DETAILED ACTION
Request for Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 4/15/26 has been entered, in which Applicant amended claims 1, 5, 7, 8, 11-13, 17, 18, and 20, cancelled claims 3, 4, and 14, and added new claim 21. Claims 1, 5, 7-13, 15-18, 20, and 21 are pending in this application and have been rejected as indicated below.
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 .
Information Disclosure Statement
No Information Disclosure Statement has yet been filed. As such, No Information Disclosure Statement has been considered.
Response to Amendment
Applicant’s amendments are acknowledged.
The 35 USC 101 rejections of claims 1, 5, 7-13, 15-18, 20, and 21 are still applied in light of Applicant’s amendments and explanations.
The 35 USC 103 rejections of claims 1, 5, 7-13, 15-18, 20, and 21 are withdrawn in light of Applicant’s amendments and explanations.
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, 5, 7-13, 15-18, 20, and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Here, under considerations of the broadest reasonable interpretation of the claimed invention, Examiner finds that the Applicant invented a method and system for assessing one or more agricultural trials in a field. Examiner formulates an abstract idea analysis, following the framework described in the MPEP as follows:
Step 1: The claims are directed to a statutory category, namely a "method" (claims 1, 5, 7-12, and 21) and "system" (claims 13, 15-18, and 20).
Step 2A - Prong 1: The claims are found to recite limitations that set forth the abstract idea(s), namely, regarding claim 1:
identifying… from the received field data, two regions in the field for assessment, the two regions including a first control region and a second test region, which is associated with an agricultural trial in the field having a specific output, wherein a target condition of the agricultural trial for causing the specific output is different between the two regions;
dividing each of the first control region and the second test region into multiple cells each associated with a different coordinate;
computing… a fitness metric for each of the multiple cells in the first control region, based on a physical distance between said cell in the first control region and each cell in the second test region;
computing… a fitness metric for each of the multiple cells in the second test region, based on a physical distance between said cell in the second test region and each cell in the first control region;
aggregating… each of the computed fitness metrics for the first control region and the second test region into an aggregate fitness metric for the two regions, the aggregate fitness metric providing a quantitative measure of a similarity between the two regions in the field
comparing… the aggregate fitness metric to a defined threshold; and
in response to the aggregate fitness metric satisfying the defined threshold:
determining… that the target condition is cause of the specific output of the trial; and
defining… a perimeter of the agricultural trial within the field, by:
filtering out ones of the fitness metrics for each of the multiple cells in the first control region and second test region below the defined threshold;
dilating a boundary defined by remaining ones of the multiple cells by a defined distance;
eroding the dilated boundary by a multiple of the defined distance;
and then dilating the eroded boundary by the defined distance; and
defining the dilated eroded boundary as the perimeter of the agricultural trial in the field
Independent claims 13 and 17 recite substantially similar claim language.
Dependent claims 5, 7-12, 15, 16, 18, 20, and 21 recite the same or similar abstract idea(s) as independent claims 1, 13, and 17 with merely a further narrowing of the abstract idea(s) to particular data characterization and/or additional data analyses performed as part of the abstract idea.
The limitations in claims 1, 5, 7-13, 15-18, 20, and 21 above falling well-within the groupings of subject matter identified by the courts as being abstract concepts, specifically the claims are found to correspond to the category of:
"Certain methods of organizing human activity- fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)" as the limitations identified above are directed to assessing one or more agricultural trials in a field and thus is a method of organizing human activity including at least commercial or business interactions or relations and/or a management of user personal behavior.
