CTNF 17/920,741 CTNF 98483 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 Claim 51 is newly added by Applicant. Claims 1, 8-9, 15-17, 19-20, 25-30, 41-42, 44-46 and 48-50 are cancelled by Applicant. Claims 2-7, 10-14, 18, 21-24, 31-40, 43, 47 and 51 are currently pending and are herein under examination. Claims 2-7, 10-14, 18, 21-24, 31-40, 43, 47 and 51 are rejected. Claims 40 and 47 are objected. Priority The instant application claims domestic benefit as a 371 of International Application No. PCT/US2021/028649 filed 04/22/2021, which claims domestic benefit to U.S. Provisional Application No. 63/014,641 filed 04/23/2020. The claims to the domestic benefit are acknowledged. As such, the effective filing date for claims 2-7, 10-14, 18, 21-24, 31-40, 43, 47 and 51 is 04/23/2020. Information Disclosure Statement The IDS filed 05/22/2023 follows the provisions of 37 CFR 1.97 and has been considered in full. A signed copy of the list of references cited from the IDS is included with this Office Action. Drawings 06-22-07 AIA The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 100, 104, 106 and 110 in Figure 1. 200, 204, 210 and 212 in Figure 2. 300, 304, 306, 310 and 312 in Figure 3. 400, 402, 404, 406, 408, 410, 412 in Figure 4. 500 and 502 in Figure 5. The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: 600 in para. [126] . Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance. Claim Objections 07-29-01 AIA Claim s 40 and 47 are objected to because of the following informalities: Claim 40, line 4, recites “; and” which should be “;”. Claim 47, line 10, recites “; and” which should be “;” . Appropriate correction is required. Claim Rejections - 35 USC § 112 35 USC 112(b) 07-30-02 AIA 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. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 18, 23-24, 31-32, 34, 40, 43 and 47 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims dependent from a rejected claim are also rejected, unless otherwise noted. Claim 18 recites the relative terms “diverse” and “uniform”, which render the claim indefinite. The terms “diverse” and “uniform” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Because diverse and uniform re subjective terms, the metes and bounds of what constitutes a diverse and uniform genetic population are unclear. Claim 18 recites the phrase “the second population is an individual” which renders the claim indefinite. It is unclear if the phrase means that the second population comprises clones of a single individual, or if the phrase means that the second population is not comprised of multiple individuals but rather one singular individual. To overcome this rejection, clarify how the phrase should be interpreted. Claims 23 and 31-32 recite “the envirotype data” which renders the claims indefinite. It is unclear if the phrase refers to envirotype data for the first or second population in claim 2. To overcome this rejection, clarify which envirotype data is being referenced. Claim 31 recites “wherein the envirotype data is grouped” which renders the claim indefinite. It is unclear if claim 31 requires an active step of grouping or if it is a product by process which defines a process previously performed to obtain the envirotype data. See MPEP 2113.I regarding product by process limitations. For examination purposes, clam 31 is being interpreted as product by process limitation. To overcome this rejection, clarify how the claim should be interpreted. Claim 31 recites “the growth stages” which lacks antecedent basis. Provide antecedent basis. Claim 31 recites “the individuals” which renders the claim indefinite. It is unclear which individuals are being referenced because both the first and second populations in claim 2 have individuals. To overcome this rejection, clarify which individuals are being referenced. Claim 34 recites “the effects” which lacks antecedent basis. Provide antecedent basis. Claim 40, line 10, recites “the population in the geographic area” which renders the claim indefinite. It is unclear which population is being referenced because lines 3 and 9 recite “a population of individuals in a geographic area”. To overcome this rejection, clarify which population is being referenced. Claim 40 step c) recites “the prediction of phenotype data of the population in the geographic area” which renders the claim indefinite. It is unclear which prediction is being referenced because both lines 6 and 10 recite “a prediction of phenotype data of the population in the geographic area”. To overcome this rejection, clarify which prediction is being referenced. Claim 43, line 13, recites “the population in the geographic area” which renders the claim indefinite. It is unclear which population is being referenced because lines 6-7 and line 12 recite “a population of individuals in a geographic area”. To overcome this rejection, clarify which population is being referenced. Claim 