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 .
Status of Claims
This communication is a Final Office Action in response to Applicant’s amendment for application number 18/587,219 received on 10/16/2025.
In accordance with Applicant’s amendment, claims 1-11, and 17-20 are amended, currently pending, and have been examined.
Priority
Applicants claim for the benefit of a prior-filed application under 35 U.S.C. 119 and/or 35 U.S.C. 120 is acknowledged.
Response to Amendment
The amendment filed on 10/16/2025 has been entered.
Applicant’s amendment necessitated the new ground(s) of rejection set forth in this Office Action.
Upon review of the amended claims, the objection previously applied to claim 8 is withdrawn.
Upon review of the amended claims, the §102 rejections previously applied to the claims are withdrawn.
Response to Arguments
Response to §101 arguments: Applicant’s arguments with respect to the §101 rejections previously applied to the original claims have been considered and are unpersuasive.
Applicant argues (Remarks at pg. 7) – “The pending claims provide for "making decisions to advance or not advance plants in a manner different than by individual human breeders, etc., for example, to account for the different data associated with the lines, as well as the volume of data known about the lines, without the data itself and/or volume of such data (e.g., based on sample size, etc.) limiting and/or impacting the decision." See, 0018, emphasis added.”, and “In this manner, the overall performance of the breeding pipeline, as a technology, is improved, generally while reducing the overall resources of the pipeline." See, 0063, emphasis added.”. In response, Examiner respectfully disagrees and notes that the items discussed above (paragraph [0018] of Applicant’s specification) are irrelevant to the analysis because these features are not recited or required by the claim. For example, the claims do not recite or require a specific “volume of such data”. Applicant’s argument lacks merit because is relies on limitations not required by the claims and it would be improper to import such limitations from the Specification. See Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875, 69 USPQ2d 1865, 1868 (Fed. Cir. 2004). See also, CollegeNet, Inc. v. Apply Yourself Inc., 418 F.3d 1225, 1231 (Fed. Cir. 2005) (while the specification can be examined for proper context of a claim term, limitations from the specification will not be imported into the claims). Furthermore, Examiner notes that the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field. Therefore, the rejections are maintained.
Applicant argues (Remarks at pg. 8) – “Importantly, it should be appreciated that Claim 1, for example, recites actually crossing the pairs of inbreds to create the specific potential hybrid(s). In this way, the claims define a specific physical transformation where a cross is created, where none existed prior.”. In response, Examiner notes that as currently recited, the claim limitations for crossing the pair(s) of the multiple inbred lines of the one or more of the ones of the potential hybrids, and whereby each of the one or more of the ones of the potential hybrids is created, planted and tested, fail to integrate the abstract idea into a practical application, or otherwise add significantly more because they provide nothing more than mere instructions to apply the abstract idea. Analogous examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include: vi. A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair, In re Brown, 645 Fed. App'x 1014, 1017 (Fed. Cir. 2016) (non-precedential).
Applicant argues (Remarks at pg. 8) – “Moreover, "[a] claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception." See, MPEP §2106.04(d). To this point, the meaningful limitations in the instant claims are established in the specific manner, or technique, in which the specific inbreds are selected to be crossed into the hybrids, which is analogous to the technique employed in McRO. That is, both claims define a specific technique (that will not monopolize the judicial exception) that outperforms conventional techniques. The pending claims do not merely recite a more specific abstract idea, but do in fact recite a specific technique (even more specific as amended herein) that is patent eligible under the analysis in McRO. That is, the pending claims define a new technique, and the specification explains how that new technique provides for advanced consideration of the specific data, as explained in, for example, 0017 and 0063 of the instant application. The pending claims improve computer implementation as well as planting, in multiple production fields, of a specific production target plan, instead of merely the alleged idea. Further, Applicant submits that the pending claims are not directed to an abstract idea because the claims are similar to the claims analyzed in the Supreme Court case Diamond v. Diehr. See Diamond v. Diehr, 450 U.S. 175 (1981). The eligible claim in Diehr recited a computer-implemented process of using temperature inputs to compute reaction times using the well-known Arrhenius equation and then using the results of those computations to cause a specific action to occur, namely that of signaling completion of a rubber curing process.“, and “In particular, Claim 1 recites the specific access of a particular trained model and to certain data specific to multiple inbred lines, and then calculating probability of advancement for individual ones of the potential hybrids in a breeding pipeline, through a specific trained model. The inbreds are then crossed consistent with the calculation. This is a specific action, which is based on the computations and specific data described above. Claim 1, as an example, arguably is even more consistent with the tenets of eligibility than the claim in Diehr, because Claim 1 recites an inventive, unique combination of computations/algorithms in the specific context, rather than a known algorithm in the particular use. This is further demonstrated by the lack of art sufficient rejection for specific claims (as explained below). Based on Diehr alone, it is clear that the pending claims are not directed to an abstract idea.”. In response, Examiner respectfully disagrees and notes that the claims, as currently presented, recite multiple abstract ideas because they amount to steps that can be performed in the human mind, or with the help of pen and paper, such as “identifying pairs of the multiple inbred lines as combinations for potential hybrids”, and “advancing one or more of the ones of the potential hybrids into the breeding pipeline”, as well as mathematical concepts to make decisions, such as “calculating a probability of advancement”. Examiner further notes that one of ordinary skill in the art would be able to perform the abstract idea steps, as currently recited, mentally, such as via human observation, evaluation, judgement, or with the help of pen and paper. Furthermore, when analyzing the additional elements to determine if they integrate the abstract idea into a practical application, or add significantly more, Examiner determines that the additional elements fail to do so because they amount to using generic computing components (such as a “computing device”), or instructions to implement the abstract idea. See MPEP 2106.05(f) and 2106.05(h).
