indefinite.
`
`However, while absolute or mathematical precision is not required, it is not enough to identify
`
`some standard for measuring the scope of the phrase.
`
`21.
`
`I understand that a patent does not satisfy the definiteness requirement of § 112
`
`merely because a court can ascribe some meaning to a patent’s claims. Instead, the claims, when
`
`read in light of the specification and the prosecution history, must provide objective boundaries
`
`for those of skill in the art.
`
`22.
`
`It is also my understanding that the fact that the patent holder can articulate a
`
`definition supported by the specification does not end the inquiry. Even if a claim term’s
`
`definition can be reduced to words, the claim is still indefinite if a person of ordinary skill in the
`
`art cannot translate the definition into meaningfully precise claim scope.
`
`23.
`
`It is also my understanding that a purely subjective claim is indefinite if sufficient
`
`guidance is lacking in the written description of the patents-in-suit such that a person of ordinary
`
`skill in the art cannot understand the scope of the claim with reasonable certainty.
`
`IV. MATERIALS REVIEWED FOR THIS DECLARATION
`
`
`24.
`
`In forming my opinions, I have reviewed the Asserted Patents, their file histories,
`
`and considered each of the documents cited in my declaration. I have also reviewed Dr. Zeger’s
`
`declaration and respond to certain of Dr. Zeger’s characterizations regarding the relevant
`
`technology. In reaching my opinions, I have relied upon my experience in the field and also
`
`considered the viewpoint of a person of ordinary skill in the art for each patent at the time of the
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`earliest claimed priority date of each of the Asserted Patents.1 As explained below, I am familiar
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`with the skill level of a person of ordinary skill in the art regarding the technology at issue as of
`
`that time.
`
`V.
`
`PERSON OF ORDINARY SKILL IN THE ART
`
`25.
`
`According to the “Description of the Related Art” in the Asserted Patents, a
`
`problem in the art was selecting the optimal data compression algorithm from the variety of
`
`available algorithms given the requirements of the particular circumstances at issue. ’535 Patent
`
`at 1:30-55. The Asserted Patents did not purport to provide any new compression methods.
`
`Instead, they focused on a system for selection of “suitable compression algorithm based on the
`
`data type” of the data to be compressed. Id. at 11:30-40.
`
`26.
`
`The technology in the Asserted Patents relates to the fields of data compression,
`
`processing, transmission, and storage. A person of ordinary skill in the art would need education
`
`and work experience in these areas.
`
`27.
`
`Based on these considerations, as well as my experience in this area, it is my
`
`opinion that a person of ordinary skill in the art would have at least a bachelor’s degree in
`
`electrical engineering, computer engineering, computer science, or the equivalent and two to
`
`three years of work experience in the fields of technology in the Asserted Patents. My definition
`
`of a person of ordinary skill in the art is identical across the Asserted Patents because they stem
`
`from a shared common specification.
`
`
`1 I provide no opinion in this Declaration regarding whether the alleged priority date is correct
`for the Asserted Patents; however, I reserve the right to address the alleged priority date of the
`Asserted Patents.
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`8
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`28.
`
`I have a good understanding of the person of ordinary skill in the art based on my
`
`knowledge and experience, and I possess the capabilities of a person of ordinary skill in the art
`
`myself.
`
`VI. OVERVIEW OF THE ASSERTED PATENTS
`
`29.
`
`The Asserted Patents generally concern the effectiveness of data compression
`
`algorithms under varying sets of conditions. The Asserted Patents disclose a system that can
`
`“select a suitable compression algorithm that provides a desired balance between execution
`
`speed (rate of compression) and efficiency (compression ratio).” Id. at 8:8-13. According to the
`
`Asserted Patents, the selected compression algorithms can include symmetrical and
`
`asymmetrical compression algorithms. E.g., id. at 9:60-10:9. According to the Asserted Patents,
`
`“an asymmetrical algorithm typically achieves higher compression ratios than a symmetrical
`
`algorithm” but “the total execution time to perform one compress and one decompress of a data
`
`set is typically greater” than that for symmetrical algorithms. Id. at 10:10-14. The Asserted
`
`Patents discuss balancing these competing factors in selecting from among symmetrical and
`
`asymmetrical compression algorithms. E.g., id. at 9:53-10:30.
