UNITED STATES PATENT AND TRADEMARK OFFICE
`
`––––––––––––––
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`
`––––––––––––––
`
`Bank of America, N.A.,
`Petitioner
`
`v.
`
`Nant Holdings IP, LLC,
`Patent Owner
`––––––––––––––
`
`Case No. IPR2021-01080
`U.S. Patent No. 8.463,030
`
`––––––––––––––
`
`DECLARATION OF PROFESSOR CHANDRAJIT BAJAJ, PHD IN
`SUPPORT OF PATENT OWNER’S PRELIMINARY RESPONSE TO
`PETITION FOR INTER PARTES REVIEW OF U.S. PATENT NO. 8,463,030
`
`Mail Stop: Patent Board
`Patent Trial and Appeal Board
`United States Patent and Trademark Office
`P.O. Box 1450
`Alexandria, VA 22313-1450
`
`1
`
`Patent Owner’s Ex. 2002, Page 1 of 18
`
`
`
`––––––––––––––
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`
`––––––––––––––
`
`Bank of America, N.A.,
`Petitioner
`
`v.
`
`Nant Holdings IP, LLC,
`Patent Owner
`––––––––––––––
`
`Case No. IPR2021-01080
`U.S. Patent No. 8.463,030
`
`––––––––––––––
`
`DECLARATION OF PROFESSOR CHANDRAJIT BAJAJ, PHD IN
`SUPPORT OF PATENT OWNER’S PRELIMINARY RESPONSE TO
`PETITION FOR INTER PARTES REVIEW OF U.S. PATENT NO. 8,463,030
`
`Mail Stop: Patent Board
`Patent Trial and Appeal Board
`United States Patent and Trademark Office
`P.O. Box 1450
`Alexandria, VA 22313-1450
`
`1
`
`Patent Owner’s Ex. 2002, Page 1 of 18
`
`
`
`
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`I, Chandrajit Bajaj, declare as follows:
`
`I.
`
`QUALIFICATIONS AND BACKGROUND
`
`1.
`
`I am currently employed as a Professor of Computer Science at the
`
`University of Texas at Austin (“UT Austin”). I hold the Computational Applied
`
`Mathematics endowed Chair in Visualization. I am also the Director of the
`
`Computational Visualization Center at UT Austin, which has been funded by the
`
`National Institutes of Health, the National Science Foundation, the Department of
`
`Energy, and the Department of Defense. The center’s personnel include twelve
`
`researchers, scientists, post-graduate students, and staff.
`
`2. My curriculum vitae (“CV”), a copy of which is included as Exhibit
`
`2003 hereto, provides details on my education, experience, publications, and other
`
`qualifications. It includes a list of all publications I have authored in the previous
`
`35 years.
`
`3.
`
`I have a Bachelor of Technology degree in Electrical Engineering,
`
`which I obtained from the Indian Institute of Technology in Delhi (IITD) in 1980.
`
`I also have a Master of Science degree and a doctorate in Computer Science from
`
`Cornell University in 1983 and 1984, respectively.
`
`4.
`
`Prior to my employment at the University of Texas (UT), I was an
`
`assistant professor, associate professor, and finally professor of Computer Sciences
`
`at Purdue University (Purdue) from 1984 until I resigned in 1997 and transferred to
`
`
`
`1
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`Patent Owner’s Ex. 2002, Page 2 of 18
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`
`
`
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`UT. During this time, I was also the Director of the Image Analysis and
`
`Visualization Center at Purdue University. I was a visiting associate professor of
`
`Computer Science at Cornell University from 1990-1991. I have also been invited
`
`for collaborative visits by several academic institutions and have presented
`
`numerous keynote presentations worldwide. I have been an editorial member of
`
`the SIAM Journal on Imaging Sciences and the ACM Transactions on Graphics,
`
`and I continue my editorial role for ACM Computing Surveys and the International
`
`Journal of Computational Geometry and Applications.
`
`5.
`
`I have spent the better part of my career, both at Purdue and UT Austin,
`
`researching, designing, teaching, and using computer systems to model, simulate,
`
`search, and visualize natural and synthetic objects, combining computational image
`
`and geometry processing. I am knowledgeable about and have much experience in
`
`both the hardware and software, including algorithms, used for capturing,
`
`analyzing, and displaying imagery in real-time permitting user interaction.