43 step c) recites “the prediction of phenotype data of the population in the geographic area” which renders the claim indefinite. It is unclear which prediction is being referenced because both lines 8-9 and line 12 recite “a prediction of phenotype data of the population in the geographic area”. To overcome this rejection, clarify which prediction is being referenced. Claim 47, line 16, recites “the population in the geographic area” which renders the claim indefinite. It is unclear which population is being referenced because lines 9-10 and line 15 recite “a population of individuals in a geographic area”. To overcome this rejection, clarify which population is being referenced. Claim 47 step c) recites “the prediction of phenotype data of the population in the geographic area” which renders the claim indefinite. It is unclear which prediction is being referenced because both lines 12-13 and line 16 recite “a prediction of phenotype data of the population in the geographic area”. To overcome this rejection, clarify which prediction is being referenced. 35 USC 112(d) 07-36 AIA The following is a quotation of 35 U.S.C. 112(d): (d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers. The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph: Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers. 07-36-01 AIA Claim 11 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 11 fails to further limit claims 2-3. Claim 11 recites “for testing performance” which is an intended use and is thus not required to be performed by the claim . Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. 07-04-03 AIA 07-04-01 Section 33(a) of the America Invents Act reads as follows: Notwithstanding any other provision of law, no patent may issue on a claim directed to or encompassing a human organism. Claim 39 is rejected under 35 U.S.C. 101 and section 33(a) of the America Invents Act as being directed to or encompassing a human organism. See also Animals - Patentability , 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (indicating that human organisms are excluded from the scope of patentable subject matter under 35 U.S.C. 101). The broadest reasonable interpretation of claim 39 includes claiming a variety of human because claims 2 and 6 recite “an individual” which encompasses a human . Claims 2-7, 10-14, 18, 21-24, 31-40, 43, 47 and 51 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and a natural phenomenon without significantly more. Step 1: Step 1 asks whether the claims recite statutory subject matter. In the instant application, claims 2-7, 10-14, 18, 21-24, 31-38 and 51 recite a method, claim 39 recites a product, claim 40 recites a method, claim 43 recites a CRM, and claim 47 recites a system. As such, these claims recite statutory subject matter ( Step 1: YES ). Step 2A, Prong 1 : Claims that recite statutory subject matter are analyzed under Step 2A, Prong 1 to determine if they recite any concepts that equate to an abstract idea, law of nature or natural phenomena. The instant claims recite the following limitations that equate to one or more categories of judicial exception: Claim 2 recites “A method for predicting phenotype data of a population in a geographic area for use in breeding, comprising: a) providing a statistical model that associates phenotype data of a first population of individuals in a first geographic area with genotype data and envirotype data of the first population; b) providing a second population of individuals in a second geographic area; c) obtaining genotype data and envirotype data of the second population in the second geographic area; and d) predicting phenotype data of the second population in the second geographic area by applying the statistical model to the genotype data and envirotype data of the second population.” Claim 3 recites “selecting one or more individuals from the second population based on the predicted phenotype data of the second population” Claim 4 recites “wherein the genotype data, phenotype data, and envirotype data of the first population is genome-wide genotype data, phenotype data, and envirotype data the genotype data and envirotype data of the second population is genome-wide genotype data and envirotype data; and further comprising: g) selecting one or more individuals from the second population based on the predicted phenotype data of the second population.” Claim 5 recites “using the selected one or more individuals in breeding.” Claim 6 recites “further comprising: e) selecting one or more individuals from the second population based on the predicted phenotype data of the second population; and f) developing one or more varieties from the selected one or more individuals, wherein the one or more varieties exhibit suitable phenotype for the second geographic area.” Claim 7 recites “wherein a) the individuals in the first population are hybrids and the individuals in the second population