`
`30.
`
`In addition, the Asserted Patents disclose that even when utilizing the same
`
`algorithm, the parameters of that algorithm can be varied to affect performance. Id. at 1:30-46.
`
`For example, the Asserted Patents disclose dictionary-based coding (e.g., Lempel Ziv), Huffman
`
`coding, table-based coding, run-length coding, and arithmetic coding. Each of the types of
`
`algorithms have parameters that can be varied (including dynamically varied) such as dictionary
`
`size, alphabet size, table size, codeword length, etc.
`
`VII. DISPUTED TERM
`
`“asymmetric compressor(s)” (’535 Pat., Cl. 12, 15, 16, 24) / “asymmetric data
`A.
`compression” (’535 Pat., Cl. 1, 10) / “asymmetric compression algorithm” /
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`“compression algorithms being asymmetric” (’610 Pat., Cl. 1, 9) / “asymmetric”
`(’610 Pat., Cl. 6, 16)
`
`31.
`
`Persons of ordinary skill in the art at the time of the claimed priority date
`
`generally discussed the “symmetry” or “asymmetry” of a compression algorithm in terms of the
`
`relative computational effort that it required for compression and decompression. For example,
`
`there may be a disparity in complexity between compression and decompression. Usually, the
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`compression process for an asymmetrical compression algorithm is more complex than the
`
`decompression process. As a result, the compression process would usually require more
`
`computation effort. The computational effort or complexity of a compression algorithm is a
`
`direct reflection of the number or complexity of steps that must be completed as part of the
`
`algorithm.
`
`32.
`
`Although persons of ordinary skill in the art generally discussed “symmetry” or
`
`“asymmetry” of a compression algorithm in terms of its computational effort, the applicant of the
`
`Asserted Patents chose to expressly define the “symmetry” or “asymmetry” of an algorithm in
`
`terms of run-time. The patent defined an asymmetrical compression algorithm as “one in which
`
`the execution time for the compression and decompression routines differ significantly.” ’535
`
`Pat. at 9:63-66. I agree with the positions recently taken by Dr. Zeger and Realtime in this
`
`litigation that the patents provide an express definition of this term.
`
`33. While computational effort or complexity of a compression algorithm may be an
`
`inherent quality of the algorithm, run-time is dependent on several factors associated with the
`
`actual systems—such as processor hardware memory and peripheral sets—that are associated
`
`with performing the compression and decompression. A person of ordinary skill in the art would
`
`understand that run-time is affected by a variety of factors including the processing power,
`
`architecture (for example, whether the processor is an RISC/CISC/FPGA, SISD, SIMD, etc.),
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`parallelism (whether the processor is distributed, pipelined, etc.) and degree of application-
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`specific dedication of the hardware used to implement the compression algorithm. In fact, the
`
`Asserted Patents recognize that the run-time of an algorithm is affected by the operating
`
`hardware used to implement the algorithm. For example, in the Asserted Patents’ discussion of
`
`the algorithmic efficiency of a given single algorithm, the Asserted Patents recognize that run-
`
`time is highly dependent on “the processing effort applied.”
`
`For a given single algorithm the effectiveness over a broad class of data sets
`including text, graphics, databases, and executable object code is highly
`dependent upon the processing effort applied. Given a baseline data set,
`processor operating speed and target architecture, along with its associated
`supporting memory and peripheral set, we define algorithmic efficiency as the
`time required to achieve a given compression ratio
`
`’535 Pat. at 5:11-21 (emphasis added).
`
`
`
`34.
`
`As noted above, the Asserted Patents also explicitly recognize a variety of factors
`
`that can affect run-time. Id. In order, to determine run-time for purposes of calculating
`
`algorithmic efficiency, the Asserted Patents disclose the creation of a baseline data set, which (as
`
`its name indicates) provides a baseline of factors that affect run-time. Id. Aside from the
`
`complexity of the algorithm, the baseline data set holds constant all of the variables that could
`
`affect the time required to achieve a given compression ratio such that algorithmic efficiency can
`
`be measured. By eliminating the influence of “processor operating speed,” “target architecture,”
`
`and “associated supporting memory and peripheral set,” the Asserted Patents recognize that each
`
`of these considerations affect run-time. These disclosures in the Asserted Patents correspond to
`
`the common understanding of person of ordinary skill in the art.
