Ex Parte Borrey et alDownload PDFPatent Trial and Appeal BoardOct 15, 201311329999 (P.T.A.B. Oct. 15, 2013) Copy Citation UNITED STATES PATENT AND TRADEMARK OFFICE __________ BEFORE THE PATENT TRIAL AND APPEAL BOARD __________ Ex parte ROLAND G. BORREY, MAURITIUS A.R. SCHMIDTLER, ROBERT A. TAYLOR, JOEL S. FECHTER, and HARI S. ASURI __________ Appeal 2011-006423 Application 11/329,999 Technology Center 2600 __________ Before DONALD E. ADAMS, JEFFREY N. FREDMAN, and ULRIKE W. JENKS, Administrative Patent Judges. FREDMAN, Administrative Patent Judge. DECISION ON APPEAL This is an appeal1 under 35 U.S.C. § 134 involving claims to a computer-implemented data processing method. The Examiner rejected the claims as obvious. We have jurisdiction under 35 U.S.C. § 6(b). We affirm. 1 Appellants identify the Real Party in Interest as Kofax, Inc. (see App. Br. 3). Appeal 2011-006423 Application 11/329,999 2 Statement of the Case Background “The present invention relates to systems and methods for improving the quality of data acquired by data acquisition devices. The user can be located locally or remotely from the data acquisition device” (Spec. 1 ¶ 0002). The Claims Claims 1-30 are on appeal. Claim 1 is representative and reads as follows: 1. A computer-implemented data processing method comprising: storing raw or normalized data from a data capture device in a computer accessible storage medium; analyzing at least portions of the raw or normalized data with a first analytic engine to determine whether the raw data is within a first set of parameters and if not, generating with the first analytic engine a first set of processor settings; processing the raw or normalized data with the first set of processor settings; and analyzing at least portions of the processed data with a second analytic engine to determine whether the processed data is within a second set of parameters and if not, generating with the second analytic engine a second set of processor settings to reprocess the raw or normalized data. The Issue The Examiner rejected claims 1-30 under 35 U.S.C. § 103(a) as obvious over Takaoka,2 Mirzaoff,3 and Vazquez4 (Ans. 4-15). 2 Takaoka, N., U.S. 6,473,535 B1, issued Oct. 29, 2002. 3 Mirzaoff et al., U.S. 6,408,094 B1, issued Jun. 18, 2002. 4 Vazquez et al., U.S. 6,763,515 B1, issued Jul. 13, 2004. Appeal 2011-006423 Application 11/329,999 3 The Examiner finds that Takaoka teaches a data processing method where raw data is captured, and analyzed “with the first analytic engine (144 of figure 3, col 14 line 60, auto setup engine) a first set of processor settings” (Ans. 5). The Examiner finds that Takaoka then processes the raw data (Ans. 5). The Examiner finds that Takaoka teaches that the user then determines if the image quality of the simulation images are proper or not (col 17 lines 25-35), which is a determination of whether the processed data is within a second set of parameters. If the simulation image is not proper, a key on the keyboard designating a change in density, etc is pressed (col 17 lines 40-50), which yields a second set of processor settings generated by the user due to the fact that the processed data is not within a second set of parameters. The prescan image data, i.e. raw data is then corrected based on the second set of processor settings (Ans. 6-7). The Examiner finds that Mirzaoff teaches determining “whether the raw data . . . is within a first set of parameters . . . and if not, the need for correction is notified to the user” (Ans. 5-6). The Examiner finds that Vazquez teaches “to incorporate image processing algorithms in image- related programs, where it is desirable to generate a program to perform the image processing algorithm developed by the user” (Ans. 7). The Examiner finds it obvious to “reprocess the raw or normalized data in order to completely remove the burden on operators having to worry about poor image outputs from automatic processing, where Vazquez’s image processing algorithm adapted to the systems of Takaoka and Mirzaoff allows operators to generate all the proper processing conditions beforehand to handle all . . . image processing scenarios” (Ans. 7). Appeal 2011-006423 Application 11/329,999 4 The issue with respect to this rejection is: Does the evidence of record support the Examiner’s conclusion that Takaoka, Mirzaoff, and Vazquez render the claims obvious? Findings of Fact 1. Takaoka teaches “an image processing apparatus and method in which proper image processing conditions can be obtained by simple operation” (Takaoka, col., ll. 25-27). 2. Takaoka teaches that the “CPU 146 of the auto setup engine 144 causes the prescan data input thereto to be sequentially stored in the RAM 148 while at the same time performing the auto setup operation” (Takaoka, col. 14, ll. 60-63). 3. Takaoka teaches that the graininess of the film image is determined based on the prescan image data, and the film image is analyzed by calculating various feature amounts including the central density in the density range and the density histogram of the film image. In the next step 512, based on the results of the film image analysis, the processing conditions (more specifically, the parameters for defining the processing conditions) for the image processing executed in the image processor 140 are arithmetically determined. (Takaoka, col. 15, ll. 27-35). 