Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/29/2025 has been entered.
Status of Claims
This communication is a Non-Final Office Action in response to Applicant’s RCE for application number 18/201,728 received on 04/20/2026.
In accordance with Applicant’s amendment, claims 1, 4, 6-7, 9-10, 15, and 17-19 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
Applicant’s amendment necessitated the new ground(s) of rejection set forth in this Office Action.
Upon review of amended claims and Applicant’s arguments, the §103 rejections previously applied to the claims are withdrawn.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 claims have been considered and are unpersuasive.
Applicant argues (Remarks at pg. 9): “These operations cannot practically be performed in the human mind. They require transforming raw, voluminous sensor data (planting rates, treatment presence/absence, headlands) from planters and harvesters and sprayers, etc. into precise trial locations through iterative geometric filtering, centroid-based grouping, geometry simplification, and boundary- constrained duplication.”. In response, Examiner respectfully disagrees and notes that the limitations for “accessing, for a target field, planting data for the target field from a data structure, the planting data associated with discrete coordinates of the target field; accessing, treatment data for the target field, the treatment data associated with discrete coordinates of the target field; identifying multiple segments of the target field having at least one common treatment feature distinct as compared to remainder segments of the target field by overlaying the treatment data and the planting data for the target field based on the coordinates associated with the treatment data and the planting data; and selecting the segments which are planted and have the at least one common treatment feature; labeling the planting data and/or the treatment data for the identified segments based on at least one characteristic associated with the planting data and/or the treatment data; generating a bounding box around the identified segments; applying a first geometric threshold to the bounding box and the identified segments by imposing the first geometric threshold on a ratio of an area of the identified segments relative to an area of the bounding box; and in response to the identified segments satisfying the first geometric threshold: identifying a centroid of each of the identified segments; aligning the centroids of the identified segments with a planting direction of the target field; grouping ones of the identified segments into at least one strip contained within the target field based on coordinates of each of the centroids of the ones of the identified segments being within a threshold distance; validating the at least one strip including the grouped ones of the identified segments, based on a length and width of the at least one strip satisfying a threshold size; building the trial based on the at least one strip, wherein the trial includes the at least one strip and at least one duplicate strip disposed along a long side of the at least one strip, wherein the at least one duplicate strip includes ones of the remainder segments of the target field lacking the at least one common treatment feature; and identifying a location of the trial identified as a location of the trial in the target field; and then comparing an output of the at least one strip and the at least one duplicate strip, to thereby provide an assessment metric for the trial based, at least in part, on the at least one characteristic associated with the planting data and/or the treatment data” recite steps that can be performed in the human mind, via observation, judgement, opinion, or with the help of pen and paper. For example, one of ordinary skill in the art would be able to reasonably identify multiple segments of the target field having at least one common treatment feature distinct as compared to remainder segments of the target field by overlaying the treatment data and the planting data for the target field based on the coordinates associated with the treatment data and the planting data; and selecting the segments which are planted and have the at least one common treatment feature with the help of pen and paper, or access planting data via observation. Therefore, the claims recite abstract ideas that fall under the “Mental Processes” abstract idea grouping.
Applicant argues (Remarks at pgs. 9-10): “The claims are also not directed to "certain methods of organizing human activity." Instead, they recite a technological process for enhancing agricultural data processing to automatically detect and locate trials in fields from sensor data-analogous to the rules-based animation in McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016). The claims improve computer functionality through an ordered combination of unconventional rules and geometric operations that automate tasks previously reliant on subjective human judgment or inaccurate manual entry. Like McRO, the specific transformation and operation sequence produces accurate, objective trial locations that "previously could only be produced by human [experts]," McRO, 837 F.3d at 1313, without preempting all field-data processing.”. In response, Examiner reminds Applicant that in the eligibility analysis of the office action dated 10/21/2025, the claim limitations were not found to be directed to such abstract idea grouping. Therefore, Applicant’s argument is unfounded. Furthermore, Examiner notes that the present claims do not provide an analogous improvement to the computer to that of McRO. The present claims are not directed to the automation of specific animation tasks that previously could only be performed subjectively by humans. Therefore, Examiner respectfully disagrees with Applicant's assertion that the present claims are directed to statutory subject matter in view of McRO. Examiner further reminds Applicant that, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”.
