DETAILED ACTION
Applicant’s response, filed 24 Sept. 2024 has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 24 Sept. 2024 has been entered.
Status of Claims
Claims 11, 13-33 are cancelled.
Claims 32-33 are newly added.
Claims 1-10 and 12 are pending.
Claims 1-10 and 12 are rejected.
Priority
The effective filing date of the claimed invention is 07 July 2021.
Claim Interpretation - 35 USC § 112(f)
The interpretation of “genetic ancestry classification system” in claim 14 under 35 U.S.C. 112(f) in the Office action mailed 28 April 2025 has been withdrawn in view of the cancellation of this claim received 23 Sept. 2025.
Claim Interpretation
Claim 1 recites “…l) administering the first dog a treatment diet determined based on (1) a current weight and the predicted adult weight of the first dog; and (2) the genetic skin disease..”. However, the claims do not require a step of determining the first diet. Therefore, the limitation recites a product by process limitation that defines the process which the treatment diet was previously determined (i.e. based on the current weight and predicted adult weight and the genetic disease). "[E]ven though product-by-process claims are limited by and defined by the process, determination of patentability is based on the product itself. The patentability of a product does not depend on its method of production. If the product in the product-by-process claim is the same as or obvious from a product of the prior art, the claim is unpatentable even though the prior product was made by a different process." In re Thorpe, 777 F.2d 695, 698, 227 USPQ 964, 966 (Fed. Cir. 1985). See MPEP 2113 I.
Claim 1 recites “…about 7g/Mcal to about g/Mcal” and “about 40 mg/Mcal to about 60 mg/Mcal”. Applicant’s specification at para. [0054] as published defines the term “about” to mean within 3 or more standard deviations or a range of up to 20% or within 5-fold of a value.
Claim Rejections - 35 USC § 112(a)
The rejection of claims 14-23 and 25 under 35 U.S.C. 112(a) in the Office action mailed 28 April 2025 has been withdrawn in view of the cancellation of these claims received 23 Sept. 2025.
Claim Rejections - 35 USC § 112(b)
The rejection of claims 14-23 and 25 under 35 U.S.C. 112(b) in the Office action mailed 28 April 2025 has been withdrawn in view of the cancellation of these claims received 23 Sept. 2025.
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.
Claims 1-10 and 12 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. This rejection is newly recited and necessitated by claim amendment.
Claim 1, and claims dependent therefrom, are indefinite for recitation of “…the one or more genetic traits comprising at least a predicted adult weight of the first dog and a risk prediction or a predisposition to a genetic skin disease”. It is unclear if the one or more genetic traits may include just one genetic trait (i.e. a predisposition to a genetic skin disease), given claim 1 recites “one or more genetic traits” and at least (1) [a predicted adult weight of the first dog and a risk prediction] or (2) a predisposition to a genetic skin disease, or alternatively if claim 1 intends for the one or more genetic traits to be required to include at least two genetic traits: (1) [a predicted adult weight of the first dog ] and (2) a risk prediction or a predisposition to a genetic skin disease. Clarification is requested via claim amendment. For purpose of examination, the claims are interpreted to encompass either option described above.
Claim 1, and claims dependent therefrom, are indefinite for recitation of “…l) administering the first dog a treatment diet determined based on (1) a current weight and the predicted adult weight of the first dog; and (2) the genetic skin disease…”. As discussed above, it is unclear which traits are required to be part of the “one or more genetic traits”. As a result, in embodiments in which the predicted adult weight is not part of the one or more genetic traits, it is unclear if the administering is contingent on the predicted adult weight being determined or if the administering would just be based on a current weight. Similarly, in embodiments in which the traits do not include “a predisposition to a genetic skin disease”, it is unclear in what way the administering is intended to be based on the genetic skin disease. Clarification is requested via claim amendment. For purpose of examination, the administering will be interpreted to be required by the claims, determined by (1) and (2) a genetic skin disease, given it is noted that (2) the genetic skin disease simply refers to the disease rather than a genetic trait of a predisposition to the disease.
Response to Arguments
Applicant's arguments filed 23 Sept. 2025 regarding 35 U.S.C. 112(b) have been fully considered but they are not persuasive because they do not pertain to the new grounds of rejection under 35 U.S.C. 112(b) set forth above.
Claim Rejections - 35 USC § 101
The rejection of claims 14-23, 25, and 27-33 under 35 U.S.C. 101 in the Office action mailed 28 April 2025 has been withdrawn in view of the cancelation of these claims received 23 Sept. 2025.
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 1-10 and 12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception(s) without significantly more. Any newly recited portion is necessitated by claim amendment.
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355). Applicant is also directed to MPEP 2106.
Step 1: The instantly claimed invention (claim 1 being representative) is directed to a method for analyzing genotypes. Therefore, the instantly claimed invention falls into one of the four statutory categories. [Step 1: YES]
Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Step 2A, Prong 1: Under the MPEP § 2106.04, the Step 2A (Prong 1) analysis requires determining whether a claim recites an abstract idea, law of nature, or natural phenomenon.
Claim 1 recites the following steps which fall under the mathematical concepts and/or mental processes groupings of abstract ideas:
a genetic ancestry classification system (algorithm) (claim 14 only) for:
a) accessing/access one or more sequence reads derived from a sample of genetic material extracted from a first dog, wherein the sample of genetic material comprises a raw genotype;
b) generating/generate a plurality of phased haplotypes based on the one or more sequence reads derived from the sample of genetic material.