4. Takaoka teaches that the fetched prescan image data and image processing conditions of the predetermined number of film images, the prescan image data and image processing conditions of one film image are fetched, and the prescan image data thus fetched are subjected to a predetermined image processing (image expansion/compression processing, color balance correction processing, density conversion processing, hyper Appeal 2011-006423 Application 11/329,999 5 gradation processing, hyper sharpness processing, or the like.) in accordance with the processing conditions fetched. (Takaoka, col. 16, ll. 41-49). 5. Takaoka teaches that “the operator visually checks the simulation image displayed on the display unit 164, and carries out various determinations, and carries out the verification work of inputting the result of determinations” (Takaoka, col. 17, ll. 17-20). 6. Mirzaoff teaches that “[c]omputer 28 receives frames of information in the form of digital pixel data in serial form from scanner 25 . . . and processes each frame of information to obtain one or more attributes about the document image in each frame of information” (Mirzaoff, col. 4, ll. 34-40). 7. Mirzaoff teaches that “[o]nce the attributes for each document image are obtained, a threshold value for each obtained attribute is selected from a database . . . the threshold values are signals which represent the minimum acceptable values for the attributes” (Mirzaoff, col. 4, ll. 51-56) 8. Mirzaoff teaches that “[a]fter the threshold values have been selected (Step 32), each obtained attribute is compared against the threshold value selected for the obtained attribute and the difference between each attribute and threshold value is obtained” (Mirzaoff, col. 4, ll. 65-67). 9. Mirzaoff teaches that “[e]valuation results are signals which may simply indicate that there is an error with the document image or may identify the type of error and/or provide more detailed information about the error” (Mirzaoff, col. 5, ll. 6-9). Appeal 2011-006423 Application 11/329,999 6 10. Vazquez teaches that in many cases users need to perform additional operations not supported by an image processing prototyping environment or need to incorporate an image processing algorithm into a larger program. In such cases, it may be desirable to enable the image processing prototyping environment to automatically generate a program to perform the image processing algorithm developed by the user. (Vazquez, col. 3, ll. 60-66). 11. Vazquez teaches that a “user may develop an image processing algorithm . . . for analyzing, processing, or manipulating various types of images, such as binary, grayscale, color, or complex images” (Vazquez, col. 4, ll. 9-15). 12. Vazquez teaches that “any of various types of image processing functions may be supported, including filtering functions, morphology functions, histogram functions, particle analysis functions, edge detection functions, etc. As the user applies each image processing function to an image, the function may be recorded as a step in a script or recipe” (Vazquez, col. 4, ll. 18-24). 13. Vazquez teaches that “[w]hen the user is satisfied with the algorithm, the user may request the image proto typing environment to automatically generate a program implementing the image processing algorithm” (Vazquez, col. 4, ll. 29-33). 14. Vazquez teaches a variety of image processing functions including: filtering functions for smoothing, edge detection, convolution, etc. morphology functions for modifying the shape of objects in an image, including erosion, dilation, opening, closing, etc. thresholding functions for selecting Appeal 2011-006423 Application 11/329,999 7 ranges of pixel values in grayscale and color images particle filtering functions to filter objects based on shape measurements. . . . an edge detection function that finds edges along a line drawn through the image with a line tool a pattern matching function that locates regions of a grayscale image that match a predetermined template a shape matching function that searches for the presence of a shape in a binary image and specifies the location of each matching shape a caliper function that computes measurements such as distances, areas, and angles based on results returned from other image processing functions a color matching function that quantifies which colors and how much of each color exist in a region of an image and uses this information to check if another image contains the same colors in the same ratio. (Vazquez, col. 13, ll. 22-61). Vazquez also teaches that “the image processing functions of the script may have associated parameters, and the user may specify one or more of these parameters that are desired to be interactively changeable or viewable” (Vazquez, col. 4, ll. 56-59). 15. The Specification teaches that the first analytic engine 714 performs geometric processing, such as for example, document orientation, background compensation, color compensation, text extraction, text/background separation, page boundary detection, streak detection, page border detection, blank page detection, conversion from RGB color representation to a YCbCr color representation, hue measurement, saturation measurement, luminescence measurement, creating a grayscale intensity histogram, creating a color histogram, color detection, gamma detection for brightness and color levels, and the like. (Spec 23 ¶ 0106). Appeal 2011-006423 Application 11/329,999 8 16. The Specification teaches that a “second analytic engine performs feature or quality processing, such as, for example, recognizing an area of poor optical character recognition, non-linear gamma, high background noise, character color distortion, and the like” (Spec. 23 ¶ 0110). 