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 18/202,250 received on 09/02/2025.
In accordance with Applicant’s amendment, claims 1-6, 8-18, and 20-24 are amended, currently pending, and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 09/02/2025 has been considered.
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 09/02/2025 has been entered.
Applicant’s reply to the Requirement for Information Under 37 C.F.R. § 1.105 is acknowledged.
Applicant’s amendment necessitated the new ground(s) of rejection set forth in this Office Action.
Response to Arguments
Response to §112(b) arguments
Upon review of the amended claims, the rejections of cancelled claims 7, and 19, are moot.
With respect to the rejections previously applied to claims 4 and 16, Applicant argues: “Claims 4 and 16 depend from Claim 3 and 15, respectively, each of which define
η
. p, and Y are defined by the specific equations in which the variables are used. Beyond the specific definition in the claims,
η
is further characterized in the specification as per seed product; and p is further characterized in the specification as probability per product. The claim terms are clear and definite.”. In response, Examiner agrees with Applicant’s argument regarding the symbol
η
. However, Examiner respectfully disagrees and notes that it is unclear what the symbols p, and Y are, as the claims fail to define these symbols. Furthermore, Applicant is reminded that while the specification can be examined for proper context of a claim term, limitations from the specification will not be imported into the claims. 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).
With respect to the rejection previously applied to claim 24, upon review of the amended claims, the rejection is withdrawn.
Response to §112(a) arguments – Upon review of the amended claims, the rejection previously applied to claim 24 is withdrawn.
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 pgs. 14-*15) – “That said, the pending claims nevertheless integrate the alleged abstract idea into a practical application. By generation of the specific target threshold (as defined by a frontier curve), generation of the dataset of target seeds in a manner dependent on the target threshold, and then generation of the allocation instructions for the target seeds, the user is presented with an objective solution to an unlimited problem of which of the seeds are to be planted in which of the target fields. The pending claims leverage more than merely the data analysis to generate the associated data sets, but rely on a novel interpretation, organization, generation, and selection based on the data. In doing so, the pending claims do more than merely link the general technology to the judicial exception. At MPEP §2106.05(e), meaningful limitations beyond generally linking may transform the judicial exception into patent eligible subject matter through a practical application. Here, the pending claims are meaningfully limited by the recited limitations, through: identifying candidate seeds from the multiple seeds for the grower, based on a model specific to the grower, wherein the model is trained on historical selected and unselected ones of the candidate seeds by the grower, independent of historical performance of the candidate seeds in the one or more growing spaces associated with the grower; and including, based on a selection by the grower, at least one seed from the identified candidate seeds in the one or more growing spaces.“. In response, Examiner notes that the steps recite the use of a model, trained on historical data, to identify seeds. The use of a model to assist a decision -which can otherwise be reasonably performed in the human mind – is not enough to integrate the abstract idea in to a practical application because it amounts to using generic computing instructions to perform the abstract idea. However, the other items discussed above (generation of the specific target threshold) are irrelevant to the analysis because these features are not recited or required by the claim. 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).
Applicant further argues (Remarks at pg. 16) – “The October 2019 Update indicates that limitation (d) in Claim 2 "specifies that the monitoring component automatically sends a control signal to the feed dispenser to dispense a therapeutically effective amount of supplemental salt and minerals mixed with the feed []. Thus, limitation (d) does not merely link the judicial exceptions to a technical field, but instead adds a meaningful limitation in that it can employ the information provided by the judicial exception (the mental analysis of whether the animal is exhibiting an aberrant behavioral pattern indicative of grass tetany) to operate the feed dispenser." See, p. 37 of Oct. 2019 Update. Similarly, Claim 2 of the present application, for example, practically applies the concept of any alleged idea to actually plant or otherwise include one of the identified candidates seed in a growing space, based on the selection process defined by the alleged judicial exception. By this meaningful limitation transforming the growing space to include the identified candidate seeds (which were not previously present), then, Claim 2 is therefore patent eligible.”. In response, Examiner respectfully disagrees and notes that even if the step to plant the seeds is considered as an additional element, it amounts to insignificant extra-solution activity as insignificant application or post-solution activity. Moreover, the agricultural planting apparatus is recited at a high level of generality, which is not enough to integrate the abstract idea into a practical application, or otherwise add significantly more to the abstract idea. This is supported by Applicant’s own disclosure, where in Fig. 8, Applicant discloses a series of generic computing components and elements (i.e., server, display, input device, cursor control, main memory, ROM, storage device, BUS, processor, communication interface, internet, ISP, local network, and host), or in par. [0093], where the specification discloses: “agricultural apparatus that may be included in the system 100 include tractors, combines, other harvesters, planters, trucks, fertilizer equipment, aerial vehicles including unmanned aerial vehicles, and any other item of physical machinery or hardware, typically mobile machinery, and which may be used in tasks associated with agriculture and/or related to operations described herein.”, effectively using an “everything-under-the-sun” approach to describe the agricultural planting apparatus. Therefore, the use of the planting apparatus to plant seeds is not enough to integrate the judicial exception into a practical application, add significantly more to the abstract idea, or otherwise provide a technical improvement because it is recited at a high level of generality. Similarly, the growing spaces are also recited at a high level of generality, which does not integrate the judicial exception into a practical application, add significantly more to the abstract idea, or otherwise provide a technical improvement. This is also supported by Applicant’s own Specification, where in at least par. [0020], the specification defines growing spaces as “Seeds to be planted in target fields (broadly, growing spaces)”.