c) determining/determine, based on linkage disequilibrium in canids and haplotype diversity, a window size for a plurality of windows of which at least one of the plurality of phased haplotypes is to be partitioned;
d) executing/execute one or more machine learning models comprising a positional Burrows-Wheeler transform to partition the at least one phased haplotype into the plurality of windows, each of the plurality of windows being sized in accordance with the determined window size;
e) compare/comparing the plurality of windows against a reference panel comprising a plurality of reference haplotypes associated with a plurality of reference populations of dogs;
f) generate/generating one or more local assignments for one or more genetic populations of dogs based on the comparison;
g) determining/determine a degree for smoothing the one or more local assignments for the one or more genetic populations of dogs based on whether the genetic material contains single-origin chromosomes or admixed chromosomes;
h) execute/executing a hidden Markov model (HMM) to smooth the one or more local assignments for the one or more genetic populations of dogs based on the determined degree;
i) determining/determine, based on one or more smoothed local assignments for the one or more genetic populations of dogs, one or more source populations associated with the first dog;
j) partitioning/partition the one or more smoothed local assignments for the one or more genetic populations of dogs into one or more of a maternally-inherited group or a paternally-inherited group; and
k) identifying/identify, based on the one or more smoothed local assignments for the one or more genetic populations of dogs and the one or more source populations, a genetic ancestry of the first dog and one or more genetic traits associated with the first dog, the one or more genetic traits comprising at least a predicted adult weight of the first dog and a risk prediction or a predisposition to a genetic skin disease;
The identified claim limitations falls into the group of abstract ideas of mental processes for the following reasons. Accessing read data derived from a sample of genetic material comprising a raw genotype involves analyzing the sequence read information of a sample of genetic material to determine a genotype, which can be practically performed in the mind. Furthermore, generating haplotypes based on the read data involves, for example, analyzing haplotype or genotype information from parents of the genetic sample to determine which alleles were inherited from the same parent. Determining, based on linkage disequilibrium and haplotype diversity, a window size of which a phased haplotype is to be partitioned, amounts to a mere analysis of data of selecting a window size by degrees of relatedness between haplotypes. The broadest reasonable interpretation of using a machine learning algorithm to generate a local assignment for one or more genetic populations by comparing the at least one phased haplotype to a reference panel of a plurality of reference haplotypes comprises inputting the values corresponding to the haplotypes (e.g. 0s and 1s) into a trained linear regression model to determine a probability that the haplotype originated from a particular reference haplotype, which can be practically performed in the mind aided with pen and paper. Furthermore using such a machine learning algorithm comprising a positional Burrows-Wheeler transform (pBWT) can be performed mentally by iterating through haplotypes to recover set-maximal matches in conjunction with a machine learning algorithm, as described in Applicant’s specification at pg. 20, line 31 to pg. 21, line 11. Furthermore, determining a degree for smoothing the local assignment based whether the genetic material contains ingle-origin chromosomes or admixed chromosomes can be performed mentally by determining whether to use a model tuned to accommodate single-origin chromosomes (e.g. if the genetic sample is believed to be pure bred) or a model tuned to accommodate admixed chromosomes (e.g. if the genetic sample is mixed). Executing a hidden Markov model (HMM) to smooth the local assignments can be performed mentally aided by pen and paper by calculating probabilities of observing sequences of local assignments and determining a most likely set of local assignments to remove any errors in the local assignments. Determining a source population based on the smoothed local assignments for the one or more genetic populations of dogs involves analyzing the one or more genetic populations to determine a common source population of the one or more genetic populations, which amounts to a mere analysis of data that can be practically performed in the mind. Next, partitioning the smoothed local assignments into a maternally and/or paternally inherited group can be performed mentally by analyzing the reference haplotypes corresponding to the smoothed local assignments and comparing them to haplotypes from parents of the original sample to determine they were inherited maternally or paternally, or organizing the local assignments based on this information. Determining a genetic ancestry and one or more genetic traits, including an adult weight and a risk prediction or a predisposition to a genetic skin disease, associated with the first dog based on the one or more local assignments and the source populations involves analyzing genetic traits and an ancestry associated with the local assignments and source populations, which amounts to a mere analysis of data. Therefore, these limitations recite a mental process. See MPEP 2106.04(a)(2) III.
Furthermore, the steps of executing a machine learning algorithm comprising a pBWT to generate one or more local assignments for one or more genetic populations and executing a hidden Markov model (HMM) to smooth the one or more local assignments further recite a mathematical concept. A claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation. See MPEP 2106.04(a)(2) I. C. In this case, the broadest reasonable interpretation of generating a local assignment for a phased haplotype using one or more machine learning algorithms comprising a pBWT encompasses calculating distances between haplotypes to identify matches, as described in Applicant’s specification at pg. 19 line 23 to pg. 20, line 2, and applying a statistical hidden markov model, as discussed in Applicant’s specification at pg. 22, lines 10-30, which amounts to a textual equivalent to calculating probabilities. Similarly, executing a hidden Markov model (HMM) to smooth the local assignment(s) amounts to a textual equivalent of performing a statistical algorithm to determine probabilities of observing various local assignment states and determine the set of local assignments with the highest likelihood. As such, executing a hidden Markov model encompasses mathematical calculations. Therefore these limitations recites a mathematical concept.
Last, the claims further recite the law of nature of a natural correlation between the genotypes/haplotypes and ancestry and genetic traits (e.g. source populations, risk prediction or predisposition to a genetic skin disease). See MPEP 2106.04(b).
Dependent claims 2-10, and 12 further recite an abstract idea and/or are part of the abstract idea. Dependent claim 2 further recites the mental process of aggregating the one or more smoothed local assignments for one or more genetic populations over both maternal and paternal chromosomes, calculating proportions associated with the one or more source populations based on the aggregations, and determining the one or more source populations based on the calculated proportions. Dependent claim 2 further recites the mathematical concept of calculating proportions associated with the one or more source populations based on the aggregations. Dependent claim 3 further recites the mathematical concept and mental process of performing the partitioning using a clustering algorithm. Dependent claim 4 further recite the mental process of determining the one or more genetic traits based on one or more of genome-wide statistics, genomic principal component analysis projections, DNA methylation profiles, or polygenic risk scores. Dependent claim 5 further recites the mental process of determining one or more genetic traits comprising a range of adult body weight, a risk prediction or a predisposition to a genetic disease, a nutrition recommendation, a behavior and temperament class prediction, a longevity estimation, an all-causes mortality prediction, a predicted pharmacological response, or a recovery time in hours for injectable anesthetics. Dependent claim 6 further recites the mental process of updating the one or more machine learning algorithms based on one or more new reference samples added to the reference panel. Dependent claim 7 further recites the mental process of applying a cross-validation across all samples in the reference panel; identifying one or more outliers based on results associated with the cross validation, and removing the identified one or more outliers. Dependent claim 7 further recites the mathematical concept of applying a cross-validation across samples to update the machine learning model, which involves iteratively applying the machine learning model to resampled groups of samples, and thus recites a mathematical concept for the same reasons discussed above for using one more machine learning algorithms for generating the one or more local assignments. Dependent claim 8 further recites the mental process of iteratively repeating the updating until a predetermine accuracy level of the one or more machine learning algorithms is reached. Dependent claim 9 further recites the mental process of generating one or more labels for one or more unlabeled samples in the reference panel, and updating the one or more machine learning models based on the generated labels. Dependent claim 10 further recites the mental process of generating a consensus genotype based on the raw genotype, and generating the phased haplotypes based on the consensus genotype by phasing the raw genotype and the consensus genotype into maternal and paternal chromosomes. Dependent claim 12 further recites the mental process and mathematical concept of executing a HMM to remove one or more errors associated with the one or more local assignments for the one or more genetic populations of dogs. Therefore, claims 1-10 and 12 recite an abstract idea and law of nature. [Step 2A, Prong 1: YES]
Step 2A: Prong 2: Under the MPEP § 2106.04, the Step 2A, Prong 2 analysis requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application for the following reasons.
Dependent claims 2-10 and 12 further recite an abstract idea, as discussed above, and apart from reciting the limitations are performed by one or more computing systems or processors, discussed below, the claims do not recite any other elements in addition to the recited judicial exception.