`
`6.
`
`In the 1970s, I majored in Electrical Engineering at the Indian Institute
`
`of Technology, with a minor in Computer Sciences. There, I was intimately
`
`involved in the design and fabrication of microprocessor-controlled circuits
`
`including the development of microprocessor controller software. In the 1980s,
`
`while at Cornell University, these past experiences from my time at Indiana
`
`Institute of Technology led to research in computational geometry, processing, and
`
`
`
`2
`
`Patent Owner’s Ex. 2002, Page 3 of 18
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`
`
`
`
`optimization. In the early 1990s, I created 3D collaborative multimedia software
`
`environments which were fully searchable, navigable for multi-person computer
`
`gaming and simulation. In 1994, I co-authored a technical paper entitled “Shastra:
`
`Multimedia Collaborative Design Environment,” Vinod Anupam and Chandrajit L.
`
`Bajaj, IEEE Multimedia 1.2 (1994) 39, 39–49.
`
`7.
`
`The increasing need for real-time computer graphics display realism
`
`without sacrificing interactivity led me also to explore fast and efficient image
`
`processing techniques such as feature selection, segmentation, detection, texture
`
`mapping with data compression, such as described in my publications “Image
`
`Segmentation Using Gradient Vector Diffusion and Region Merging”, “Detecting
`
`Circular and Rectangular Particles Based on Geometric Feature Detection in
`
`Electron Micrographs”, “Compression-Based 3D Texture Mapping for Real-Time
`
`Rendering,” and “3D RGB Image Compression for Interactive Applications.”
`
`During this time, I was also intimately involved with the development of a new
`
`synthetic-natural hybrid data compression MPEG (Motion Pictures Expert Group)
`
`standard. Additionally, I applied and received a joint patent “Encoding Images of
`
`3-D Objects with Improved Rendering Time and Transmission Process,” August
`
`2002, US Patent 6,438,266.
`
`8. My work with encoding, transmission and reconstructing 3-D objects
`
`led me to explore image processing and geometric modeling techniques such as
`
`
`
`3
`
`Patent Owner’s Ex. 2002, Page 4 of 18
`
`
`
`
`
`surface reconstruction from CT scans, point clouds, segmentation, and texture
`
`mapping with data compression, such as those described in my publications:
`
`“Multi-Component Heart Reconstruction from Volumetric Imaging,” and
`
`“Automatic Reconstruction of Surfaces and Scalar Fields from 3D Scans”.
`
`9.
`
`In the mid-2000s, I began to create spatially-realistic 3D graphical
`
`environments of natural molecules and cells with a combination of different types
`
`of acquired and reconstructed imagery within which a user may explore, query,
`
`and learn. My publication titled “From Voxel Maps to Models,” that appeared in
`
`an Oxford University Press book titled Imaging Life: Biological Systems from
`
`Atoms to Tissues, c. 15 (Gary C. Howard, William E. Brown & Manfred Auer eds.)
`
`(2014), is an example of my research in computational imaging.
`
`10. Over the course of my career, I have participated in the design and use
`
`of several computer systems, spanning handhelds,
`
`laptops, and graphics
`
`workstations to PC/Linux clusters, as well as very large memory supercomputers
`
`for capturing, modeling, processing and displaying acquire and simulated data of
`
`diverse scientific phenomena. My experience with computer modeling, imaging,
`
`computer graphics, and scientific visualization encompasses the use of interactive
`
`technologies, such as 3D pointers and mice, smart IR touch-screens, motion
`
`detection and gesture-based user interfaces, and multi-display walls, in many
`
`different fields and industrial settings, such as interactive games, medicine (e.g.,
`
`
`
`4
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`Patent Owner’s Ex. 2002, Page 5 of 18
`
`
`
`
`
`molecular, biomedical, and industrial diagnostics), oil and gas exploration, geology,
`
`cosmology, and military industries.
