DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Status Claims 52- 81 are currently pending and under examination herein. Claims 52-81 are rejected. Priority The i nstant application claims priority to PCT/ US2021 /025433 filed 04/01/2021 and U . S . Provisional Application 63004204 fi l ed 04/02/2020. In this action, claims 52- 81 are examined as though they had an effective filing date of 04/02/2020 . In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s). Information Disclosure Statement The information disclosure statements (IDS) submitted on 06/02/2023, 06/06/2023, and 04/18/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement s are being considered by the examiner. One reference from the IDS filed on 06/02/2023, The Most Popular Dog Breeds of 2019 (Cite No. 004) was lined through because it was not found amongst the submitted references. However, a document on The Most Popular Dog Breeds of 2020 was found amongst those submitted. This may have been a minor error. The document was considered as a replacement and did not seem to have any impact on the examination of the instant claims. Drawings The drawings filed on 09/29/2022 are accepted. A n accepted petition for color drawings is noted . Specification The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Links were found on pages 59 (Paragraph 0197) , 77 (Paragraph 0291) , 97 (Paragraph 0365), 100 (Paragraph 0393), and 101 (Paragraph 0399) of the specification. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// , www., or other browser-executable code. See MPEP § 608.01. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b ) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. Claim s 63, 67, and 69 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims 63, 67, and 69 are indefinite because they refer to tables that are not present in the claims. It is not proper to refer to tables in the specification , as this renders the me te s and bounds of the claims unclear. Th is rejection can be overcome by placing the tables or information from the tables within the claims. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 52-81 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and a natural law without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter ( Step 1 : YES ) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea or natural law ( Step 2A , Prong 1 ). Claims 52-79 are directed to a method and claims 80-81 are directed to systems. In the instant application, the claims recite the following limitations that equate to an abstract idea or natural law : Claim s 52 , 80, and 81 recite the limitation - applying a trained machine learning algorithm to the genotype data to determine a predicted pigmentation phenotype based at least in part on the quantitative values of the plurality of genetic variants . Based on the broadest reasonable interpretation, applying a trained machine learning algorithm to the genotype data to determine a predicted phenotype could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Additionally, this describes a natural correlation between genotype s and phenotype s , which classifies the limitation as a law of nature. Claim s 52 , 80, and 81 also recite identifying the canine subject as having the predicted pigmentation phenotype . Based on the broadest reasonable interpretation, identifying the canine subject could practically be done by the human mind . This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claims 53-79 depend on claim 52 , and thus contain the above issues due to said dependence. Claim 70 recites the limitation - applying the trained machine learning algorithm comprises determining a weighted sum of the quantitative values of the plurality of genetic markers. Based on the broadest reasonable interpretation, determining these values could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claims 71-72 depend on claim 70 , and thus contain the above issues due to said dependence. Claim 71 recites the limitation - the weighted sum is determined using a plurality of pre-determined weights associated with the plurality of genetic markers. Based on the broadest reasonable interpretation, determining these values could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 72 depend s on claim 71 , and thus contain the above issues due to said dependence. Claim 72 recites the limitation - the plurality of pre-determined weights associated with the plurality of genetic markers is determined by performing a genome- wide association study comprising a multiple linear regression. Based on the broadest reasonable interpretation, determining these values could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 73 recites the limitation - determining a second pigmentation phenotype of a second canine subject . Based on the broadest reasonable interpretation, determining a phenotype could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Additionally, this describes a natural correlation between genotype s and phenotype s , which classifies the limitation as a law of nature. Claim 73 also recites determining an expected range of pigmentation phenotypes of a potential offspring of the canine subject and the second canine subject. Based on the broadest reasonable interpretation, determining the