are inbred lines or hybrids, b) the individuals in the first population are inbred lines, breeding populations, or hybrids, and the individuals in the second population are segregating lines from breeding populations; or c) the individuals in the first population are parental lines and the individuals in the second population are filial lines derived from the parental lines.” Claim 10 recites “further comprising using the selected one or more individuals in breeding.” Claim 11 recites “wherein the selection is for testing performance of the selected one or more individuals in a field. Claim 12 recites “wherein the selected one or more individuals are segregating lines, inbred lines, or hybrid lines.” Claim 13 recites “wherein the selection is applied using a selection intensity.” Claim 14 recites “further comprising producing offspring from the selected one or more individuals.” Claim 18 recites “wherein: a) the second population is a genetically diverse population, b) the second population is a genetic uniform population; or c) the second population is an individual.” Claim 21 recites “wherein the first geographic area and the second geographic area are the same geographic area. Claim 22 recites “wherein the second geographic area is a target breeding zone or a target market zone.” Claim 23 recites “wherein the envirotype data is time data, location data, weather data, soil data, companion organism data, management data, crop canopy data, cultivation area data, or a combination thereof.” Claim 24 recites “wherein: a) the time data is century data, decade data, year data, season data, month data, day data, hour data, minute data, second data, or a combination thereof; b) the location data is latitude, longitude, altitude, or a combination thereof; c) the weather data is temperature, humidity, pressure, zonal wind speed, meridional wind speed, long-wave radiation, fraction of total precipitation that is convective, convective available potential energy, potential evaporation, precipitation hourly total, short-wave solar radiation, photoperiod, or a combination thereof; d) the soil data is soil type, soil structure, soil moisture, soil depth, soil organic matter content, soil density, soil pH, soil fertility, soil salinity, or a combination thereof; e) the companion organism data is soil fauna, insects, animals, weeds, or a combination thereof; f) the management data is intercropping management, covercropping management, rotating cropping management, or a combination thereof; and/or g) the crop canopy data is obtained from an aerial platform.” Claim 31 recites “wherein the envirotype data is grouped according to the growth stages of the individuals.” Claim 32 recites “wherein the envirotype data is an envirotype map.” Claim 33 recites “wherein the second population of individuals are a crop selected from the group consisting of maize, soybean, wheat, sorghum, barley, oats, rice, millet, canola, cotton, cassava, cowpea, safflower, sesame, tobacco, flax, sunflower, a grain crop, a vegetable crop, an oil crop, a forage crop, an industrial crop, a woody crop, and a biomass crop.” Claim 34 recites “wherein the statistical model estimates the effects of genetic markers in interaction with the envirotype on the phenotype of the individuals of the first population.” Claim 35 recites “wherein the statistical model comprises a genotype variable, an envirotype covariate, and an interaction term between the genotype variable and the envirotype covariate.” Claim 36 recites “wherein the statistical model is a linear regression model, a logistic regression model, a Bayesian ridge regression model, a lasso regression model, an elastic net regression model, a decision tree model, a gradient boosted tree model, a neural network model, or a support vector machine model.” Claim 37 recites “wherein the predicted phenotype data of the second population are genomic estimated breeding values (GEBVs).” Claim 38 recites “wherein building the statistical model further comprises training the statistical model, tuning the statistical model, validating the statistical model, and/or updating the statistical model.” Claim 39 recites “A variety developed by the method of claim 6.” Claim 40 recites “b) apply a statistical model to the genotype data and envirotype data of the population to obtain a prediction of phenotype data of the population in the geographic area, wherein the statistical model is configured to receive genotype data and envirotype data of a population of individuals in a geographic area and output a prediction of phenotype data of the population in the geographic area;” Claim 43 recites “b) apply a statistical model to the genotype data and envirotype data of the population to obtain a prediction of phenotype data of the population in the geographic area, wherein the statistical model is configured to receive genotype data and envirotype data of a population of individuals