`
`11. During this time at UT Austin, I also designed and implemented
`
`scalable solutions for inverse problems in microscopy, spectroscopy, biomedical
`
`imaging, constructing spatially realistic and hierarchical 3D models, development
`
`of search/scoring engines for predicting energetically favorable multi-molecular
`
`and cellular complexes, and statistical analysis and interrogative visualization of
`
`neuronal form-function.
`
`12.
`
`I have courtesy appointments and supervise M.S. and Ph.D. students
`
`from several UT Austin departments, including biomedical and electrical
`
`engineering, neurobiology, and mathematics. I currently serve on the editorial
`
`boards for the International Journal of Computational Geometry and Applications
`
`and the ACM Computing Surveys. Much of my work involves issues relating to
`
`interactive image processing, feature extraction, 3D modeling, bio-informatics,
`
`computer graphics, and computational visualization. Examples of my publications,
`
`including peer-reviewed publications, are listed in my CV.
`
`13.
`
`I currently serve on the editorial boards for the International Journal of
`
`Computational Geometry and Applications and the ACM Computing Surveys.
`
`Much of my work involves issues relating to interactive image processing, 3D
`
`modeling, bio-informatics, computer graphics, and computational visualization.
`
`
`
`5
`
`Patent Owner’s Ex. 2002, Page 6 of 18
`
`
`
`
`
`Examples of my publications, including peer-reviewed publications, are listed in
`
`my curriculum vitae.
`
`14. As set forth in my CV, I have authored approximately 167 peer-
`
`reviewed journal articles, 34 book chapters (which were also peer reviewed), and
`
`154 peer-reviewed conference publications.
`
`15.
`
`I have written and edited four books on topics ranging from image
`
`processing, geometric modeling, and visualization techniques to algebraic
`
`geometry and its applications. I have given 165 invited speaker keynote
`
`presentations. I am a Fellow of the American Association for the Advancement of
`
`Science, a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a
`
`Fellow of the Society of Industrial and Applied Mathematics (SIAM), and also a
`
`Fellow of the Association of Computing Machinery (also known as ACM), which
`
`is the world’s largest education and scientific computing society. The ACM
`
`Fellow is ACM’s most prestigious member grade and recognizes the top 1% of
`
`ACM members for their outstanding accomplishments in computing and
`
`information technology and/or outstanding service to ACM and the larger
`
`computing community.
`
`II.
`
`SCOPE OF WORK
`
`16.
`
`I was asked by counsel for Patent Owner Nant Holdings IP, LLC
`
`(“Nant”) to review U.S. Patent No. 8,463,030 (the “’030 patent”) (Ex. 2001). I
`
`
`
`6
`
`Patent Owner’s Ex. 2002, Page 7 of 18
`
`
`
`
`
`receive $700 per hour for my services. No part of my compensation is dependent
`
`on my opinions or on the outcome of this proceeding.
`
`17.
`
`I also reviewed the present Petition for Inter Parties Review of U.S.
`
`Patent No. 8,463,030 (IPR2021-01080) and the exhibits and declarations
`
`associated therewith, including U.S. Patent No. 6,512,919 to Nobuo Ogasawara
`
`(“Ogasawara”) (Ex. 2004).
`
`18.
`
`I was asked to provide my understanding of whether Ogasawara
`
`discloses the limitations of claim 1 of the ’030 patent, and in particular, the
`
`requirement of “an object identification platform configured to obtain the acquired
`
`data, recognize the object as a target object based on the acquired data, and
`
`determine object information associated with the target object.” In my opinion, it
`
`does not.
`
`19. Ogasawara contains a passing reference to “[a]dvanced pattern
`
`recognition software” which may purportedly be used
`
`to “enhance
`
`the
`
`performance” of a “wireless videophone” and provide “the capability to capture
`
`merchandise information from items that are not identified by either a bar code or
`
`an alpha-numeric label.” Ogasawara at col. 23:12-31.
`
`20.
`
`In these above few lines on the last column before the claims,
`
`Ogasawara describes using such software to “allow[] a consumer to capture a
`
`ideographic image of an apple, for example, and to have the apple by recognized,”
`
`
`
`7
`
`Patent Owner’s Ex. 2002, Page 8 of 18
`
`
`
`
`
`and also notes this “capability is useful for any merchandise item having a distinct
`
`or identifiable shape or other visually identifiable characteristic.” Id. 23:16-22.
