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`EXHIBIT G
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`B. Obviousness Under 35 U.S.C. § 103
`In addition to the anticipatory references described in these PreliminaryFinal
`Invalidity Contentions, the Asserted Claims of the Asserted Patents are invalid based
`on obviousness. In general, a claimed invention is unpatentable if the differences
`between it and the prior art “are such that the subject matter as a whole would have
`been obvious at the time the invention was made to a person having ordinary skill in
`the art.” 35
`
` U.S.C. § 103(a); Graham v. John Deere Co., 383 U.S. 1, 13-14 (1966).
`EachPursuant to Defendants’ Final Election of Asserted Prior Art, each prior art
`reference identified above and described in the charts attached as the Exhibits
`A-01-H-31- 31, either alone or in combination with other prior art (identified in the
`Exhibits A-01- H-31 and Appendix A, also renders the Asserted Claims of the Asserted
`Patents invalid as obvious. Various combinations of the references would have
`naturally been considered as part of the exercise of ordinary skill by one skilled in the
`art. In particular, each prior art reference may be combined with (1) information known
`to persons skilled in the art at the time of the alleged invention, (2) information
`regarding the state of the art at the time of the alleged inventions (3) any of the other
`anticipatory prior art references, and/or (3) any of the additional prior art identified
`above and in the charts attached hereto. Specific combinations of prior art, by way of
`example, are provided below. In addition, Bank of America incorporates by reference
`each and every prior art reference of record in the prosecution of the Asserted Patents
`and related applications,
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`B.
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`C.
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`including the statements made therein by the applicant, as well as the prior art discussed
`in the specification.
`In view of the Supreme Court’s KSR International Co. v. Teleflex Inc., 550 U.S.
`398, 127 S. Ct. 1727, 1739 (2007) (“KSR”) decision, the PTO issued a set of
`Examination Guidelines. See Examination Guidelines for Determining Obviousness
`Under 35 U.S.C. § 103 in View of the Supreme Court Decision in KSR International
`Co. v. Teleflex Inc., 72 Fed. Reg. 57, 526 (Oct. 10, 2007). Those Guidelines summarized
`the KSR decision, and identified various rationales for finding a claim obvious,
`including those based on other precedents. Those rationales include:
`A.
`Combining prior art elements according to known methods to yield
`predictable results;
`Simple substitution of one known element for another to obtain predictable
`results;
`Use of known technique to improve similar devices (methods, or products)
`in the same way;
`Applying a known technique to a known device (method, or product) ready
`for improvement to yield predictable results;
`“Obvious to try”—choosing from a finite number of identified, predictable
`solutions, with a reasonable expectation of success;
`Known work in one field of endeavor may prompt variations of it for use
`in either the same field or a different one based on design incentives or
`other market forces if the variations would have been predictable to one of
`ordinary skill in the art;
`Some teaching, suggestion, or motivation in the prior art that would have
`led one of ordinary skill to modify the prior art reference or to combine
`prior art reference teachings to arrive at the claimed invention.
`
`G.
`
`D.
`
`E.
`
`F.
`
`Id. at 529.
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`Bank of America contends that one or more of these KSR rationales applies in
`considering the obviousness of the Asserted Claims in the Patents-in SuitAsserted
`Patents in accordance with S.P.R. 2.5.
`1. Background and State of the Art
`Consistent with Plaintiff’sPlaintiffs’ admissions noted in Section III.D.,
`machine vision done by way of image processing for symbol identification and object
`recognition was well-known before the alleged November 6, 2000 priority date of the
`Asserted Patents. Research on a machine’s ability to understand text began in the late
`1980’s, and “commercial systems [were] built to read text on a page, to find fields on
`a form, and to locate lines and symbols on a diagram.” O’Gorman, Document Image
`Analysis at iii. By the late 90’s, “the results of research work in document processing
`and optical character recognition (OCR) can be seen and felt every day.” Id. As
`shown below, known document processing techniques by 1997 include at least textual
`and graphical processing. Id. at 2.
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`In practice, “document image analysis has been in use for a couple of decades
`(especially in the banking business for computer reading of numeric check codes).”
`Id. at 3.