Response to §103 arguments
Applicant’s arguments (Remarks at pgs. 18-19) with respect to the §103 rejections previously applied to the claims have been considered and are unpersuasive.
Applicant argues (Remarks at pg. 18-19): “Overall, the claims' modelling of training data is directed to what the grower is selecting and not selecting, and is not based on the specific performance of the seeds. In other words, the Bull is directed to modeling of something different as compared to the pending claims. There is no disclosure in Bull of any modeling being independent of performance, while also including seeds not selected by the grower. As such, what is claimed is not taught by Reich (as admitted by the Office) and also not taught by Bull. The suggested combination of the reference is also therefore deficient.”. In response, Examiner respectfully disagrees and notes that Bull discloses using data from crop rotation, a process known in the art were crops planted vary by season in order to produce better long-term outcomes. Therefore, one of ordinary skill in the art would’ve reasonably considered the crop rotation data to incorporate data from crops not planted in a particular season. With respect to the modeling independent of performance, Examiner respectfully disagrees with Applicant’s interpretation of Bull’s disclosure in par. [0125], where Bull discloses: “The agronomic model may also comprise calculated agronomic properties which describe either conditions which may affect the growth of one or more crops on a field, or properties of the one or more crops, or both. Additionally, an agronomic model may comprise recommendations based on agronomic factors such as crop recommendations, irrigation recommendations, planting recommendations, fertilizer recommendations, fungicide recommendations, pesticide recommendations, harvesting recommendations and other crop management recommendations. The agronomic factors may also be used to estimate one or more crop related results, such as agronomic yield.”. Although in par. [0125] Bull discloses estimating yield for the crops, such estimation is an output of the model, and not considering “historical performance” to identify seeds to plant. Furthermore, even if it was interpreted as using historical performance to identify seeds to plant, this feature is optional and therefore, not necessary to produce the recommendations – supported by Bull in the same paragraph, where it discloses “The agronomic factors may also be used to estimate one or more crop related results, such as agronomic yield.”.
Applicant further argues (Remarks at pg. 19): “Further, in rejecting Claim 1, the Office cites to Reich to disclose including at least one seed in one or more growing spaces. See, Office action dated May 2, 2025, at p. 16. In doing so, the Office cites to specific data records related to the crop seed data describing seed and yield properties. See, 0059. There is no indication in the cited part of Reich that an identified seed is actually being planted or otherwise included in one or more growing spaces. What's more, the Office's note about data records being equivalent to selection by a grower is not relevant to planting or including the seed in a growing space. Reich as cited is deficient, whereby the suggested combination based on the same is also deficient.”. In response, Examiner respectfully disagrees and notes that under BRI, the limitation for “including, based on a selection by the grower, at least one seed from the identified candidate seeds in the one or more growing spaces”, can be interpreted as information that shows the selected seed was planted in the growing space, which is taught by the agricultural data records disclosed by Reich in at least par. [0059].
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
From claims 13, and 22: “… the agricultural planting apparatus operates to plant…”.
From Claim 24: “…the agricultural planting apparatus configured to…”.
When looking at the specification, par. [0093] discloses “Additional examples of agricultural apparatus that may be included in the system 100 include tractors, combines, other harvesters, planters, trucks, fertilizer equipment, aerial vehicles including unmanned aerial vehicles, and any other item of physical machinery or hardware, typically mobile machinery, and which may be used in tasks associated with agriculture and/or related to operations described herein.”. This is to be the interpretation given by Examiner to the agricultural planting apparatus recited in the claims.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
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.