Claim 1 recites the following additional elements:
I) administering the first dog administering the first dog a treatment diet determined based on (1) a current weight and the predicted adult weight of the first dog; and (2) the genetic skin disease, the treatment diet comprising energy density less than 4100 kcal/kg, an amount of fat less than 20% w/w, an amount of carbohydrates less than 25% w/w, an amount of protein more than 20% w/w, and an amount of dietary fiber more than 5% w/w, linoleic acid in an amount from about 7 g/Mcal to about 9 g/Mcal, and zinc in an amount from about 40 mg/Mcal to about 60 mg/Mcal
The step of administering the first dog a treatment diet, as claimed, does not integrate the recited judicial exception into a practical application of effecting a particular treatment because the treatment step is not particular and/or does not have a significant relationship with the judicial exception and instead amounts to mere instructions to apply the exception. Claim 1 recites the administering is “based on (a) a current weight and the predicted adult weight…; and (2) the genetic skin disease, the treatment diet comprises energy density less than 4100 kcal/kg, an amount of fat…, and amount of carbohydrates…”. Only the energy density corresponds to a weigh of the dog (i.e. per kg), and no other components of the diet clearly depend on the predicted weight or the genetic skin disease. In other words, the administration step is similar to “administering a suitable medication to the patient” given the specific diet requirements (energy, fat, protein, carbohydrates) encompass a broad range of diets with different compositions and is mere “based on” certain elements determined by the judicial exception. Furthermore, the claims do not establish a relationship regarding how the predicted weight (i.e. part of the judicial exception) is being used to inform the diet, given the diet is based on a current (i.e. measured weight). Last, the claims do not establish any relationship between the diet and the genetic skin disease other than saying the diet is “based on” the skin disease, or even use the “predisposition to a genetic skin disease” in determining the diet. Overall, the claims instead attempt to cover any solution to identifying a diet with no restriction on how the result is accomplished, and no description for how the diet is determined “based on” the judicial exception. Thus the additional element amounts to mere instructions to apply the exception, which does not integrate a judicial exception into a practical application because this type of recitation is equivalent to the words "apply it". See MPEP 2106.05(f).
Therefore, the additionally recited element amounts to mere instructions to apply the exception, and as such, the claims as a whole do no integrate the abstract idea into practical application. Thus, claims 1-10 and 12 are directed to an abstract idea and law of nature. [Step 2A, Prong 2: NO]
Step 2B: In the second step it is determined whether the claimed subject matter includes additional elements that amount to significantly more than the judicial exception. See MPEP § 2106.05.
The claims do not include any additional steps appended to the judicial exception that are sufficient to amount to significantly more than the judicial exception for the following reasons.
Dependent claims 2-10 and 12 further recite an abstract idea and law of nature, as discussed above, the claims do not recite any other elements in addition to the recited judicial exception as discussed above.
Claim 1 recites the following additional elements:
I) administering the first dog administering the first dog a treatment diet determined based on (1) a current weight and the predicted adult weight of the first dog; and (2) the genetic skin disease, the treatment diet comprising energy density less than 4100 kcal/kg, an amount of fat less than 20% w/w, an amount of carbohydrates less than 25% w/w, an amount of protein more than 20% w/w, and an amount of dietary fiber more than 5% w/w, linoleic acid in an amount from about 7 g/Mcal to about 9 g/Mcal, and zinc in an amount from about 40 mg/Mcal to about 60 mg/Mcal
Administering a dog diet comprising energy density less than 4100 kcal/kg, an amount of fat less than 20% w/w, an amount of carbohydrates less than 25% w/w, an amount of protein more than 20% w/w, and an amount of dietary fiber more than 5% w/w is well-understood, routine, and conventional. This position is supported by Duque-Saldarriaga et al. (Assessment of energy content in dog foods, 2017, Arch. Zootec. 66(254), pg. 279-286; newly cited) and Gautam et al. (Scientific dog feeding for good health and its preparation: A review, 2018, Journal of Entomology and Zoology Studies, 6(2), pg. 1683-1689; newly cited). Duque-Saldarriaga reviews the energy content in dog foods (Abstract), and discloses digestible energy included in dog foods by market segment, and discloses various commercial dog foods for adult dogs with less than 4100 kcal/kg compared to food for puppies which contains a higher calorie content (Table II; pg. 284, col. 1, para. 5 to col. 2, para. 2). Gautum reviews the nutrition of pet animals and formulations of commercial brands (Abstract), and overviews dog diet specification guidelines for maintenance and different growth stages (pg. 1686, col. 1, para. 3 Tables) and different types of commercially available pet foods (Table 1 on pg. 1684). Gautum discloses dogs should be given 24% protein, 5% fat, and 5% fiber for maintenance and provides examples of puppies receiving 28% protein and 17% fat (pg. 1686 Tables 1-2). Gautum discloses that carbohydrates are essential for preparing dry kibble and play an important role in the composition of most commercial pet food, and further discloses cereal and grain products not replace a considerable portion of meat in dog food, while canned pet food can be prepared without carbohydrates (i.e. less than 25% carbohydrates) (pg. 1685, col. 1, para. 4 to col. 2, para. 2). Gautum further discloses that feeding animals a reduced carbohydrate diet is also standard given some animals have an intolerance to carbohydrates, and that extra fiber is generally given to dogs to reduce and prevent obesity (pg. 1685, col. 1, para. 3 to col. 2, para. 2).
Including linoleic acid and zinc in the dog diet is also well-understood, routine, and conventional. This position is supported by Gautum (cited above), Watson et al., WO 2020055856 A1 (newly cited), George Watson et al., US 6,331,567 B1 (newly cited), and Marsh et al., WO 1998056263 A1 (newly cited). First, Gautum further discloses overviews mineral supplements in dog food, which are essential nutrients needed in the body, and describes Zinc as commonly found in dog foods (pg. 1685, col. 2, para. 5 and Table on pg. 1685). Gautum additionally discloses essential fatty acids are required in the body, and that for dogs, arachidonic acid can be synthesized from linoleic acid (pg. 1685, col. 1, para. 3), demonstrating linoleic acid is a source of essential fatty acids in dogs. Furthermore, Watson discloses diets for dogs containing the recited amounts of linoleic acid and zinc (Abstract; pg. 3, lines 1-32). George Watson similarly discloses a diet for enhancing the skin of an animal comprising 6g/400kcal of linoleic acid (Abstract) and 50 mg zinc (claims 1-7; Abstract). Marsh similarly discloses a diet for pets comprising 6g of linoleic acid and 40 mg of Zinc (Abstract). Therefore, the composition of linoleic acid and zinc for improving skin condition in a pet is conventional, and given pets are also given diets (i.e. caloric) in addition to supplements, as demonstrated by Gautum, the claimed diet is conventional even considered in combination.
Therefore, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself. [Step 2B: NO]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea and natural correlation without significantly more. For additional guidance, applicant is directed generally to applicant is directed generally to the MPEP § 2106.