`Similarly, the Asserted Patents also acknowledge that symbol identification
`using “identification markings such as barcodes, special targets, or written language”
`existed before the invention, and that it was desirable to use such systems for
`searching and retrieving data. E.g., ’529 patent at 1:56–59. The Asserted Patents
`describe these techniques as “traditional methods for linking objects to digital
`information,” which include “applying a barcode, radio or optical transceiver or
`transmitter, or some other means of identification to the object, or modifying the data
`or object so as to encode detectable information in it.” Id. at 2:13–18. They further
`explain that by using the purported invention, these “traditional methods” are not
`required because the “data or object can be identified solely by its visual appearance.”
`Id. at 2:18 –19. Notably, the Asserted Patents admit that “detect[ing] and decode[ing]
`symbols, such as barcodes or text, in the input image” could be “accomplished via
`algorithms, software, and/or hardware components” that were “commercially
`available (The HALCON software package from MVTec is an example).” ’004
`patent, 14:49–54; ’252 patent, 13:56–61;
`’036 patent, 14:35–40; ’897 patent 14:59–64; ’278 patent, 14:54–59. Indeed, by the
`release of HALCON software 5.2 in July 2000, the HALCON software could
`determine if an image contained one or more recognizable symbols, decode the
`recognizable symbols according to type, such as barcode (via a barcode reader) and
`characters (via OCR), and return information about the decoded symbols. See, e.g.,
`MVTec00002.001-
`019, MVTec00601-1459;
`at
`see
`also
`generally
`MVTec00001-08179.
`Various symbol recognition techniques were also well-known. These techniques
`include, for example, “1D and 2D barcodes, magnetic ink character recognition
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` (MICR), optical character recognition (OCR), optical mark recognition (OMR),
`radio frequency identification (RF/ID), UV/IR identification technologies.” Rhoads at
`7:6–9.
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`Various object recognition techniques to identify objects were also known before
`the alleged November 6, 2000 priority date. See Bolle at 1:14–22; Shapiro, Computer
`Vision at 107.5 107.5 For example, a “basic approach” for recognizing objects is to use
`a “vector of measurements.” Shapiro at 107; see also PicToSeek at 102; VeggieVision
`IEEE at 1; QBIC Paper at 24. These measurements include “color, shape, and texture
`features” extracted in software. Shapiro at 109; see also PicToSeek at 102;
`VeggieVision IEEE at 1; QBIC Paper at 24. Once those features are extracted, a
`distance measurement is calculated between the extracted features and features of
`known images stored on a database. See Shapiro at 110–113; see also PicToSeek at 102;
`VeggieVision IEEE at 1; QBIC Paper at 24. Object recognition systems can also “use
`histograms to perform this recognition” where the system “either develops a gray scale
`histogram or a color histogram from a (color) image containing an object.” Bolle at
`1:15–16; see also PicToSeek at 103; VeggieVision IEEE at 3; QBIC Paper at 24.
`Prior to extracting image features, software may use “image processing” methods
`in order to “reduce noise in the image” or if “certain details need to be emphasized.”
`Shapiro at 145; see also PicToSeek at 108; VeggieVision IEEE at 2-3. These “image
`processing” methods include “masking” to assist in edge detection (Shapiro at 156),
`“grey-level mapping” (Shapiro at 147), “removal of small image regions” that include
`“noise” or “low level detail” (Shapiro at 150), and image “segmentation” (Shapiro at
`305). See also PicToSeek at 108; VeggieVision IEEE at 2-3; License plate recognition
`SPIE at 29-32.
`
`5 See also T. Gevers et al., PicToSeek: Combining Color and Shape Invariant Features for Image
`Retrieval (“PicToSeek”), at 102 (“In this paper, we focus on the problem of retrieving images
`containing instances of particular objects.”); Bolle, R.M., et al, VeggieVision: A Produce Recognition
`System, Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96,
`Sarasota, FL, USA, 1996, pp. 244-251 (“VeggieVision IEEE”), at 1 (“In this paper, we present a
`trainable produce recognition system for supermarkets and grocery stores.”); Neubauer, Claus,
`License plate recognition with an intelligent camera, SPIE 3838, Mobile Robots XIV, (September
`1999) (“License plate recognition SPIE”), at 29 (“This paper describes a robust car identification
`system for surveillance of parking lot entrances…”); QBIC Paper (“QBIC lets users find pictorial
`information in large image and video databases based on color, shape, texture, and sketches.”).
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`305). See also PicToSeek at 108; VeggieVision IEEE at 2-3; License plate recognition
`SPIE at 29-32.