`
`Ogasawara does not provide any explanation whatsoever as to what this “advanced
`
`pattern recognition software” is or how it would operate, the algorithms it would
`
`use to recognize pattern, or any other details regarding how it could recognize a
`
`pattern.
`
`III. LEGAL UNDERSTANDING OF ANTICIPATION
`
`21.
`
`I understand that in order to anticipate a claimed invention, a prior art
`
`reference must disclose all elements of the claim within the four corners of the
`
`documents and must disclosed those elements in the same arrangement as the
`
`purportedly anticipated claim. Microsoft Corp. v. Biscotti Inc., 878 F.3d 1052,
`
`1068 (Fed. Cir. 2017). I also understand that, under Federal Circuit precedent, a
`
`prior art reference cannot anticipate a claimed invention unless the allegedly
`
`anticipatory disclosure cited as prior art is enabled by the reference. In re Antor
`
`Media Corp., 689 F.3d 1282, 1288 (Fed. Cir. 2012).
`
`IV. OVERVIEW OF THE ’030 PATENT
`
`22. The ’030 patent discloses “technology and processes that can
`
`accommodate linking objects and images to information via a network such as the
`
`Internet” based on imaging performed by a mobile device and in a manner that
`
`“requires no modification to the linked object.” Ex. 2001 (’030 patent) 3:24-27.
`
`
`
`8
`
`Patent Owner’s Ex. 2002, Page 9 of 18
`
`
`
`
`
`The image recognition algorithms disclosed in the ’030 patent allow for “fast and
`
`reliable detection and recognition of images and/or objects based on their visual
`
`appearance in an image, no matter whether shadows, reflections, partial
`
`obscuration, and variations in viewing geometry are present.” Id. 3:42-50. The
`
`specification of the ’030 patent identifies specific algorithms capable of
`
`performing the object image recognition steps of the invention using, among other
`
`things, steps involving the processes of segmentation, decomposition, and
`
`comparison. See, e.g., id. 6:3-12:23.
`
`23. The ’030 patent explains
`
`that
`
`the
`
`invention provides for “a
`
`‘decomposition’, in the Input Image Decomposition 34, of a high-resolution input
`
`image into several different types of quantifiable salient parameters” which
`
`“allows for multiple independent convergent search processes of the database to
`
`occur in parallel” and leads to improved “match robustness.” Id. 6:3-14. The ’030
`
`patent provides that this “Input Image Decomposition process” may consists of the
`
`following individual steps:
`
`
`
`9
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`Patent Owner’s Ex. 2002, Page 10 of 18
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`
`
`
`
`
`
`Id. 6:19-28. Each of these steps is described in detail in the body of the ’030
`
`patent’s specification. See id. 6:30-41 (Radiometric Correction), 6:42-49
`
`(Segmentation), 50-64 (Segment Group Generation), 6:65-7:4 (Bounding Box
`
`Generation), 7:5-12 (Geometric Normalization), 7:13-30 (Wavelet
`
`Decomposition), 7:31-55 (Color Cube Decomposition), 7:56-62 (Shape
`
`Decomposition), 7:63-8:5 (Low-Resolution Grayscale Image Generation).
`
`24. The ’030 patent then describes a process for comparing the outputs of
`
`each of these segmentation and decomposition steps to a database to produce “a
`
`match score” for that value, culminating in the calculation of a “combined match
`
`score” for a given object:
`
`
`
`10
`
`Patent Owner’s Ex. 2002, Page 11 of 18
`
`
`
`
`
`
`
`25. The ’030 patent describes how each of these parameter comparisons
`
`may be performed. See id. 8:28-53 (comparison of Each Input Image Segment
`
`Group), (comparison for Each Database Object), 8:57-61 (comparison for Each
`
`View of this Object), 8:62-67 (comparison for Each Segment Group in this View
`
`of this Database Object), 9:1-45 (Shape Comparison), 9:46-10:7 (Grayscale
`
`Comparison), 10:8-38 (Wavelet Comparison), 10:39-64 (Color Cube Comparison).