17. The Specification teaches that “virtual reacquisition enhanced by an analytic engine may be implemented as software, firmware, hardware, or any combination of software, firmware, or hardware” (Spec. 8 ¶ 0033). Principles of Law The Supreme Court explains that “the [obviousness] analysis need not seek out precise teachings directed to the specific subject matter of the challenged claim, for a court can take account of the inferences and creative steps that a person of ordinary skill in the art would employ.” KSR Int’l v. Teleflex Inc., 550 U.S. 398, 418 (2007). “The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.” Id. at 416. “If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” Id. at 417. Moreover, an “[e]xpress suggestion to substitute one equivalent for another need not be present to render such substitution obvious.” In re Fout, 675 F.2d 297, 301 (CCPA 1982). Analysis Claim Interpretation We begin with claim interpretation, since before a claim is properly interpreted, its scope cannot be compared to the prior art. Claim 1 has a preamble, limiting the claim to “computer-implemented data processing” and four steps, “storing raw or normalized data”, “analyzing . . . the raw or Appeal 2011-006423 Application 11/329,999 9 normalized data with a first analytic engine”, “processing the raw or normalized data”, and “analyzing . . . the processed data with a second analytic engine”. The phrases at issue are “computer-implemented data processing” and “analytic engine”. We agree with Appellants that “computer-implement data processing” refers to processes that are solely performed on computers (see, e.g., App. Br. 14). The term “analytic engine” is not defined by the Specification. The Specification teaches that the first analytic engine 714 performs geometric processing, such as for example, document orientation, background compensation, color compensation, text extraction, text/background separation, page boundary detection, streak detection, page border detection, blank page detection, conversion from RGB color representation to a YCbCr color representation, hue measurement, saturation measurement, luminescence measurement, creating a grayscale intensity histogram, creating a color histogram, color detection, gamma detection for brightness and color levels, and the like. (Spec 23 ¶ 0106; FF 15). The Specification teaches that a “second analytic engine performs feature or quality processing, such as, for example, recognizing an area of poor optical character recognition, non-linear gamma, high background noise, character color distortion, and the like” (Spec. 23 ¶ 0110; FF 16). The Specification teaches that “virtual reacquisition enhanced by an analytic engine may be implemented as software, firmware, hardware, or any combination of software, firmware, or hardware” (Spec. 8 ¶ 0033; FF 17). Appeal 2011-006423 Application 11/329,999 10 Thus, interpreted in light of the Specification, the term “analytic engine” reasonably encompasses software that performs image processing (FF 15-17). We now address the claims in light of these interpretations. Claim 1 Takaoka teaches a multi-step image processing method (FF 2) in which Takaoka teaches a first step where “the prescan data input thereto to be sequentially stored in the RAM 148 [random access memory]” (Takaoka, col. 14, ll. 60-63; FF 2), where RAM 148 is a computer accessible storage medium. Takaoka teaches a second step where “the film image is analyzed by calculating various feature amounts including the central density in the density range and the density histogram of the film image” using specific software, which is reasonably interpreted as the “fist analytic engine.” Takaoka teaches a third step where “the prescan image data and image processing conditions of one film image are fetched, and the prescan image data thus fetched are subjected to a predetermined image processing” (Takaoka, col. 16, ll. 43-45; FF 4). Finally, Takaoka teaches a fourth step where “the operator visually checks the simulation image displayed on the display unit 164, and carries out various determinations, and carries out the verification work of inputting the result of determinations” (Takaoka, col. 17, ll. 17-20; FF 5). Mirzaoff teaches processing images (FF 6) to obtain image parameters or threshold values which are analyzed to determine whether they fall within acceptable values (FF 7-9). Vazquez teaches a method where a user develops a software script to perform image processing in the place of the user (FF 11-12) so that when “the user is satisfied with the algorithm, the user may request the image Appeal 2011-006423 Application 11/329,999 11 proto typing environment to automatically generate a program implementing the image processing algorithm” (Vazquez, col. 4, ll. 29-33; FF 12). Applying the KSR standard of obviousness to the findings of fact, we find it would have been obvious to modify the final user image check of Takaoka (FF 5) with the user-developed automated software script of Vazquez since Vazquez teaches that “it may be desirable to enable the image processing prototyping environment to automatically generate a program to perform the image processing algorithm developed by the user” (Vazquez, col. 3, ll. 63-66; FF 10). Replacing the human user