Response to Arguments
Applicant's arguments filed 23 Sept. 2025 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant remarks the claims reflect a technical improvement of genetic ancestry classification and genetic traits prediction by utilizing a local ancestry classifier, a global ancestry classifier, and machine-learning models for improving the accuracy of classification and physical traits of a dog based on raw DNA sequences, and thus the claim is not directed to an abstract idea (Applicant’s remarks at pg. 6, para. 5 to pg. 7, para. 2).
This argument is not persuasive. It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements or by the additional element(s) in combination with the recited judicial exception. See MPEP 2106.05(a).
In the instant case, the alleged improvement in “genetic ancestry classification” amounts to an improvement in the abstract idea itself (i.e. classifying genetic ancestry), which is not an improvement to technology. Furthermore, as noted in the above rejection, claim 1 only recites an administering a diet step as an additional element (i.e. not a step involved in improving ancestry classification), and thus, the alleged improvement is not provided by any additional elements, as required by MPEP 2106.05(a).
Applicant remarks that claims 1-10 and 12 are directed to a treatment method for a dog and not an abstract idea, similar to example 49 of the USPTO July 2025 Subject Matter Eligibility Examples which recites “the appropriate treatment is compound X eye drops”, and even if the claims are considered to recite an abstract idea, the abstract idea is integrated into a practical application due to the limitation of administering the first dog a treatment diet as claimed, which is particular to the dog with the specifically identified genetic traits, and the treatment diet being determined based on (1) a current weight and the predicted weight) and (2) the genetic skin disease that the first dog has a risk for, which is a solution at the same level as example 49 (Applicant’s remarks at pg. 6, para. 5 and pg. 7, para. 3 to pg. 8, para. 2).
This argument is not persuasive. MPEP 2106.04(d)(2) explains that when determining whether a claim applies or uses a recited judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, the following factors are relevant: a. the particularity or generality of the treatment or prophylaxis; b. whether the limitation(s) have more than a nominal or insignificant relationship to the exception(s). MPEP 2106.04(d)(2) further explains examiners may find it helpful to evaluate other considerations such as the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)), and the field of use and technological environment consideration (see MPEP § 2106.05(h)), when making a determination of whether a treatment or prophylaxis limitation is particular or general.
For the reasons discussed in the above rejection the administered treatment amounts to mere instructions to apply the exception and furthermore does not appear to have a significant relationship with the judicial exception. First, while Applicant alleges the first dog is at risk for the genetic skin disease, claim 1 does not even require that the one or more genetic traits comprises a predisposition to a genetic skin disease (as opposed to merely “a risk prediction”).The first dog does not clearly have, or is not clearly predisposed to, the genetic skin disease for which the diet is based on. Furthermore, the claim does not provide any details regarding how the treatment diet is determined “based on” the predicted weight and the current weight. For example, the claims encompass any dog predicted to weigh from 15 lbs to 100 lbs being administered a treatment diet comprising 4000 kcal/kg and the same amounts of protein, fat, or carbohydrates. Overall, the claims attempt to cover any solution to identifying a diet with no restriction on how the result is accomplished, and no description for how the diet is determined “based on” the judicial exception. Thus the additional element amounts to mere instructions to apply the exception, which does not integrate a judicial exception into a practical application because this type of recitation is equivalent to the words "apply it". See MPEP 2106.05(f). This is in contrast to claim 2 in example 49, which specifically identifies a glaucoma patient as a high risk of post-implantation inflammation and then administers a particular treatment “Compound X eye drops” which treats the inflammation the patient is at risk for.
Claim Rejections - 35 USC § 103
The rejections of claims 1-10 and 12 under 35 U.S.C. 103 in the Office action mailed 28 April 2025 has been withdrawn in view of claim amendments received 23 Sept. 2025. However, after further consideration a new grounds of rejection is set forth below.
The rejections of claims 14-23, 25, and 27-33 under 35 U.S.C. 103 in the Office action mailed 28 April 2025 has been withdrawn in view of the cancellation of these claims received 23 Sept. 2025.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
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 1-3, 5, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Wilton (2020) in view of Rosenfeld (2006), Butterwick (2020), and Watson (2020), as evidenced by Freyman (2021). This rejection is newly recited and necessitated by claim amendment.
Cited references:
Wilton et al., US 2022/0051751 A1, effectively filed 13 Aug. 2020 based on priority to U.S. Provisional App. No. 62/706,396 (previously cited);
Rosenfeld et al., US 2006/0008815 A1; Pub. Date: 12 Jan. 2006 (previously cited);
Butterwick et al., US 2020/0058405 A1; Pub. Date; 20 Feb. 2020 (previously cited); and
Watson et al., WO 2020055856 A1; Pub. Date: 19 March 2020 (newly cited).
Freyman et al., WO 2021/016114 A1; Pub. Date: 28 Jan. 2021 (previously cited); and
Regarding claim 1, Wilton discloses a method implemented using a computer system (i.e. each method step is performed by an ancestry classification computer system) ([0004]), computer readable non-transitory storage media ([0042]) for performing the following steps:
Wilton discloses a) obtaining sequence data (i.e. sequence reads) derived from a genetic sample of an individual (i.e. a first animal), including an unphased genotype for the individual (FIG. 4;[0045]; [0054]-[0055], e.g. genotype obtained from DNA sample; [0069]) (accessing/access one or more sequence reads derived from a sample of genetic material associated with a first…, wherein the sample of genetic material comprises a raw genotype).
Wilton discloses b) phasing the unphased genotype of the sequence reads to generate phased haplotypes (FIG. 4; [0057]; [0060]; [0063], e.g. classifying haplotypes; [0065]) (generating/generate a plurality of phased haplotypes based on the one or more sequence reads derived from the sample of genetic material).
Wilton discloses c) determining a window size based on an expected length of a recombination-free segment ([0109]).
Wilton discloses c) if a switch error occurs within a haplotype window, the assumption that the haplotype segment within the window has a single ancestry may no longer be valid, and thus it is necessary to choose a window size small enough to ensure that most windows are free of switch errors from recombination (i.e. nearby alleles are more likely to share a single ancestry), such that the window size is determined based on linkage disequilibrium (i.e. the nonrandom association of alleles), while longer windows contain more haplotype information (i.e. haplotype diversity) ([0138]-[0139]). Thus Wilton discloses balancing the window size of the haplotypes based on linkage disequilibrium and haplotype diversity (c) determining, based on linkage disequilibrium…and haplotype diversity, a window size for a plurality of windows of which at least one of the plurality of phased haplotypes is to be partitioned).