`
`35.
`
`In addition, claim 14 of the ’535 Patent requires that the time to compress and
`
`decompress be measured on a “common host system.” This limitation confirms that the
`
`execution time of an algorithm is dependent on the operating hardware. The claim eliminates
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`variations in processing power that may exist on different systems by requiring the run-time to
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`be measured on a common host system, i.e., a single system utilizing the same processing power
`
`for compression and decompression. Unlike claim 14 and the discussion of run-time in the
`
`context of algorithmic efficiency, the definition of an “asymmetric compression algorithm” does
`
`not require the creation of a baseline that holds variables, such as processing speed, constant. As
`
`a result, the execution time can vary with different hardware. Given all of the variables that may
`
`affect execution time discussed above, this variation can be significant.
`
`36.
`
`The fact that the run-time of a given algorithm can vary with the hardware is
`
`evidenced by clear, real world examples at the time of the claimed priority date. In real-time live
`
`broadcast video systems, for example, the compression process must be conducted at frame rate
`
`in order to ensure the video feed is transmitted in real-time. Frame rate refers to how frequently a
`
`frame of video is displayed. The compression rate must keep up with the frame rate in order to
`
`eliminate unsuitable delays in video delivery. Consequently, in such a real-time streaming
`
`situation, the goal from the distribution side is to make compression and decompression times as
`
`similar as possible. Because compression is nearly always more complex, more powerful or
`
`specific processing hardware is required to achieve runtime parity. Of course, the similarity of
`
`compressing and decompressing times are dependent on the capabilities of the client device that
`
`performs the decompression. Conversely, in video-on-demand (VOD) applications, the videos
`
`may be pre-compressed at a much slower run-time, and stored until it is requested to be
`
`streamed. This is because the VOD application does not need to keep up with a live feed. As a
`
`result, the processing power and existence of application specific hardware and memory is less
`
`critical.
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`37.
`
`Because the patent defines “asymmetry” in terms of run-time and the hardware
`
`capabilities of the compression device and decompression device respectively have a significant
`
`effect on compression run-time and decompression run-time, a person of ordinary skill in the art
`
`would need to know the capabilities of the compression and decompression device in cases
`
`where compression and decompression are performed on separate devices with different or
`
`unknown properties. It would be impossible for a person of ordinary skill in the art to evaluate a
`
`compression device to know whether the utilized algorithm was “symmetric” or “asymmetric”
`
`according to the patentee’s express definition without knowing the capabilities of the
`
`corresponding client decompression device. This is because the determination of “asymmetry”
`
`would depend on the run-time of the compression algorithm on its device (for example, a server),
`
`versus the runtime of the decompression algorithm on its device (for example, a client device).
`
`38.
`
`In addition, the Asserted Patents define an “asymmetric” algorithm as “one in
`
`which the execution time for the compression and decompression routines differ significantly.”
`
`’535 Pat. at 9:63-66. The Asserted Patents, however, do not contain any criteria by which a
`
`person of skill in the art could evaluate whether differences are “significant” as opposed to
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`insignificant.
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`39.
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`Based on these preceding considerations, it is my opinion that, a person of
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`ordinary skill, in view of the Asserted Patent’s express definition, would not be readily able to
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`determine whether a given algorithm is asymmetric based on how that term is defined and used
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`in the claimed invention.
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`I declare under penalty of perjury under the laws of the United States, Texas, and Colorado
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`that the information and opinions in this declaration are true and correct to the best of my
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`knowledge, and that I would testify to the same under oath in court.
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`13
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`Case 1:17-cv-02097-RBJ Document 135-1 Filed 11/07/18 USDC Colorado Page 17 of 111
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`Executed November 7, 2018.
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`___________________
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`Alan Bovik
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`14
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