in a geographic area and output a prediction of phenotype data of the population in the geographic area;” Claim 47 recites “b) applying a statistical model to the genotype data and envirotype data of the population to obtain a prediction of phenotype data of the population in the geographic area, wherein the statistical model is configured to receive genotype data and envirotype data of a population of individuals in a geographic area and output a prediction of phenotype data of the population in the geographic area;” Claim 51 recites “further comprising building the statistical model by associating phenotype data of the first population with genotype data and envirotype data obtained for the first population.” Limitations reciting a mental process . Claims 2-4, 6, 31, 34, 38, 40, 43, 47 and 51 c ontain limitations recited at such a high level of generality that they equate to a mental process because they 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)), which the courts have identified as concepts that can be practically performed in the human mind. The paragraphs below discuss the broadest reasonable interpretation (BRI) of the limitations in these claims that recite a mental process. Claims 2, 34, 38, 40, 43, 47 and 51 include using pen and paper to collect data and perform calculations using a linear regression based on the gather data to generate a prediction. Claims 3, 4 e), 6 e), includes making a mental determination to make a selection. Claim 31 includes organizing data. Limitations reciting a mathematical concept . Claims 2, 34-35, 37-38, 40, 43, 47 and 51 recite limitations that equate to a mathematical concept because they are similar to the concepts of 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)), which the courts have identified as mathematical concepts. The paragraph below discusses the broadest reasonable interpretation (BRI) of the limitations in these claims that recite a mathematical concept. Claims 2 f), 34-35, 37-38, 40 b), 43 b), 47 b) and 51 include performing calculations using any of the models recited in claim 37, wherein the output are numerical predictions such as breeding values. Limitation reciting a natural phenomenon . Claim 39 claims a physical product of a variety previously developed by claim 6. The BRI of claim 39 includes a variety of plant, wherein a plant is a product of nature. See MPEP 2106.04(b)(II) regarding products of nature. Limitations reciting methods of organizing human activity . Claims 5-6, 10 and 14 recite using a selected individual for breeding, developing a variety, and producing offspring. The BRI of these claims includes using a human. Thus, these claims are similar to managing personal behavior, relationships, or interactions between people. See MPEP 2106.04(a)(2)(II)(C). Limitations included in the recited judicial exception . Claims 7, 12-13, 18, 21-24, and 32-33 further limit limitations that are part of the judicial exception but do not alter the fact that they are part of the recited judicial exception. Claim 11, as discussed above in 35 USC 112(d), recites an intended use and is thus not required to be performed. As such, claims 2-7, 10-14, 18, 21-24, 31-40, 43, 47 and 51 recite an abstract idea and a natural phenomenon ( Step 2A, Prong 1: YES ). Additional Elements : Once limitations have been identified that recite a judicial exception, the claims are evaluated for additional elements. The additional elements are then analyzed under Step 2A, Prong 2 then Step 2B . However, claims 2-7, 10-14, 18, 21-24 and 31-39 do not recite any additional elements and thus cannot be integrated into a practical application or provide significantly more. Conversely, claims 40, 43 and 47 do recite additional elements which are listed below: Claim 40 recites “a computer-implemented method for predicting phenotype data of a population in a geographic area for use in breeding, comprising: a) receiving genotype data and envirotype data of a population of individuals in a geographic area; and c) outputting the prediction of phenotype data of the population in the geographic area.” Claim 43 recites “A non-transitory computer-readable storage medium storing one or more programs for predicting phenotype data of a population in a geographic area for use in breeding, the one or more programs comprising instructions, which when executed by one or more processors of an electronic device having a display, cause the electronic device to: a) receive genotype data and envirotype data of a population of individuals in a geographic area; and c) output the prediction of phenotype data of the population in the geographic area.” Claim 47 recites “An electronic device for predicting phenotype data of a population in a geographic area for use in breeding, comprising: a display; one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: a) receiving genotype data and envirotype data of a population of individuals