Wilton discloses d) executing a machine learning algorithm by dividing the phased haplotypes into the determined window sizes ([0137]-[0139]; [0114]; FIG. 12, #1206) and inputting the windows into the machine learning algorithm to obtain predicted ancestries (i.e. local assignments) for each window (FIG. 12, #1208; [0115], e.g. ancestry predicted for particular segment). Wilton further discloses, additionally the machine learning model can include a hidden Markov model incorporated into a positional Burrows-Wheler transform (pBWT) (i.e. a machine learning model comprising a pBWT) to classify the ancestry in a window ([0107]), explained in more detail in Freyman, incorporated into by Wilton by reference ([0107], e.g. PCT/US2020/042628; [0124], e.g. method in PCT/US2020/042628 used to determine ancestry based on ancestry of related copy in a window). Specifically, Freyman discloses the pBWT of Wilton comprises applying a positional burrows wheeler transform with a hidden Markov model to identify identity by descent segments ([0066]; [0210]) by identifying matching haplotype segments between haplotype strings using reference haplotype data ([0013]; [0076]; [00225]; FIG. 5D), and that the IBS segment then used to determine ethnicity and/or ancestry (e.g. as performed in Wilton) ([0004]; [0214]; [0225]) (d) executing one or more machine learning models comprising a positional Burrows-Wheeler transform to partition that at least one phased haplotype into the plurality of windows, each of the plurality of windows being sized in accordance with the determined window size).
Wilton further discloses e) executing the machine learning algorithm compares the segments to reference data of DNA sequences from reference individuals and their corresponding ancestries (FIG. 12, e.g. classifier trained using reference data; [0118]-[0019]) (e) comparing the plurality of windows against a reference panel comprising a plurality of reference haplotypes associated with a plurality of reference populations…).
Wilton discloses f) executing the machine learning algorithm to generate the ancestry prediction for each segment (i.e. generating the local assignments) compares the segments to reference data of DNA sequences from reference individuals and their corresponding ancestries (FIG. 12, e.g. classifier trained using reference data; [0118]-[0019]) (f) generating one or more local assignments for one or more genetic populations…based on the comparison).
Wilton discloses g) training an ensemble smoothing hidden markov model using ancestry classifications from a plurality of individuals, with different models trained or optimized for certain ancestries, to correct the local ancestry estimates of the machine learning models ([0140]-[0141]; ([0150]). Wilton further discloses the training can be based on broad or narrow categories of ancestry (i.e. a degree of smoothing) ([0150]), and that different models can be trained on a subset of admixed individuals or a set of unadmixed individuals ([0152]). Wilton further discloses selecting the trained HMM smoothers for smoothing the individual’s classifications by comparing the hard ancestry calls of the individual to the ancestry of the training panels for each trained HMM to determine HMM models that closely represent the hard ancestry calls of the individual ([0163]-[0164]), which includes HMM models specific to admixed and/or unadmixed individuals as noted above ([0152]), such that the smoothing is based on whether the individual contains unadmixed or admixed chromosomes) (determining a degree of smoothing the one or more local assignments for the one or more genetic populations… based on whether the genetic material contains single-origin chromosomes or admixed chromosomes).
Wilton discloses h) executing the trained smoothing HMM to perform smoothing on the ancestry assignments of the machine learning models ([0140]-[0141]; FIG. 4, #408; [0060], e.g. smoothing using HMM) (executing a hidden Markov model (HMM) to smooth the one or more local assignments for the one or more genetic populations… based on the determined degree).
Wilton discloses i) determining proportions of each ancestry based on the smoothed local ancestry estimates of the segments (FIG. 4, #410-414; FIG. 28, e.g. ancestry composition) (determining/determine based on the one or more local assignments for the one or more genetic populations…, one or more source populations associated with the first…).
Wilton discloses g) partitioning the local ancestry assignments of each segment into paternal and maternal haplotypes (FIG. 31, e.g. ancestry assignments of each segment split by maternal/paternal origin; [0258]) (partitioning/partition the one or more local assignments for the one or more genetic populations… into one or more of a maternally-inherited group or a paternally inherited group).
Wilton discloses k) identifying, based local ancestry assignments and the proportions of each ancestry, a genetic ancestry of the individual (FIG. 27-28; FIG. 31, e.g. ancestry report generated based on source populations of parents, which are based on local assignments as discussed above) (k) identifying, based on the one or more smoothed local assignments for the one or more genetic populations of.. and the one or more source populations, a genetic ancestry of the first…).
Further regarding claim 1 and dependent claim 5 , Wilton does not disclose the following limitations:
Regarding claim 1, Wilton does not disclose the method is applied to data from a genetic sample of a dog, such that the linkage disequilibrium is of canids or the one or more genetic populations are of dogs, and instead discloses applying the method to a human individual (FIG. 31), and furthermore does not disclose k) identifying based on the one or more local assignments for the one or more genetic populations and the one or more source populations… one or more genetic traits associated with the first dog, the one or more genetic traits comprising at least a predicted adult weight of the first dog and a risk prediction or a predisposition to a genetic skin disease.
Regarding claim 5, Wilton does not disclose the one or more genetic traits comprise one or more of: a risk prediction or a nutritional recommendation.
However, regarding claim 1, Rosenfeld discloses a method of inferring breeds of dogs (i.e. source populations of a dog) (Abstract) based on identified SNP and haplotype markers of a dog (i.e. genotypes and haplotypes) ([0064]; [0224]). Rosenfeld further discloses these markers can be used to determine parentage (i.e. maternal and paternal lineage) or identify breed specificity and to assign a dog to one or more breeds, and then overviews classes of models that can be used to assign individuals to a population, including model-based methods that use statistical techniques to analyze such genomic data ([0025]; [0133]). Rosenfeld discloses using the inferred dog breeds to diagnose a health condition or predisposition in the dog or determine genetic traits and behavior characteristics (i.e. genetic traits) (Abstract; [0015]), and displaying results pertaining to the information on genetic traits associated with haplotypes to infer a trait of a dog ([0037]). Rosen discloses the inferred health condition or genetic trait can be a body mass (i.e. adult weight) or predisposition to obesity (i.e. a prediction relating to an adult weight) ([0062]-[0063]; [0084]-[0086]). Given the condition and genetic trait of a body mass are genetic predictions, the predicted body mass is considered an “adult weight”.
Rosenfeld further discloses that many software programs have been developed that can handle large volumes of data, such as TFPGA, Arlequin, GDA, GENEPOP, GeneStrut, POPGENE, and Structure, and that these programs for population genetics can be applied to classify canines into populations ([0134]). Last, Rosenfeld discloses it is important for canine owners to know the breed from which a particular animal may arise because animals of the same breed have similar behavioral characteristics and predispositions to disease ([0015]).
