12190418 (P.T.A.B. Sep. 11, 2015)

Ex Parte CHEN et al

Patent Trial and Appeal BoardSep 11, 2015

12190418 (P.T.A.B. Sep. 11, 2015)

UNITED STATES PATENT AND TRADEMARK OFFICE UNITED STATES DEPARTMENT OF COMMERCE United States Patent and Trademark Office Address: COMMISSIONER FOR PATENTS P.O. Box 1450 Alexandria, Virginia 22313-1450 www.uspto.gov APPLICATION NO. FILING DATE FIRST NAMED INVENTOR ATTORNEY DOCKET NO. CONFIRMATION NO. 12/190,418 08/12/2008 Dingding CHEN 1391-762.01 8778 36177 7590 09/11/2015 KRUEGER ISELIN LLP (HAL) P. O. BOX 1906 CYPRESS, TX 77410-1906 EXAMINER PELLETT, DANIEL T ART UNIT PAPER NUMBER 2122 MAIL DATE DELIVERY MODE 09/11/2015 PAPER Please find below and/or attached an Office communication concerning this application or proceeding. The time period for reply, if any, is set in the attached communication. PTOL-90A (Rev. 04/07) UNITED STATES PATENT AND TRADEMARK OFFICE ____________ BEFORE THE PATENT TRIAL AND APPEAL BOARD ____________ Ex parte DINGDING CHEN, SYED HAMID, and MICHAEL C. DIX ____________ Appeal 2013-005140 Application 12/190,418 Technology Center 2100 ____________ Before CAROLYN D. THOMAS, JEFFREY S. SMITH, and TERRENCE W. McMILLIN, Administrative Patent Judges. SMITH, Administrative Patent Judge. DECISION ON APPEAL Appeal 2013-005140 Application 12/190,418 2 STATEMENT OF THE CASE This is an appeal under 35 U.S.C. § 134(a) from the Examiner’s Final Rejection of claims 1–18 and 30–34, which are all the claims pending in the application. We have jurisdiction under 35 U.S.C. § 6(b). We affirm. Representative Claim 1. A visualization method that comprises: obtaining a data set having a dimensionality that is to be reduced; identifying kernels that represent clusters within the data set; representing low-dimensionality coordinates of each kernel as a corresponding gene on a chromosome in a population of such chromosomes; subjecting said population to evolutionary computation to select a dimensionality reduction mapping; and displaying the kernels at locations based on their low- dimensionality coordinates as determined from the selected dimensionality reduction mapping. Prior Art Agrafiotis US 7,039,621 B2 May 2, 2006 Stockwell US 2008/0154809 A1 June 26, 2008 Gillen US 7,565,833 B2 July 28, 2009 Ujjwal Maulik & Sanghamitra Bandyopadhyay, Genetic algorithm- based clustering technique, Pattern Recognition 33, 1455–1465 (2000). Appeal 2013-005140 Application 12/190,418 3 Frans van den Bergh & Andries P. Engelbrecht, A Cooperative Approach to Particle Swarm Optimization, 8 IEEE Transactions on Evolutionary Computation 225–239 (2004). N. Chakraborti et al., A Study of the Cu Clusters Using Gray-Coded Genetic Algorithms and Differential Evolution, 25(1) JOURNAL OF PHASE EQUILIBRIA AND DIFFUSION 16–21 (2004). Examiner’s Rejections Claims 1, 3, 4, 7, 8, 13, and 18 stand rejected under 35 USC § 103(a) as being unpatentable over Agrafiotis and Maulik. Claims 2, 14–16, 30–32, and 34 stand rejected under 35 U.S.C. § 103(a) as being unpatentable over Agrafiotis, Maulik, Bergh. Claims 5, 6, and 17 are rejected under 35 U.S.C. § 103(a) as being unpatentable over Agrafiotis, Maulik, and Stockwell. Claims 9–12 stand rejected under 35 U.S.C. § 103(a) as being unpatentable over Agrafiotis, Maulik, and Gillen. Claim 33 stands rejected under 35 U.S.C. § 103(a) as being unpatentable over Agrafiotis, Maulik, Bergh, and Chakraborti. Appeal 2013-005140 Application 12/190,418 4 ANALYSIS We adopt the findings of fact made by the Examiner in the Final Rejection and Examiner’s Answer. We agree with the decisions made by the Examiner for the reasons given in the Examiner’s Answer. We highlight the following for emphasis. Section 103 rejection of claims 1, 3, 4, 7, 8, 13, and 18 Appellants contend the prior art does not teach “subjecting said population to evolutionary computation to select a dimensionality reduction mapping” as recited in claim 1. Br. 9–11. Appellants’ contention is inconsistent with Paragraph 5 of Appellants’ Specification, which admits that techniques for dimensionality reduction using genetic algorithms were known in the art at the time of invention. We sustain the rejection of claim 1 under 35 U.S.C. § 103. Appellants do not present arguments for separate patentability of claims 3, 4, 8, 13, and 18, which fall with claim 1. Appellants further contend the prior art does not teach the “fitness function that includes a measure of linear correlation between distances in the original data set and distances in a reduced-dimension data set,” as recited in claim 7. Br. 11. However, Appellants have not provided persuasive evidence to show employing a fitness function including a measure of linear correlation between distances