in a geographic area; and c) outputting the prediction of phenotype data of the population in the geographic area.” These above recited additional elements are analyzed below under both Step 2A, Prong 2 and Step 2B : Step 2A, Prong 2 : 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). The judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflect an improvement to a computer, technology, or technical field (MPEP § 2106.04(d)(1) and 2106.5(a)), require a particular treatment or prophylaxis for a disease or medical condition (MPEP § 2106.04(d)(2)), implement the recited judicial exception with a particular machine that is integral to the claim (MPEP § 2106.05(b)), effect a transformation or reduction of a particular article to a different state or thing (MPEP § 2106.05(c)), nor provide some other meaningful limitation ( MPEP § 2106.05(e)). Rather, the claims include limitations that equate to an equivalent of the words “apply it” and/or to instructions to implement an abstract idea on a computer (MPEP § 2106.05(f)) and to insignificant extra-solution activity (MPEP § 2106.05(g)). The paragraphs below discuss the additional elements recited above in the instant claims. Regarding the computer-implemented method of claim 40, the CRM of claim 43, and the electronic device in claim 47 comprising a display, processors, and programs stored in memory. There are no limitations that these components require anything other than a generic computer. Therefore, these limitations equate to mere instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp . 573 U.S. at 223, 110 USPQ2d at 1983. Regarding the limitations in claims 40, 43 and 47 of receiving data and outputting data, these limitations equate to invoking a computer as a tool to perform an existing process such as receiving, storing, and transmitting data (MPEP 2106.05(f)(2)). These limitations also equate to insignificant extra-solution activity of necessary data gathering and outputting because they gather data to perform the recited judicial exception and then output the result of the judicial exception (MPEP 2106.05(g)(3)). As such, claims 2-7, 10-14, 18, 21-24, 31-40, 43, 47 and 51 are directed to an abstract idea and a natural phenomenon ( Step 2A, Prong 2: NO ). Step 2B : 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 ). These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these claims recite additional elements that equate to instructions to apply the recited exception in a generic way and/or in a generic computing environment (MPEP § 2106.05(f)) and to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The paragraphs below discuss the additional elements recited above in the instant claims. Regarding the computer-implemented method of claim 40, the CRM of claim 43, and the electronic device in claim 47 comprising a display, processors, and programs stored in memory. There are no limitations that these components require anything other than a generic computer. Therefore, these limitations equate to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept in Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). Regarding the limitations in claims 40, 43 and 47 of receiving data and outputting data, these limitations equate to receiving/transmitting data over a network, which the courts have established as WURC limitation of a generic computer in buySAFE, Inc. v. Google, Inc ., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). Regarding the limitations in claims 43 and 47 of storing instructions in memory, these limitations equate to storing information in memory, which the courts have established as a WURC function of a generic computer in Versata Dev. Group, Inc. v. SAP Am., Inc ., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). When these additional elements are considered individually and in combination, they do not provide an inventive concept because they equate to WURC functions/components of a generic computer. Therefore, these additional elements do not transform the claimed judicial exception into a patent-eligible application of the judicial exception and do not amount to significantly more than the judicial exception itself ( Step 2B: No ). As such, claims 2-7, 10-14, 18, 21-24, 31-40, 43, 47 and 51 are not patent eligible . Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) 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. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15 AIA Claim s 2-7, 10-12, 18, 21-24, 31-35, 37-40, 43, 47 and 51 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Messina et al. (“Messina”; European Journal of Agronomy 100 (2018): 151-162) . The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims. Claim 2 : A method for predicting phenotype data of a population in a geographic area for use in breeding, comprising: a) providing a statistical model that associates phenotype data of a first population of individuals in a first geographic area with genotype data and envirotype data of the first population; Messina predicts crop yield by using a combination of a crop growth model (CGM) and whole genome prediction (WGP) methods called CGM-WGP (abstract). An estimation set is used to train the CGM-WGP and comprises individuals within populations who have both phenotype and genotype data (pg. 152, col. 1, para. 1). The training data also includes data from multiple environments (pg. 152, col. 2, para. 2). See the equation of the model in section 2.1. and in Figure 1. The different environments are being interpreted as geographic areas. Alternatively, Messina provides an example of an empirical breeding experiment using the CGM-WGP model using four double haploid populations tested in different environmental fields in Chile (−33.797°S,−70.807°W) (pg. 155, col. 1, para. 2). b) providing a second population of individuals in a second geographic area; c) obtaining genotype data and envirotype data of the second population in the second geographic area; A prediction set is used to validate the model, which contains genotype data (pg. 152, col. 1, para. 1). Figure 1 shows observed data for an individual which includes environment data. d) predicting phenotype data of the second population in the second geographic area by applying the statistical model to the genotype data and envirotype data of the second population. For an empirical breeding experiment, Messina recites “A leave-one-family-out cross-validation was conducted to assess prediction accuracy for grain yield. Here the CGM-WGP model was selected using an estimation data set based on the yield data from both the NWL and WL environments for three of the four DH populations and then used to predict the yield values of the fourth DH population for both the NWL and WL environments. This process was repeated until each population was left out once. Since the estimation set comprised yield data from both the yield in the NWL and WL environments this is a multi-environment estimation set” (pg. 155, col. 2, para. 2). Additionally, sec. 3.5 uses a parametrized CGM-WGP model and applies it to a test population that only contains genotype and location data. Claim 3 : The CGM-WGP model is used to predict crop yield and is used in plant breeding, specifically in early hybrid advancement stages of breeding program (abstract) (sec. 5). Thus, the test populations would be selected based on their precited crop yield (Figure 6). Claim 4 : CGM-WGP is based on whole genome prediction (abstract) (pg. 155, col. 1, para. 3). “The WGP methodology seeks to simultaneously estimate the allelic values at all available polymorphic marker loci across the genome” (pg. 151, col. 2, para. 2). Figure 1 and equation in sec. 2.1 show that for the training population phenotypic traits and environmental variables are associated with each genetic trait ( genome-wide genotype, envirotype and phenotype data of first population ). Then the prediction set models genetic markers in different environment types ( genome-wide genotype and envirotype data of the second population ). DH line 1 was predicted to perform better that DH line 2 in environments where yield is greater than 877gm -2 but not below this threshold (sec. 3.5) ( selecting one individual based on predicted phenotype ). Claims 5-6, 10 and 39 : CGM-WGP is used for plant breeding applications to select plants that have a breeding value (abstract) (pg. 151, col. 1). Crop yield was virtually predicted in the US corn belt using two DH lines with untested phenotypes based on their genotypes (Figure 6) (pg. 155, col. 1, last para.) (sec. 3.5). Of the two DH lines, DH line 1 was predicted to perform better than DH line 2 in environments where yield was greater than 877 gm -2 (pg/ 160, col. 1, para. 1). The model is used in early hybrid advancement stages of a breeding program (pg. 160, col. 1, para. 2). Thus, the top predicted performer is selected for advancement in early hybrid stages of a breeding program which includes environmental trials ( instant claims 5 and 10 and steps e and f of instant claim 6 ). This procedure would result in different crop varieties ( instant claim 39 ). Claims 7 and 18 : “The four DH populations, referred to as DHPop1, DHPop2, DHPop3, DHPop4, were created from biparental crosses between six inbred lines, referred to as I1, I2, …, I6. The two parents of DHPop1, I1 and I2, were different from the four inbred lines, I3, I4, I5 and I6, used as parents to create the other three DH populations. One of the four remaining inbred lines, I3, was used as a common parent for all three remaining DH populations; DHPop2 parents I3/I4, DHPop3 parents I3/I5 and DHPop4 parents I3/I6. Thus, there is a closer pedigree relationship, based on the half-sib structure, between DHPop2, DHPop3 and DHPop4 than there is between any of these three populations and DHPop1” ( the second population is genetically