`2. Explanations of Obviousness under S.P.R. 2.5.2
`As discussed in more detail below and as demonstrated in the appended claim
`charts, the aforementioned or similarly known techniques were used throughout
`various industries, including the financial, consumer, and retail industries. Employees
`at companies in these industries knew of and/or used these techniques, were actively
`pursuing a number of well-developed solutions, and also
`invented and/or
`commercialized their own systems in connection with the subject matter and claims of
`the Asserted Patents. To that end, the Asserted Claims simply combine elements well
`known in the art and yield no more than one skilled in the art would expect from such
`a combination. When confronted with the alleged problems described in the Asserted
`Patents—that there is “a need to identify an object that has been digitally captured
`from a database of images without requiring modification or disfiguring of the
`object”—one of ordinary skill in the art at the time of the alleged inventions would
`have been motivated to consider the techniques taught by the prior art cited in these
`Preliminary Invalidity Contentions, and to combine such teachings to arrive at the
`alleged invention claimed in the Asserted Patents. This is demonstrated by the cited
`prior art, which disclose all of the elements of the Asserted Claims, as well as
`motivations to modify or combine their individual teachings.
`The cited prior art share commonalities in terms of their general applicability to
`the use of image processing techniques to identify a target based on symbolic and/or
`object information. Indeed, the field of image processing was well developed and
`used to solve various problems related to the concepts of identifying an object based
`on symbolic information (e.g., barcodes), visual characteristics (e.g., shape, color,
`texture), and a combination of symbolic information and visual characteristics.
`The references discussed below utilize myriad types of image processing in
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` computing devices, including personal digital assistants, mobile phones, and
`other portable computing devices, to identify targets in various fields of endeavor.
`Indeed,
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`utilizing personal digital assistants, mobile phones, and other portable computing
`devices, to identify objects based on symbols and/or object characteristics were well
`known in the art. Each of the prior art references show that known image processing
`techniques were used in portable computing devices in different industries and could be
`substituted with other image processing techniques according to known methods to
`yield predictable results. The prior art references also show that it was common and
`known to substitute one image processing technique in portable computing devices for
`another image processing technique to obtain predictable results and improve the
`functionality of portable computer devices in the same way, such as, e.g., utilizing
`different image processing techniques to identify objects under different circumstances
`and to retrieve information about the object. The prior art references also show that it
`was efficient to distribute image processing between the front end (mobile device) and
`backend (server).
`For example, Ogasawara (U.S. Pat. No. 6,512,919) teaches an electronic
`shopping system for conducting transactions on a wireless videophone. Ogasawara at
`Abstract. “[A]n integral digital camera is used to scan the images of bar codes of
`purchased items, and pattern recognition software resident either in the videophone or
`in the server, translates the bar code image data into an alpha-numeric product
`identification.” Id. at 3:13–17. Additionally, the program can also detect icons in
`captured images. For example, “product specific icons might represent the stylistic
`outlines of a loaf of bread, carton of milk, a bunch of broccoli.” Id. at 20:62–65. The
`system also has advanced pattern recognition capability to “capture merchandise
`information from items that are not identified by either a bar code or an alpha-numeric
`label.” After processing, the “results are then transmitted back to the wireless
`videophone for display to the customer.” Id. at 22:57–58.
`As yet another example, Rhoads (U.S. Pat. No. 6,947,571) discloses a “cell phone
`equipped with a 2D optical sensor, enabling a variety of applications.” Rhoads at
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`Abstract. The analysis of the image data “can be accomplished in various known ways.”
`Id., 9:35-36. Object identification is assisted by clues such as shape, color, slight
`movement, and “characteristic markings.” See id., 9:52-10:7. Object orientation uses
`similar clues along with “edge-detection algorithms.” Id., 10:19-21. The technology has
`“adaptability for use with everyday objects (e.g., milk cartons)” and is “well suited for
`countless applications.” Id., 5:57-59. Rhoads discloses that the invention can be used
`for facilitating commercial transactions. Id., 31:26-29. Additionally, the invention can
`be used in a variety of document validation contexts. See id., 16:36-17:22.
`Another example is Mault (U.S. Pat. App. 20020047867), which discloses a
`portable computing device which can take pictures of food, food packaging, or barcodes
`and generate relevant information. Id., Abstract. Non-packaged food images are
`analyzed and compared to previously stored images of food. Id., [0076]. Mault uses
`“[i]mage processing, image recognition, and pattern recognition algorithms” to “assist
`identification” where “[s]tored images can be identified by comparison with previously
`stored images.” Id., [0077]. Mault explains that the device can record images
`corresponding to food, barcode data, alphanumeric codes, and printed data to create a
`record. Id., [0006].