`
`Ultimately, this comparison process results in “a normalized matching score” for
`
`each object image comparison that represent “independent assessments of the
`
`match of salient features of the input image to database images.” Id. 10:65-11:3.
`
`The specification also explains that, “[t]o minimize the effect of uncertainties in
`
`any single comparison process,” each of these independent assessments may be
`
`processed through a “root sum of squares relationship” (depicted below) in order to
`
`produce “a combined match score for an image.” Id. 11:3-14.
`
`
`
`11
`
`Patent Owner’s Ex. 2002, Page 12 of 18
`
`
`
`
`
`
`
`26. The ’030 patent also makes clear that the results of this image analysis
`
`process need not result in perfect one-to-one matches with image information
`
`already contained in the comparison database. Id. 11:35-55. Indeed, the
`
`specification explains that as an initial step, a feature parameter “carrying greatest
`
`weight from the input image” may be “compared first to find statistical matches
`
`and near-matches in all database records.” Id. 11:41-43. This results in a
`
`“normalized interim score (e.g., scaled value from zero to one, where one is perfect
`
`match and zero is not match).” Id. 11:43-45. These variable match scores are then
`
`compiled and assessed as part of a final “Combined Match Score evaluation,”
`
`which can be used to recognize an object in the image. Id. 11:64-67.
`
`27. Once objects in
`
`the
`
`image have been
`
`identified using
`
`these
`
`comprehensive search, detection and recognition algorithmic approaches, the ’030
`
`patent further discloses facilitating a transaction related to the object through a
`
`mobile device, by retrieving and delivering information related to the object to a
`
`user via a network connection. Id. 3:60-4:5. For example, the patent explains that
`
`once the “image is analyzed and the object or image of interest is detected and
`
`recognized,” the “network address of information corresponding to that object is
`
`
`
`12
`
`Patent Owner’s Ex. 2002, Page 13 of 18
`
`
`
`
`
`transmitted” back to the mobile device, “allowing the mobile device to access
`
`information using the network address.” Id. 3:67-4:5.
`
`V. OVERVIEW OF OGASAWARA
`
`28. Ogasawara discloses “an electronic shopping system” which
`
`“facilitates purchase transaction via a wireless videophone.” Ex. 2004 at Abstract.
`
`In one embodiment, Ogasawara teaches a “store server 10 in communication with a
`
`commercial telephone network 14,” a “wireless telephone 18,” an “external” or
`
`“built-in” bar code scanner, and a “catalog 21 of the items which can be
`
`purchased” that “contains a bar code 22 for each such item.” Id. 4:66-5:48. The
`
`patent describes how a user can dial a store’s telephone number upon arrival to
`
`automatically download a “personal shopping application” that will allow for the
`
`scanning of product bar codes and permit the telephone to “facilitate[] purchase
`
`transactions.” Id. 6:5-57.
`
`
`
`13
`
`Patent Owner’s Ex. 2002, Page 14 of 18
`
`
`
`
`
`
`
`29.
`
`In a separate embodiment, Ogasawara replaces this wireless telephone
`
`with a “wireless videophone” with “a digital camera 236 in place of a bar code
`
`scanner.” Id. 18:11-22. In the case of this “videophone” embodiment, Ogasawara
`
`explains that, “the tailored purchase transaction program might additionally
`
`include character recognition and/or pattern recognition, as well as bar code
`
`decode, software.” Id. 18:15-19. In the absence of a discrete bar code reader,
`
`Ogasawara explains this software “would allow the wireless videophone to
`
`function in a manner similar to the wireless telephone and bar code scanner
`
`embodiment” described elsewhere. Id. 18:19-22. Specifically, it could be used “to
`
`
`
`14
`
`Patent Owner’s Ex. 2002, Page 15 of 18
`
`
`
`
`
`either decode the bar code videographic data or to perform pattern recognition
`
`functions on a [sic] icon-like pattern captured by the digital video camera.” Id.
`
`18:34-37.