diverse population ) (pg. 155, col. 1, para. 3). The CGM-WGP model was trained based on three of the four DH populations ( first population and second population are hybrids ) (sec. 2.7). Claim 11 : Claim 11 is rejected for its dependency on claims 2-3, which are also rejected. Claim 11 is interpreted as an intended use, as discussed above in 35 USC 112(d) . Claim 12 : DH line 1 was predicted to perform better than DH line 2 (pg. 160, col. 1, para. 1). The DH lines are F1 hybrids (pg. 154, col. 2, para. 1). Claim 21 : The estimation set and prediction set are derived from the same DH lines, which are acquired from environments types (pg. 155, col. 1, para. 1) (pg. 155, col. 2, para. 2). Environment types are being interpreted as geographic locations. Claim 22 : Target environments were selected in the US corn-belt where DH line crop yield was simulated (Figure 6) (pg. 161, col. 1, last para.). Claims 23-24 : “The environmental inputs (e.g., soil type, temperature) of environment j are represented by E j , and denotes the residual variance for yield in that environment” ( the soil data is soil type ) (Figure 1) (sec. 2.1). Claim 31 : Claim 31 is being interpreted as a product by process. MPEP 2113.I recites “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.” As such, the environment input variables of Messina read on the product of envirotype data even though it was produced using a different process. Claim 32 : Figure 6 shows a map ( envirotype map ) of simulated yields for major maize growing regions in the corn belt for 34 individuals of a breeding population with CGM parameters estimated from genetic marker data using the CGM-WGP model (sec. 3.5). Yields were simulated for two DH lines in 2263 grids in the corn belt (sec. 3.5). Claim 33 : The DH populations are maize (sec. 2.5). Claim 34 : Figure 1 and section 2.1 show the equation that models effects of genetic markers in different environments to predict a phenotype of crop yield. Claim 35 : CGM-WGP models gene x environment interactions to predict phenotypes (abstract). The model is: PNG media_image1.png 70 399 media_image1.png Greyscale (sec. 2.1). The envirotype covariate is PNG media_image2.png 50 48 media_image2.png Greyscale which denotes the residual variance for yield in a environment (sec. 2.1). T ti is the unobserved value for a physiological trait t and for individual i , and the prior for the unobserved T ti uses B ot as a trait specific intercept, z tik denotes the marker score of individual i at marker k for trait t and u tk the effect of marker k for trait t (ge notype variable ). The Gaussian density function N represents an interaction term. Claim 37 : Messina predicts crop yield of a prediction set containing genotype data using CGM-WGP trained on an estimation set containing genotype x environment data and phenotype data (pg. 152, col. 1, para. 1). WGP predicts breeding values (pg. 151, col. 1). Because Messina uses whole genome SNP data from a test population to predict crop yield crop (pg. 155, col. 1, para. 3), the predicted phenotype can be considered a genomic estimated breeding value. Claims 38 and 51 : CGM-WGP was trained using estimation sets which contain genotype, environment, and phenotype data (pg. 152, col. 2, para. 2). Equation in section 2.1 shows the correlation of genetic markers to different environmental variables associated with crop yield (Figure 1) (sec. 2.7). Claims 40, 43 and 47 : Steps (a)-(c) in claims 40, 43 and 47 have been taught above in regards to claim 2. The difference between these claims and claim 2 are a computer-implemented method, a non-transitory computer readable medium, and an electronic device containing a display, processor, memory, and programs stored in the memory. Messina performs computer simulations (pg. 152, col. 2, para. 2). A generic computer contains a display, processor and memory, and because Messina performs their method using software such as R and ASREML (sec. 2.2 and 2.7), there are stored programs in the memory . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-103 AIA The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. 07-23-aia AIA 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. 