Regarding claims 1 and 5, Rosenfeld further discloses the breed prediction (i.e. a source population) can be used to determine a genetic risk, such as hip dysplasia, and also a nutritional regiment ([0020]). Rosenfeld further discloses using the breed identity tool can determine the appropriate preventative measures for ensuring the health of their dog ([0020]).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the method, system, and product of Wilton to have determined and utilized genotypes and haplotypes from a genetic sample of a dog, rather than a human, for breed and parentage (i.e. maternal and paternal local assignment) predictions, as shown by Rosenfeld ([0025]; [0064]; [0133]; [0224]), in the model of Wilton, and additionally determined one or more genetic traits comprising at least a predicted adult weight of the first dog and a risk prediction and nutritional recommendation based on the determined breeds (i.e. based on the source population and local assignments), as shown by Rosenfeld (Abstract; [0015];[0020]; [0064]-[0064]; [0084]-[0086]). One of ordinary in the art would have been motivated to combine the methods of Wilton and Rosenfeld to allow pet owners to know the behavioral characteristics and predispositions of disease, including body mass, of their pet, as shown by Rosenfeld ([0015]; [0086]), thus facilitating the determination of appropriate preventative measures for ensuring the health of an owner’s dog, as shown by Rosenfeld ([0020]). This modification would have had a reasonable expectation of success because the input into the model of Wilton includes haplotypes, which Rosenfeld discloses can be used to predict ancestry in dogs, as discussed above, and Rosenfeld discloses a reference database with known haplotypes ([0048]; [0055]) and that method for determining haplotypes are known in the art and can be applied to determine each haplotype in a dog genome ([0060]). Furthermore, Rosenfeld generally discloses that software programs developed for population genetics can also be applied to dog genomes ([0134]), and thus one of ordinary skill in the art would expect the population genetics model of Wilton to apply to the dog genotype and haplotype information of Rosenfeld.
Further regarding claim 1, while Rosenfeld generally discloses managing diet composition (i.e. a particular diet), and the age and weight at which diet changes based on the trait inferred from the dog, which is based on the genetic ancestry (Abstract; [0015]; [0020]-[0022], e.g. traits based on ancestry; [0053], e.g. particular diet based on traits), Wilton in view of Rosenfeld do not disclose administering the first dog a treatment diet determined based on (1) a current weight and the predicted adult weight of the first dog; and (2) the genetic skin disease, the treatment diet comprising energy density less than 4100 kcal/kg, an amount of fat less than 20% w/w, an amount of carbohydrates less than 25% w/w, an amount of protein more than 20% w/w, and an amount of dietary fiber more than 5% w/w, linoleic acid in an amount from about 7 g/Mcal to about 9 g/Mcal, and zinc in an amount from about 40 mg/Mcal to about 60 mg/Mcal
However, this limitation was known to one of ordinary skill in the art, before the effective filing date of the claimed invention, as shown by Butterwick and Watson.
Regarding claim 1, Butterwick discloses a method for maintaining optimal growth of animals (Abstract), and discloses various diet compositions according to a dog’s weight and age ([0076]-[0077]), including a diet for puppy/adolescence maintenance including 3000-4000 kcal/kg, 11-20% fat, 25-50% carbohydrates, 27-38% protein, and 4-18% fiber, which overlaps or lies close to the claimed ranges of fat, carbohydrates, protein, and fiber. Butterwick further discloses using biomarkers including dog breed and weight to obtain optimal growth curves for the dog ([0057]; [0060]; [0071]), and altering dietary regimens to achieve a healthy body weight ([0075]). Butterwick further discloses maintaining a display of information (i.e. a generated output), recommendations (i.e. the diet recommendations), and outputs relating to the monitoring and/or evaluation of the animal to allow for healthy, optimal growth ([0025]).
Furthermore, Watson disclose a method of using a diet comprising linoleic acid and zinc for improving skin quality of healthy pet animals or for treating pet animals affected with a disease causing a skin or dermatological disorder (Abstract; pg. 1, lines 9-11), wherein the pet is a dog (pg. 3, line 22). Watson discloses embodiments in which the diet comprises 7 g/Mcal to 9 g/Mcal of linoleic acid and further comprises 40 mg/Mcal to 60 mg/Mcal of zinc (pg. 3, lines 5-8, 14-17).
Regarding the process in which the above diet was determined (i.e. based on (1) and (2)), the limitation recites a product by process limitation that defines the process which the treatment diet was previously determined (i.e. based on the current weight and predicted adult weight and the genetic disease). Given Butterwick and Watson discloses the treatment diet, Butterwick and Watson disclose the same product as the claims. See MPEP 2113 I.
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the method of Wilton in view of Rosenfeld to have administered the particular diet to the first dog, as shown by Butterwick and Watson, discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Wilton in view of Rosenfeld with Butterwick and Watson in order to allow for healthy and optimal growth of the animal, as shown by Butterwick ([0025]) and to further improve the skin quality of the dog through the use of linoleic acid and zinc, as shown by Watson (Abstract; pg. 1, lines 9-11). This modification would have had a reasonable expectation of success given Rosenfeld discloses the method of inferring dog ancestry can be used to manage diet, as discussed above, and thus the method and diet of Butterwick and Watson is applicable to the method of Wilton in view of Rosenfeld.
Furthermore, with respect to the particular diet composition in claim 1, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the fat, carbohydrate, protein, and fiber content of the particular diet through routine experimentation of the diet composition within the prior art conditions of achieving a healthy body weight of the animal according to an optimal growth curve of the dog, as shown by Butterwick (([0057]; [0060]; [0071]; [0075]). See MPEP 2144.05 II. A.
Regarding claim 2, Wilton further discloses determining the one or more source populations comprises the following steps ([0249])
Wilton further discloses aggregating the assignments of each window across chromosomes into a maternal haplotype and a paternal haplotype (FIG. 31; [0258]) (aggregating the one or more local assignments for the one or more genetic populations over both maternal and paternal chromosomes).
Wilton discloses calculating ancestry proportions associated with each ancestry prediction (i.e. the one or more source populations) based on the maternal and paternal origin (i.e. based on the aggregations) (FIG. 31; [0258]) (calculating proportions associated with the one or more source populations based on the aggregations).
Wilton further discloses determining the one or more source populations by comparing the calculated proportions to a threshold (FIG. 26A; [0246]-[0247], e.g. ancestries percentages below the threshold are merged into broader categories until threshold is met) (determining the one or more source populations based on the calculated proportions).
Regarding claim 3, Wilton further discloses the partitioning the smoothed local ancestry assignments of each segment into paternal and maternal haplotypes comprises using a clustering process (FIG. 31, e.g. ancestry assignments of each segment split by maternal/paternal origin; [0256], e.g. final assignments for each maternal/paternal haplotype based on clustering; [0258]) (wherein the partitioning is based on one or more clustering algorithms).
Regarding claim 12, Wilton in view of Rosenfeld, as applied to claim 1 above, disclose the ancestry classification can be applied to dogs, such that the one or more genetic populations are of dogs. Wilton further discloses executing the HMM to smooth the ancestry predictions of the segments (i.e. the local assignments) comprises removing errors associated with the ancestry predictions of the windows ([0140]-[0141], e.g. local classifier contains errors, and error correction is implemented via “smoothing” using an HMM) (wherein executing the HMM to smooth the one or more local assignments for the one or more genetic populations of dogs comprises removing, one or more errors associated with the one or more local assignments for the one or more genetic populations).