Applicant argues (Remarks at pg. 11): “This is not mere automation using generic computing elements but a particular, ordered combination that improves computer-based agricultural data processing-similar to the improved network filtering in BASCOM Global Internet Servs., Inc. v. AT&T Mobility LLC, 827 F.3d 1341 (Fed. Cir. 2016). The flowchart in FIG. 6 and FIGS. 3A-3C embody this process, illustrating an application of the elements in a meaningful way beyond generally linking to a technological environment.”. In response, Examiner respectfully disagrees and notes that Applicant’s claims are not analogous to BASCOM because Applicant’s claims, as currently recited, do not improve computer functionality. Applicant’s claims rely on generic computing components to perform the abstract limitations to identify trials in fields by analyzing information. More specifically, Applicant’s claims rely on the additional elements by a computing device from claim 1, and a non-transitory computer-readable storage medium comprising executable instructions and at least one processor from claim 15. 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).
Response to §103 arguments – Applicant’s arguments with respect to the §103 rejections previously applied to the claims have been considered. Specifically, Applicant’s arguments related to the grouping based on centroid coordinates and applying a geometric threshold to the bounding box are persuasive. Therefore, the rejections under 35 USC 103 are withdrawn.
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, 4, 6, 7, 9, 10, 15, and 17-19 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
Claims 1, 4, 6-7, and 9-10 are directed to a method (i.e., Process), claims 15, and 17-19 are directed to a computer-readable storage medium (i.e., Manufacture). Therefore, the 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 and 15 recite a method, and a computer-readable storage medium. 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 1 recites a computer-implemented method for identifying trials in fields with the following abstract limitations: “accessing, for a target field, planting data for the target field from a data structure, the planting data associated with discrete coordinates of the target field; accessing, treatment data for the target field, the treatment data associated with discrete coordinates of the target field; identifying multiple segments of the target field having at least one common treatment feature distinct as compared to remainder segments of the target field by overlaying the treatment data and the planting data for the target field based on the coordinates associated with the treatment data and the planting data; and selecting the segments which are planted and have the at least one common treatment feature; labeling the planting data and/or the treatment data for the identified segments based on at least one characteristic associated with the planting data and/or the treatment data; generating a bounding box around the identified segments; applying a first geometric threshold to the bounding box and the identified segments by imposing the first geometric threshold on a ratio of an area of the identified segments relative to an area of the bounding box; and in response to the identified segments satisfying the first geometric threshold: identifying a centroid of each of the identified segments; aligning the centroids of the identified segments with a planting direction of the target field; grouping ones of the identified segments into at least one strip contained within the target field based on coordinates of each of the centroids of the ones of the identified segments being within a threshold distance; validating the at least one strip including the grouped ones of the identified segments, based on a length and width of the at least one strip satisfying a threshold size; building the trial based on the at least one strip, wherein the trial includes the at least one strip and at least one duplicate strip disposed along a long side of the at least one strip, wherein the at least one duplicate strip includes ones of the remainder segments of the target field lacking the at least one common treatment feature; and identifying a location of the trial identified as a location of the trial in the target field; and then comparing an output of the at least one strip and the at least one duplicate strip, to thereby provide an assessment metric for the trial based, at least in part, on the at least one characteristic associated with the planting data and/or the treatment data.”. Independent claim 15 recites a non-transitory computer-readable storage medium for identifying trials in fields with the following abstract limitations: “access, for a target field, planting data for the target field from a data structure, the planting data associated with discrete coordinates of the target field; access treatment data for the target field, the treatment data associated with discrete coordinates of the target field; identify multiple segments of the target field having at least one common treatment feature distinct as compared to remainder segments of the target field by overlaying the treatment data and the planting data for the target field based on the coordinates associated with the treatment data and the planting data; and selecting the segments which are planted and have the at least one common treatment feature; label the planting data and/or the treatment data for the identified segments based on at least one characteristic associated with the planting data and/or the treatment data; generate a bounding box around the identified segments; apply a first geometric threshold to the bounding box and the identified segments by imposing the first geometric threshold on a ratio of an area of the identified segments relative to an area of the bounding box; and in response to the identified segments segment(s) satisfying the first geometric threshold: identify a centroid of each of the identified segments; align the centroids of the identified segments with a planting direction of the target field; group ones of the identified segments into at least one strip contained within the target field based on coordinates of each of the centroids of the ones of the identified segments being within a threshold distance; validate the at least one strip including the grouped ones of the identified segments, based on a length and width of the at least one strip satisfying a threshold size; build the trial in the target field based on the at least one strip, wherein the trial includes the at least one strip and at least one duplicate strip disposed along a long side of the at least one strip, wherein the at least one duplicate strip includes ones of the remainder segments of the target field lacking the at least one common treatment feature; and identify a location of the trial in the target field; and then compare an output of the at least one strip and the at least one duplicate strip, to thereby provide an assessment metric for the trial based, at least in part, on the at least one characteristic associated with the planting data and/or the treatment data.”. But for the additional elements recited in the claims, the limitations of claims 1 and 15 could be accomplished mentally, such as by human observation, evaluation, judgement, opinion, or with the help of pen and paper.
