DETAILED ACTION
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
The information disclosure statement (IDS) submitted on February 4, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
Abstract: Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided. (examiner emphasis added)
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.
Invoked despite absence of “means”
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 limitation(s) is/are:
“a receiving unit” in claims 13-14
“a processing unit” in claims 1, 13-14
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Based on the above 35 U.S.C. 112(f) interpretation, corresponding 35 USC § 112(a) and 35 USC § 112(b) rejections were considered. Upon reviewing the specification it was determined 35 USC § 112(a) and 35 USC § 112(b) rejections were unnecessary (see chart below).
Unit
Structure/Algorithm (when computer implemented)
Receiving unit
Structure: paragraph 0070, "The receiving unit typically relates to a receiving interface that is connectively coupled to the processing unit. The receiving unit is capable of communicating with a remote device such as a data source computing device having stored thereon the original satellite imaging data captured by a satellite, or with a local or remote memory device having stored thereon the historic satellite imaging data. " paragraph 0071, "In one embodiment, the receiving unit may be a computing device"
Processing unit
Structure: paragraph 0072, " The term processing unit typically relates to a general-purpose processing device such as a microprocessor, microcontroller, central processing unit, or the like. "
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.
Claim 14 is rejected under 35 U.S.C. 101 because the claimed inventio is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because data per se and/or computer programs do not fall into one of the four categories of statutory invention (machine, process, manufacture, composition). Regarding claim 14, the claim is drawn toward a “a computer program element”. As described in MPEP 2106, data per se and computer programs do not fall into one of the four statutory categories. Therefore, since claim 14 is drawn to a computer program element, the claim is not eligible for patent protection.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. See MPEP 2106 and 2106.03 for guidance. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter of a process, machine, manufacture, or composition of matter. “A computer program” (or “computer program product”, “computer readable media”) as recited is not patent eligible subject matter because it is “software/data per se”. Furthermore, it is not a remedy when such “software/data” are claimed as a product without any structural recitations. “Thus, a product claim to a software program that does not also contain at least one structural limitation (such as a ‘means plus function’ limitation) has no physical or tangible form, and thus does not fall within any statutory category.” MPEP 2106.03(I). A recommended remedy for claiming a computer program is to have it embodied within a “non-transitory” computer readable medium. See also USPTO Published 2019 Patent Eligibility Guidance.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-8, 11 and 13-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Publication No. 2022/0164736 to Johnson (hereinafter Johnson).
Regarding independent claim 1, Johnson discloses A computer-implemented method for selecting at least one agricultural field for production of plant propagation material of a crop plant (paragraph 0175, “FIGS. 18A-C are examples of the computer dashboard user interface of the present invention.” paragraph 0127, “Models can be applied to the data in a variety of ways depending on the objective of the user, for example: Select a field with the highest probability (score) of growing a crop with the desired attribute metrics; ”) comprising the steps:
a) providing historic satellite imaging data of at least one agricultural field from at least one previous growing period to a processing unit (paragraph 0181, “Another data example is NDVI and imagery Data. Examples of vegetation indexes can include: NDVI (normalized difference vegetation index); Tasseled Cap Index; Perpendicular Vegetation Index; IPVI; Soil Adjusted Vegetation Index (SAVI); TSAVI; MSAVI; Atmospherically Resistant Vegetation Index (ARVI); Global Environmental Monitoring Index (GEMI); Soil Wetness Index; Other special indexes derived from imagery. ”…“Historical data is available for many years in the past. Data is available for all acres, globally.”… “ Satellite data for annual crops is only valuable during brief period of time, in-season. Data availability during critical times of the crop cycle can be poor or good; it is not consistent. ”);