range of phenotypes could practically be done by the human mind . This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claims 74-77 depend on claim 73 , and thus contain the above issues due to said dependence. Claims 74-76 recites the limitation - determining a recommendation indicative of whether or not to breed the first canine subject and the second canine subject together, based on the expected range of pigmentation phenotypes of the potential offspring of the canine subject and the second canine subject . Based on the broadest reasonable interpretation, determining a recommendation and an expected range of phenotypes could practically be done by the human mind . This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 77 depend on claim 74 , and thus contain the above issues due to said dependence. Claim 77 recites the limitation – based at least in part on the expected range of pigmentation phenotypes of the potential offspring of the canine subject and the second canine subject. Based on the broadest reasonable interpretation, determining an expected range of phenotypes could practically be done by the human mind . This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 79 recites the limitation - the trained machine learning algorithm comprises a linear regression or a logistic regression. Based on the broadest reasonable interpretation, applying this machine learning algorithm could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. These limitations recite concepts of determining , predicting, and identifying information and applying algorithms that are so generically recited that they can be practically performed in the human mind as claimed, which falls under the “Mental processes” and “Mathematical concepts” grouping of abstract ideas. These recitations are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Additionally, the limitations describe natural correlations between genotype s and their corresponding phenotype s , which fall under natural laws. This is similar to a correlation between the presence of myeloperoxidase in a bodily sample (such as blood or plasma) and cardiovascular disease risk ( Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1361, 123 USPQ2d 1081, 1087 (Fed. Cir. 2017)) that the courts have identified as a law of nature. As such, claims 52-81 recite an abstract idea and law of nature ( Step 2A , Prong 1: YES ). Claims found to recite a judicial exception under Step 2A , Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not ( Step 2A , Prong 2 ). These judicial exceptions are not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology (MPEP § 2106.04(d)(1)). Rather, the claims provide insignificant extra-solution activity (MPEP § 2106.05(g)) and provide mere instructions to apply a judicial exception (MPEP § 2106.05(f)). Specifically, the claims recite the following additional elements: Claim 52 recites a computer-implemented method for determining a pigmentation phenotype of a canine subject, comprising: (a) receiving genotype data for the canine subject, wherein the genotype data comprises quantitative values of each of a plurality of genetic markers, wherein the plurality of genetic markers comprises genetic variants . Claim 53 recites the canine subject is a dog. Claim 54 recites the dog is a purebred dog. Claim 55 recites the dog is a mixed breed dog. Claim 56 recites obtaining the genotype data by assaying a biological sample obtained from the canine subject. Claim 57 recites the biological sample comprises a blood sample, a saliva sample, a swab sample, a cell sample, or a tissue sample. Claim 58 recites the assaying comprises sequencing the biological sample or derivatives thereof. Claim 59 recites the quantitative values are indicative of a presence or absence in the genotype data of each of the plurality of genetic variants. Claim 60 recites the plurality of genetic variants is selected from the group consisting of single nucleotide polymorphisms (SNPs), insertions or deletions (indels), microsatellites, and structural variants. Claim 61 recites the pigmentation phenotype comprises a coat color intensity phenotype, a ticking phenotype, a roaning phenotype, or a tongue pigmentation phenotype. Claim 62 recites the pigmentation phenotype comprises a coat color intensity phenotype. Claim 63 recites the plurality of genetic markers comprises one or more markers selected from the group listed in Table 8. Claim 64 recites the plurality of genetic markers comprises one or more SNPs of a genetic locus selected from the group consisting of canFam3.1 chr2 : 74.7Mb , chr20 : 55.8Mb , and chr2 : 10.9Mb . Claim 65 recites the pigmentation phenotype comprises a ticking phenotype. Claim 66 recites the pigmentation phenotype comprises a roaning phenotype. Claim 67 recites the plurality of genetic markers comprises one or more markers selected from the group listed in Table 11. Claim 68 recites the pigmentation phenotype comprises a tongue pigmentation phenotype. Claim 69 recites the plurality of genetic markers comprises one or more markers selected from the group listed in Table 13. Claim 77 recites generating a social connection between a first person associated with the first canine subject and a second person associated with the second canine subject . Claim 78 recites