`In another example, Sizer (U.S. Pat. No. 6,036,086) discloses a capture device
`that scans and captures transaction data from marks contained on an object. Sizer at
`Abstract. Sizer teaches that scannable marks include bar codes, alphanumeric
`characters, or Xerox glyphs on the object’s surface. Id., 3:57–59. The capture device
`sends the data over a network to a processor that interprets the scanned marks. Id.,
`2:66–3:8. Sizer further teaches that the user has the ability to determine that the
`appropriate data has been captured by viewing it on the capture device’s display before
`it is sent over the network to be interpreted. Id., 10:38–49.
`As another example, Harris (U.S. Pat. No. 6,666,377) discloses a portable
`computing device for scanning codes to retrieve information about an object. Id., 1:58-
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`2:2. The code can be scanned by either using a camera or a commercial bar code scanner
`attached to a portable computer or a personal digital assistant. Id., 2:3-14. The codes
`can be used for various applications, and can contain various different types of
`information. Id., Abstract.
`As another example, Wilz (U.S. Pat. No. 6,505,776) discloses a body wearable
`device for identifying bar codes and accessing information resources based on the bar
`code. Id., Abstract. The body wearable device is a computing platform and can access
`servers by way of the Internet. Id. In response to reading symbols, the body wearable
`device access information over the Internet and the information is displayed on a display
`panel. Id.
`As another example, Ehrhart (U.S. Pat. No. 6,722,569) discloses a system that
`utilizes an optical reader that determines if an acquired image should be characterized
`as a color photograph or as including a graphical symbol, e.g., bar codes, text, OCR
`symbols, or signatures. Id., Abstract. The system will then decode the graphical symbol
`or associate the acquired image with at least one other acquired image in a database.
`Id., Abstract.
`Recognizing the object in the image based on myriad image processing
`techniques was also known and also utilized in different industries in the same or similar
`ways, as discussed in the exemplary references below. Indeed, the exemplary
`references below illustrate that it was known to use different image processing
`techniques for computing devices in different fields to achieve the same results such as,
`e.g., recognizing an object in different circumstances to retrieve information about the
`object and then further conduct some transaction related to the object.
`The prior art references show that image processing techniques could be applied
`to different fields. For example, Bolle teaches a trainable image-processing system for
`recognizing objects within a scene. Bolle at Abstract. It generally discusses object
`recognition for identifying produce, which may be useful at a grocery store checkout
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`counter. Id., 5:52–55. To start, “a target object to be recognized is imaged by camera
`120.” Id., 8:13–14. Then, the image is “segmented 220 from its background” by the
`computer 140 in order to “separate the target object 131 from the background so that
`the system 100 can compute characteristics of separated object 131 image pixels
`independently of the background of the scene.” Id., 8:15–21. The computer receives the
`image and performs a computation process “to determine features of the target object”
`which include, for example, “color, shape, texture, density.” Bolle’s system also
`provides a user display where information such as price, weight, and quantity of the
`produce items are displayed, and users can interact with the system. See e.g., id., Figs.
`15–17. Mohan (U.S. Patent No. 6,434,257) lists Maarten Bolle as an additional inventor
`and further elaborates on Bolle’s image processing technique.
`Another example that used image processing for retail is McQueen (U.S. Pat. No.
`6,069,696), which discloses a system for automated identification and classification of
`objects, such as, e.g., groceries. Id., Abstract. In various embodiments, object
`recognition uses information from an image such as color, size, shape, density, and
`texture, and use this information to assist in the identification process. Id., 2:32-36. In
`some other embodiments, an optical code reader is also located on the same system and
`can be used as an additional way to gather information to further identify an object. Id.,
`2:39-50.
`It would have been obvious to improve prior art systems or devices like
`Ogasawara, Rhoads, Sizer, Harris, and Wilz, with prior art like McQueen, Bolle, or
`Mault, which use image processing techniques to determine various visual
`characteristics of an object and then matches those characteristics with reference
`characteristics of known objects stored in a database to identify the object, so as to
`provide for systems or devices that recognize a variety of objects with or without a
`barcode or other identifier. This art is in the same field and a POSITA would recognize
`that such combination would be a combination of familiar elements according to known
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`methods that would yield predictable results to arrive at the Asserted Claims.