Applicant argues (Remarks at pg. 9) – “Here, the human mind is not equipped to perform the recited steps of Claim 1, or any other recited claim. The pending claims recite, at least, accessing a trained model specific to a segment, the segment defined by a relative maturity (RM) and a region, the model trained using historic hybrid field performance data and parental line genomic data; calculating, by the computing device, with the trained model, a probability of advancement for individual ones of the potential hybrids in a breeding pipeline; and advancing one or more of the ones of the potential hybrids into the breeding pipeline; wherein advancing one or more of the ones of the potential hybrids includes crossing the pair(s) of the multiple inbred lines of the one or more of the ones of the potential hybrids.”. In response, Examiner respectfully disagrees and reiterates that, as currently recited, the steps recited in the claim limitations can reasonably be performed in the human mind, or with the help of pen and paper by one of ordinary skill in the art, for example: “identifying pairs of the multiple inbred lines as combinations for potential hybrids”, “advancing one or more of the ones of the potential hybrids into the breeding pipeline”, and “calculating a probability of advancement”. Under BRI, these steps can be performed by analyzing a data set of, for example, 10 datapoints, which one of ordinary skill in the art would be able to do in their mind, or with the help of pen and paper.
Applicant argues (Remarks at pg. 10) – “The claims do not provide a naked recitation of a specific mathematical concept, but rather leverage specific calculation, using a specifically trained model, in the context of assessing specific potential hybrids for advancement. As explained in the October 2019 Update PEG, these claimed limitations are more than a mere relation between, for example, reaction rate and temperature, a conversion of binary code, a relationship between radio activity and antenna conductor, or a calculated alarm limit value, etc.”. In response, Examiner respectfully disagrees and notes that the claim limitation for calculating a probability is consistent with what the MPEP describes as “mathematical concepts”: Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I). The concept of calculating a probability for supporting a decision is not new to the art and does not provide an improvement to technology. Therefore, the claim recites limitations that fall under the “Mathematical Concepts” abstract idea grouping.
Applicant argues (Remarks at pg. 10) – “What's more, the claims do not simply "apply" the alleged exception. Initially, the pending claims recite detail at how the specific solution to the problem is accomplished. The specific type of data that is accessed, along with a specifically trained model, whereby a specific probability is calculated, provides an explicit indication of what data is used in what manner to provide the technical solution. See, MPEP § 2106.05(f)(1). What's more, as amended, for example, Claim 1 recites crossing the pair(s) of the multiple inbred lines of the one or more of the ones of the potential hybrids. The cross is executed to create the specific potential hybrids, which are identified through unconventional techniques. In this way, the computing device is not merely executing an existing process, but instead, is functioning differently. The claims do not amount to "applying" the alleged idea, and are instead eligible.”. In response, Examiner respectfully disagrees and notes that the claims, as presented, recite a combination of abstract ideas, together with steps to implement the abstract ideas. Furthermore, the additional elements (for the reasons discussed above, as well as in the updated 101 rejections section below) fail to integrate the abstract idea, or otherwise add significantly more to the abstract idea.
Therefore, the rejections are maintained.
Response to §103 arguments: Applicant’s arguments with respect to the §103 rejection previously applied to the original claims have been considered. However, said argument is considered moot by the new ground(s) of rejection necessitated by the amendment.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of those findings is provided below, in accordance with the “2019 Revised Patent Subject Matter Eligibility Guidance” (published on 01/07/2019 in Fed. Register, Vol. 84, No. 4 at pgs. 50-57, hereinafter referred to as the “2019 PEG”) and further clarified in the “October 2019 Update: Subject Matter Eligibility” published on 10/17/2019) and as further set forth in MPEP 2106.
Step 1: The claimed invention is analyzed to determine if it falls outside one of the four statutory categories of invention. See MPEP 2106.03
Claims 1-11 are directed to a Method (i.e., Process), and claims 17-20 are directed to a System (i.e., Machine). Therefore, claims 1-11, and 17-20 are directed to patent eligible categories of invention. Accordingly, the claims satisfy Step 1 of the eligibility inquiry.
Step 2A, Prong 1: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether they recite a judicial exception. See MPEP 2106.04
Independent claim 1 recites a method for defining advancement of agricultural products in breeding. As drafted, the limitations recited by claim 1 fall under the “Mental Processes” abstract idea grouping by setting forth activities that could be performed mentally by a human (including an observation, evaluation, judgment, opinion). Additionally, some of the limitations recited by claim 1 also fall under the “Mathematical Concepts” abstract idea grouping for mathematical relationships, mathematical formulas or equations, mathematical calculations. The limitations recited by claim 1 are:
accessing, by a computing device, a trained model specific to a segment, the segment defined by a relative maturity (RM) and a region, the model trained using historic hybrid field performance data and parental line genomic data; (But for the additional elements recited in the claim limitation – underlined – to be analyzed under steps 2A, prong 2, and 2B, the step for “accessing” could be accomplished mentally, such as by human observation. Furthermore, even if considered as an additional element, the "accessing" step would, at most, be considered insignificant extra-solution activity.);
accessing, by the computing device, data specific to multiple inbred lines, the data including best linear unbiased predictions (BLUPs) for one or more traits of the multiple inbred lines; (But for the additional elements recited in the claim limitation – underlined – to be analyzed under steps 2A, prong 2, and 2B, the step for “accessing data” could be accomplished mentally, such as by human observation. Furthermore, even if considered as an additional element, the "accessing" step would, at most, be considered insignificant extra-solution activity.);
identifying pairs of the multiple inbred lines as combinations for potential hybrids; (The “identifying” step can be accomplished mentally such as via human observation, evaluation, or judgement.);
calculating, by the computing device, with the trained model, a probability of advancement for individual ones of the potential hybrids in a breeding pipeline; and (But for the additional elements recited in the claim limitation – underlined – to be analyzed under steps 2A, prong 2, and 2B, the step for “calculating a probability” can be accomplished mentally such as via human observation, evaluation, or judgement. Furthermore, the calculating a probability claim limitation recites an abstract idea directed to “Mathematical Concepts”.);
advancing one or more of the ones of the potential hybrids into the breeding pipeline, based on the calculated probability of advancement for the individual ones of the potential hybrids, wherein advancing one or more of the ones of the potential hybrids includes crossing the pair(s) of the multiple inbred lines of the one or more of the ones of the potential hybrids. (But for the additional elements recited in the claim limitation – underlined – to be analyzed under steps 2A, prong 2, and 2B, the step for “advancing one or more of the ones of the potential hybrids into the breeding pipeline” can be accomplished mentally such as via human observation, evaluation, judgement, or with the help of pen and paper. Additionally, this claim limitation recites mathematical relationships and mathematical calculations to obtain a probability of advancement.).