identifying the canine subject as having the predicted pigmentation phenotype with an accuracy of at least about 70%. Claim 80 recites computer system for determining a pigmentation phenotype of a canine subject, comprising: a database that is configured to store genotype data for the canine subject, wherein the genotype data comprises quantitative values of each of a plurality of genetic markers, wherein the plurality of genetic markers comprises genetic variants; and one or more computer processors operatively coupled to the database, wherein the one or more computer processors are individually or collectively programmed . Claim 81 recites a non-transitory computer-readable medium comprising machine-executable code that, upon execution by one or more computer processors, implements a method for determining a coat color intensity phenotype of a canine subject, the method comprising: (a) receiving genotype data for the canine subject, wherein the genotype data comprises quantitative values of each of a plurality of genetic markers, wherein the plurality of genetic markers comprises genetic variants . There are no limitations that indicate that the claimed determining, predicting, and identifying information and applying algorithms require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. There is no indication that these steps are affected by the judicial exception in any way and thus do not integrate the recited judicial exception into a practical application. As such, claims 52-81 are directed to an abstract idea and natural law ( Step 2A , Prong 2 : NO ). Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself ( Step 2B ). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite conventional additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The claims also recite conventional additional elements that represent insignificant extra-solution activities. The instant claims recite the additional elements listed above. As discussed above, there are no additional limitations to indicate that the claimed determining, predicting, and identifying information and applying algorithms require anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea or natural law using a generic computer do not render an abstract idea or natural law eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. As specified in MPEP 2106.05(g), extra-solution activities can be understood as incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Insignificant extra-solution activities include mere data gathering, selecting a particular data source or type of data to be manipulated, and displaying information. Additionally, Syvänen (2001, Nature Reviews Genetics, Vol. 2: 930-942) explains that the detection of genetic variants such as SNPs and the utilization of that kind of genetic data for further investigations has long been a conventional laboratory practice (Page 932, Column 2, Paragraph 1: These assays are frequently used for large-scale genotyping of SNPs today ) . The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself ( Step 2B : No ). As such, claims 52-81 are not patent eligible. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness . This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. Claims 52-53, 56-62, 70-72, 78-81 are rejected under 35 U.S.C. 103 as being unpatentable over Branicki et al. (2011, Human Genetics, Vol. 129: 443-454), in view of Bannasch et al. (2020, Genes, Vol. 11, No. 75, 1-13). Italicized text from reference art. Applicable c laims include: Claim 52. A computer-implemented method for determining a pigmentation phenotype of a canine subject, comprising: (a) receiving genotype data for the canine subject, wherein the genotype data comprises quantitative values of each of a plurality of genetic markers, wherein the plurality of genetic markers comprises genetic variants; and (b) applying a trained machine learning algorithm to the genotype data to determine a predicted pigmentation phenotype based at least in part on the quantitative values of the plurality of genetic variants; and (c) identifying the canine subject as having the predicted pigmentation phenotype. Claim 53. The method of claim 52, wherein the canine subject is a dog. Claim 56. The method of claim 52, further comprising obtaining the genotype data by assaying a biological sample obtained from the canine subject. Claim 57. The method of claim 56, wherein the biological sample comprises a blood sample, a saliva sample, a swab sample, a cell sample, or a tissue sample. Claim 58. The method of claim 56, wherein the assaying comprises sequencing the biological sample or derivatives thereof. Claim 59. The method of claim 52, wherein the quantitative values are indicative of a presence or absence in the genotype data of each of the plurality of genetic variants. Claim 60. The method of claim 52, wherein the plurality of genetic variants is selected from the group consisting of single nucleotide polymorphisms (SNPs), insertions or deletions (indels), microsatellites, and structural variants. Claim 61. The method of claim 52, wherein the pigmentation phenotype comprises a coat color intensity phenotype, a ticking phenotype, a roaning phenotype, or a tongue pigmentation phenotype. Claim 62. The method of claim 61, wherein the pigmentation