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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1:
According to the first part of the analysis, in the instant case, claims 1-9 are directed to a method, claim 10-16 are directed to using a system to perform the method, and claims 17-20 are directed to a system to implement a learning model. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Regarding claim 1:
A method for estimating an expected planting quality indicator, comprising:
receiving, via a processor, a plurality of types of data associated with planting via an agricultural planting implement;
combining, via the processor, the plurality of types of data to calculate an expected planting quality indicator;
wherein the expected planting quality indicator is calculated by: determining an optimal planting quality indicator; and
comparing the optimal planting quality value to the collected plurality of types of data associated with planting via the agricultural planting implement.
Step 2A Prong 1:
“receiving, via a processor, a plurality of types of data associated with planting via an agricultural planting implement” is directed to mental step of data gathering.
“combining, via the processor, the plurality of types of data to calculate an expected planting quality indicator” is directed to math because the processing of calculating an expected planting quality indicator (EPQ) is a classic exercise in Multivariate Data Analysis and Predictive Modeling.
“wherein the expected planting quality indicator is calculated by:
determining an optimal planting quality indicator” is directed to math because expected planting quality indicator is calculating an expected value involves mathematical models that simulate crop growth and development based on variables like temperature, soil nutrients, and moisture. Optimal implies finding the maximum and minimum value of a function. Soil and planting quality indices are created by normalizing raw data and then combining them using mathematical processes such as addition, multiplication, or weighted average processes.
“comparing the optimal planting quality value to the collected plurality of types of data associated with planting via the agricultural planting implement” is directed to math because comparing the optimal planting quality value to the collected plurality of types of data is an exercise in applied mathematics within the field of statistics, geometry, and optimization. When an agricultural implement operates, it is performing a high speed data matching calculation.
Each limitation recites in the claim is a process that, under BRI covers performance of the limitation in the mind. Nothing in the claim elements precludes the steps from practically being performed in the mind. Thus, the claim recites a mental process.
Further, the claim recites the step of "combining, via the processor, the plurality of types of data to calculate an expected planting quality indicator; wherein the expected planting quality indicator is calculated by: determining an optimal planting quality indicator; and comparing the optimal planting quality value to the collected plurality of types of data associated with planting via the agricultural planting implement” which as drafted, under BRI recites a mathematical calculation. The grouping of "mathematical concepts” in the 2019 PED includes "mathematical calculations" as an exemplar of an abstract idea. 2019 PEG Section |, 84 Fed. Reg. at 52. Thus, the recited limitation falls into the "mathematical concept" grouping of abstract ideas. This limitation also falls into the “mental process” group of abstract ideas, because the recited mathematical calculation is simple enough that it can be practically performed in the human mind, e.g., scientists and engineers have been solving the Arrhenius equation in their minds since it was first proposed in 1889.
Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. See October Update at Section I(C)(i) and (iii).
Additional Elements:
Step 2A Prong 2:
“A method for estimating an expected planting quality indicator, comprising” recited in the preamble does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“receiving, via a processor, a plurality of types of data associated with planting via an agricultural planting implement” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“combining, via the processor, the plurality of types of data to calculate an expected planting quality indicator” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“wherein the expected planting quality indicator is calculated by: determining an optimal planting quality indicator” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“comparing the optimal planting quality value to the collected plurality of types of data associated with planting via the agricultural planting implement” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
The claim is merely selecting data, manipulating or analyzing the data using math and mental process, and displaying the results.
This is similar to electric power: MPEP 2106.05(h) vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
The claim as a whole does not meet any of the following criteria to integrate the judicial exception into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Step 2B:
“A method for estimating an expected planting quality indicator, comprising” recited in the preamble does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“receiving, via a processor, a plurality of types of data associated with planting via an agricultural planting implement” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“combining, via the processor, the plurality of types of data to calculate an expected planting quality indicator” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“wherein the expected planting quality indicator is calculated by: determining an optimal planting quality indicator” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“comparing the optimal planting quality value to the collected plurality of types of data associated with planting via the agricultural planting implement” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
The claim is therefore ineligible under 35 USC 101.
