Ex Parte Ghosh et alDownload PDFPatent Trial and Appeal BoardSep 16, 201410869343 (P.T.A.B. Sep. 16, 2014) Copy Citation 1 UNITED STATES PATENT AND TRADEMARK OFFICE __________ BEFORE THE PATENT TRIAL AND APPEAL BOARD __________ Ex parte JAYATI GHOSH, CHARLES D. TROUP, and XIANGYANG ZHOU __________ Appeal 2012-001101 Application 10/869,343 Technology Center 1600 __________ Before DEMETRA J. MILLS, LORA M. GREEN, and JEFFREY N. FREDMAN, Administrative Patent Judges. MILLS, Administrative Patent Judge. DECISION ON APPEAL This is an appeal under 35 U.S.C. § 134. The Examiner has rejected the claims for obviousness. We have jurisdiction under 35 U.S.C. § 6(b). STATEMENT OF CASE The following claims are representative. 1. A computer-implemented method of automatically separating multiple microarray images from at least one single combined image containing multiple microarray images thereon, said method comprising: receiving as an input said at least one single combined image containing multiple microarray images thereon, each microarray image corresponding to a single microarray and comprising a plurality of features; automatically locating said features in said microarray images using a computer; Appeal 2012-001101 Application 10/869,343 2 automatically determining, using said computer, the boundaries of each microarray image based on the locations of said features corresponding to that microarray image; automatically cropping, using said computer, said single combined image, using said automatically determined boundaries to split said single combined image into separate, independent images, thereby forming a group of single independent images, each single independent image containing only one microarray image, wherein all other image data from said single combined image has been removed from each said single independent image; automatically storing, using said computer, said single independent images as separate files: and displaying or outputting at least one of said single independent images in a user readable format. Cited References Gaidoukevitch et al. US 6,498,863 B1 Dec. 24, 2002 Le Cocq US 2004/0243341 A1 Dec. 2, 2004 Jesús Angulo & Jean Serra, Automatic analysis of DNA microarray images using mathematical morphology, 19 BIOINFORMATICS 553-562 (2003). Grounds of Rejection 1. Claims 1–4, 7–17, 19–22, 25–33, and 36 are rejected under 35 U.S.C. § 103(a) as being unpatentable over Gaidoukevitch in view of Angulo. 2. Claims 5, 6, 23, 24, 34, and 35 are rejected under 35 U.S.C. § 103(a) as being unpatentable over Gaidoukevitch in view of Angulo and Le Cocq. FINDINGS OF FACT The Examiner’s findings of fact are set forth in the Answer at pages 5–12. The following facts are highlighted. Appeal 2012-001101 Application 10/869,343 3 1. The Specification, page 12, discloses that Current methods for such pre-processing [of microarrays] require manual cropping and naming or cataloguing of the cropped images, as noted above. Embodiments of the present invention eliminate the need for such manual tasks, thereby reducing the chances for erroneously naming or organizing the cropped images, and simplifying pre-processing by automating it. 2. The Specification discloses that To analyze results obtained from microarray experiments where multiple microarrays are provided on a single slide, the researcher or analyst first needs to have the multiple images which are produced from the multiple microarrays located on the single slide, separated or cropped, so that the researcher or analyst can work with data from a single microarray (i.e., single image) at a time, since generally the researcher or analyst is interested in observing the data from only one microarray at a time. (Id.) ISSUE The issue is: Does the cited prior art support the Examiner’s finding that the claimed subject matter is obvious? PRINCIPLES OF LAW In making our determination, we apply the preponderance of the evidence standard. See, e.g., Ethicon, Inc. v. Quigg, 849 F.2d 1422, 1427 (Fed. Cir. 1988) (explaining the general evidentiary standard for proceedings before the Office). The Board “determines the scope of claims in patent applications not solely on the basis of the claim language, but upon giving Appeal 2012-001101 Application 10/869,343 4 claims their broadest reasonable construction ‘in light of the specification as it would be interpreted by one of ordinary skill in the art.’” Phillips v. AWH Corp., 415 F.3d 1303, 1316 (Fed. Cir. 2005) (quoting In re Am. Acad. of Sci. Tech. Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004). “In rejecting claims under 35 U.S.C. § 103, the examiner bears the initial burden of presenting a prima facie case of obviousness. Only if that burden is met, does the burden of coming forward with evidence or argument shift to the applicant.” In re Rijckaert, 9 F.3d 1531, 1532 (Fed. Cir. 1993) (citations omitted). In order to determine whether a prima facie case of obviousness has been established, we consider the factors set forth in Graham v. John Deere Co., 383 U.S. 1, 17 (1966): (1) the scope and content of the prior art; (2) the differences between the prior art and the claims at issue; (3) the level of ordinary skill in the relevant art; and (4) objective evidence of nonobviousness, if present. “The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.” KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007). Our mandate is to give claims their broadest reasonable interpretation. In re Am. Acad. of Sci. Tech Ctr, 367 F.3d at 1364. “An essential purpose of patent examination is to fashion claims that are precise, clear, correct, and unambiguous. Only in this way can uncertainties of claim scope be removed, as much as possible, during the administrative process.” In re Zletz, 893 F.2d 319, 322 (Fed. Cir. 1989). In In re Venner, the court held that broadly providing an automatic or mechanical means to replace a manual activity which accomplished the Appeal 2012-001101 Application 10/869,343 5 same result is not sufficient to distinguish over the prior art. In re Venner, 262 F.2d 91, 95 (CCPA 1958). ANALYSIS Claims 1–4, 7–17, 19–22, 25–33, and 36 are rejected under 35 U.S.C. § 103(a) as being unpatentable over Gaidoukevitch in view of Angulo.1 We agree with the Examiner’s fact finding, statement of the rejection and responses to Appellants’ arguments as set forth in the Answer. We find that the Examiner has provided evidence to support a prima facie case of obviousness. We provide the following additional comment to argument set forth in the Answer. Claim Interpretation We interpret the term “cropping” in claim 1 to reasonably encompass “separating” multiple arrays into smaller arrays. (FF2.) Discussion Rejection 1 Claims 1 and 4 The Examiner finds that “Gaidoukevitch teach[es] how an array may be partitioned into sub-arrays (Figures 2-3 and col. 8, lines 14-17).” (Ans. 5.) “Gaidoukevitch et al. teach storing the original image and then restoring images after manipulation (col. 21, lines 32-46) (i.e. storing images as files), as in claims 1 and 36.” (Ans. 6.) “Gaidoukevitch et al. teach filtering based 1 Where claims are argued as a group in the Brief, we select the first indicated claim as representative. For example, on page 10 of the Brief, claims 2 and 20 are argued together, and we select claim 2 as representative. Claims separately argued by Appellants are addressed separately. Appeal 2012-001101 Application 10/869,343 6 on ‘threshold’ values (col. 7, lines 44-47) and a ‘default value’ for the boundaries (col. 12, lines 15-19). The default values of Gaidoukevitch et al. are applied during the setting of array boundaries (col. 12, lines 25-28), as in ‘applied during said cropping’ recited in claim 4.” (Ans. 14.) Appellants contend that At most, the references teach automatically finding the boundaries of the individual micro-arrays. Neither reference teaches cropping the images and outputting them to separate files. Furthermore, there would be no reason to perform such an operation within the methods of either of the references, since each reference is dedicated to processing the entire image of the multiple arrays to determine the properties of each of the spots. (Reply Br. 2.) We are not persuaded. As argued by the Examiner, Gaidoukevitch teaches how an array may be partitioned (separated) into sub- arrays and stored. (Ans. 5.) Thus, one of ordinary skill in the art would understand that the system of Gaidoukevitch would have been capable of partitioning an array to multiple sub-arrays where each sub-array would be composed of a single image. We agree with the Examiner that storing such a single image in a single image file would have been an obvious approach to the person of ordinary skill. Furthermore, Appellants admit in the Specification at page 12, that their invention is to merely automate well known current methods of manually cropping and naming cropped images. (FF1.) It is not inventive to automate a manual process. In In re Venner, the court held that broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art. Venner, 262 F.2d at 95. Appellants have failed to rebut the Appeal 2012-001101 Application 10/869,343 7 Examiner’s prima facie case of obviousness for claims 1 and 4 by a preponderance of the evidence and the rejections of claims 1 and 4 are affirmed. Claims 3, 7, 9, 17, 19–22, 25–31, 33, and 36 fall with representative claim 1. Other claims separately argued by Appellants are addressed below. Claim 2 With respect to Claims 2 (which we select as representative), the Examiner finds that Gaidoukevitch et al. teach providing images (Abstract) of microarrays which contain more than one array, which they call “sub-arrays” (i.e. multiple array images) (see Figures 2-3 and col. 6, lines 44-48). Gaidoukevitch teach how an array may be partitioned into sub-arrays (Figures 2-3 and col. 8, lines 14-17). Gaidoukevitch