phenotype comprises a coat color intensity phenotype. Claim 70. The method of claim 52, wherein applying the trained machine learning algorithm comprises determining a weighted sum of the quantitative values of the plurality of genetic markers. Claim 71. The method of claim 70, wherein the weighted sum is determined using a plurality of pre-determined weights associated with the plurality of genetic markers. Claim 72. The method of claim 71, wherein the plurality of pre-determined weights associated with the plurality of genetic markers is determined by performing a genome- wide association study (GWAS) comprising a multiple linear regression. Claim 78. The method of claim 52, further comprising identifying the canine subject as having the predicted pigmentation phenotype with an accuracy of at least about 70%. Claim 79. The method of claim 52, wherein the trained machine learning algorithm comprises a linear regression or a logistic regression. Claim 80. A computer system for determining a pigmentation phenotype of a canine subject, comprising: a database that is configured to store genotype data for the canine subject, wherein the genotype data comprises quantitative values of each of a plurality of genetic markers, wherein the plurality of genetic markers comprises genetic variants; and one or more computer processors operatively coupled to the database, wherein the one or more computer processors are individually or collectively programmed to:5 (a) apply a trained machine learning algorithm to the genotype data to determine a predicted pigmentation phenotype based at least in part on the quantitative values of the plurality of genetic variants; and (b) identify the canine subject as having the predicted pigmentation phenotype. Claim 81. A non-transitory computer-readable medium comprising machine-executable code that, upon execution by one or more computer processors, implements a method for determining a coat color intensity phenotype of a canine subject, the method comprising:(a) receiving genotype data for the canine subject, wherein the genotype data comprises quantitative values of each of a plurality of genetic markers, wherein the plurality of genetic markers comprises genetic variants; (b) applying a trained machine learning algorithm to the genotype data to determine a predicted pigmentation phenotype based at least in part on the quantitative values of the plurality of genetic variants; and (c) identifying the canine subject as having the predicted pigmentation phenotype. Regarding Claim s 52, 80 , and 81 , Branicki et al. teaches (Claim 52.a ) receiving genotype data that comprises quantitative values of genetic variants (Page 447, Column 1, Paragraph 1: All ascertained SNPs including the R and r variants were tested for association with each hair color category ). Branicki et al. also teaches (Claim 52.b ) applying a trained machine learning algorithm to the genotype data to determine a predicted pigmentation phenotype (Page 447, Column 1, Paragraph 2: We used a multinomial logistic regression model for the prediction analysis (for predicting hair color from the SNPs)). Branicki et al. also teaches (Claim 52.c ) identifying a subject as having the predicted pigmentation phenotype (Page 449, Column 1, Paragraph 2: Overall, hair color prediction with 13 DNA components from 11 genes showed very good accuracy ). Branicki et al. teaches the above limitations were run on a computer system that also inherently contains non-transitory computer readable mediums (Claims 80 and 81) (Page 448, Column 1, Paragraph 2: we conducted a prediction analysis using the multinomial LASSO regression model implemented in the R library glmnet v1.1 -4 ). Regarding Claim 70 , Branicki et al. teach applying the trained machine learning algorithm comprises determining a weighted sum of the quantitative values of the plurality of genetic markers (Page 447, Column 1, Paragraph 2: We used a multinomial logistic regression model for the prediction analysis ; The equations listed include sums ( ) that utilize probabilities ( ) as weights). Regarding Claim 71 , Branicki et al. teach the weighted sum is determined using pre-determined weights associated with genetic markers (Page 447, Column 1, Paragraph 2: Let 1, 2, 3, and 4 denote the probability of blond, brown, red, and black, respectively ; represent probabilities used as weights ). Regarding Claim 78 , Branicki et al. teach identifying the subject as having the predicted pigmentation phenotype with an accuracy of at least about 70% (Page 450, Column 2, Paragraph 1: the expected true positive rates are 78% for black and 88% for red ). Regarding Claim 79 , Branicki et al. teach the trained machine learning algorithm comprises a linear regression or a logistic regression (Page 447, Column 1, Paragraph 2: We used a multinomial logistic regression model for the prediction analysis ). Branicki et al. does not teach the subject was a canine ( Claim s 52 , 78, 80-81 ). Branicki et al. also does not teach the subject is a dog ( Claim 53 ) . Branicki et al. also does not teach assaying a biological sample obtained from the canine subject ( Claim 56 ). Branicki et al. also does not teach the nature of the biological sample ( Claim 57 ). Branicki et al. also does not teach sequencing the biological