as taught by Maulik (p. 1458, section 3.2.3; see also p. 1456 (“One such measure of similarity may be the Euclidean distance D ”)) in the original and reduced data sets of the admitted prior art was “uniquely challenging or difficult for one of ordinary skill in the art.” Leapfrog Enters., Inc. v. Fisher–Price, Inc., 485 F.3d 1157, Appeal 2013-005140 Application 12/190,418 5 1162 (Fed. Cir. 2007) (citing KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 419 (2007)). We sustain the rejection of claim 7 under 35 U.S.C. § 103. Section 103 rejection of claims 2, 14–16, 30–32, and 34 Appellants contend the prior art does not teach “refining the dimensionality reduction mapping using a particle swarm optimization search” as recited in claim 2. Br. 12. Appellants’ contention is inconsistent with paragraph 5 of Appellants’ Specification, which teaches that reducing dimensions using genetic algorithm particle swarm optimization was known in the prior art at the time of invention. Appellants present arguments for the patentability of claims 14–16, 30–32, and 34 (Br. 12) similar to those presented for claim 2 which we find unpersuasive. We sustain the rejection of claims 2, 14–16, 30–32, and 34 under 35 U.S.C. § 103. Section 103 rejection of claims 5, 6, and 17 Appellants contend the prior art does not teach “the evolutionary computation employs a multi-objective fitness function with a measure of kernel pair distance error and a measure of linear correlation with a prediction variable” as recited in claim 5. Br. 13. According to Appellants, paragraph 94 of Stockwell merely teaches three user specified fitness function options that may be selected by a user. Id. The Examiner finds the fitness function with linear correlation as taught by Stockwell teaches the claimed “measure of linear correlation,” and the categorical dependent variable as taught by Stockwell teaches the Appeal 2013-005140 Application 12/190,418 6 claimed “prediction variable.” Ans. 29. The Examiner further finds the combined teachings of the prior art teach the limitations recited in claim 5. Id. Appellants have not provided persuasive evidence or argument to rebut the Examiner’s findings. We sustain the rejection of claim 5 under 35 U.S.C. § 103. Appellants do not present arguments for separate patentability of claims 6 and 17, which fall with claim 5. Section 103 rejection of claims 9–12 Appellants contend Gillen does not teach the application of a dimensionality reduction process to any of the types of data sets of claims 9– 12. App. Br. 13–14. The Examiner relies on Gillen to teach the various types of data sets recited in claims 9–12. See Ans. 20–23, 29–30. Appellants have not provided persuasive evidence to show applying the known techniques for reducing dimensions using genetic algorithms as admitted on page 5 of Appellants’ Specification to the data sets of Gillen was uniquely challenging or difficult for one of ordinary skill in the art. We sustain the rejection of claims 9–12 under 35 U.S.C. § 103. Section 103 rejection of claim 33 Appellants contend Chakraborti does not teach “the software configures the processor to carry out the evolutionary computation with at least eight bits of resolution in each dimension of the low-dimensionality data space, and wherein positions within the low-dimensionality data space are expressed using Gray coding” as recited in claim 33. The Examiner relies on Chakraborti to teach the use of Gray coding, and concludes Appeal 2013-005140 Application 12/190,418 7 applying the Gray coding used in Chakraborti to the dimensionality reduction mapping using genetic algorithms known in the prior art yields the predictable result of avoiding Hamming Cliff problems as taught by Chakraborti. Appellants have not provided persuasive evidence or argument to rebut the Examiner’s findings. We sustain the rejection of claim 33 under 35 U.S.C. § 103. DECISION The rejection of claims 1, 3, 4, 7, 8, 13, and 18 under 35 U.S.C. § 103(a) as unpatentable over Agrafiotis and Maulik is affirmed. The rejection of claims 2, 14–16, 30–32, and 34 under 35 U.S.C. § 103(a) as unpatentable over Agrafiotis, Maulik, and Bergh is affirmed. The rejection of claims 5, 6, and 17 under 35 U.S.C. § 103(a) as unpatentable over Agrafiotis, Maulik, and Stockwell is affirmed. The rejection of claims 9–12 under 35 U.S.C. § 103(a) as unpatentable over Agrafiotis, Maulik, and Gillen is affirmed. The rejection of claim 33 under 35 U.S.C. § 103(a) as unpatentable over Agrafiotis, Maulik, Bergh, and Chakraborti is affirmed. No time period for taking any subsequent action in connection with this appeal may be extended under 37 C.F.R. § 1.136(a)(1)(iv). See 37 C.F.R. § 41.50(f). AFFIRMED ELD