b) determining, based on the imaging data, if the crop plant was grown on the at least one field in at least one previous growing period by the processing (paragraph 0166, “Some data processing and analysis may be required to extract additional information from the data. For example, satellite data analysis to quantify a field's susceptibility to stress including the number of high stress days, the number field acres impacted and the frequency. Consistent susceptibility to stress is an indicator of future stress which will impact attribute metrics. Crop rotation data to determine the most crop grown on a field the past year or for a number of years. For example, potatoes following corn is high risk.” Paragraph 0182, “Another data example is crop rotation data. Crop rotations have a big impact of several attributes including yield and quality. Crops that follow certain crops can be susceptible to disease, pests, fertility, soil heath, and erosion issues. The past three years rotation when selecting a field. Data is available for all fields from USDA NASS and other commercial sources and is available for all fields and without farmer assistance. Data is at field level resolution. ”);
c) providing information by the processing unit if the crop plant had been grown on the at least one field in at least one previous growing period (paragraph 0166, “Some data processing and analysis may be required to extract additional information from the data. For example, satellite data analysis to quantify a field's susceptibility to stress including the number of high stress days, the number field acres impacted and the frequency. Consistent susceptibility to stress is an indicator of future stress which will impact attribute metrics. Crop rotation data to determine the most crop grown on a field the past year or for a number of years. For example, potatoes following corn is high risk.”); and
d) selecting, based on the provided information, at least one suitable agricultural field for production of plant propagation material, wherein the crop plant has not been grown on the at least one agricultural field for at least one previous growing period (paragraph 0126, “ the Model is applied to identify and predict which fields have the highest probability of growing the crop with the desired outcome attribute metrics. The ability of a field to grow the crop with the desired outcome attribute metrics can be expressed as a percentage probability or by some other scoring mechanism such as scale of 10 to 1, odds as in gambling, or on a color scale.” Paragraph 0182, “Another data example is crop rotation data. Crop rotations have a big impact of several attributes including yield and quality. Crops that follow certain crops can be susceptible to disease, pests, fertility, soil heath, and erosion issues. The past three years rotation when selecting a field. Data is available for all fields from USDA NASS and other commercial sources and is available for all fields and without farmer assistance. Data is at field level resolution. ”).
Regarding dependent claim 2, the rejection of claim 1 is incorporated herein. Additionally, Johnson further discloses further comprising step a1) after step a) or step b) of determining based on the imaging data field boundaries by the processing unit, using a field boundary detection model, wherein the field boundary detection model is a machine-learning (paragraph 0183, “Another data example is field shape and size data. The shape and size of a field impacts production practices and therefore attribute metrics. One objective when selecting a field is to identify fields with optimal shape and size. The ratio of acres to borders is one example characterizing a field's shape and size.” Paragraph 0120, “The one or more machine learning operations can include one or more of: a generalized linear model; a generalized additive model; a non-parametric regression operation; a random forest classifier; a spatial regression operation; a Bayesian regression model; a time series analysis; a Bayesian network; a Gaussian network; a decision tree learning operation; an artificial neural network; a recurrent neural network; a reinforcement learning operation; a linear/non-linear regression operation; a support vector machine; a clustering operation; and a genetic algorithm operation.”).
Regarding dependent claim 3, the rejection of claim 1 is incorporated herein. Additionally, Johnson further discloses wherein the historic satellite imaging data comprises time-resolved imaging data over the at least one previous growing period (paragraph 0181, “Another data example is NDVI and imagery Data. Examples of vegetation indexes can include: NDVI (normalized difference vegetation index); Tasseled Cap Index; Perpendicular Vegetation Index; IPVI; Soil Adjusted Vegetation Index (SAVI); TSAVI; MSAVI; Atmospherically Resistant Vegetation Index (ARVI); Global Environmental Monitoring Index (GEMI); Soil Wetness Index; Other special indexes derived from imagery. ”…“Historical data is available for many years in the past. Data is available for all acres, globally.” … “ Satellite data for annual crops is only valuable during brief period of time, in-season. Data availability during critical times of the crop cycle can be poor or good; it is not consistent. ”).