sample ( Claim 58 ). Branicki et al. also does not teach the quantitative values are indicative of a presence or absence in the genotype data of each genetic variant ( Claim 59 ). Branicki et al. also does not teach the type of genetic variants ( Claim 60 ). Branicki et al. also does not teach the types of pigmentation phenotype s included ( Claim 61 ). Branicki et al. also does not teach the pigmentation phenotype comprises a coat color intensity phenotype ( Claim 62 ). Branicki et al. also does not teach the pre-determined weights associated with the genetic markers are determined by performing a genome-wide association comprising a multiple linear regression ( Claim 72 ) . Regarding Claim s 52 , 78, 80-81, Bannasch et al. teaches linking genetic variants to the coat coloration of dogs (Page 2, Paragraph 4: By analyzing sequence coverage from whole genome sequence data, a previously identified copy number variant was found to be associated with the red color intensity ). Establishing the link between genetic variants and coat color phenotypes would allow the methods of Branicki et al. to be applied to predict dog hair color based on genetic variants (see reason to combine below). This is applicable to all dependent claims. Regarding Claim 53 , Bannasch et al. teaches the subject is a dog (Page 2, Paragraph 5: Blood and saliva samples were collected from privately owned dogs ). Regarding Claim 56 , Bannasch et al. teaches assaying a biological sample obtained from the canine subject (Page 2, Paragraph 6: Single-nucleotide variant genotyping of was performed (on the biological samples) on the Illumina Canine HD 170 K BeadChip ). Regarding Claim 57 , Bannasch et al. teaches the biological sample comprises a blood sample, a saliva sample, a swab sample, a cell sample, or a tissue sample (Page 2, Paragraph 5: Blood and saliva samples were collected from privately owned dogs ). Regarding Claim 58 , Bannasch et al. teach the assaying comprises sequencing the biological sample (Page 2, Paragraph 6: Single-nucleotide variant genotyping was performed (on the biological samples) on the Illumina Canine HD 170 K BeadChip ). Regarding Claim 59 , Bannasch et al. teach the quantitative values are indicative of a presence or absence in the genotype data of each genetic variant (Page 3, Paragraph 1: Final analysis was performed with 105,678 single nucleotide variants ). Regarding Claim 60, Bannasch et al. teach the genetic variants are selected from the group consisting of single nucleotide polymorphisms (SNPs), insertions or deletions (indels), microsatellites, and structural variants (Page 3, Paragraph 1: Final analysis was performed with 105,678 single nucleotide variants ). Regarding Claim 61 , Bannasch et al. teach the pigmentation phenotype comprises a coat color intensity phenotype, a ticking phenotype, a roaning phenotype, or a tongue pigmentation phenotype (Page 4, Paragraph 1: A GWAS was performed to identify a region in the genome that is associated with this variation in coat color ). Regarding Claim 62 , Bannasch et al. teach the pigmentation phenotype comprises a coat color intensity phenotype (Page 4, Paragraph 1: A GWAS was performed to identify a region in the genome that is associated with this variation in coat color ). Regarding Claim 72 , Bannasch et al. teach the pre-determined weights associated of the genetic markers are determined by performing a genome- wide association study (GWAS) comprising a multiple linear regression (Page 4, Paragraph 1: A GWAS was performed to identify a region in the genome that is associated with this variation in coat color ; Page 7, Paragraph 1: Linear regression analysis of 15 NSDTR with color intensity ratios from root to tip and estimated ddPCR genomic copy number) . It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify Branicki et al. with Bannasch et al. because both utilize the relationship between genotype and hair phenotype. Branicki et al. uses genotype to predict phenotype in humans. Bannasch et al. uses phenotype to predict genotype in dogs. The utilization of genotypic data of one organism could be swapped for the other as there is no difference in the nature of DNA that would make analytical techniques used for human s not applicable to dogs . Additionally, Branicki et al. explain that other genes related to hair color could be explored with their methods (Page 452, Column 2, Paragraph 1: it shall be tested in future studies if and to what extent SNPs from other genes with recently reported hair color association not used here add to the hair color prediction accuracy as presented ). After Bannasch et al. disclosed the link between genetic variants and dog coat phenotype, it would be obvious to use the analytical framework of Branicki et al. to predict phenotype from genotype in dogs. Therefore, it would have been obvious to someone of ordinary skill in the art at the time of the effective filling date to combine the methods from the references indicated above. Furthermore, one of ordinary skill in the art would predict that the method taught by Bannasch et al could be readily added to the method of Branicki et al. with a reasonable expectation of success because they utilize genotypic and ph eno typic data of closely related species (mammals) to make predictions . Accordingly, claims 52-53, 56-62, 70-72, 78-81 taken as a whole would have been prima facie obvious before the effective filing date and are rejected under 35 U.S.C. 103. Claims 52-64, 70-72, 78-81 are rejected under 35 U.S.C. 103 as being unpatentable over Branicki et al., as applied to 52-53, 56-62, 70-72, 78-81 above, in view of Bannasch et al. , as applied to claims 52-53, 56-62, 70-72, 78-81 above, and in further view of Deane-Coe et al. (2018, Plos Genetics, Vol. 14, No. 10: 1-15) . Italicized text from reference art. Applicable c laims include: For claims 52-53, 56-62, 70-72, 78-81 , see above Claim 54. The method of claim 53, wherein the dog is a purebred dog. Claim 55. The method of claim 53, wherein the dog is a mixed breed dog. Claim 63. The method of claim 62, wherein the plurality of genetic markers comprises one or more markers selected from the group listed in Table 8. Claim 64. The method of claim 62, wherein the plurality of genetic markers comprises one or more SNPs of a genetic locus selected from the group consisting of canFam3.1 chr2 : 74.7Mb , chr20 : 55.8Mb , and chr2 : 10.9Mb . Branicki et al. and Bannasch et al. teach claims 52-53, 56-62, 70-72, 78-81 (see above). Branicki et al. and Bannasch et al. do not teach if the dog is a purebred or a mixed breed dog ( Claims 54-55 ). Branicki et al. and Bannasch et al. also do not teach the genetic ma r kers of table 8 ( Claim 63 ). Branicki et al. and Bannasch et al. also do not teach utilizing genetic markers from CanFam3.1 ( Claim 64 ). Regarding Claim 54 - 55 , Deane-Coe et al. teach the utilization of purebred dog s and mixed breed dog s (Page 8, Paragraph 3: Most were owners of mixed-breed dogs, and 21% were owners of purebred dogs ). Regarding Claim 63 , Deane-Coe et al. teach the plurality of genetic markers comprises one or more selected from Table 8. The markers BICF2P1302896 , TIGRP2P30892_rs8643466 , BICF2P202986 , TIGRP2P31085_rs8981024 , BICF2S245539 , BICF2G630433130 , BICF2P828524 , BICF2G630655699 , BICF2S23541470 were found in a supplemental data file (coe_etal_canine_eye_color_GWAS_N3180_discovery_panel.assoc.txt) deposited on Dryad (see Data Availability Statement) . Regarding Claim 64 , Deane-Coe et al. teach the utilization of the canFam3.1 reference genome (Page 5, Paragraph 2: The sequence aligned with greater than 98% homology to CanFam3.1 ). SNPs were found in a supplemental the data file (coe_etal_canine_eye_color_GWAS_N3180_discovery_panel.assoc.txt) deposited on Dryad (see Data Availability Statement), which included those from CanFam3.1 chromosomes 2, 20, and 21. It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify Branicki et al. and Bannasch et al. with Deane-Coe et al. because, as discussed above, all utilize the relationship between genotypes and phenotypes related to pigmentation and are applicable to genomic data from dogs. Deane-Coe et al. provide SNPs related to dog phenotypes that could be imple me nted using the methods of Branicki et al. (Page 452, Column 2, Paragraph 1: it shall be tested in future studies if and to what extent SNPs from other genes with recently reported hair color association not used here add to the hair color prediction accuracy as presented ). Deane-Coe et al. are concerned with pigmentation of the eye in dogs. However, the genes are related to those that produce pigmentation in hair and have utilize similar sets of genetic markers, hence the overlap in SNPs tested between Deane-Coe et al. for eye color and the instant application for coat color. Therefore, it would have been obvious to someone of ordinary skill in the art at the time of the effective filling date to combine the methods from the references indicated above. Furthermore, one of ordinary skill in the art would predict that the method taught by Deane-Coe et al. could be readily added to the method s of Branicki et al. and Bannasch et al. with a reasonable expectation of success because they utilize genotypic and phenotypic data of closely related species (mammals) to make predictions . Accordingly, claims 52-64, 70-72, 78-81 taken as a whole would have been prima facie obvious before the effective filing date and are rejected under 35 U.S.C. 103. Claims 52-72 and 78-81 are rejected under 35 U.S.C. 103 as being unpatentable over Branicki et al., as applied to 52-53, 56-62, 70-72, 78-81 above, in view of Bannasch et al. , as applied to claims 52-53, 56-62, 70-72, 78-81 above, and in further view of Deane-Coe et al. , as applied to claims 52-64, 70-72, 78-81 above, and Schmutz and Berryere (2007, Animal Genetics, Vol. 38: 539-549 ; IDS filed 06/02/2023 ) . Italicized text from reference art. Applicable c laims include: See above for Claims 52-64, 70-72, 78-81 Claim 65. The method of claim 61, wherein the pigmentation phenotype comprises a ticking phenotype. Claim 66. The method of claim 61, wherein the pigmentation phenotype comprises a roaning phenotype. Claim 67. The method of claim 66, wherein the plurality of genetic markers comprises one or more markers selected from the group listed in Table 11. Claim 68. The method of claim 61, wherein the pigmentation phenotype comprises a tongue pigmentation phenotype. Claim 69. The method of claim 68, wherein the plurality of genetic markers comprises one or more markers selected