But for the following limitation, independent claim 17 recites a system for defining advancement of agricultural products in breeding with limitations that are largely similar to those set forth in claim 1. Therefore, the same analysis applies to claim 17.
whereby each of the one or more of the ones of the potential hybrids is created, planted and tested. (This step is an additional element to be analyzed under steps 2A, prong 2, and 2B).
Independent claims 1, and 17 recite additional elements beyond the abstract idea for consideration under Step 2A, Prong 2, and Step 2B below. The additional elements are: computing device, trained model specific to a segment, the model trained using historic hybrid field performance data and parental line genomic data, crossing the pair(s) of the multiple inbred lines of the one or more of the ones of the potential hybrids, and whereby each of the one or more of the ones of the potential hybrids is created, planted and tested.
Dependent claims 2-11, and 18-20 further narrow the abstract idea and introduces the following additional elements for consideration under said steps below:
From claim 19: planter.
Step 2A, Prong 2: An evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the judicial exception into a practical application of the exception. See MPEP 2106.04(d).
Independent claims 1/17 recite additional elements of computing device, trained model specific to a segment, the model trained using historic hybrid field performance data and parental line genomic data, crossing the pair(s) of the multiple inbred lines of the one or more of the ones of the potential hybrids, and whereby each of the one or more of the ones of the potential hybrids is created, planted and tested.
Regarding the computing additional elements (computing device), this additional element has been evaluated but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements (based on Examiner’s interpretation set forth in Claim Interpretation section above) or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h).
With respect to the limitations for trained model specific to a segment, and the model trained using historic hybrid field performance data and parental line genomic data, these limitations fail to integrate the abstract idea into a practical application because the provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
With respect to the limitations crossing the pair(s) of the multiple inbred lines of the one or more of the ones of the potential hybrids, and whereby each of the one or more of the ones of the potential hybrids is created, planted and tested, these limitations fail to integrate the abstract idea into a practical application because they provide nothing more than mere instructions to apply an exception. Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include: vi. A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair, In re Brown, 645 Fed. App'x 1014, 1017 (Fed. Cir. 2016) (non-precedential).
With respect to the planter introduced in claim 19, the planter has been considered under Step 2A Prong Two, however the planter is recited at a high level of generality and fails to provide a technical improvement or otherwise integrate the abstract idea into a practical application. Dependent claims 2-11, and 18, and 20 recite the same abstract ideas (“mental processes” and “mathematical concepts”) as the independent claims along with further steps/details falling under the scope of the abstract idea itself, along with the same or substantially same additional elements addressed.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
Step 2B: The claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for "inventive concept." See MPEP 2106.05.
Regarding the computing additional elements, namely computing device, this/these additional element(s) has/have been evaluated, but fail to add significantly more to the claims because they amount to using generic computing elements (computer hardware) or instructions/software (engine) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (network computing environment, the internet, online) and does not amount to significantly more than the abstract idea itself. Furthermore, Applicant’s specification recites the computing device at a high level of generality, which does not add significantly more to the abstract idea. Therefore, the computing additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
With respect to the limitations for trained model specific to a segment, and the model trained using historic hybrid field performance data and parental line genomic data, these limitations fail to add significantly more to the abstract idea because the provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
With respect to the limitations crossing the pair(s) of the multiple inbred lines of the one or more of the ones of the potential hybrids, and whereby each of the one or more of the ones of the potential hybrids is created, planted and tested, these limitations fail to add significantly more to the abstract idea because they provide nothing more than mere instructions to apply an exception. Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include: vi. A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair, In re Brown, 645 Fed. App'x 1014, 1017 (Fed. Cir. 2016) (non-precedential).
With respect to the planter introduced in claim 19, the planter has been considered under Step 2A Prong Two, however the planter is recited at a high level of generality and fails to provide a technical improvement or otherwise add significantly more to the abstract idea. Dependent claims 2-11, and 18, and 20 recite the same abstract ideas (“mental processes” and “mathematical concepts”) as the independent claims along with further steps/details falling under the scope of the abstract idea itself, along with the same or substantially same additional elements addressed.
Furthermore, even if the accessing step(s) is/are interpreted as additional elements, this activity at most amounts to insignificant extra-solution activity, which does not add significantly more to the abstract idea, as noted in MPEP 2106.05(g). Furthermore, the accessing insignificant extra-solution activity has been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
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.
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.
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.
Claim(s) 1-11, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chavali et al. (WO 2019113468 A1, hereinafter “Chavali”), in view of Bhagat et al. (US 20220383428 A1, hereinafter “Bhagat”).