check of Takaoka with the use of a second software program (or analytic engine) which analyzes the image data processed by the first software program (analytic engine) of Vazquez would have been obvious since it would permit automation of the human analysis step. This is consistent with Leapfrog Enterprises Inc. v. Fisher-Price Inc., 485 F.3d 1157 (Fed. Cir. 2007), where the court found that one of ordinary skill in the art would have found it obvious to combine an old electromechanical device with electronic circuitry “to update it using modern electronic components in order to gain the commonly understood benefits of such adaptation, such as decreased size, increased reliability, simplified operation, and reduced cost.” Id. at 1163. “The combination is thus the adaptation of an old idea or invention … using newer technology that is commonly available and understood in the art.” Id. Appellants contend that “the Examiner has failed to show the second portion of this limitation, specifically ‘with a second analytic engine’ to determine whether the processed data is within a second set of parameters” (App. Br. 11). Appellants contend that “claim 1 clearly states that it is a Appeal 2011-006423 Application 11/329,999 12 ‘computer-implemented data processing method,’ and does not include any human steps” (App. Br. 11). We find this argument unpersuasive because it fails to address the combination of references, focusing solely on Takaoka. We agree with Appellants that claim 1 is limited to computer processing steps and cannot include human performed steps. However, the rejection relies upon the combination of Takaoka and Vazquez (and Mirzaoff), where Vazquez teaches that image processing operations performed by humans may be computerized to generate programs or scripts which will then automatically execute the image processing operation (FF 10-12). Vazquez is therefore relied upon to teach that the human user image processing of Takaoka can be replaced by an automated, computerized script or software developed by the human user, at which point the entire method would then be performed by a computer, thereby satisfying the preamble of claim 1. See In re Keller, 642 F.2d 413, 425 (CCPA 1981) (“The test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art.”) Appellants contend that the “Examiner has not attempted to show a second analytic engine in any of the arguments presented in the rejection of claim 1, nor is a second analytic engine taught by the combination of art used in the rejection of claim 1, which simply fail to disclose a second analytical engine” (App. Br. 11). Appeal 2011-006423 Application 11/329,999 13 We are not persuaded. Consistent with our claim interpretation above, a “second analytic engine” is simply a second software which performs image processing on the data processed by the first software (or analytic engine). Takaoka teaches a first software processing (FF 3) followed by a second processing of the image by a human. Therefore, the script or software of Vazquez (FF 10-12), when incorporated in the place of the Takaoka’s human processing (FF 5), is reasonably interpreted as satisfying the second analytic engine requirement. Appellants contend that “the second analytic engine generates a second set of processor settings. However, using the Examiner’s rationale, the human simply chooses a predetermined set of parameters by striking a key on a keyboard, which effectively precludes the human from generating a second set of parameters” (App. Br. 12). We are not persuaded. When Vazquez teaches generating the script, or software, which will function in the place of the human processing, Vazquez teaches that “the image processing functions of the script may have associated parameters, and the user may specify one or more of these parameters that are desired to be interactively changeable or viewable” (Vazquez, col. 4, ll. 56-59; FF 14). Thus, Vazquez expressly teaches generating second set of parameters associated with the script (i.e. software). Appellants contend that the “the combination of Vazquez with Takaoka and Mirzaoff to alleviate an issue in Takaoka which is not present in the claimed invention, is improper” (App. Br. 13). Appellants contend that “the rejection of claim 1 relies on an improper ‘obvious to try’ rationale” (App. Br. 13). Appeal 2011-006423 Application 11/329,999 14 We are not persuaded. We have already explained the Examiner’s reason to combine is so that “image processing automation completely replaces the need for operators to function like a second analytic engine” (Ans. 7-8). Therefore, the fact that the prior art’s reasons for performing the claimed steps might be different than Appellants’ does not demonstrate that performing the claimed would have been unobvious. See KSR, 550 U.S. at 419 (“In determining whether the subject matter of a patent claim is obvious, neither the particular motivation nor the avowed purpose of the patentee controls. What matters is the objective reach of the claim. If the claim extends to what is obvious, it is invalid under § 103.”). We also do not find this to be an “obvious to try” situation. There would be an absolutely reasonable expectation that the ordinary artisan would have been able to generate a script according to the method of Vazquez for use in the place of the human review in Takaoka. There is virtually no unpredictability whatsoever in performing this substitution, yielding a very reasonable expectation of success. Kubin stated that “[r]esponding to concerns about uncertainty in the prior art influencing the purported success of the claimed combination, this court [in O’Farrell] stated: ‘[o]bviousness does not require absolute predictability of success … all that is required is a reasonable expectation of success.”’ In re Kubin, 561 F.3d 1351, 1360 (Fed. Cir. 2009) (citing In re O’Farrell, 853 F.2d 894, 903- 904 (Fed. Cir. 1988)). Claim 2 Appellants contend that “the ‘second set of parameters’ cited by the Examiner from Takaoka and Vazquez are not a teaching of ‘reprocessing the Appeal 2011-006423 Application 11/329,999 15 raw or normalized data with the second set of processor settings, wherein the second set of parameters is different than the first set of parameters,’ as required by claim 2” (App. Br. 15). The Examiner finds that “Takaoka’s first analytic engine . . . checks image data against a 1st set of parameters. Such a 1st set of parameters can include things like expansion-to-compression ratio, color balance, density conversion, gradation compression and hyper sharpness” (Ans. 26). The Examiner finds that “when a human (but later modified by Vazquez to be an automated script functioning as the second analytic engine) analyzes the processed image output by Takaoka’s first analytic engine (i.e. auto setup engine), the only three (3) types of parameters used are frame, color balance and density” (Ans. 27). The Examiner finds that the “1st set of parameters is not entirely the same as the 2nd set of parameters” (Ans. 27). We find that the Examiner has the better position. The Examiner has reasonably explained why the first and second software (analytic engines) of the prior art will result in the use of two different sets of parameters (Ans. 26-27). Claim 3 Appellants contend that the prior art “does not disclose ‘analyzing at least portions of the processed data with the second analytic engine to determine whether the processed data is within the second set of parameters,’ as required by claim 3” (App. Br. 16). We are not persuaded. As discussed above, we find that Takaoka and Vazquez render obvious a “second analytic engine”. Vazquez also teaches that “the image processing functions of the script may have associated parameters, and the user may specify one or more of these parameters that Appeal 2011-006423 Application 11/329,999 16 are desired to be interactively changeable or viewable” (Vazquez, col. 4, ll. 56-59; FF 14). This is an express teaching that the script or software comprising the second analytic engine processes data using a “second set of parameters” (FF 10-14). We also incorporate the Examiner’s findings (Ans. 26-27). Claims 9 and 10 Appellants contend that “a review of Takaoka does not reveal that this reference, nor any other cited reference in the combination of prior art, teaches that the second set of parameters are associated with processed data analyses” (App. Br. 17). We are not persuaded. We have already explained that the Vazquez teaches a second set of parameters (FF 14) and Vazquez also clearly teaches “an image processing algorithm . . . for analyzing, processing, or manipulating various types of images, such as binary, grayscale, color, or complex images” (Vazquez, col. 4, ll. 9-15; FF 11). This image processing algorithm is reasonably interpreted as satisfying the requirement of claims 9 and 10 for “processed data analyses with regard to the target data” (FF 10- 14). Claims 17 and 18 Appellants contend that the “distinction between raw or normalized data is an indication that the ‘processed data analyses’ are distinct from a normalizing process. Accordingly, the Examiner’s contentions are improper in this regard” (App. Br. 18). The Examiner finds that “since Takaoka discloses that image data is corrected in some manner during scanning (which can be interpreted as normalization), then this scanned image data, used by the first analytic Appeal 2011-006423 Application 11/329,999 17 engine (i.e. auto setup engine) to compare with the 1st set of parameters, would thus be associated with processed data analyses” (Ans. 28). We find that the Examiner has the better position. Claim 12 encompasses the use of raw or “normalized” data, which is processed according to first set of parameters which parameters claim 17 requires to be associated with processed data analysis. Takaoka teaches that “based on the results of the film image analysis, the processing conditions (more specifically, the parameters for defining the processing conditions) for the image processing executed in the image processor 140 are arithmetically determined” (Takaoka, col. 15, ll. 27-35; FF 3). This is an example of the raw film image being processed, after an initial analysis, according to a first set of parameters (FF 3). Conclusion of Law The evidence of record supports the Examiner’s conclusion that Takaoka, Mirzaoff, and Vazquez render the claims obvious. SUMMARY In summary, we affirm the rejection of claims 1-3, 9, 10, 17, and 18 under 35 U.S.C. § 103(a) as obvious over Takaoka, Mirzaoff, and Vazquez. Pursuant to 37 C.F.R. § 41.37(c)(1), we also affirm the rejection of claims 4-8, 11-16, and 19-30, as these claims were not argued separately. Appeal 2011-006423 Application 11/329,999 18 No time period for taking any subsequent action in connection with this appeal may be extended under 37 C.F.R. § 1.136(a). AFFIRMED lp Copy with citationCopy as parenthetical citation