from the group listed in Table 13. Branicki et al. and Bannasch et al. teach Claims 52-53, 56-62, 70-72, 78-81 (see above). Regarding Claim 67, Deane-Coe et al. teach the genetic markers comprises one or more selected from the group listed in Table 11 . The markers chr38_11085443 , BICF2P941536 , chr38_11111286 , BICF2S23536290 , BICF2P1396284 , BICF2S23332370 were found in a supplemental data file (coe_etal_canine_eye_color_GWAS_N3180_discovery_panel.assoc.txt) deposited on Dryad (see Data Availability Statement) . Regarding Claim 69 , Deane-Coe et al. teach the genetic markers comprises one or more markers selected from the group listed in Table 13 . The markers BICF2G630433130 , EMB_chr20_5843762 , BICF2G630133910 , chr37_28548746 , BICF2P888958 , BICF2S2332765 , BICF2S2332764 , BICF2G630133940 , BICF2G630133952 , BICF2G630133969 , BICF2G630133994 , BICF2G630134000 , BICF2G630134015 , chr37_28616075 , BICF2G630134036 , BICF2P387308 , chr37_28636710 , BICF2G630134058 , BICF2G630134067 , BICF2G630134080 , BICF2G630134084 were found in a supplemental data file (coe_etal_canine_eye_color_GWAS_N3180_discovery_panel.assoc.txt) deposited on Dryad (see Data Availability Statement) . Branicki et al. , Bannasch et al. , and Deane-Coe et al. do not teach a ticking ( Claim 65 ), a roaning ( Claim 66 ), or a tongue pigmentation ( Claim 68 ) phenotype . Regarding Claim 65 , Schmutz and Berryere suggest a ticking pigmentation phenotype (Page 546, Column 1, Paragraph 2: another type of spotting that consists of very small spots on a white background ). Regarding Claim 66 , Schmutz and Berryere suggest a roaning pigmentation phenotype (Page 546, Column 1, Paragraph 4: Roan is a pattern consisting of intermingled pigmented and unpigmented hairs ). Regarding Claim 68 , Schmutz and Berryere suggest the pigmentation phenotype comprises a tongue pigmentation phenotype (Page 546, Column 2, Paragraph 3: Chow Chow and Chinese Shar-Pei typically have a melanistically pigmented tongue). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify Branicki et al. , Bannasch et al. , and Deane-Coe et al. with Schmutz and Berryere because all are concerned with the relationship between genotypes and phenotypes related to pigmentation and are applicable to the utilization of genetic data from dogs. Schmutz and Berryere suggest dog coat phenotypes and their links to specific breeds and genetic s that one of ordinary skill in the art could apply to prediction methods of Branicki et al. (Page 452, Column 2, Paragraph 1: it shall be tested in future studies if and to what extent SNPs from other genes with recently reported hair color association not used here add to the hair color prediction accuracy as presented ). Therefore, it would have been obvious to someone of ordinary skill in the art at the time of the effective filling date to combine the methods from the references indicated above. Furthermore, one of ordinary skill in the art would predict that the method taught by Schmutz and Berryere could be readily added to the method s of Branicki et al., Bannasch et al., and Deane-Coe et al. with a reasonable expectation of success because they all are concerned with phenotypic data related to the underlying genotypic mechanisms in closely related species (mammals) . Accordingly, claims 52-72 and 78-81 taken as a whole would have been prima facie obvious before the effective filing date and are rejected under 35 U.S.C. 103. Claims 52-53, 56-62, and 70-81 are rejected under 35 U.S.C. 103 as being unpatentable over Branicki et al. , as applied to claims 52-53, 56-62, 70-72, 78-81 above , in view of Bannasch et al. , as applied to claims 52-53, 56-62, 70-72, 78-81 above, and in further view of Rosenfeld et al. (U.S. Pre - Grant Publication 20090162859 A1) . Italicized text from reference art. Applicable c laims include: See above for claims 52-53, 56-62, 70-72, 78-81 Claim 73. The method of claim 52, further comprising determining a second pigmentation phenotype of a second canine subject, and determining an expected range of pigmentation phenotypes of a potential offspring of the canine subject and the second canine subject. Claim 74. The method of claim 73, further comprising determining a recommendation indicative of whether or not to breed the first canine subject and the second canine subject together, based on the expected range of pigmentation phenotypes of the potential offspring of the canine subject and the second canine subject. Claim 75. The method of claim 74, further comprising determining a recommendation indicative of breeding the first canine subject and the second canine subject together, when the expected range of pigmentation phenotypes of the potential offspring of the canine subject and the second canine subject includes a pre-determined pigmentation phenotype. Claim 76. T he method of claim 74, further comprising determining a recommendation against breeding the first canine subject and the second canine subject together, when the expected range of pigmentation phenotypes of the potential offspring of the canine subject and the second canine subject does not include a pre-determined pigmentation phenotype. Claim 77. The method of claim 74, further comprising generating a social connection between a first person associated with the