Regarding dependent claim 4, the rejection of claim 3 is incorporated herein. Additionally, Johnson further discloses wherein step b) comprises determining from the imaging data time-resolved vegetation index data selected from Normalized Difference Vegetation Index (NDVI) Data and/or Leaf Area Index (LAI) Data, Normalized Difference Water Index (NDWI), Enhanced Vegetation Index (EVI) Data and/or any other vegetation based indices data (paragraph 0181, “Another data example is NDVI and imagery Data. Examples of vegetation indexes can include: NDVI (normalized difference vegetation index); Tasseled Cap Index; Perpendicular Vegetation Index; IPVI; Soil Adjusted Vegetation Index (SAVI); TSAVI; MSAVI; Atmospherically Resistant Vegetation Index (ARVI); Global Environmental Monitoring Index (GEMI); Soil Wetness Index; Other special indexes derived from imagery. ”… “For field selection purposes NDVI is a simple graphical indicator that can be used to assess whether or not the target being observed contains live green vegetation. Data is available for all fields (not only contracted farmers-fields) and without farmer assistance. Accuracy and resolution vary from supplier to supplier.”).
Regarding dependent claim 5, the rejection of claim 4 is incorporated herein. Additionally, Johnson further discloses wherein step b) comprises classifying the at least one field by crop plant according to the time-resolved vegetation index data (paragraph 0181, “Another data example is NDVI and imagery Data. Examples of vegetation indexes can include: NDVI (normalized difference vegetation index); Tasseled Cap Index; Perpendicular Vegetation Index; IPVI; Soil Adjusted Vegetation Index (SAVI); TSAVI; MSAVI; Atmospherically Resistant Vegetation Index (ARVI); Global Environmental Monitoring Index (GEMI); Soil Wetness Index; Other special indexes derived from imagery. ”… “For field selection purposes NDVI is a simple graphical indicator that can be used to assess whether or not the target being observed contains live green vegetation. Data is available for all fields (not only contracted farmers-fields) and without farmer assistance. Accuracy and resolution vary from supplier to supplier.” Classifying the data according to the NDVI as live green vegetation is read as classifying the field by crop (i.e. it is a live vs dead crop)).
Regarding dependent claim 6, the rejection of claim 5 is incorporated herein. Additionally, Johnson further discloses wherein the classification is performed by using a classification model, wherein the classification model is obtainable by machine-learning (Paragraph 0120, “The one or more machine learning operations can include one or more of: a generalized linear model; a generalized additive model; a non-parametric regression operation; a random forest classifier; a spatial regression operation; a Bayesian regression model; a time series analysis; a Bayesian network; a Gaussian network; a decision tree learning operation; an artificial neural network; a recurrent neural network; a reinforcement learning operation; a linear/non-linear regression operation; a support vector machine; a clustering operation; and a genetic algorithm operation.”).
Regarding dependent claim 7, the rejection of claim 6 is incorporated herein. Additionally, Johnson further discloses wherein the machine-learning is supervised machine-learning, wherein the training data is obtained by annotation of satellite imaging data with ground truth data (paragraph 0068, “ Training the model is an iterative process.” paragraph 0121, “A portion of the known outcome attribute metrics from the sample locations, for example, 25% to 30% of the known total, will be “set aside” and used later to determine the accuracy of the Model after the Model is trained. Using this approach, the trained Model, can be applied to a set of growing condition metrics to predict the probability of growing a crop with the desired outcome attributes. These predictions are then compared to the known outcome metrics and accuracy of the Model determined. If the accuracy is not acceptable, the Model can be retrained using additional sample data, new algorithms, or new assumptions. This process can continue until the accuracy of the Model is optimized or has reached an acceptable level of performance.”).
Regarding dependent claim 8, the rejection of claim 1 is incorporated herein. Additionally, Johnson further discloses wherein the imaging data is obtained by using Synthetic Aperture Radar (SAR), or Light Detection and Ranging (LIDAR) via satellites (paragraph 0085, “Field data, previously described, publicly or commercially available data, including, for example: soils, topography, boundaries (CLU), organic matter; historical including crops grown and rotation; satellite, LIDAR.”).