`In the financial industry, there are several examples of using known image
`processing techniques for use in check processing, as Plaintiff appearsPlaintiffs appear
`to be interpreting the claim limitations in the Asserted Claims. For example, Hyde
`(U.S. Pat. No. 6,038,553) discloses an automated system for cashing checks by
`imaging the check using a camera. Id., 2:12-26. Hyde teaches a system in which a
`check processing server compares information on the check with criteria derived from a
`check cashing database. Hyde at Abstract. The check cashing transaction module
`includes optical character recognition (OCR) software. Id., 2:38-55. The check
`cashing transaction module prompts the customer to insert the check to be cashed into
`the check receiver, where both sides of the check are scanned and the MICR line is
`read. Id., 2:56-3:2. The check cashing module compares the amount entered by the
`customer with digits amount read by the OCR software and verifies that the check is
`signed and endorsed. Id.
`Steger (U.S. Pat. No. 5,594,226) is another example that discloses an invention
`for scanning codes printed on checks for verification. Steger explains that a mobile
`scanner reads bar code information printed on a check and that the information is then
`transmitted remotely to a bank. Id., Abstract. Steger further teaches that the bar code
`data corresponds to an individual’s account. Id., 2:9–13. The system transmits the
`account information to a bank to verify the account, and after performing the
`verification process, the bank transmits information back to the terminal that initiated
`the scan. Id., 5:63–67.
`As another example, Krouse (U.S. Patent No. 6,097,834) discloses a system for
`generating data from an image of a check with an optical scanner for scanning a check
`for a financial transaction. Recognition characteristics are generated from the scanned
`image and are compared to respective sets of reference recognition characteristics of
`other transaction documents, e.g., checks, in a database having different respective
`formats to determine a match. Krouse at Abstract. Then, the information from the
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`transaction document is extracted from the image for use in processing a transaction.
`Id.
`
`As another example, Funk (U.S. Patent No. 5,832,463) discloses an automated
`system and method for checkless check transaction including a device with a digital
`camera that captures the image of a check and transfers checking account information,
`check amount, and the check image electronically over a network to a checkless
`transaction system including a database. Funk at Abstract. Then the checkless
`transaction system, having received all relevant transaction data, performs an electronic
`settlement and electronic post to an account, and the image of the check may be
`reviewed. Id. at 4:20-36.
`As another example, Deaton (U.S. Patent No. 6,351,735) discloses automatic
`check reading techniques which enable the detection of a customer’s checking account
`number on a check, and then provide check verification. Id., Abstract. Deaton
`elaborates on the check processing process and how the check data is then parsed and
`processed in order to deposit the amount in the customer’s account. Id., 27:21-29.
`As another example, IBM’s QBIC system was designed for applications that
`included large amounts of business-related information, including multi-media content,
`including for financial institutions. See, e.g., IBM 000789.
`The utility of image processing to recognize objects, identify and decode
`symbols, and retrieve information about the objects and/or symbols was also applied to
`fields outside of the financial industry to improve devices and yielded predictable
`results. The exemplary references discussed below illustrate that known image
`processing techniques were used to improve similar devices in the same way.
`For example, Carlton (U.S. Pat. No. 6,977,743) discloses an image processing
`transaction system and method for use in a network, e.g., the Internet. Id., Abstract.
`The imaging appliance is operable to capture an image, and thereafter image
`transformations are performed. Id. In one exemplary embodiment, the image is
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`transmitted to the service provider for processing by a host processing engine
`co-located at the image processing service provider. Id. In a second exemplary
`embodiment, the host processing engine and associated list of available transforms are
`downloaded for
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` locally processing the image by the imaging appliance. Id.
`As another example, Yamakita (JPH10289243) discloses a method of
`recognizing a person by taking a picture of a person by using an electronic camera.
`Yamakita at Abstract. The image is sent to a database and is matched with information
`to identify the person by using pattern recognition process. Id.
`As another example, Yuasa (JPH1091634) discloses a search engine that uses an
`image to search for what the image is and display the explanation on a display. Yuasa
`at Abstract. The image feature attributes that are used for classifying and searching
`images “include the color tone of the entire photograph, a histogram, a combination
`pattern of adjacent colors, a contour of the subject, and the like.” Id. at [0009].
`As another example, Tian (U.S. Pat. No. 6,339,651 B1) also discloses capturing
`an image of a license plate and processing the image to recognize characters. Id., at
`Abstract. Tian teaches that character regions of the license plate image are recognized
`and provided with a confidence score based on the probability of a correct match. Id.,
`1:47–65. Another example of image processing in the license plate field is Tyan (U.S.