first canine subject and a second person associated with the second canine subject, based at least in part on the expected range of pigmentation phenotypes of the potential offspring of the canine subject and the second canine subject. Branicki et al. and Bannasch et al. teach Claims 52-53, 56-62, 70-72, 78-81 (see above). Regarding Claim 73 , Branicki et al. teach determining a pigmentation phenotype of multiple (2 or greater) individuals (Page 447, Column 2, Paragraph 2: in each replicate 80% individuals were used as the training set and the remaining samples were used as the testing set ). Branicki et al. and Bannasch et al. do not teach determining an expected range of phenotypes of a n offspring of canine subject s ( Claim 73 ). Branicki et al. and Bannasch et al. also do not teach determining a recommendation indicative of whether or not to breed canines ( Claims 74-76 ) . Branicki et al. and Bannasch et al. also do not teach generating a social connection between people based on the expected range of pigmentation phenotypes of the potential canine offspring ( Claim 77 ). Regarding Claim 73 , Rosenfeld et al. teaches determining an expected range of phenotypes of a potential offspring of the canine subject and the second canine subject (Page 3. Paragraph 0031: This invention provides a method for determining the optimum male and female parent to maximize the genetic components of dominance and epistasis thus maximizing heterosis and hybrid vigor in the animals ). Regarding Claim 74 . Rosenfeld et al. teaches determining a recommendation indicative of whether or not to breed the first canine subject and the second canine subject together, based on the expected range of phenotypes of the potential offspring of the canine subject and the second canine subject (Page 7, Paragraph 00701: Genetic traits are predicted in the laboratory and forwarded electronically to a breeder. The breeder then uses this information to sort and manage animals to maximize profitability and marketing potential ). Regarding Claims 75 and 76 . Rosenfeld et al. teaches determining a recommendation indicative of breeding or against breeding when the expected range of phenotypes of the potential offspring include or do not include, respectively, a pre-determined phenotype (Page 7, Paragraph 0072: The present invention provides a method for improving profits related to breeding a companion animal subject. The method includes drawing an inference regarding a trait of the companion animal subject from a nucleic acid sample of the companion animal subject ). Rosenfeld et al. makes a similar prediction as the result of breeding, see Regarding Claim 74 of the current rejection above. The recommendation to breed or not to breed will therefor e be based on maximizing profits based on the phenotype predicted from the genotypic data. Regarding Claim 77 , Rosenfeld et al. teaches generating a social connection between a first person associated with the first canine subject and a second person associated with the second canine subject, based at least in part on the expected range of pigmentation phenotypes of the potential offspring of the canine subject and the second canine subject (Page 14, Paragraph 0134: the database can include information regarding phenotypes and/or genetic traits that are associated with some or all of the SNPs and/or haplotypes ; Page 14, Paragraph 138: The parts of the database can be internal databases, or external databases that are accessible to users ). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify Branicki et al. and Bannasch et al. with Rosenfeld et al. because all are concerned with the relationship between genotypes and phenotypes and are applicable to the utilization of genetic data from dogs . While Branicki et al. and Bannasch et al. are con c er n ed with hair phenotype, Rosenfeld et al. is concerned with general phenotypes related to an animal’s ability to be a human companion, which also includes the hair color of dogs (Page 6, Paragraph 0063: a method of the present invention can infer, for example, coat quality/texture/color ). Rosenfeld et al. extends this core relationship between genotype and phenotype to a subsequent generation and the desirability of the offspring. Therefore, it would have been obvious to someone of ordinary skill in the art at the time of the effective filling date to combine the methods from the references indicated above. Furthermore, one of ordinary skill in the art would predict that the method taught by Rosenfeld et al. could be readily added to the method of Branicki et al. and Bannasch et al with a reasonable expectation of success because they all are concerned with phenotypic data of closely related species (mammals) to make predictions . Accordingly, claims 52-53, 56-62, and 70-81 taken as a whole would have been prima facie obvious before the effective filing date and are rejected under 35 U.S.C. 103. Double Patenting No double patenting issues were identified with the instant claims . Conclusion No Claims are allowed. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT BLAKE H ELKINS whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-2649 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday-Friday 8- 5PM . 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