Therefore, the invention is prima facie obvious.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Wilton in view of Rosenfeld, Butterwick, and Watson, as evidenced by Freyman, as applied to claim 1 above, and further in view of Becker (2021). This rejection is newly recited and necessitated by claim amendment.
Cited reference: Becker et al., Resource profile and user guide of the Polygenic Index Repository, 2021, Nature Human Behavior, 5, pg. 1744-1758 and Suppl.; Pub. Date: 17 June 2021 (previously cited).
Regarding claim 4, Wilton in view of Rosenfeld, Butterwick, and Watson, disclose the method of claim 1 as applied above.
Further regarding claim 4,, Wilton in view of Rosenfeld, Butterwick, and Watson, as applied to claim 1 above, does not disclose identifying the one or more genomic traits is further based on one or more of genome-wide statistics, genomic principal component analysis (PCR) projections, DNA methylation profiles, or polygenic risk scores.
However, Becker discloses a method for using polygenic indexes (i.e. polygenic risk scores) for predicting phenotypes (Abstract; pg. 1745, col. 2, para. 1) comprising determining a plurality of genetic traits including a predisposition to a genetic disease (Table 1, Phenotypes 18-19, 22, 26-27), based on polygenic risk scores (Abstract; suppl. pg. 5, para. 2). Becker further discloses predicting traits based on polygenic risk scores can be incorporated into clinical practice for preventative medicine and can engage people in research to stoke their interest and educate them about genomics (Suppl. pg. 6, para. 3; Suppl. pg. 18, para. 4).
It would have been prima facie obvious, to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Wilton in view of Rosenfeld, Butterwick, and Watson, as applied to claims 1 and 14 above, to have further determined the one or more genetic traits comprising a predisposition to a genetic disease based on polygenic risk scores, as shown by Becker (Abstract; pg. 1745, col. 2, para. 1; Table 1; Suppl. pg. 5, para. 2), thus arriving at the inventions of claims 4 and 17. One of ordinary skill in the art would have been motivated to combine the methods of Wilton in view of Rosenfeld, Butterwick, and Watson, and Becker to facilitate preventative medicine, as shown by Becker (Suppl. pg. 6, para. 3; Suppl. pg. 18, para. 4), given Rosenfeld also discloses it is important to focus on preventative therapeutics in dogs rather than acute treatment ([0003]). This modification would have had a reasonable expectation of success because Rosenfeld infers traits based on multiple SNPs ([0032]; [0037]), such that the genetic traits of Becker are applicable to the method of Wilton in view of Rosenfeld, Butterwick, and Watson.
Therefore, the invention is prima facie obvious.
Claims 6 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Wilton in view of Rosenfeld, Butterwick, and Watson, as evidenced by Freyman, as applied to claim 1 above, and further in view of Watson (2018). This rejection is newly recited and necessitated by claim amendment.
Cited reference: Watson (referred to as Watson (2018) below), Keeping Your Machine Learning Models Up-To-Date, 2018, Center for Open Source Data and AI Technologies, pg. 1-9 (previously cited).
Regarding claims 6 and 8-9 Wilton in view of Rosenfeld, Butterwick, and Watson disclose the method of clam 1 as applied above.
Further regarding claim 6, Wilton in view of Rosenfeld, as applied to claim 1 above, do not disclose updating the one or more machine learning algorithms based on one or more new reference samples added to the reference panel.
Further regarding claim 8 Wilton in view of Rosenfeld, Butterwick, and Watson, as applied to claim 1 above, does not disclose the updating is repeatedly iterated until a predetermined accuracy level of the one or more machine learning algorithms is reached.
Further regarding claim 9, While Wilton discloses the reference samples of the reference panel are labeled training data ([0122]), Wilton similarly does not disclose the updating comprises generating one or more labels for one or more unlabeled samples, wherein the updating is based on the generated one or more labels.
However, regarding claims 6 and 8-9 Watson (2018) discloses a method for updating machine learning models (pg. 1, para. 2) which comprises continuously evaluating and retraining machine learning models by saving new training data (e.g. new labeled reference samples in the reference panel of Wilton) as it is received (pg. 2, para. 6 to pg. 3, para. 2), and continuously retraining the machine learning model until a threshold accuracy is achieved (pg. 5, para. 4; pg. 7, para 1-3). Watson further discloses that data changes over time and machine learning systems should take into account this new data to keep models up to date (pg. 2, para. 3).
It would have been prima facie obvious, to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Wilton in view of Rosenfeld, Butterwick, and Watson, as applied to claim 1 above, to have updated the machine learning algorithm based on newly labeled training data added to the training data, and repeatedly iterated the updating of the one or more machine learning models until a predetermined accuracy level of the one or more machine learning algorithms is reached, as shown by Watson (2018) (pg. 2, para. 6 to pg. 3, para. 2; pg. 5, para. 4; pg. 7, para. 1-3), thus arriving at the inventions of claims 6 and 8-9. One of ordinary skill in the art would have been motivated to combine the method of Wilton in view of Rosenfeld, Butterwick, and Watson, as applied to claim 1 above, and Watson (2018) to keep the machine learning model of Wilton up to date by taking into account new data, as shown by Watson (2018) (pg. 2, para. 3). This modification would have had a reasonable expectation of success because Wilton discloses training a machine learning model, such that that the retaining method of Watson (2018) is applicable to the model of Wilton.
Therefore the invention is prima facie obvious.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Wilton in view of Rosenfeld, Butterwick, Watson, and Watson (2018), as evidenced by Freyman, as applied to claim 6 above, and further in view of Clarke (2021). This rejection is newly recited and necessitated by claim amendment.
Cited reference: Clarke, How to use the Isolation Forest model for outlier detection, 2021, Practical Data Science, pg. 1-6; Pug. Date: 14 March 2021 (previously cited).
Regarding claim 7, Wilton in view of Rosenfeld, Butterwick, Watson, and Watson (2018) disclose the method of claim 6, as applied above.
Regarding claim 7, Wilton further discloses applying a cross-validation to the reference samples of the machine learning model ([0228]) (applying/apply a cross-validation across all samples in the reference panel), and additionally removing outliers from the training data ([0135]).
Further regarding claim 7, Wilton in view of Rosenfeld, Butterwick, Watson, and Watson (2018), as applied to claim 6 above do not disclose identifying, based on results associated with the cross-validation by a detection algorithm, one or more outliers; and removing the identified one or more outliers from the reference panel.