Step 2A - Prong 2: Claims 1, 5, 7-13, 15-18, 20, and 21 are found to clearly be directed to the abstract idea identified above because the claims, as a whole, fail to integrate the claimed judicial exception into a practical application, specifically the claims recite the additional elements of:
" further comprising generating a plot of the fitness metric for the field per location of a first region of the two regions and/or a second regions of the two regions" (claim 11), "wherein the plot includes one or more visual distinctions indicative of the fitness metric, the visual distinction including one or more of color, shading, hatching, or shape" (claim 12), “further comprising compiling, by the agricultural computer system, a map of the field and overlaying the perimeter of the agricultural trial on the map of the field, at a display in communication with the agricultural computer system, thereby illustrating a location of the agricultural trial in the field on the display,” (claim 21), however the aforementioned elements directed to the receiving of user input/selection of data to view via a dashboard and displaying corresponding data via the dashboard merely amount to generic GUI elements of a general purpose computer used to "apply" the abstract idea (MPEP 2106.05(f)) and/or is merely an attempt at limiting the abstract idea of assessing one or more agricultural trials in a field to a particular field of use/technological environment of a GUI dashboard (MPEP 2106.05(h)) and therefore the GUI dashboard input and display of data fails to integrate the abstract idea into a practical application;
" A computer-implemented method for use… , by an agricultural computer system… receiving… field data collected by farm equipment operating in a field, the field data including at least one of planting data and/or harvest data,” and “A non-transitory computer-readable storage medium comprising executable instructions for assessing a trial in a field, which when executed by at least one processor, cause the at least one processor to,” (claims 1, 13, and 17) however the aforementioned elements merely amount to generic components of a general purpose computer used to "apply" the abstract idea (MPEP 2106.0S(f)) and thus fails to integrate the recited abstract idea into a practical application, furthermore the high-level recitation of a computer-implemented method for use in assessing one or more agricultural trials in a field is at most an attempt to limit the abstract to a particular field of use (MPEP 2106.0S(h), e.g.: "For instance, a data gathering step that is limited to a particular data source (such as the Internet) or a particular type of data (such as power grid data or XML tags) could be considered to be both insignificant extra-solution activity and a field of use limitation. See, e.g., Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (limiting use of abstract idea to the Internet); Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data); Intellectual Ventures I LLC v. Erie lndem. Co., 850 F.3d 1315, 1328-29, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017) (limiting use of abstract idea to use with XML tags).") and/or merely insignificant extra-solution activity (MPE 2106.05(g)) and thus further fails to integrate the abstract idea into a practical application;
Step 2B: Claims 1, 5, 7-13, 15-18, 20, and 21 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 merely amount to a general purpose computer that attempts to apply the abstract idea in a technological environment (MPEP 2106.0S(f)), including merely limiting the abstract idea to a particular field of use of assessing one or more agricultural trials in a field, as explained above, and/or performs insignificant extra-solution activity, e.g. data gathering or output, (MPEP 2106.0S(g)), as identified above, which is further found under step 2B to be merely well-understood, routine, and conventional activities as evidenced by MPEP 2106.0S(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser's back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to assessing one or more agricultural trials in a field.
Claims 1, 5, 7-13, 15-18, 20, and 21 are accordingly rejected under 35 USC§ 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
Note: The analysis above applies to all statutory categories of invention. As such, the presentment of any claim otherwise styled as a machine or manufacture, for example, would be subject to the same analysis
For further authority and guidance, see:
MPEP § 2106
https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility
Subject Matter Overcoming Prior Art
Claims 1, 5, 7-13, 15-18, and 20-21 are found to overcome the prior art rejection. The claims would be found to be allowable if they overcame the 35 USC 101 rejection.