Claim 4, and 16 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 4, and 16 are rejected under 35 USC 112(b) because the bounds of the claimed invention are unclear. In particular, the claims recite: “wherein the model is further based on: p =
p
=
1
1
+
e
^
-
η
and Y ∼ Bernoulli (p). It is unclear what the following symbols are: p, and Y. The drafting of the claim is not clear, such that the introduction of these claim elements is supported by what they actually are. A person having ordinary skill in the art at the time of the invention’s filing would not readily recognize the meaning of claim limitations p, and Y. Therefore, the bounds of the claim are unclear.
For the purpose of compact prosecution, Examiner is interpreting Equation (4) as the commonly known logistic function used to determine the probability of a seed being selected. Examiner also interprets Equation (5) as being informational of the fact that the probability of a seed being selected is approximated by the Bernoulli distribution, rather than part of the equation itself.
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 1-6, 8-18, and 20-24 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 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
Claim(s) 1-6, and 8-12 is/are directed to a method (i.e., Process), claim(s) 13-18, and 20-21 is/are directed to a non-transitory computer-readable storage medium (i.e., Manufacture), and claim(s) 22-24 is/are directed to a system (i.e., Machine). Therefore, all claims 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 claims 1, 13, and 22 recite a method, a system, and a computer-readable storage medium for processing data to conduct an attribution analysis. As drafted, the limitations recited by the independent claims fall under the “Mental Processes” abstract idea group by setting forth activities that could be performed mentally by a human (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Independent claim 1recites a method for use in identifying candidate seeds with the following limitations:
identifying, by a computing device, multiple seeds for a grower, the multiple seeds suitable to be selected by the grower for planting in one or more growing spaces associated with the grower; (But for the additional elements – underlined – recited in this claim limitation, the step for “identifying multiple seeds for a grower“ could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.).
accessing, by the computing device, data from a data server, the data including seed data representative of each of the multiple seeds; (But for the additional elements – underlined – recited in this claim limitation, the step for “accessing data” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper. Additionally, even if considered as an additional element, this step amounts to insignificant extra-solution activity as mere data gathering).
identifying, by the computing device, candidate seeds from the multiple seeds for the grower, based on a model specific to the grower, wherein the model is trained on historical selected and unselected ones of the candidate seeds by the grower, independent of historical performance of the candidate seeds in the one or more growing spaces associated with the grower; (But for the additional elements – underlined – recited in this claim limitation, the step for “identifying candidate seeds“ could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.).
outputting, by the computing device, the identified candidate seeds to the grower or a user associated with the grower; (But for the additional elements – underlined – recited in this claim limitation, the step for “outputting the identified candidate seeds“ could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper. Additionally, even if considered as an additional element, this step amounts to insignificant extra-solution activity as insignificant application).
and including, based on a selection by the grower, at least one seed from the identified candidate seeds in the one or more growing spaces. (Even if considered as an additional element, this limitation amounts to insignificant extra-solution activity as insignificant application).
But for the following limitations, the rest of the limitations recited by claims 13 and 22 recite are largely similar to the limitations recited by claim 1, therefore the same analysis applies.
and based on a selection of one(s) of the identified candidate seeds from the grower or the user associated with the grower, generate planting instructions for an agricultural planting apparatus to plant the one(s) of the identified candidate seeds in the one or more growing spaces associated with the grower, whereby the agricultural planting apparatus operates to plant the one(s) of the identified candidate seeds in the one or more growing spaces in response to the planting instructions. (But for the additional elements – underlined – recited in this claim limitation, the step to “generate planting instructions“ could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper. Additionally, even if considered as an additional element, the step “to plant the one(s) of the identified candidate seeds in the one or more growing spaces” amounts to insignificant extra-solution activity as insignificant application/post-solution activity).
The additional elements beyond the abstract idea for consideration under Step 2A, Prong 2, and Step 2B recited by the independent claims are: by a computing device, data server, model, model is trained on historical selected and unselected ones of the candidate seeds, one or more growing spaces, and agricultural planting apparatus.
Dependent claims 2-6, 8-12, 14-18, 20-21, and 23-24 further narrow the abstract idea and do not introduce further additional elements for consideration. That is, the dependent claims include the same additional elements recited by the independent claims.
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).