Regarding claims 1/17: Chavali discloses a method and a system for defining advancement of agricultural products in breeding ([0002] The present disclosure generally relates to methods and systems for use in plant breeding, and in particular to methods and systems for identifying a set of progenies, from a pool of potential progenies, based on prediction frameworks and/or optimization frameworks, and populating a breeding pipeline with the identified set of progenies.) with limitations for:
accessing, by a computing device, a trained model specific to a segment, the segment defined by… …and a region, ([0031] In this exemplary embodiment, the selection engine 110 is configured to generate a prediction model, based on the historical data; [0033] once the prediction model is generated, the selection engine 110 further is configured to determine a prediction score, based on the prediction model, for each of the progenies in the pool of progenies introduced in the progeny start phase 104 and included in the cultivation and testing phase 106.; [0084] the selection engine 110 may evaluate performance of the method(s) and select, if necessary, the one that provides the best prediction for a given crop and/or a given region);
the model trained using historic hybrid field performance data and parental line genomic data; ([0016] Progeny are generally organisms which descend from one or more parent organisms of the same species. Progeny may refer to, for example, a universe of all possible progenies from a particular breeding program, a subset of all possible progenies, or offspring from a plant which exhibits one or more different phenotypes, etc. Progenies may further include all offspring from a line and/or a cross in a given generation, certain offspring from a cross, or individual plants, etc.; [0017] As used herein, the term “origin” refers to the parent(s) of progeny, and is therefore interpreted as either singular or plural, as applicable. The phenotypic data, trait distribution, ancestry, genetic sequence, commercial success, and additional information of the origin are generally known and may be stored in memory described herein. Hereditary genetics indicate the traits of the parent(s) to be passed to the progeny. And, mutations, genetic recombination, and/or directed genetic modification may alter the genotype and resulting phenotype of the progeny vis-a-vis the origin.; [0019] genotypic data may be used, in connection or in combination with the phenotypic data described herein (or otherwise) (e.g., to further supplement the phenotypic data and/or to further inform the models, algorithms, and/or predictions herein, etc.), in one or more exemplary implementations, to aid in the selection of groups of progenies and/or identification of sets of progenies consistent with the description herein.);
accessing, by the computing device, data specific to multiple inbred lines, the data including best linear unbiased predictions (BLUPs) for one or more traits of the multiple inbred lines; ([0034] That said, it should be appreciated that the selection engine 110 may be configured to determine the prediction score based on ranking phenotypic data and/or on derived phenotypic data (e.g ., best linear unbiased prediction (BLUP), etc.) associated with the progenies included in the data structure 112.);
identifying pairs of the multiple inbred lines as combinations for potential hybrids; ([0021] As shown in FIG. 1, the system 100 generally includes a breeding pipeline 102, which is provided to select a set of progenies from a pool of progenies to be advanced toward commercial product development. The breeding pipeline 102 generally defines a pyramidal progression, whereby it starts with a large number of potential progenies and successively narrows (e.g., reduces) the number of potential progenies to preferred and/or desired progenies. While the breeding pipeline 102 is configured to employ the selections provided herein, the breeding pipeline 102 may be configured to employ one or more other techniques which may include a wide range of methods known in the art, often depending on the particular plant and/or organism for which the breeding pipeline 102 is provided.);
calculating, by the computing device, with the trained model, a probability of advancement for individual ones of the potential hybrids in a breeding pipeline; ([0044] FIG. 3B (columns P-S) … These metrics are obtained from machine learning models that are trained to predict a probability of the parent in the identified cross advancing each stage of the breeding pipeline.);
and advancing one or more of the ones of the potential hybrids into the breeding pipeline, based on the calculated probability of advancement for the individual ones of the potential hybrids, ([0031] In this exemplary embodiment, the selection engine 110 is configured to generate a prediction model, based on the historical data, in whole or in part, included in the data structure 112 and/or provided via one or more user inputs, decisions, and/or iterations, where the prediction model indicates a probability of an origin, progeny, etc., for example, being “advanced” (e.g., to the validation phase 108, etc.) as defined in the past based on a set of data, such as, for example, phenotypic data. The selection engine 110 may employ any suitable technique and/or algorithm to generate the prediction model (also referred to as a prediction algorithm). The techniques may include, without limitation, random forest, support vector machine, logistic regression, tree based algorithms, naive Bayes, linear/logistic regression, deep learning, nearest neighbor methods, Gaussian process regression, and/or various forms of recommendation systems techniques, methods and/or algorithms ( See “Machine learning: a probabilistic perspective” by Kevin P. Murphy (MIT press, 2012), which is incorporated herein by reference in its entirety, to provide a manner of determining a probability of advance for a given set of data ( e.g ., yield, height, and standability for maize, etc.)).);
Furthermore, claim 1 also recites the following limitation, also taught by Chavali:
wherein advancing one or more of the ones of the potential hybrids includes crossing the pair(s) of the multiple inbred lines of the one or more of the ones of the potential hybrids. ([0025] In the progeny start phase 104, a pool of potential progenies is provided from one or more sets of origins. The origins may be selected by a breeder, for example, or otherwise, depending on the particular type of plant, etc. The origins may also be selected, for example, based on origin selection systems and/or based (at least in part) on the methods and systems disclosed in U.S. Pat. App. 15/618,023, titled “Methods for Identifying Crosses for use in Plant Breeding,” the entire disclosure of which is incorporated herein by reference. Once the origins are selected, the pool of progenies is created from multiple crosses of the origins. The pool of progenies is then directed to the cultivation and testing phase 106, in which the progenies are planted or otherwise introduced into one or more growing spaces, such as, for example, greenhouses, shade houses, nurseries, breeding plots, fields (or test fields), etc. As needed, in some applications of the breeding pipeline 102, the pool of progenies may be combined with one or more tester plants, to yield a plant product suitable for introduction into the cultivation and testing phase 106.).