Reasons for Overcoming the Prior Art
The following is a statement of reasons for the indication of overcoming the prior art rejection:
The following limitations of claim 1,
…
receiving, by an agricultural computer system, field data collected by farm equipment operating in a field, the field data including at least one of planting data and/or harvest data;
identifying, by the agricultural computer system, from the received field data, two regions in the field for assessment, the two regions including a first control region and a second test region, which is associated with an agricultural trial in the field having a specific output, wherein a target condition of the agricultural trial for causing the specific output is different between the two regions;
dividing each of the first control region and the second test region into multiple cells each associated with a different coordinate;
computing, by the agricultural computer system, a fitness metric for each of the multiple cells in the first control region, based on a physical distance between said cell in the first control region and each cell in the second test region;
computing, by the agricultural computer system, a fitness metric for each of the multiple cells in the second test region, based on a physical distance between said cell in the second test region and each cell in the first control region;
aggregating, by the agricultural computer system, each of the computed fitness metrics for the first control region and the second test region into an aggregate fitness metric for the two regions, the aggregate fitness metric providing a quantitative measure of a similarity between the two regions in the field
comparing, by the agricultural computer system, the aggregate fitness metric to a defined threshold; and
in response to the aggregate fitness metric satisfying the defined threshold:
determining, by the agricultural computer system, that the target condition is cause of the specific output of the trial; and
defining, by the agricultural computer system, a perimeter of the agricultural trial within the field, by:
filtering out ones of the fitness metrics for each of the multiple cells in the first control region and second test region below the defined threshold;
dilating a boundary defined by remaining ones of the multiple cells by a defined distance;
eroding the dilated boundary by a multiple of the defined distance;
and then dilating the eroded boundary by the defined distance; and
defining the dilated eroded boundary as the perimeter of the agricultural trial in the field
in combination with the remainder of the claim limitations are neither taught nor suggested, singularly or in combination, by the prior art of record. Furthermore, neither the prior art, the nature of the problem, nor knowledge of a person having ordinary skill in the art provides for any predictable or reasonable rationale to combine prior art teachings. Independent claims 13 and 17, and dependent claims 5, 7-12, 15, 16, 18, and 20-21 likewise overcome the prior art rejection.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
The closest prior art of record is described as follows:
Ruff et al. (U.S. Patent Application Publication Number 2019/0057461) - The abstract provides for the following: A system for implementing a trial in one or more fields is provided. In an embodiment, an agricultural intelligence computing system receives field data for a plurality of agricultural fields. Based, at least in part, on the field data for the plurality of agricultural fields, the agricultural intelligence computing system identifies one or more target agricultural fields. The agricultural intelligence computing system sends, to a field manager computing device associated with the one or more target agricultural fields, a trial participation request. The server receives data indicating acceptance of the trial participation request from the field manager computing device. The server determines one or more locations on the one or more target agricultural fields for implementing a trial and sends data identifying the one or more locations to the field manager computing device. When the agricultural intelligence computing system receives application data for the one or more target agricultural fields, the agricultural intelligence computing system determines whether the one or more target agricultural fields are in compliance with the trial. The agricultural intelligence computing system then receives result data for the trial and, based on the result data, computes a benefit value for the trial.
Acedo et. al. (U.S. Patent Application Publication Number 2022/0312661) - The abstract provides for the following: The invention(s) include systems and methods for receiving and processing agriculture-associated samples with sample processing architecture structured to rapidly return outputs characterizing effects of agriculture inputs and practices over time. Control instructions based upon outputs of the systems and methods are then executed for maintaining or improving performance of crops and agriculture sites (e.g., in relation to yield, in relation to nutrient characteristics) in a sustainable manner (e.g., environmentally sustainable manner). System and method outputs can further be used to affect modifications to product treatments generated by associated manufacturers.
Hu (U.S. Patent Application Publication Number 2018/0260504) - The abstract provides for the following: In an embodiment, a computer-implemented method of selecting sampling locations in a field is disclosed. The method comprises receiving, by a processor, input data including a map for a management zone in a field indicating one or more values of a set of agricultural characteristics for each of a plurality of locations in the management zone. The method further comprises identifying, by the processor, a set of values for the set of agricultural characteristics for each of a group of locations in the management zone based on map. In addition, the method comprises normalizing a set of model values for the set of agricultural characteristics used by an agricultural modeling tool and the set of values of the set of agricultural characteristics for each of the group of locations in the management zone. The method also comprises selecting one of the group of locations as a sampling location based on the normalized set of model values, the normalized sets of values for the group of locations, and a first distance constraint related to a distance to a boundary of the management zone. Finally, the method comprises causing display of information regarding the selected location.