Regarding dependent claim 11, the rejection of claim 1 is incorporated herein. Additionally, Johnson further discloses wherein the imaging data is provided for at least the two previous growing periods (paragraph 0181, “ Historical data is available for many years in the past. Data is available for all acres, globally.” Paragraph 0182, “Another data example is crop rotation data. Crop rotations have a big impact of several attributes including yield and quality. Crops that follow certain crops can be susceptible to disease, pests, fertility, soil heath, and erosion issues. The past three years rotation when selecting a field. Data is available for all fields from USDA NASS and other commercial sources and is available for all fields and without farmer assistance.”).
Regarding independent claim 13, the rejection of claim 1 applies directly. Additionally, Johnson further discloses A system for selecting an agricultural field for production of plant propagation material of a crop plant (paragraph 0175, “FIGS. 18A-C are examples of the computer dashboard user interface of the present invention.” paragraph 0127, “Models can be applied to the data in a variety of ways depending on the objective of the user, for example: Select a field with the highest probability (score) of growing a crop with the desired attribute metrics; ” claim 1, “A system for identifying optimal growing conditions to achieve specific outcomes and for predicting the probability of successfully growing a crop”), the system comprising
a) a receiving unit (paragraph 0188, “given a set of growing condition metrics, such as practices, fields, soils, timing, and other inputs score the probabilities of growing a crop with the desired outcome attribute metrics.” The entity “given” the metrics is read as the receiving unit) configured to receive historic imaging data of at least one agricultural field from at least one previous growing period (see claim 1 analysis);
b) a processing unit (paragraph 0071, “ Models are created using Artificial Intelligence/Machine Learning (AI/ML) techniques. Training techniques, applied to data and research, are used to identify the causative relationship between growing condition metrics with desired outcome attributes metrics.” The processing unit is read as the computer needed to run the machine learning models) configured to
- determine, based on the imaging data, if the crop plant had been grown on the at least one field in at least one previous growing period (see claim 1 analysis); and
- provide information if the crop plant had been grown on the at least one field in at least one previous growing period (see claim 1 analysis); and
- select, based on the provided information, at least one suitable agricultural field for production of plant propagation material, wherein the crop plant has not been grown on the at least one agricultural field for at least one previous growing period (see claim 1 analysis).
Regarding dependent claim 14, the rejection of claim 1 is incorporated herein. Additionally, Johnson further discloses a computer program element with instructions, which, when executed on computing devices of a computing environment (see Johnson claim 1, 3 and 9), is configured to carry out the steps of the method according to claim 1 (see claim 1 analysis) in a system comprising
a) a receiving unit (paragraph 0188, “given a set of growing condition metrics, such as practices, fields, soils, timing, and other inputs score the probabilities of growing a crop with the desired outcome attribute metrics.” The entity “given” the metrics is read as the receiving unit)configured to receive historic imaging data of at least one agricultural field from at least one previous growing period (see claim 1 analysis);
b) a processing unit (paragraph 0071, “ Models are created using Artificial Intelligence/Machine Learning (AI/ML) techniques. Training techniques, applied to data and research, are used to identify the causative relationship between growing condition metrics with desired outcome attributes metrics.” The processing unit is read as the computer needed to run the machine learning models) configured to
- determine, based on the imaging data, if the crop plant had been grown on the at least one field in at least one previous growing period (see claim 1 analysis); and
- provide information if the crop plant had been grown on the at least one field in at least one previous growing period (see claim 1 analysis); and
- select, based on the provided information, at least one suitable agricultural field for production of plant propagation material, wherein the crop plant has not been grown on the at least one agricultural field for at least one previous growing period (see claim 1 analysis).