`
`30. Finally, on the final page of its specification, Ogasawara provides a
`
`single paragraph suggesting that its “wireless videophone” embodiment could be
`
`enhanced through the use of “[a]dvanced pattern recognition software.” Id. 21:12-
`
`31. Ogasawara does not provide any detail as to what this software is, how it
`
`operates, or what its capabilities and limitations may or not be. In fact,
`
`Ogasawara’s only description of this purported software comes by way of an
`
`“example” included in this paragraph:
`
`Advanced pattern recognition software allows a consumer to capture a
`videographic image of an apple, for example, and to have the apple be
`recognized as such by the pattern recognition software. This capability
`is useful for any merchandise item having a distinct or identifiable
`shape or other visually identifiable characteristic.
`
`Id. 23:16-22.
`
`VI. OGASAWARA DOES NOT DISCLOSE OR ENABLE THE
`ADVANCED OBJECT RECOGNITION CLAIMED BY THE ’030
`PATENT
`
`31.
`
`I understand that Petitioner contends the singular paragraph in
`
`Ogasawara referring to “[a]dvanced pattern recognition software” discloses claim 1
`
`of the ’030 patent’s requirement of a “an object identification platform configured
`
`to obtain the acquired data, recognize the object as a target object based on the
`
`
`
`15
`
`Patent Owner’s Ex. 2002, Page 16 of 18
`
`
`
`
`
`acquired data, and determine object information associated with the target object.”
`
`Petition at 16-18, 32-36. I also understand that Petitioner has submitted a
`
`declaration from its expert—Dr. Jeffrey J. Rodriguez—in support of this claim.
`
`See Ex. 1003 at ¶¶ 70, 84-85, 127. In my opinion, both Petitioner and Dr.
`
`Rodriguez are incorrect.
`
`32. A POSITA at the time of Ogasawara—in or around March 1999—
`
`would not have understood the cursory and undefined reference to “[a]dvanced
`
`pattern recognition software” in Ogasawara’s specification to disclose to the kinds
`
`of advanced object image recognition disclosed and claimed by the ’030 patent.
`
`During that time period, a POSITA would have understood that image processing
`
`technology was not yet sophisticated enough to engage in true object recognition
`
`regardless of irregularities in the object, lighting, field of view, or viewing
`
`geometry as described as claimed by the ’030 patent. As a result, in my opinion a
`
`POSITA would not understand Ogasawara to disclose this limitation of the ’030
`
`patent within its four corners and that Ogasawara thus cannot be considered
`
`anticipatory to claim 1 of the ’030 patent.
`
`33. The scant description of this purported “[a]dvanced image recognition
`
`software” in Ogasawara only confirms this point. Ogasawara’s only substantive
`
`description of this purported software is couched in terms of its limitations, noting
`
`that this software only functions when an object contains a highly “distinct or
`
`
`
`16
`
`Patent Owner’s Ex. 2002, Page 17 of 18
`
`
`
`identifiable shape or other visually identifiable characteristic.” Ex. 2004 at 23:16-
`
`22.
`
`34. Even to the extent Ogasawara could be considered to disclose the
`
`object recognition techniques of the ’030 patent—and to be clear, I disagree that it
`
`could be—I also understand that Ogasawara cannot be considered anticipatory
`
`because it fails to actually enable those advanced techniques. As discussed above,
`
`Ogasawara provides no explanation or guidance as to how any “[a]dvanced pattern
`
`recognition software” would work, how it could be implemented in the
`
`embodiments described in its specification, or what its capabilities or limitations
`
`would be beyond the need for a specific “distinct or identifiable shape or other
`
`visually identifiable characteristic.” In my opinion, a POSITA reading these
`
`disclosures would not understand them to enable the advanced object recognition
`
`techniques of claim 1 of the ’030 patent.
`
`VII. CONCLUSION
`
`35.
`
`I declare that the information contained in this declaration is true and
`
`accurate to the best of my knowledge. If called upon to testify, I would do so
`
`consistent with the statements and opinions contained in this declaration.
`
`Dated: September 21, 2021
`
`Chandrajit Bajaj, Ph.D.
`
`17
`
`Patent Owner’s Ex. 2002, Page 18 of 18
`
`
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