teach automatically locating objects (i.e. features) (col. 3, lines 18-21) and determining a grid overlay (i.e. determining boundaries) (col. 3, lines 5-18) which corresponds to the underlying sub-arrays (col. 14, lines 8-13). Sub-grids can be identified and objects can be repositioned within their corresponding subgrids (col. 6, lines 23-35). Furthermore, Gaidoukevitch et al. teach that array boundaries can be set for the image to define the area of interest so that objects on the image outside the defined array are not included (col. 11, line 60 to col. 12, line 4 and lines 25-29), as in claims 1 and 36. (Ans. 5.) The Examiner further finds that Gaidoukevitch et al. teach a grid overlay (i.e. boundaries) (col. 9, line 44 to col. 10, line 3) and a deviation of plus or minus two degrees from the overlaid boundaries (col. 9, lines 52-55), which reads on the limitation “offset by predetermined boundary parameters.” Also, see Gaidoukevitch Figure 9 as Appeal 2012-001101 Application 10/869,343 8 compared to Applicant's Figure 9, both of which show an offset in the image boundaries, as in claim 2. (Ans. 6.) Appellants contend that the Examiner maintains that the passage in Gaidoukevitch at column 9, lines 52–55 is equivalent to the teaching of an offset used to define the cropping boundaries. The cited passage teaches that the multiple-array image is rotated such that the rows and columns of the spots deviate from horizontal and vertical alignment by no more than plus or minus two degrees. There is no teaching of an offset from the boundaries of the edges of the individual micro-array sub-images, i.e., a linear displacement from a boundary. (Reply Br. 2.) According to Appellants’ Specification, page 25 ¶ [00103], “[u]sing the offset values, the degree of rotation [of the microarray] can be readily calculated.” Rotation is performed if any is needed. (Page 25 ¶ [00102].) In FIG. 11 of Gaidoukevitch at a step 1110, the actual angle of orientation between rows and columns of the array and boundaries of the image is determined Finally, at a step 1112, the image is rotated to its desired angle of orientation. This is different from prior art approaches in which alignment of the overlay grid is either performed manually or depends on orientation markers included on the substrate of the array and captured in the image. The present invention allows users to correct automatically the orientation of the array independent of any guides that may be included on the substrate. (Col. 10, ll. 24–37.) Appeal 2012-001101 Application 10/869,343 9 Appellants have failed to provide evidence or scientific argument that rebuts the Examiner’s finding that the microarray image rotation calculations, deviations and corrections of Gaidoukevitch do not teach the offset of claim 2. The rejection of claim 2 is affirmed. Appellants have further failed to provide argument or evidence to rebut the Examiner’s prima facie case of obviousness of claim 4, dependent upon claim 2 and addressed with claim 1 above. Claims 8, and 10–13 Appellants argue that, With respect to Claims 8, the Examiner maintains that Angulo teaches naming the individual cropped images, since the images in Figure 2a refer to array 1a and array 1b. First, as noted above, the images in Figure 2a are not images of a single cropped micro-array image. Second, the computer did not generate these names, the authors of the article generated those names. (Reply Br. 2.) Appellants have failed to provide a preponderance of the evidence to show that Gaidoukevitch combined with Angulo does not teach that microarray naming and storing is known. Appellants further admit that they are automating a well-known, manual naming process. (FF1.) The rejections of claims 8 and 10–13 is affirmed. Appeal 2012-001101 Application 10/869,343 10 Claims 14 and 32 The Examiner finds that Gaidoukevitch et al. teach one or more computer processors for carrying the method (i.e. batch mode)(col. 28, lines 13-22), as in claim 14. Since a limiting definition of batch mode was not provided, “batch mode” as been interpreted to mean the same as “batch processing” which is commonly understood by those in the art to mean executing a series of jobs on a computer. Since a plurality of processors are taught, this reads on having a series of jobs are executed as a batch process (i.e. in batch mode). (Ans. 15.) Appellants argue that, [A] system with more than one processor could as easily employ all of the processors to carry out one single job or even the same thread within a single job as employ them to carry out a series of different jobs. The additional processors merely allow the computer to execute more instructions at the same time, but such execution is not inherently equivalent to batch processing and that “[t]he Examiner has not pointed to any teaching in Gaidoukevitch of performing multiple image array analysis on the multiple processors, such that each