Furthermore, claim 17 also recites the following limitation, also taught by Chavali:
whereby each of the one or more of the ones of the potential hybrids is created, planted and tested. ([0025] In the progeny start phase 104, a pool of potential progenies is provided from one or more sets of origins. The origins may be selected by a breeder, for example, or otherwise, depending on the particular type of plant, etc. The origins may also be selected, for example, based on origin selection systems and/or based (at least in part) on the methods and systems disclosed in U.S. Pat. App. 15/618,023, titled “Methods for Identifying Crosses for use in Plant Breeding,” the entire disclosure of which is incorporated herein by reference. Once the origins are selected, the pool of progenies is created from multiple crosses of the origins. The pool of progenies is then directed to the cultivation and testing phase 106, in which the progenies are planted or otherwise introduced into one or more growing spaces, such as, for example, greenhouses, shade houses, nurseries, breeding plots, fields (or test fields), etc. As needed, in some applications of the breeding pipeline 102, the pool of progenies may be combined with one or more tester plants, to yield a plant product suitable for introduction into the cultivation and testing phase 106.).; [0026] Once the progenies are grown in the cultivation and testing phase 106, each is tested (again as part of the cultivation and testing phase 106 in this example) to derive and/or collect phenotypic data for the progeny, whereby the phenotypic data is stored in one or more data structures, as described below. In connection therewith, the testing may include, for example, any suitable techniques for determining phenotypic data. Such techniques may include any number of tests, trials, or analyses known to be useful for evaluating plant performance, including any phenotyping known in the art. In preparation for such testing, samples of embryo and/or endosperm material/tissue may be harvested/removed from the progenies in a way that does not kill or otherwise prevent the seeds or plants from surviving the ordeal. For example, seed chipping may be employed to obtain tissue samples from the progenies for use in determining desired phenotypic data. Any other methods of harvesting samples of tissue can also be used, as conducting assays directly on the tissue of the seeds that do not require samples of tissue to be removed. In certain embodiments, the embryo and/or endosperm remain connected to other tissue of the seeds. In certain other embodiments, the embryo and/or endosperm are separated from other tissue of the seeds ( e.g ., embryo rescue, embryo excision, etc.). Common examples of phenotypes that may be assessed through such testing include, without limitation, disease resistance, abiotic stress resistance, yield, seed and/or flower color, moisture, size, shape, surface area, volume, mass, and/or quantity of chemicals in at least one tissue of the seed, for example, anthocyanins, proteins, lipids, carbohydrates, etc., in the embryo, endosperm or other seed tissues. As an example, where a progeny (e.g., cultivated from a seed, etc.) has been selected or otherwise modified to produce a particular chemical (e.g, a pharmaceutical, a toxin, a fragrance, etc.), the progeny can be assayed to quantify the desired chemical.).
Chavali doesn’t explicitly teach the following limitations from claims 1/17:
…the segment defined by a relative maturity (RM)…
Bhagat teaches:
…the segment defined by a relative maturity (RM)… ([0057] In connection therewith, as above, it should be appreciated that the training data set, while including data representative of the different growing spaces 102-106, may be filtered prior to training the model 400. Various manners of filtering the training data, or the data in general, in association with modeling the yield deltas may be applied. FIG. 4 illustrates a first technique for filtering data accessed from the data servers 114a-b based on relative maturity (RM). As shown in FIG. 4, a region including Illinois (representative of including the growing spaces 102-106) is separated by bands (indicated by coloring/hatching) associated with RM. The RM is indicative of the class of seed products that will likely reach their yield potential within the length of the growing season that is typical for a region, which provides a basis to link performance of different seeds to one another, etc.)
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Chavali with Bhagat’s feature(s) listed above. One would’ve been motivated to do so, so that the agricultural computer system 116 may be configured to build, or define, the training data set by filtering the accessed data, for the given region (Bhagat; [0057]). By incorporating the teachings of Bhagat, one would’ve been able to train the model specific to the segment defined by relative maturity.
Regarding Claim 2: Chavali further teaches:
wherein the trained model includes a random forest model; ([0031] The selection engine 110 may employ any suitable technique and/or algorithm to generate the prediction model (also referred to as a prediction algorithm). The techniques may include, without limitation, random forest, support vector machine, logistic regression, tree based algorithms, naive Bayes, linear/logistic regression, deep learning, nearest neighbor methods, Gaussian process regression, and/or various forms of recommendation systems techniques, methods and/or algorithms ( See“Machine learning: a probabilistic perspective” by Kevin P. Murphy (MIT press, 2012), which is incorporated herein by reference in its entirety, to provide a manner of determining a probability of advance for a given set of data ( e.g ., yield, height, and standability for maize, etc.)).);
and wherein the segment is defined by… …and the region. ([0031] In this exemplary embodiment, the selection engine 110 is configured to generate a prediction model, based on the historical data; [0033] once the prediction model is generated, the selection engine 110 further is configured to determine a prediction score, based on the prediction model, for each of the progenies in the pool of progenies introduced in the progeny start phase 104 and included in the cultivation and testing phase 106.; [0084] the selection engine 110 may evaluate performance of the method(s) and select, if necessary, the one that provides the best prediction for a given crop and/or a given region);
Chavali doesn’t explicitly teach:
and wherein the segment is defined by the RM…
Bhagat teaches:
and wherein the segment is defined by the RM… ([0057] In connection therewith, as above, it should be appreciated that the training data set, while including data representative of the different growing spaces 102-106, may be filtered prior to training the model 400. Various manners of filtering the training data, or the data in general, in association with modeling the yield deltas may be applied. FIG. 4 illustrates a first technique for filtering data accessed from the data servers 114a-b based on relative maturity (RM). As shown in FIG. 4, a region including Illinois (representative of including the growing spaces 102-106) is separated by bands (indicated by coloring/hatching) associated with RM. The RM is indicative of the class of seed products that will likely reach their yield potential within the length of the growing season that is typical for a region, which provides a basis to link performance of different seeds to one another, etc.)
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Chavali with Bhagat’s additional feature(s) listed above. One would’ve been motivated to do so, so that the agricultural computer system 116 may be configured to build, or define, the training data set by filtering the accessed data, for the given region (Bhagat; [0057]). By incorporating the teachings of Bhagat, one would’ve been able to train the model specific to the segment defined by relative maturity.