The dependent claims (3-11, and 16-19) further narrow the abstract idea and do not introduce any additional elements for consideration.
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 from claim 1, and a non-transitory computer-readable storage medium comprising executable instructions and at least one processor from claim 15, 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).
The dependent claims (3-11, and 16-19) further narrow the abstract idea and do not introduce any additional elements for consideration.
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 by a computing device from claim 1, and a non-transitory computer-readable storage medium comprising executable instructions and at least one processor from claim 15, 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).
The dependent claims (3-11, and 16-19) further narrow the abstract idea and do not introduce any additional elements for consideration.
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.
Accordingly, claims 1, 4, 6, 7, 9, 10, 15, and 17-19 are rejected under 35 USC 101.
Allowable Over Prior Art
Claims 1, 4, 6-7, 9-10, 15, and 17-19 are allowable over the available field of prior art. Independent claims 1/15, as drafted, are rendered neither obvious nor anticipated by the available field of prior art. The claims overcome the prior art such that none of the cited prior art references can be applied to form the bases of a 35 USC 102 rejection nor can they be applied to form the basis of a 35 USC 103 rejection when the limitations are read in the particular environment of the claims. Therefore, the claims may be allowable if amended to overcome the rejections under 35 USC 101, as set forth above.
Regarding Claims 1/15: the claim is rendered neither obvious nor anticipated by the available field of prior art. With respect to claims 1/15, the closest prior art of the record is listed:
Rowan et al. (US 20180132423 A1, hereinafter “Rowan”) teaches “accessing, by a computing device, for a target field, planting data for the target field from a data structure, the planting data associated with discrete coordinates of the target field; accessing, by the computing device, treatment data for the target field, the treatment data associated with discrete coordinates of the target field; identifying, by the computing device, multiple segments of the target field having at least one common treatment feature distinct as compared to remainder segments of the target field…; and selecting the segments which are planted and have the at least one common treatment feature; labeling, by the computing device, the planting data and/or the treatment data for the identified segments based on at least one characteristic associated with the planting data and/or the treatment data; and in response to the identified segments satisfying the first geometric threshold: identifying, by the computing device, a centroid of each of the identified segments; aligning, by the computing device, the centroids of the identified segments with a planting direction of the target field”.
However, Rowan doesn’t explicitly teach:
by overlaying the treatment data and the planting data for the target field based on the coordinates associated with the treatment data and the planting data;
generating, by the computing device, a bounding box around the identified segments;
applying, by the computing device, a first geometric threshold to the bounding box and the identified segments
by imposing the first geometric threshold on a ratio of an area of the identified segments relative to an area of the bounding box;
grouping, by the computing device, ones of the identified segments into at least one strip contained within the target field
based on coordinates of each of the centroids of the ones of the identified segments being within a threshold distance;
validating, by the computing device, the at least one strip including the grouped ones of the identified segments, based on a length and width of the at least one strip satisfying a threshold size;
building, by the computing device, the trial based on the at least one strip, wherein the trial includes the at least one strip and at least one duplicate strip disposed along a long side of the at least one strip, wherein the at least one duplicate strip includes ones of the remainder segments of the target field lacking the at least one common treatment feature;
and identifying, by the computing device, a location of the trial identified as a location of the trial in the target field;
and then comparing an output of the at least one strip and the at least one duplicate strip, to thereby provide an assessment metric for the trial based, at least in part, on the at least one characteristic associated with the planting data and/or the treatment data.
Anderson (Anderson SL and Murray SC (2020) R/UAStools:plotshpcreate: Create Multi-Polygon Shapefiles for Extraction of Research Plot Scale Agriculture Remote Sensing Data. Front. Plant Sci. 11:511768.) teaches “generating, by the computing device, a bounding box around the identified segments; applying, by the computing device, a first geometric threshold to the bounding box and the identified segments; grouping, by the computing device, ones of the identified segments into at least one strip contained within the target field”.