Regarding the computing additional elements, namely by a computing device, data server, model, model is trained on historical selected and unselected ones of the candidate seeds, these additional elements have been evaluated but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements 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). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (generic computing environment). With respect to the one or more growing spaces, and agricultural planting apparatus, the one or more growing spaces, and agricultural planting apparatus have been considered under Step 2A Prong Two, however the one or more growing spaces, and agricultural planting apparatus are recited at a high level of generality and fail to provide a technical improvement or otherwise integrate the abstract idea into a practical application – further supported by Applicant’s own specification, such as in [0093], where it defines the agricultural planting apparatus as “agricultural apparatus that may be included in the system 100 include tractors, combines, other harvesters, planters, trucks, fertilizer equipment, aerial vehicles including unmanned aerial vehicles, and any other item of physical machinery or hardware, typically mobile machinery, and which may be used in tasks associated with agriculture and/or related to operations described herein”, virtually any type of machine used in agriculture, and [0020], where the specification defines growing spaces as “Seeds to be planted in target fields (broadly, growing spaces)”.
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.
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 by a computing device, data server, model, model is trained on historical selected and unselected ones of the candidate seeds, 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. Applicant’s specification recites the computing additional elements at a high level of generality. 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 one or more growing spaces, and agricultural planting apparatus, the one or more growing spaces, and agricultural planting apparatus have been considered under Step 2A Prong Two, however the one or more growing spaces, and agricultural planting apparatus are recited at a high level of generality and fail to provide a technical improvement or otherwise add significantly more to the abstract idea – further supported by Applicant’s own specification, such as in [0093], where it defines the agricultural planting apparatus as “agricultural apparatus that may be included in the system 100 include tractors, combines, other harvesters, planters, trucks, fertilizer equipment, aerial vehicles including unmanned aerial vehicles, and any other item of physical machinery or hardware, typically mobile machinery, and which may be used in tasks associated with agriculture and/or related to operations described herein”, virtually any type of machine used in agriculture, and [0020], where the specification defines growing spaces as “Seeds to be planted in target fields (broadly, growing spaces)”.
Furthermore, even if the accessing data, outputting the identified candidate seeds, and to plant the one(s) of the identified candidate seeds in the one or more growing spaces steps are interpreted as additional elements, these activities at most amount to insignificant extra-solution activity (mere data gathering and insignificant application, respectively), which does not add significantly more to the abstract idea, as noted in MPEP 2106.05(g). Additionally, the accessing data 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. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to amount to significantly more than the abstract idea itself. The ordered combination of elements in the claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to 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.
Claims 1, 5-6, 8-13, 17-18, and 20-24 are rejected under 35 U.S.C. 103 as being unpatentable over Reich et al. (US 20200005166 A1), in view of Bull et al. (US 20200005401 A1).
Regarding claims 1/13/22: Reich teaches a computer-implemented method ([0057] a computer-implemented method), a non-transitory computer-readable storage media ([0145] Such instructions, when stored in non-transitory storage media accessible to processor 404, render computer system 400), and a system ([0145] Such instructions, when stored in non-transitory storage media accessible to processor 404, render computer system 400) for use in identifying seeds with limitations for:
identifying, by a computing device, multiple seeds for a grower, the multiple seeds suitable to be selected by the grower for planting in one or more growing spaces associated with the grower: [0059] A computer system and a computer-implemented method that are disclosed herein for generating a set of target success yield group of hybrid seeds that have a high probability of a successful yield on one or more target fields.
accessing, by the computing device, data from a data server, the data including seed data representative of each of the multiple seeds: [0057] teaches: receiving, over a digital data communication network at a server computer system, grower datasets specifying agricultural fields of growers and inventories of hybrid products or seed products of the growers
identifying, by the computing device, candidate seeds from the multiple seeds for the grower: [0191] At step 1015, the hybrid seed filtering instructions 182 provide instruction to select a subset of one or more hybrid seeds from the candidate set of hybrid seeds that have a probability of success value greater than or equal to a target probability filtering threshold.
based on a model specific to the grower: [0064] Hybrid seed filtering instructions within the server computer system are configured to select a subset of the hybrid seeds that have probability of success values greater than a target probability filtering threshold. The server computer system includes hybrid seed normalization instructions configured to generate representative yield values for hybrid seeds in the subset of the one or more hybrid seeds based on the historical agricultural data. (Examiner’s Note: One of ordinary skill in the art would reasonably interpret the historical agricultural data disclosed in Reich to be specific to the grower using the claimed invention, as a result, the model created and its hyperparameters will be specific to the grower.)