Claim 10 is similar to claim 1 but recites a system for estimating an expected planting quality value, comprising: a processor; a memory and/or a non-transitory computer readable medium that stores executable instructions that, when executed by the processor, perform operations, the operations comprising the steps as in claim 1. These additional elements fail to integrate the abstract idea into a practical application. These limitations are recited at a high level of generality and do not add significantly more to the judicial exception. These elements are generic computing devices that perform generic functions. Using generic computer elements to perform an abstract idea does not integrate an abstract idea into a practical application. See 2019 Guidance, 84 Fed. Reg. at 55. Moreover, “the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.” Alice, 573 U.S. at 223; see also FairWarninglP, LLCv. latric SysInc., 839 F.3d 1089, 1096 (Fed. Cir. 2016) (citation omitted) (“[T]he use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter”).
On the record before us, we are not persuaded that the hardware of claim 10 integrates the abstract idea into a practical application. Nor are we persuaded that the additional elements are anything more than well-understood, routine, and conventional so as to impart subject matter eligibility to claim 10.
Regarding claim 17:
A system for estimating an expected planting quality value of an agricultural planting implement, the system comprising: at least one processor and at least one memory configured to implement a learning model, the learning model generated from training data, wherein the learning model is trained with a method comprising the steps of:
reviewing a plurality of data types associated with planting via the agricultural planting implement; and
identifying a classifier in the form of an expected planting quality value that corresponds to an operational quality of planting based upon the plurality of data types; and
wherein the learning model is stored on one or more non-transitory computer readable media comprising instructions comprising:
collecting, in real time, data associated with planting via the agricultural planting implement; and
generating and displaying the expected planting quality value for the collected data via a display.
Step 2A Prong 1:
“reviewing a plurality of data types associated with planting via the agricultural planting implement” is directed to mental step of identifying type of data.
“identifying a classifier in the form of an expected planting quality value that corresponds to an operational quality of planting based upon the plurality of data types” is directed to math because this process involves translating real-world agricultural scenarios into mathematical forms to analyze and predict outcomes. The identification of such a classifier relies on several mathematical disciplines: machine learning classifiers: classifiers are mathematical families of algorithms, probability and statistics: many classifiers are based on probabilistic theories.
“collecting, in real time, data associated with planting via the agricultural planting implement” is directed to mental step of data gathering.
“generating and displaying the expected planting quality value for the collected data via a display” is directed to mental step of outputting result of analyzing data.
Each limitation recites in the claim is a process that, under BRI covers performance of the limitation in the mind. Nothing in the claim elements precludes the steps from practically being performed in the mind. Thus, the claim recites a mental process.
Further, the claim recites the step of “identifying a classifier in the form of an expected planting quality value that corresponds to an operational quality of planting based upon the plurality of data types” which as drafted, under BRI recites a mathematical calculation. The grouping of "mathematical concepts” in the 2019 PED includes "mathematical calculations" as an exemplar of an abstract idea. 2019 PEG Section |, 84 Fed. Reg. at 52. Thus, the recited limitation falls into the "mathematical concept" grouping of abstract ideas. This limitation also falls into the “mental process” group of abstract ideas, because the recited mathematical calculation is simple enough that it can be practically performed in the human mind, e.g., scientists and engineers have been solving the Arrhenius equation in their minds since it was first proposed in 1889.
Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. See October Update at Section I(C)(i) and (iii).
Additional Elements:
Step 2A Prong 2:
“A system for estimating an expected planting quality value of an agricultural planting implement, the system comprising: at least one processor and at least one memory configured to implement a learning model, the learning model generated from training data, wherein the learning model is trained with a method comprising the steps of:” recited in the preamble does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“reviewing a plurality of data types associated with planting via the agricultural planting implement” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“identifying a classifier in the form of an expected planting quality value that corresponds to an operational quality of planting based upon the plurality of data types” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“wherein the learning model is stored on one or more non-transitory computer readable media comprising instructions comprising: collecting, in real time, data associated with planting via the agricultural planting implement” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“generating and displaying the expected planting quality value for the collected data via a display” is directed to insignificant activity and does not integrate the judicial exception into a practical application. See MPEP 2106.05(g).