However, Anderson doesn’t explicitly teach:
by overlaying the treatment data and the planting data for the target field based on the coordinates associated with the treatment data and the planting data;
by imposing the first geometric threshold on a ratio of an area of the identified segments relative to an area of the bounding box;
based on coordinates of each of the centroids of the ones of the identified segments being within a threshold distance;
validating, by the computing device, the at least one strip including the grouped ones of the identified segments, based on a length and width of the at least one strip satisfying a threshold size;
building, by the computing device, the trial based on the at least one strip, wherein the trial includes the at least one strip and at least one duplicate strip disposed along a long side of the at least one strip, wherein the at least one duplicate strip includes ones of the remainder segments of the target field lacking the at least one common treatment feature;
and identifying, by the computing device, a location of the trial identified as a location of the trial in the target field;
and then comparing an output of the at least one strip and the at least one duplicate strip, to thereby provide an assessment metric for the trial based, at least in part, on the at least one characteristic associated with the planting data and/or the treatment data.”
Johannesson (US 20200201269 A1) teaches “building, by the computing device, the trial based on the at least one strip, wherein the trial includes the at least one strip and at least one duplicate strip disposed along a long side of the at least one strip, wherein the at least one duplicate strip includes ones of the remainder segments of the target field lacking the at least one common treatment feature; and identifying, by the computing device, a location of the trial identified as a location of the trial in the target field; and then comparing an output of the at least one strip and the at least one duplicate strip, to thereby provide an assessment metric for the trial based, at least in part, on the at least one characteristic associated with the planting data and/or the treatment data.”.
However, Johannesson doesn’t explicitly teach:
by overlaying the treatment data and the planting data for the target field based on the coordinates associated with the treatment data and the planting data;
by imposing the first geometric threshold on a ratio of an area of the identified segments relative to an area of the bounding box;
based on coordinates of each of the centroids of the ones of the identified segments being within a threshold distance;
validating, by the computing device, the at least one strip including the grouped ones of the identified segments, based on a length and width of the at least one strip satisfying a threshold size;
Richt (US 20180020622 A1) teaches “by overlaying the treatment data and the planting data for the target field based on the coordinates associated with the treatment data and the planting data;”.
However, Richt doesn’t explicitly teach:
by imposing the first geometric threshold on a ratio of an area of the identified segments relative to an area of the bounding box;
based on coordinates of each of the centroids of the ones of the identified segments being within a threshold distance;
validating, by the computing device, the at least one strip including the grouped ones of the identified segments, based on a length and width of the at least one strip satisfying a threshold size;
Schmeer (WO 2021122962 A1) teaches “validating, by the computing device, the at least one strip including the grouped ones of the identified segments, based on a length and width of the at least one strip satisfying a threshold size;”.
However, Schmeer doesn’t explicitly teach:
by imposing the first geometric threshold on a ratio of an area of the identified segments relative to an area of the bounding box;
based on coordinates of each of the centroids of the ones of the identified segments being within a threshold distance;
Anderson teaches polygon dimension arguments that can be specified to accurately create the proper plot dimensions and buffer dimensions desired. However, Anserson doesn’t explicitly teach “by imposing the first geometric threshold on a ratio of an area of the identified segments relative to an area of the bounding box; based on coordinates of each of the centroids of the ones of the identified segments being within a threshold distance;”.
Therefore, the present claims are rendered neither obvious nor anticipated by the available field of prior art. However, these claims are not in condition for allowance because they remain rejected under 35 USC 101, as set forth in the instant office action. See the details above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kurzhanskiy et al. (US 9942440 B2), which discloses detection and identification a field's boundaries is performed in a workflow based on processing images of the field captured at different times, relative to a defined seed point.
Nolan et al. "Multi-UAS path planning for non-uniform data collection in precision agriculture," 2017 IEEE Aerospace Conference, Big Sky, MT, USA, 2017, pp. 1-12, which discloses defining the grid in the R-domain, define a minimum bounding box to encompass the augmented boundary. In that minimum bounding box, define a mesh grid of points with the spacing as defined above and find the points in that mesh contained in the polygon defined by the transformed boundary points in the R-domain.
Guo et al. Aerial Imagery Analysis - Quantifying Appearance and Number of Sorghum Heads for Applications in Breeding and Agronomy. Front Plant Sci. 2018 Oct 23, which discloses UAV-based imagery from agronomy experiments and scouting of production fields.
Mardanisamani et al. Automatic Microplot Localization Using UAV Images and a Hierarchical Image-Based Optimization Method. Plant Phenomics. 2021 Dec 8, which discloses automated analysis of aerial crop images.
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/G.J.T./Examiner, Art Unit 3625
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625
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