Regarding dependent claim 15, the rejection of claim 14 is incorporated herein. Additionally, Johnson further discloses a computer readable medium having stored the computer program element of claim 14 (see claim 14 analysis; see also Johnson claim 1, 3 and 9)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Johnson as applied to claim 1 above, and further in view of U.S. Publication No. 2011/0113030 to Hunter et al. (hereinafter Hunter).
Regarding dependent claim 9, the rejection of claim 1 is incorporated herein. Additionally, Johnson further discloses wherein imaging data of at least two agricultural fields is provided (paragraph 0007, “ These relationships are captured in a model that can be broadly applied across numerous fields or sub-fields to predict the probably that the field will grow plants with the desired attributes, score performance, and quantify attributes.”),
wherein the selection of step d) is also based on the information on the presence of the crop plant at the at least one second field during a previous growing period (paragraph 0007, “ These relationships are captured in a model that can be broadly applied across numerous fields or sub-fields to predict the probably that the field will grow plants with the desired attributes, score performance, and quantify attributes.” paragraph 0181, “ Historical data is available for many years in the past. Data is available for all acres, globally.” Paragraph 0182, “Another data example is crop rotation data. Crop rotations have a big impact of several attributes including yield and quality. Crops that follow certain crops can be susceptible to disease, pests, fertility, soil heath, and erosion issues. The past three years rotation when selecting a field. Data is available for all fields from USDA NASS and other commercial sources and is available for all fields and without farmer assistance.”).
Johnson fails to explicitly disclose as further recited. However, Hunter discloses and wherein at least two of the agricultural fields are adjacent fields (Figure 13, elements 1206 and 1302; paragraph 0085, “The tool may also provide for identifying other fields, objects, or points of interest which may be adjacent, or near a selected field. ”), or wherein the boundaries of at least two of the agricultural fields are up to 10 km apart.
Johnson is directed toward, “The present invention identifies the optimal genetics, environment, and management practices and predicts the probability of growing a crop with the desired attributes, quantifies the attribute, scores relative performance, and identifies actions management can take to increase probability of growing plants with specific attributes (abstract).” Hunter is directed toward, “A system for aggregating data obtained from different organizations within a seed company or within multiple seed companies is provided (abstract).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, Johnson and Hunter are directed toward similar methods of endeavor of analyzing various data for crop determination. Further, one of ordinary skill in the art before the effective filing date of the claimed invention would easily understand adjacent fields can impact one another more than fields that are far away. For example if an adjacent field floods or has a fungus, it is more likely that the fields around that adjacent field will have similar conditions. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Hunter in order to take into account adjacent field data.
Regarding dependent claim 10, the rejection of claim 9 is incorporated herein. Additionally, Hunter further discloses wherein imaging data of one agricultural field and all adjacent fields is provided (Figure 13, elements 1206 and 1302; paragraph 0085, “The tool may also provide for identifying other fields, objects, or points of interest which may be adjacent, or near a selected field. ”), and wherein the selection of step d) is based on the information on the presence of the crop plant at the field and all adjacent fields during a previous growing period (paragraph 0085, “The tool may also provide for identifying other fields, objects, or points of interest which may be adjacent, or near a selected field. ”).
One of ordinary skill in the art before the effective filing date of the claimed invention would easily understand adjacent fields can impact one another more than fields that are far away. For example if an adjacent field floods or has a fungus, it is more likely that the fields around that adjacent field will have similar conditions. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Hunter in order to take into account adjacent field data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Publication No. 2021/0350478 discloses, “A computer-implemented method for recommending agricultural activities is implemented by an agricultural intelligence computer system in communication with a memory (abstract).”
U.S. Publication No. 2020/0226375 discloses, “A computer-implemented method for determining field boundaries and crop forecasts in each field is provided (abstract).”
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Courtney J. Windsor whose telephone number is (571)272-3956. The examiner can normally be reached Monday - Friday 8:00 - 4:00.
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/COURTNEY JOAN NELSON/Primary Examiner, Art Unit 2661
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