`Patent No. 6,473,517).
`A POSITA would have known to use the portable computer devices described
`above with each of the different image processing techniques discussed above because
`doing so would merely be the use of a known technique to improve similar devices
`(methods, or products) in the same way. A POSITA would have recognized that the
`portable computer devices would be improved through the use of different image
`processing and matching techniques so that objects could more easily be imaged and
`detected. Doing so would be simply substituting image processing and matching
`techniques to improve portable computing devices and would have predicably allowed
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`the portable computer devices to recognize a wide variety of objects either with
`or without a symbol, e.g., a barcode. Such uses would be a natural extension of the
`functionalities of portable computer devices. A POSITA would have also known to use
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` any and all methods/components disclosed in the references described above, such as
`a portable device with a camera, database, server, and display to arrive at the
`invention of the Asserted Claims as each of the elements of the Asserted Claims were
`all known in the prior art.
`For these reasons, the teachings described above are complementary, and a
`POSITA would recognize that their combination would be a combination of familiar
`elements according to known methods that would yield predictable results. Also, the
`substitution of one known image processing technique for another would have been
`used to obtain predictable results and a known way to improve similar devices. The
`wide applicability of image processing techniques in various fields with the same
`predictable results, i.e., recognizing an object, would have led a POSAPOSiTA to
`understand it would have been obvious to try different combinations of the art
`discussed in these contentions.
`Moreover, a multitude of companies sold products that utilized symbol and object
`recognition as described by the Asserted Patents. For example, a number of companies
`introduced portable computing devices that utilized image processing to identify objects
`and symbols, such as DigiMarc MediaBridge, IBM InfoScope, Fujitsu TeamPad 500,
`Sony InfoStick, HP Cooltown, and BarPoint System. “A good quality CCD (Charge
`Coupled Device) digital camera provides the best MediaBridge performance.” 66
`“Client side [of the InfoScope System] consists of a color camera attached personal
`digital organizer.”77 The TeamPad 500 was advertised as “the only handheld
`computer available with integrated printer, scanner, and mag stripe reader…”8 “The
`InfoStick consists” of a “SONY CCD-MC1 as a CCD camera,” “three input
`buttons,” and a
`
`6 Alattar, Adnan, "Smart Images" Using Digimarc's Watermarking Technology, Proc. SPIE 3971,
`Security and Watermarking of Multimedia Contents II, (25 January 2000) (“Smart Images”), at 7.
`7 Ismail Haritaoglu, InfoScope Link from Real World to Digital Information Space (“InfoScope
`Paper”), at 3.
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`8 TeamPad 500 Flyer dated 2001 (“TeamPad 500 Flyer”), at 2.
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`available with integrated printer, scanner, and mag stripe reader…”8 “The InfoStick
`consists” of a “SONY CCD-MC1 as a CCD camera,” “three input buttons,” and a
`“display” to show the information received at the device.99 The HP CoolTown
`creators imagined its users carrying devices like “PDAs” with “network access and
`barcode or other sensors” or “[c]ameras with short-range networks.”1010 BarPoint
`users can “scan product UPC barcodes into a handheld device to wirelessly access
`BarPoint.com.”1111 DigiMarc MediaBridge, for example, disclosed a portable web
`camera connected to a computer that could be used to identify an object via an
`embedded symbol. See Smart Images at 7. IBM InfoScope, Fujitsu TeamPad, and
`Sony InfoStick disclosed portable computing devices that could do the same.
`InfoScope, which discloses the use of a personal digital assistant to translate foreign
`languages and identify buildings. See InfoScope Paper at 1. Fujitsu TeamPad
`disclosed a portable computing device that could be used to identify objects in the
`retail context using barcodes. See TeamPad 500 Flyer, at 1. Sony InfoStick and HP
`Cooltown disclosed the use of a portable computer device to identify objects in the
`consumer entertainment context. See InfoStick at 2; CoolTown at 4.
`A number of companies were also developing image processing techniques. For
`example, asat least as early as 1999, at least1995 and by no later than November 5,
`2000, the QBIC System was known, used, and/or sold to the public in the United
`States. The QBIC system is a “Query by Image and Video Content: the QBIC
`System.” ,” and includes methods for database population with images (including
`video) and features of those images, extracting features (such as color, shape, and
`texture) for both input and stored imag