Regarding Claim 3: Chavali further teaches:
wherein the BLUPs include BLUPs based on an interval, the interval including a number of years; ([0034] the selection engine 110 may be configured to determine the prediction score based on ranking phenotypic data and/or on derived phenotypic data ( e.g ., best linear unbiased prediction (BLUP), etc.) associated with the progenies included in the data structure 112.; [0029] that data structure 112 may include data indicative of various different characteristics and/or traits of the plants for the current and/or the last one, two, five, ten, fifteen, or more or less years of the plants through the cultivation and testing phase 106, or other growing spaces included in or outside the breeding pipeline 102, and also present data from the cultivation and testing phase 106.);
and/or wherein the one or more traits of the multiple inbred lines includes yield. ([0018] “Phenotypic data” as used herein includes, but is not limited to, information regarding the phenotype of a given progeny (e.g., a plant, etc.), or a population of progeny (e.g, a group of plants, etc.). Phenotypic data may include the size and/or heartiness of the progeny (e.g, plant height, stalk girth, stalk strength, etc.), yield, time to maturity, resistance to biotic stress (e.g, disease or pest resistance, etc.), resistance to abiotic stress (e.g, drought or salinity resistance, etc.), growing climate, or any additional phenotypes, and/or combinations thereof.).
Regarding Claim 4: Chavali further teaches:
wherein identifying the pairs of the multiple inbred lines includes identifying all unique pairs of one male of the inbred lines and one female of the inbred lines. ([0075] Further in the above equations, the term c.sub.M is a characteristics vector for male progenies. The term c.sub.R is a characteristics vector for female progenies.).
Regarding Claim 5: Chavali doesn’t teach:
further comprising, prior to calculating the probability of advancement, eliminating other ones of the potential hybrids, based on inclusion of the other ones of the potential hybrids in a database of prior hybrids.
Bhagat teaches:
further comprising, prior to calculating the probability of advancement, eliminating other ones of the potential hybrids, based on inclusion of the other ones of the potential hybrids in a database of prior hybrids. ([0076] The agricultural computer system 116 may then be programmed, or configured, optionally, to reduce the set of candidates seeds (e.g., by filtering, selection, etc.), based on the parameters of the specific candidate seeds.; Par. [0096] teaches crop rotation. Examiner notes that one of ordinary skill in the art would reasonably interpret crop rotation as the process of alternating crops to be grown between harvests, ensuring different crops are planted. Said person of ordinary skill in the art would reasonably interpret crop rotation to being equivalent to eliminating hybrids based on prior hybrids.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Chavali with Bhagat’s additional feature(s) listed above. One would’ve been motivated to do so in order to filter out candidate seeds inconsistent with the plant type and with the traits of the target seed (Bhagat; [0076]). By incorporating the teachings of Bhagat, one would’ve been able to eliminate hybrids prior to calculating the probability of advancement.
Regarding Claim 6: Chavali further teaches:
wherein each identified pair includes a male one of the multiple inbred lines and a female one of the multiple inbred lines ([0075] Further in the above equations, the term c.sub.M is a characteristics vector for male progenies. The term c.sub.R is a characteristics vector for female progenies.).
Regarding Claim 7: Chavali further teaches:
accessing historical hybrid field performance data associated with multiple test inbred lines and the region, ([0018] “Phenotypic data” as used herein includes, but is not limited to, information regarding the phenotype of a given progeny ( e.g ., a plant, etc.), or a population of progeny (e.g, a group of plants, etc.). Phenotypic data may include the size and/or heartiness of the progeny (e.g, plant height, stalk girth, stalk strength, etc.), yield, time to maturity, resistance to biotic stress (e.g, disease or pest resistance, etc.), resistance to abiotic stress (e.g, drought or salinity resistance, etc.), growing climate, or any additional phenotypes, and/or combinations thereof.; [0036] The selection engine 110 is further configured to identify a set of progenies, from the group of progenies, to advance to a next iteration of the cultivation and testing phase 106 and/or to advance to the validation phase 108. To do so, the selection engine 110 is configured to employ one or more additional algorithms, as described herein or otherwise, for example, to account for a predicted performance of the particular progeny (e.g., based on the prediction score, etc. Examiner notes that one of ordinary skill in the art would reasonably consider yield as a measure of hybrid field performance.);
the historical hybrid field performance data including BLUPs for one or more traits of the multiple test inbred lines and fate data for multiple hybrids including pairs of the multiple test inbred lines relative to a stage of the breeding pipeline; ([0015] In particular, the pool of progenies is reduced, initially, for example, to a group of progenies based on a prediction score for each of the progenies, which is indicative of a success of the progeny based on past selections of progenies (e.g., based on phenotypic data, etc.) and/or available relevant data associated with the progenies.; [0034] the selection engine 110 may be configured to determine the prediction score based on ranking phenotypic data and/or on derived phenotypic data (e.g., best linear unbiased prediction (BLUP), etc.) associated with the progenies included in the data structure 112. In such embodiments, the data is ranked with a top X number of progenies selected for advancement herein, whereby the rank is employed as a prediction score (e.g., TRUE/FALSE, etc.) for each progeny above a threshold (as compared to any modeling of the data included in the data structure 112).;
and training the model based on at least a portion of the historical hybrid field performance data. ([0032] the prediction model (or SVM model) training involves solving a convex optimization problem, which finds the optimal hyperplane (linear or nonlinear), which would be able to separate the positive and negative samples, based on the phenotypic data; [0053] teaches historical data being used to train the given models.).