07-20-02-aia AIA 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 13-14 are rejected under 35 USC 103 for being unpatentable over Messina et al. (“Messina”; European Journal of Agronomy 100 (2018): 151-162), as applied to claims 2-3, in view of Yonezawa et al. (“Yonezawa”; Heredity 82, no. 4 (1999): 401-408). The limitations of claims 2-3 have been taught above by Messina in section Claim Rejections - 35 USC § 102. Claims 13-14 : Messina uses WGP in large commercial breeding programs to evaluate a fraction of new hybrids in multi-environmental trials (abstract) (pg. 151, col. 1-2). Messina implements CGM-WGP in a functional breeding program (pg. 152, col. 2, para. 1). However, Messina does not select a hybrid for a trial based on selection intensity to then produce offspring of a selected hybrid. Yonezawa evaluates a selection efficiency metric in plant breeding that produces optimum selection procedures to produce new commercial varieties (abstract). Yonezawa recites “Figure 2 describes how selection intensity influences the selection efficiency S/t. The selection intensity in a range 0.05 < α < 0.10 is optimum, giving the highest selection efficiency (S/t) with the optimum selection cycles of six and seven” (pg. 404, col. 2, para. 2). Thus, a plant is selected based on selection intensity and is used in breeding cycles ( producing offspring from the selected individual ). It would have been prima facie obvious to have modified the plant breeding program implemented with a CGM-WGP model in Messina by selecting hybrids for trials based on intensity selection which includes cycles for breeding as taught by Yonezawa. Motivation is taught by Yonezawa who recites that their method provides optimum selection procedures to give the most opportunities for producing a sufficiently high genetic gain required for recognition of a new commercial plant variety (abstract) (pg. 406, col. 2, last para.). The method also helps solve a problem in plant breeding of whether to test many individuals with a low input assessment or a few individuals with a high input assessment (pg. 407, col. 2, para. 2). There would have been a reasonable expectation of success to select hybrids based on selection intensity because selection intensity selects plants to be parents in each cycle (i.e. each generation of plants). Claim 36 is rejected under 35 USC 103 for being unpatentable Messina et al. (“Messina”; European Journal of Agronomy 100 (2018): 151-162), as applied to claim 2, in view of Crossa et al. (“Crossa”; rends in plant science 22, no. 11 (2017): 961-975). The limitations of claim 2 have been taught above by Messina in section Claim Rejections - 35 USC § 102. Claim 36 : Messina discloses a Bayesian generalized linear hierarchical model to perform crop yield predictions (sec. 2.1) (Figure 1). However, Messina does not disclose a neural network for predictions. Crossa reviews genomic selection (GS) in plant breeding (abstract). Crossa recites “Deep machine-learning methods using neural networking appear promising to increase the accuracy of genomic-enabled prediction” (pg. 973, para. 3). It would have been prima facie obvious to have substituted the Bayesian generalized linear hierarchical model in Messina with a deep neural network to increase prediction accuracy as taught by Crossa (Crossa at pg. 973, para. 3). The result of substituting these models would have yielded predictable results because they both use genotype, phenotype, and environmental variables to predict phenotypes of a test population (Crossa at pg. 969, last para – pg. 970, para. 2). Conclusion No claims are allowed . Notable, but not relied upon, prior art includes: Bhat et al. ( Frontiers in genetics 7 (2016): 221), Talbot et al. ( Journal of Heredity 108, no. 2 (2017): 207-216), Calus ( animal 4, no. 2 (2010): 157-164), Robertsen et al. ( Agronomy 9, no. 2 (2019): 95), and Beans et al. ( BioScience 67, no. 7 (2017): 593-599). Inquiries Any inquiry concerning this communication or earlier communications from the examiner should be directed to Noah A. Auger whose telephone number is (703)756-4518. The examiner can normally be reached M-F 7:30-4:30 EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached at (571) 272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /N.A.A./Examiner, Art Unit 1687 /KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685 Application/Control Number: 17/920,741 Page 2 Art Unit: 1687 Application/Control Number: 17/920,741 Page 3 Art Unit: 1687 Application/Control Number: 17/920,741 Page 4 Art Unit: 1687 Application/Control Number: 17/920,741 Page 5 Art Unit: 1687 Application/Control Number: 17/920,741 Page 6 Art Unit: 1687 Application/Control Number: 17/920,741 Page 7 Art Unit: 1687 Application/Control Number: 17/920,741 Page 8 Art Unit: 1687 Application/Control Number: 17/920,741 Page 9 Art Unit: 1687 Application/Control Number: 17/920,741 Page 10 Art Unit: 1687 Application/Control Number: 17/920,741 Page 11 Art Unit: 1687 Application/Control Number: 17/920,741 Page 12 Art Unit: 1687 Application/Control Number: 17/920,741 Page 13 Art Unit: 1687 Application/Control Number: 17/920,741 Page 14 Art Unit: 1687 Application/Control Number: 17/920,741 Page 15 Art Unit: 1687 Application/Control Number: 17/920,741 Page 16 Art Unit: 1687 Application/Control Number: 17/920,741 Page 17 Art Unit: 1687 Application/Control Number: 17/920,741 Page 18 Art Unit: 1687 Application/Control Number: 17/920,741 Page 19 Art Unit: 1687 Application/Control Number: 17/920,741 Page 20 Art Unit: 1687 Application/Control Number: 17/920,741 Page 21 Art Unit: 1687 Application/Control Number: 17/920,741 Page 22 Art Unit: 1687