processor works on a separate image.” (Br. 14–15.) We are not persuaded. Appellants have failed to provide a preponderance of the evidence to show that the multiple processors of Gaidoukevitch are not capable of performing batch processing on a separate image. Further, the claims do not require each processor to operate on a separate image, but simply require a “batch mode.” The Examiner’s Appeal 2012-001101 Application 10/869,343 11 interpretation is reasonable in the absence of any evidence or substantive argument to the contrary. The rejection of claims 14 and 32 are affirmed. Claims 15 and 16 Claims 15 and 16 are affirmed for the reasons of record set forth on pages 7 and 16 of the Examiner’s Answer. Claim 17 falls with claim 16. Rejection 2 Claims 5, 6, 23, 24, 34, and 35 are rejected under 35 U.S.C. § 103(a) as being unpatentable over Gaidoukevitch in view of Angulo and Le Cocq. The Examiner admits that Gaidoukevitch et al. in view of Angulo et al. do not teach the limitations of claims 5, 6, 24, 34 and 35 which recite locating features using a projection based algorithm, projecting the two dimensional array to form one dimension, peak picking the one dimensional data set, estimating spacing between features, projecting the two dimensional array in the second of two dimensions, peak picking the one dimensional data set representative of the values of the second dimension, and generating coordinates. (Ans. 10.) However, the Examiner relies on Le Cocq to make up for this deficiency in Gaidoukevitch and Angulo. Le Coqc [sic] teaches processing of microarray data by automatically locating features on the microarray using a projection based algorithm (¶[16). LeCoqc [sic] teach reducing (i.e. projecting) the two dimensional array in a first of the two dimensions to form a one dimensional dataset representative of Appeal 2012-001101 Application 10/869,343 12 the values in the first dimension (¶ [016); peak picking the one dimensional dataset and determining which picked peaks to retain for further processing, based on predetermined peak height and peak width thresholds (¶ [0020 and 0058); estimating spacing between the features based on a statistical determination of a most frequent distance between centers of retained peaks which are adjacent one another; projecting the two dimensional array in the second of the two dimensions to form a one dimensional dataset representative of the values in the second dimension ¶ [0011, ¶ [0016, and ¶ [0058); peak picking the one dimensional dataset representative of the values in the second dimension, and determining which picked peaks to retain for further processing, based on predetermined peak height and peak width thresholds (¶ [0126); estimating spacing between the features based on a statistical determination of a most frequent distance between centers of retained peaks which are adjacent one another (¶ [0016); and generating coordinates for the features on the array, relative to X and Y axes referring to the first and second dimensions, based on the picked peaks and peak spacing (¶ [0016 and ¶ [0080). (Ans. 10–11.) Appellants contend that As noted above with respect to independent claims 1, 19, and 33 from which claims 5-6, 23-24 and claim 34 respectively depend, the combination of Gaidoukevitch and Angulo does not teach the limitations of the base claims regarding the cropping of one image into sub-images and storing those sub-images as separate files. Independent claim 35 also includes the limitations in question. LeCocq does not provide the missing teachings. Second, the algorithm used in LeCocq is different from that used in Gaidoukevitch or Angulo. The Examiner has not pointed to any teaching that the method of LeCocq would shorten the processing time of the method taught in Gaidoukevitch. Accordingly, Applicant submits that the Examiner has not made a prima facie case for obviousness with respect to claims 5, 6, 23, 24, 34, and 35. Appeal 2012-001101 Application 10/869,343 13 (Br. 16.) We have concluded above that Gaidoukevitch and Angulo teach the limitations of the base claims regarding the cropping of one image into sub- images and storing those sub-images as separate files. In addition, LeCocq is in the same field of endeavor as the claimed invention and it is analogous prior art as it is “reasonably pertinent to the particular problem with which the inventor is involved.” In re Bigio, 381 F.3d 1320, 1325 (Fed. Cir. 2004). Appellants have not shown otherwise. Appellants have failed to rebut the Examiner’s prima facie case of obviousness by a preponderance of the evidence and the rejection of representative claim 5 is affirmed. Claims 6, 23, 24, 34, and 35 fall with claim 5. CONCLUSION OF LAW The cited references support the Examiner’s obviousness rejections which are affirmed for the reasons of record. 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 cdc Copy with citationCopy as parenthetical citation