Regarding Claim 8: Chavali further teaches:
further comprising validating the model based on data reserved from the accessed historical hybrid field performance data. ([0018] “Phenotypic data” as used herein includes, but is not limited to, information regarding the phenotype of a given progeny ( e.g ., a plant, etc.), or a population of progeny (e.g, a group of plants, etc.). Phenotypic data may include the size and/or heartiness of the progeny (e.g, plant height, stalk girth, stalk strength, etc.), yield, time to maturity, resistance to biotic stress (e.g, disease or pest resistance, etc.), resistance to abiotic stress (e.g, drought or salinity resistance, etc.), growing climate, or any additional phenotypes, and/or combinations thereof.; [0032] the prediction model (or SVM model) training involves solving a convex optimization problem, which finds the optimal hyperplane (linear or nonlinear), which would be able to separate the positive and negative samples, based on the phenotypic data; [0053] teaches historical data being used to train the given models.); [0054] Once this data set is provided with the input data and response variable, the user segregates the data set, either randomly or along a logical delineation ( e.g ., year, month, etc.), into a training set, a validation set, and a testing set. The data set may be segregated, for example, into a set ratio of 70:20:10, respectively (or otherwise). With these three distinct data sets, the modeling is initiated for the training set of data by the selection of an algorithm, as listed above. If, for example, a random forest is selected as a potential algorithm for creating this prediction score, the user, in general, selects a well-supported coding package that implements random forests in a suitable coding language, such as R or python. Once the package and the language have been selected, for example scikit-leam in python, the user commences the process of building the code framework to specify, build, train, validate, and test the model.; [0055] When the framework is built, it is connected to the training data set, the validation set, and the testing set, in their appropriate locations. Thereafter, the algorithm hyperparameters, which are the parameters that define the structure of the algorithm itself, are tuned. Some random-forest-specific examples of these hyperparameters include tree size, number of trees, and number of features to consider at each split, but the specific nature of the hyperparameters will vary from algorithm to algorithm (and/or based on user inputs, phenotypes, etc.). To begin the tuning process, the model is trained using an initial set of hyperparameters— which can be chosen based on past experience, an educated guess, at random, or by other suitable manner, etc. During the training process, the algorithm will attempt to minimize the error between the classifications it is making and the true response values included in the data set. Once this process is complete, the error rate reported from the training process is validated through evaluation of the error rate of the trained model on the separate validation data set.; [0056] Once a model is generated through the training, validation and/or cross- validation as described above (i.e., based on the training and validation data sets), the model is further evaluated on the test data set to determine an expected performance of the model on data that is, at that time, new, unseen data to the model.).
Regarding Claim 9: Chavali further teaches:
further comprising outputting the calculated probability of advancement for the individual ones of the potential hybrids to a user. ([0043] The presentation unit 206 outputs, or presents, to a user of the computing device 200 (e.g., a breeder, etc.) by, for example, displaying and/or otherwise outputting information such as, but not limited to, selected progeny, progeny as commercial products, and/or any other types of data as desired.; [0089] As will be appreciated based on the foregoing specification, the above- described embodiments of the disclosure may be implemented using computer programming or engineering techniques, including computer software, firmware, hardware or any combination or subset thereof, wherein the technical effect may be achieved by performing at least one of the following operations: (a) accessing a data structure including data representative of a pool of progenies; (b) determining, by at least one computing device, a prediction score for at least a portion of the pool of progenies based on the data included in the data structure, the prediction score indicative of a probability of selection of the progeny based on historical data; (c) selecting, by the at least one computing device, a group of progenies from the pool of progenies based on the prediction score; (d) identifying, by the at least one computing device, a set of progenies, from the group of progenies, based on at least one of an expected performance of the group of progenies, risks associated with ones of the group of progenies and a deviation of the group of progenies from at least one profile; and (e) directing the set of progenies to a testing and cultivation phase of a breeding pipeline and/or to a validation phase of the breeding pipeline.).
Regarding Claim 10: Chavali further teaches:
wherein the crop includes corn ([0023] In this exemplary embodiment, the breeding pipeline 102 is described with reference to, and is generally directed to, corn or maize and traits and/or characteristics thereof.).
Regarding Claim 11: Chavali further teaches:
planting at least one plant, from the crossing of the pair(s) of the multiple inbred lines of the one or more of the ones of the potential hybrids, in a field included in the breeding pipeline, ([0025] Once the origins are selected, the pool of progenies is created from multiple crosses of the origins. The pool of progenies is then directed to the cultivation and testing phase 106, in which the progenies are planted or otherwise introduced into one or more growing spaces, such as, for example, greenhouses, shade houses, nurseries, breeding plots, fields (or test fields), etc.);
whereby the probability associated with the one or more of the ones of the potential hybrids is validated. ([0031] In this exemplary embodiment, the selection engine 110 is configured to generate a prediction model, based on the historical data, in whole or in part, included in the data structure 112 and/or provided via one or more user inputs, decisions, and/or iterations, where the prediction model indicates a probability of an origin, progeny, etc., for example, being “advanced” (e.g., to the validation phase 108, etc.) as defined in the past based on a set of data, such as, for example, phenotypic data. The selection engine 110 may employ any suitable technique and/or algorithm to generate the prediction model (also referred to as a prediction algorithm). The techniques may include, without limitation, random forest, support vector machine, logistic regression, tree based algorithms, naive Bayes, linear/logistic regression, deep learning, nearest neighbor methods, Gaussian process regression, and/or various forms of recommendation systems techniques, methods and/or algorithms ( See“Machine learning: a probabilistic perspective” by Kevin P. Murphy (MIT press, 2012), which is incorporated herein by reference in its entirety, to provide a manner of determining a probability of advance for a given set of data ( e.g ., yield, height, and standability for maize, etc.)).).
Regarding Claim 18: Chavali further teaches:
wherein the trained model includes a random forest model; ([0031] the selection engine 110 is configured to generate a prediction model, based on the historical data, in whole or in part, included in the data structure 112 and/or provided via one or more user inputs, decisions, and/or iterations, where the prediction model indicates a probability of an origin, progeny, etc., for example, being “advanced” (e.g., to the validation phase 108, etc.) as defined in the past based on a set of data, such as, for example, phenotypic data. The selection engine 110 may employ any suitable technique and/or algorithm to generate the prediction model (also referred to as a prediction algorithm). The techniques may include, without limitation, random forest, support vector machine, logistic regression, tree based algorithms, naive Bayes, linear/logistic regression, deep learning, nearest neighbor methods, Gaussian process regression, and/or various forms of recommendation systems techniques, methods and/or algorithms ( See“Machine learning: a probabilistic perspective” by Kevin P. Murphy (MIT press, 2012), which is incorporated herein by reference in its entirety, to provide a manner of determining a probability of advance for a given set of data ( e.g ., yield, height, and standability for maize, etc.)).);
wherein the BLUPs include BLUPs based on an interval, the interval including a number of years; ([0034] the selection engine 110 may be configured to determine the prediction score based on ranking phenotypic data and/or on derived phenotypic data ( e.g ., best linear unbiased prediction (BLUP), etc.) associated with the progenies included in the data structure 112.; [0029] that data structure 112 may include data indicative of various different characteristics and/or traits of the plants for the current and/or the last one, two, five, ten, fifteen, or more or less years of the plants through the cultivation and testing phase 106, or other growing spaces included in or outside the breeding pipeline 102, and also present data from the cultivation and testing phase 106.);
and wherein the one or more traits of the multiple inbred lines includes yield. ([0018] “Phenotypic data” as used herein includes, but is not limited to, information regarding the phenotype of a given progeny (e.g., a plant, etc.), or a population of progeny e.g, a group of plants, etc.). Phenotypic data may include the size and/or heartiness of the progeny e.g, plant height, stalk girth, stalk strength, etc.), yield, time to maturity, resistance to biotic stress (e.g, disease or pest resistance, etc.), resistance to abiotic stress (e.g, drought or salinity resistance, etc.), growing climate, or any additional phenotypes, and/or combinations thereof.).