However, Clarke overviews a method for isolation forest outlier detection (i.e. a detection algorithm), which comprises performing cross-validation on a training dataset to identify the optimal hyperparameters for an isolation forest machine learning model (pg. 1, para. 3-4; pg. 4, para. 2 to pg. 5, para. 1), and then removing the outliers associated with the optimal model (i.e. based on the cross-validation) from the training data (pg. 5, para. 2). Clarke further discloses removing the outliers from the training data improves classification performance of the machine learning model (pg. 6, para. 1).
It would have been prima facie obvious, to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the method, product, and system of Wilton in view of Rosenfeld, Butterwick, Watson, and Watson (2018), as applied to claim 6 above, to have identified, based on results associated with the cross-validation by a detection algorithm, one or more outliers, and to have removed the identified one or more outliers from the reference panel, as shown by Clarke (pg. 1, para. 3-4; pg. 4, para. 2 to pg. 5, para. 1). One of ordinary skill in the art would have been motivated to combine the method of Wilton in view of Rosenfeld, Butterwick, Watson, and Watson (2018), as applied to claim 6 above, with the method of Clarke et al. to improve classification performance of a machine learning model, as shown by Clarke (pg. 6, para. 1), given Wilton also uses a machine learning model for classification. This modification would have had a reasonable expectation of success because Wilton utilizes a machine learning model for classification, such that the outlier detection method of Clarke is applicable to the method of Wilton
Therefore, the invention is prima facie obvious.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Wilton in view of Rosenfeld, Butterwick, and Watson, as evidenced by Freyman, as applied to claim 1 above, and further in view of Trubetskoy (2015). This rejection is newly recited and necessitated by claim amendment.
Cited reference: Trubetskoy et al., Consensus Genotyper for Exome Sequencing (CGES): improving the quality of exome variant genotypes, 2015, Bioinformatics, 31(2), pg. 187-19 (previously cited).
Regarding claim 10, Wilton in view of Rosenfeld, Butterwick, and Watson disclose the method of claim 1, as applied above.
Further regarding claim 10¸ Wilton further discloses generating the phased haplotypes is based on the raw genotype (FIG. 4; [0057]; [0060]) and phasing the one or more genotypes into maternal and paternal chromosomes (FIG. 31; [0258]) (…wherein generating the phased haplotypes comprises phasing the raw genotype into maternal and paternal chromosomes).
Further regarding claim 10, Wilton in view of Rosenfeld, Butterwick, and Watson as applied to claim 1 above, does not disclose generating, based on the raw genotype, a consensus genotype, wherein generating the plurality of phased haplotypes is further based on the consensus genotype, and wherein generating each of the plurality of phased haplotypes comprises phasing…the consensus genotype into maternal and paternal chromosomes)
However, Trubetskoy discloses a method for improving the quality of variant genotypes (Abstract) comprising generating consensus genotypes using a voting scheme based on raw genotype calls from variant calling algorithms (pg. 189, col. 1, para. 7; pg. 191, col. 1, para. 1; Fig. 1). Trubetskoy further discloses combining the raw genotype outputs from multiple models can account for false-positive variant calls (pg. 192, col. 1, para. 3 to col. 1, para. 2) and improve variant classification performance (Abstract).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to modified the method of phasing haplotypes based on genotypes of Wilton (FIG. 4 and 31; [0057]; [0060]; [0258]) to have generated and used consensus genotypes from the raw genotypes, as shown by Trubetskoy (pg. 189, col. 1, para. 7; pg. 191, col. 1, para. 1; Fig. 1), thus arriving at the inventions of claim 10. One of ordinary skill in the art would have been motivated to combine the method of Wilton in view of Rosenfeld, Butterwick, and Watson with Trubetskoy. in order to utilize genotypes with a lower false-positive rate and provide improved genotype calls, as shown by Trubetskoy (Abstract). This modification would have had a reasonable expectation of success because Wilton utilizes genotype data, and thus the genotype data of Trubetskoy is applicable to the method of Wilton in view of Rosenfeld, Butterwick, and Watson.
Therefore, the invention is prima facie obvious.
Response to Arguments
Applicant's arguments filed 23 Sept. 2025 regarding 35 U.S.C. 103 have been fully considered but they are not persuasive.
Applicant remarks that claim 1 has been amended to recite features of “the one or more genetic traits comprising at least a predicted adult weight of the first dog and a risk prediction or a predisposition to a genetic skin disease” and Wilton in view of Rosenfeld and Freyman do not disclose the recited features of claim 1 (Applicant’s remarks at pg. 9, para. 3). Applicant remarks that while Rosenfeld discloses determining certain genetic traits such as hip dysplasia, diabetes, hypertension, atherosclerosis, autoimmune disorders, kidney disease, and neurological disease, it fails to disclose “a risk prediction or a predisposition to a genetic skin disease”, and further remarks Freyman does not make up for this deficiency (Applicant’s remarks at pg. 9, para. 4).
This argument is not persuasive. The broadest reasonable interpretation of claim 1 requires that the one or more genetic traits comprise at least a predicted adult weight of the first dog and a risk prediction or a predisposition to a genetic skin disease”. Therefore, under the broadest reasonable interpretation of the claim, the genetic traits may comprise “a risk prediction” in addition to the predicted weight, or may comprise “a predisposition to a genetic skin disease” in addition to the predicted weight. As noted by Applicant, and discussed in the above rejection, Rosenfeld does disclose a risk prediction relating to hip dysplasia.
Applicant remarks that claim 1 has been amended to recite features of “administering the first dog a treatment….”, and Wilton in view of Rosenfeld and Freyman do not disclose the recited features of claim 1 (Applicant’s remarks at pg. 9, para. 3). Applicant further remarks regarding the administering step, that the Examiner concedes Wilton in view of Rosenfeld does not disclose this feature, and that Butterwick does not disclose determining a treatment diet based on a current weight and the predicted dog weight, and also does not disclose the treatment diet additionally comprises linoleic acid and zinc as claimed, and thus the references do not disclose each and every limitation of claim 1 (Applicant’s remarks at pg. 9, para. 5 to pg. 10, para. 2).
This argument is not persuasive. First, claim 1 does not require any steps of determining the treatment diet based on (1) a current weight and the predicted adult weight of the first dog; and (2) the genetic skin disease. Instead, this limitation only serves to define the process in which the treatment diet was determined. Given Butterwick in combination with Watson discloses the treatment diet recited in claim 1, Butterwick in combination with Watson disclose the same product as recited in claim 1, even if the treatment was determined by a different process. See MPEP 2113 I.
Furthermore, while it is agreed Butterwick does not disclose the linoleic acid and zinc as part of the diet, the newly cited reference Watson discloses this limitation as applied in the above rejection.
Applicant remarks that claims 4 and 6-10 are non-obvious over the cited references for the same reasons as discussed above for claim 1 (Applicant’s remarks at pg. 10, para. 3 to pg. 12, para. 2).
This argument is not persuasive for the same reasons discussed above for claim 1.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