outputting, by the computing device, the identified candidate seeds to the grower or a user associated with the grower: Fig. 22. [0033] FIG. 22 illustrates an example graphical screen display for displaying output recommendations.
including, based on a selection by the grower, at least one seed from the identified candidate seeds in the one or more growing spaces: [0059] teaches: a target success yield group of hybrid seeds may be generated using a server computer system that is configured to receive, over a digital data communication network, one or more agricultural data records that represent crop seed data describing seed and yield properties of one or more hybrid seeds and first field geo-location data for one or more agricultural fields where the one or more hybrid seeds were planted. (Examiner’s Note: One of ordinary skill in the art would reasonably interpret the grower’s agricultural data records that represent crop seed data as being equivalent to the selection by the grower.)
However, Reich does not explicitly teach the following limitations, further taught by Bull:
wherein the model is trained on historical selected and unselected ones of the candidate seeds by the grower, independent of historical performance of the candidate seeds in the one or more growing spaces associated with the grower ([0097] the mobile application comprises an integrated software platform that allows a grower to make fact-based decisions for their operation because it combines historical data about the grower's fields with any other data that the grower wishes to compare.; [0124] 2.4. Process Overview-Agronomic Model Training.; [0125] teaches: the agricultural intelligence computer system 130 is programmed or configured to create an agronomic model. In this context, an agronomic model is a data structure in memory of the agricultural intelligence computer system 130 that comprises field data 106.; [0164] In an embodiment, specific field data within the agricultural data records may also include crop rotation data. Soil nutrient management for fields may depend on factors such as establishing diverse crop rotations and managing the amount of tillage of the soil. For example, some historical observations have shown that a “rotation effect” of rotating between different crops on a field may increase crop yield by 5 to 15% over planting the same crop year over year.; [0228] At step 1215 of FIG. 12, the server computer 108 generates one or more yield ranking scores for the grower's one or more fields using the first set of historical agricultural data.; [0258] The computer system may store a trained machine learning model that is programmed to generate output data specifying a respective probability of success (POS) of planting one type of hybrid seed over another using features of the seeds as input.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Reich with Bull’s feature(s) listed above. One would’ve been motivated to do so in order to train the machine learning model and/or to modify the machine learning model to be more specific to the grower's field (Bull; [0258]). By incorporating the teachings of Bull, one would’ve been able to train the ml model using grower specific historical data that includes past selection of seeds for the growing spaces.
Additionally, claims 13, and 22 also recite the following limitation, further taught by Reich:
and based on a selection of one(s) of the identified candidate seeds from the grower or the user associated with the grower, generate planting instructions for an agricultural planting apparatus to plant the one(s) of the identified candidate seeds in the one or more growing spaces associated with the grower, whereby the agricultural planting apparatus operates to plant the one(s) of the identified candidate seeds in the one or more growing spaces in response to the planting instructions. ([0107] remote sensor 112 comprises one or more sensors that are programmed or configured to produce one or more observations. Remote sensor 112 may be aerial sensors, such as satellites, vehicle sensors, planting equipment sensors, tillage sensors, fertilizer or insecticide application sensors, harvester sensors, and any other implement capable of receiving data from the one or more fields. In an embodiment, application controller 114 is programmed or configured to receive instructions from agricultural intelligence computer system 130. Application controller 114 may also be programmed or configured to control an operating parameter of an agricultural vehicle or implement. For example, an application controller may be programmed or configured to control an operating parameter of a vehicle, such as a tractor, planting equipment, tillage equipment, fertilizer or insecticide equipment, harvester equipment, or other farm implements such as a water valve. Other embodiments may use any combination of sensors and controllers, of which the following are merely selected examples.; [0113] examples of sensors 112 that may be used with seed planting equipment such as planters, drills, or air seeders include seed sensors, which may be optical, electromagnetic, or impact sensors; downforce sensors such as load pins, load cells, pressure sensors; soil property sensors such as reflectivity sensors, moisture sensors, electrical conductivity sensors, optical residue sensors, or temperature sensors; component operating criteria sensors such as planting depth sensors, downforce cylinder pressure sensors, seed disc speed sensors, seed drive motor encoders, seed conveyor system speed sensors, or vacuum level sensors; or pesticide application sensors such as optical or other electromagnetic sensors, or impact sensors. In an embodiment, examples of controllers 114 that may be used with such seed planting equipment include: toolbar fold controllers, such as controllers for valves associated with hydraulic cylinders; downforce controllers, such as controllers for valves associated with pneumatic cylinders, airbags, or hydraulic cylinders, and programmed for applying downforce to individual row units or an entire planter frame; planting depth controllers, such as linear actuators; metering controllers, such as electric seed meter drive motors, hydraulic seed meter drive motors, or swath control clutches; hybrid selection controllers, such as seed meter drive motors, or other actuators programmed for selectively allowing or preventing seed or an air-seed mixture from delivering seed to or from seed meters or central bulk hoppers; metering controllers, such as electric seed meter drive motors, or hydraulic seed meter drive motors; seed conveyor system controllers, such as controllers for a belt seed delivery conveyor motor; marker controllers, such as a controller for a pneumatic or hydraulic actuator; or pesticide application rate controllers, such as metering drive controllers, orifice size or position controllers.