Zonlehoua Coulibali et al. “Site-specific machine learning predictive fertilization models for potato crops in Eastern Canada.” – Statistical modeling is commonly used to relate the performance of potato (Solanum tubero-sumL.) to fertilizer requirements. Prescribing optimal nutrient doses is challenging because of the involvement of many variables including weather, soils, land management, genotypes, and severity of pests and diseases. Where sufficient data are available, machine learning algorithms can be used to predict crop performance. The objective of this study was to determine an optimal model predicting nitrogen, phosphorus and potassium requirements for high tuber yield and quality (size and specific gravity) as impacted by weather, soils and land management variables. We exploited a data set of 273 field experiments con ducted from 1979 to 2017in Quebec (Canada). We developed, evaluated and compared predictions from a hierarchical Mitscherlich model, k-nearest neighbors, random forest, neural networks and Gaussian processes. Machine learning models returned R 2 2 values of 0.49–0.59 for tuber marketable yield prediction, which were higher than the Mitscherlich model R (0.37). The models were more likely to predict medium-size tubers (R 0.69) and tuber specific gravity (R 2 =0.58–0.67) than large-size tubers (R 2 2 =0.60 =0.55–0.64) and marketable yield. Response surfaces from the Mitscherlich model, neural networks and Gaussian processes returned smooth responses that agreed more with actual evidence than discontinuous curves derived from k-nearest neighbors and random forest models. When conditioned to obtain optimal dosages from dose-response surfaces given constant weather, soil and land management conditions, some disagreements occurred between models. Due to their built-in ability to develop recommendations within a probabilistic risk assessment framework, Gaussian processes stood out as the most promising algorithm to support decisions that minimize economic or agronomic risks..
Doug Sauder et al. (WIPO Patent Publication Number WO 2016/200699) - The abstract provides for the following: Described herein are systems and methods for agricultural data analysis. In one embodiment, a computer system for monitoring field operations includes a database for storing agricultural data including yield and field data and at least one processing unit that is coupled to the database. The at least one processing unit is configured to execute instructions to monitor field operations, to store agricultural data, to automatically determine whether at least one correlation between different variables or parameters of the agricultural data exceeds a threshold, and to perform analysis of the agricultural data to identify a category of man-made issues or other issues that have potentially caused the correlation when at least one correlation occurs between different variables or parameters of the agricultural data.
Response to Argument
Applicant’s arguments filed 4/15/2026 have been fully considered but they are not fully persuasive.
Applicant argues that the amended claims transform data, offers technical benefits, and solves a specific technical problem. (See Applicant’s Remarks, 4/15/2026, pgs. 10-15). Examiner disagrees with Applicant’s assertions. As described above in the 35 USC 101 rejection, the claim recites an abstract idea that is analogous to organizing human activities and utilizes technology to facilitate implementation of the abstract ideas. Each of the steps set forth by the Examiner as abstract constitute actions that could be performed by a person and are written as directions to be performed (e.g. grouping inputting, calculating, comparing, etc.). The application of a machine learning process to these abstract concepts does not rise to the level of eligibility in and of itself. Additionally, the problems solved by applying the machine learning process are solely directed towards improving the abstract idea rather than the underlying technology. Examiner can not find any improvement to technology recited in the claims. The machine learning elements as claimed are recited at a high level of generality and merely use computer technology as a tool to apply the abstract idea.. (See e.g. MPEP 2106.05(f): (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. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it"). Additionally, other than an output of data there is no particular application of the technology recited by the claims. There is nothing in the claims that improves the underlying technology but instead the technology is present to facilitate implementation of the abstract ideas in a particular technological environment. Accordingly, the 35 USC 101 rejection has been maintained.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW H. DIVELBISS whose telephone number is (571) 270-0166. The fax phone number is 571-483-7110. The examiner can normally be reached on M-Th, 7:00 - 5:00. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O’Connor can be reached on (571) 272-6787.
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/M.H.D/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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