Regarding Claim 19: Chavali further teaches:
wherein the at least one computing device is configured, in order to advance the one or more of the ones of the potential hybrids into the breeding pipeline, to: automatically direct the one or more of the ones of the potential hybrids into the breeding pipeline; ([0022] In certain breeding pipeline embodiments (e.g ., large industrial breeding pipelines, etc.), testing, selections, and/or advancement may be directed to hundreds, thousands, or more origins, progenies, etc., in multiple phases and at several locations over several years to arrive at a reduced set of origins, progenies, etc., which are then selected for commercial product development. In short, the breeding pipeline 102 is configured, by the testing, selections, etc., included therein, to reduce a large number of origins, progenies, etc., down to a relatively small number of superior-performing commercial products.);
whereby the probability associated with the one or more of the ones of the potential hybrids is validated. ([0031] In this exemplary embodiment, the selection engine 110 is configured to generate a prediction model, based on the historical data, in whole or in part, included in the data structure 112 and/or provided via one or more user inputs, decisions, and/or iterations, where the prediction model indicates a probability of an origin, progeny, etc., for example, being “advanced” (e.g., to the validation phase 108, etc.) as defined in the past based on a set of data, such as, for example, phenotypic data. The selection engine 110 may employ any suitable technique and/or algorithm to generate the prediction model (also referred to as a prediction algorithm). The techniques may include, without limitation, random forest, support vector machine, logistic regression, tree based algorithms, naive Bayes, linear/logistic regression, deep learning, nearest neighbor methods, Gaussian process regression, and/or various forms of recommendation systems techniques, methods and/or algorithms ( See “Machine learning: a probabilistic perspective” by Kevin P. Murphy (MIT press, 2012), which is incorporated herein by reference in its entirety, to provide a manner of determining a probability of advance for a given set of data (e.g ., yield, height, and standability for maize, etc.)).).
Chavali doesn’t teach:
generate executable instructions for a planter to plant at least one plant, consistent with the one or more of the ones of the potential hybrids, in a field included in the breeding pipeline;
and transmit the executable instructions to the planter, to cause the planter to plant the at least one plant, consistent with the one or more of the ones of the potential hybrids, in the field,
Bhagat teaches:
generate executable instructions for a planter at least one plant, consistent with the one or more of the ones of the potential hybrids, in a field included in the breeding pipeline; ([0041] In connection therewith, it should be appreciated that the seeds planted in the different growing spaces may be in (or associated with) different categories (or statuses or availabilities or maturities, etc.), for example, within a commercial or breeding pipeline; [0081] Alternatively, this may include the agricultural computer system 116 generating planting instructions (e.g., scripts, etc.) based on the selected candidate seeds and providing the instructions to a planter whereby the planter operates, in response to the instructions, to plant the selected candidate seeds in the target field); [0123] seeds and planting instructions 1108 are programmed to provide tools for seed selection, hybrid placement;);
and transmit the executable instructions to the planter, to cause the planter to plant the at least one plant, consistent with the one or more of the ones of the potential hybrids, in the field, ([0081] This may include the grower/user receiving the selected candidate seeds and operating a planter to plant the seeds.; [0099] planting instructions generated by the agricultural computer system 116 and transmitted to a planter agricultural apparatus that then control operation of the planter agricultural apparatus to plant certain selected seeds).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Chavali with Bhagat’s additional feature(s) listed above. One would’ve been motivated to do so, so that the grower is able to test the recommendation, and the seed seller associated with the agricultural computer system 116 (and/or the agricultural computer system 116 itself) is programmed or able to make recommendations of seeds to be included in the growing spaces 106 (Bhagat; [0081]). By incorporating the teachings of Bhagat, one would’ve been able to generate instructions and send those instructions to a planter to cause the planter to plant the hybrid according to the instructions.
Regarding Claim 20: Chavali further teaches:
further comprising separating each of the potential hybrids into one of multiple groups based on the probability of advancement for the potential hybrid; and ([0015] Uniquely, the methods and systems herein permit identification of a set of progenies, from a pool of progenies, to be included in a breeding pipeline. In particular, the pool of progenies is reduced, initially, for example, to a group of progenies based on a prediction score for each of the progenies, which is indicative of a success of the progeny based on past selections of progenies (e.g., based on phenotypic data, etc.) and/or available relevant data associated with the progenies.);
wherein automatically directing, by the computing device, the one or more of the ones of the potential hybrids into the breeding pipeline includes automatically directing the potential hybrids separated into a particular one of the multiple groups into the breeding pipeline. ([0035] the selection engine 110 is configured to select ones of the progenies (from the pool) to be included in a group of progenies. The selection may be based on the prediction scores relative to one or more thresholds, or it may be based on the prediction scores relative to one another, or otherwise.).
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIEL J TORRES CHANZA whose telephone number is (571)272-3701. The examiner can normally be reached Monday thru Friday 8am - 5pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached on (571)270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/G.J.T./Examiner, Art Unit 3625
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625