Regarding Claim 5: Reich further teaches:
wherein the data representative of the multiple seeds includes, for each seed, one or more of: commercial name, brand, product identifier, ratings, trait stack(s), product category, relative maturity, stalk strength, wilt, green snap, leaf blight, dry down, harvest appearance, emergence, root strength, test weight, seeding growth, leaf spot, stalk rot, and/or drought tolerance: [0069] Examples of field data 106 include: (b) harvest data (for example, crop type, crop variety, crop rotation, whether the crop is grown organically, harvest date, Actual Production History (APH), expected yield, yield, crop price, crop revenue, grain moisture, tillage practice, and previous growing season information), (d) planting data (for example, planting date, seed(s) type, relative maturity (RM) of planted seed(s), seed population).
However, Reich doesn’t explicitly teach:
and wherein the model is further trained on historical selected ones of the candidate seeds by at least one other grower in a region of said grower
Bull teaches:
and wherein the model is further trained on historical selected ones of the candidate seeds by at least one other grower in a region of said grower ([0097] teaches: the mobile application comprises an integrated software platform that allows a grower to make fact-based decisions for their operation because it combines historical data about the grower's fields with any other data that the grower wishes to compare.; [0122] In an embodiment, the agricultural intelligence computer system 130 is programmed or configured to create an agronomic model. In this context, an agronomic model is a data structure in memory of the agricultural intelligence computer system 130 that comprises field data 106, such as identification data and harvest data for one or more fields. The agronomic model may also comprise calculated agronomic properties which describe either conditions which may affect the growth of one or more crops on a field, or properties of the one or more crops, or both. Additionally, an agronomic model may comprise recommendations based on agronomic factors such as crop recommendations, irrigation recommendations, planting recommendations, fertilizer recommendations, fungicide recommendations, pesticide recommendations, harvesting recommendations and other crop management recommendations.; [0124] FIG. 3 illustrates a programmed process by which the agricultural intelligence computer system generates one or more preconfigured agronomic models using field data provided by one or more data sources. FIG. 3 may serve as an algorithm or instructions for programming the functional elements of the agricultural intelligence computer system 130 to perform the operations; [0163] These sets of fields where agricultural data records are collected may also be the same fields designated as target fields for planting newly selected crops. In yet other embodiments, sets of fields owned and operated by a grower may provide agricultural data records used by other growers; [0228] At step 1215 of FIG. 12, the server computer 108 generates one or more yield ranking scores for the grower's one or more fields using the first set of historical agricultural data.; [0230] FIG. 14 illustrates aspects of integration of assignment permutations into a machine learning algorithm. In the example of FIG. 14, a trained ML classifier 1400 is used to calculate the outcome of an assignment of a product to a field.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Reich and Bull with Bull’s additional feature(s) listed above. One would’ve been motivated to do so in order to train the machine learning model and/or to modify the machine learning model to be more specific to the grower's field (Bull; [0258]). By incorporating the teachings of Bull, one would’ve been able to train the ml model using grower specific historical data that includes past selection of seeds for the growing spaces.
Regarding Claim 6: Reich further teaches:
further comprising receiving an input from the grower or the user associated with the grower prior to identifying the multiple seeds, the input indicative of the grower and a type of seed ([Fig. 17] Data Inputs 1702, Grower Data (seed order, equipment types, and operations management).; [0242] teaches: data inputs 1702 may include data obtained from client computers used by growers, such as data collected via client computers 104 (FIG. 1) and/or the FIELD VIEW software that has been previously described; such data may specify field locations, size, density and harvest metrics. Data inputs 1702 also may include grower data such as seed orders, equipment types and operations management parameters. Unlike prior approaches, embodiments use grower-specific inputs that ass