The claim is merely selecting data, manipulating or analyzing the data using math and mental process, and displaying the results.
This is similar to electric power: MPEP 2106.05(h) vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Claim 17 recites the additional element(s) of using generic AI/ML technology, i.e. a learning model, the learning model generated from training data, wherein the learning model is trained with a method to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of the learning model merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of the learning model to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
The claim as a whole does not meet any of the following criteria to integrate the judicial exception into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Step 2B:
“A system for estimating an expected planting quality value of an agricultural planting implement, the system comprising: at least one processor and at least one memory configured to implement a learning model, the learning model generated from training data, wherein the learning model is trained with a method comprising the steps of:” recited in the preamble does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“reviewing a plurality of data types associated with planting via the agricultural planting implement” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“identifying a classifier in the form of an expected planting quality value that corresponds to an operational quality of planting based upon the plurality of data types” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“wherein the learning model is stored on one or more non-transitory computer readable media comprising instructions comprising: collecting, in real time, data associated with planting via the agricultural planting implement” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“generating and displaying the expected planting quality value for the collected data via a display” is directed to insignificant activity and does not amount to significantly more than the judicial exception in the claim. See MPEP 2106.05(g) and 2106.05(d)(ii), third list, (iv).
The claim is therefore ineligible under 35 USC 101.
Regarding claim 2, “updating a setting of the agricultural planting implement based on the expected planting quality indicator” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 3, “wherein the setting is updated automatically via the processor” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 4, “displaying the expected planting quality indicator on a display” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 5, “displaying a suggested change to one or more settings of the agricultural planting implement to improve the expected planting quality indicator” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 6, “storing the expected planting quality indicator and the plurality of types of data to a memory” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 7, “analyzing a plurality of expected planting quality indicators based upon the plurality of types of data to improve the agricultural planting implement” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 8, “wherein at least one of the plurality of types of data comprises ambient weather conditions” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 9, “wherein at least one of the plurality of types of data comprises GPS data” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 11, “wherein the processor is part of a display” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 12, “wherein the display is configured to display the expected planting quality value” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 13, “wherein the display comprises a graphical user interface” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 14, “wherein a user can make a change to one or more settings of the agricultural planting implement, via the graphical user interface, based upon the expected planting quality value” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 15, “wherein the graphical user interface comprises a map, and wherein the expected planting quality value is shown relative to a location on the map” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 16, “wherein the collected plurality of types of data and the expected planting quality value are saved in the memory and/or the non-transitory computer readable medium as a data pair comprising a location and the expected planting quality value” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 18, “wherein the expected planting quality value is stored on the at least one memory and/or the one or more non-transitory computer readable media” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 19, “wherein the instructions of the one or more non-transitory computer readable media further comprise generating and displaying, via the display, suggestions for improving the expected planting quality value” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 20, “wherein the plurality of data types comprises GPS data, ambient weather conditions, and settings of the agricultural planting implement” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Hence the claims 1-20 are treated as ineligible subject matter under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
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) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hubner et al. (US 2022/0000008 A1).
Regarding claims 1 and 10, Hubner et al. disclose a system and method for estimating an expected planting quality indicator (par. 0064: "predict the quality of furrow", par. 0091: "likely furrow quality metric"), comprising:
receiving, via a processor (control system 208, including a processor 284 and a data capture logic 314), a plurality of types of data (Fig.6, par. 0063, 0064, 0067, 0082-0083) associated with planting via an agricultural planting implement (100, 101);
combining, via the processor, the plurality of types of data to calculate an expected planting quality indicator (par. 0063, 0094-0095);
wherein the expected planting quality indicator is calculated by:
determining an optimal planting quality indicator (par. 0072, 0096: "furrow quality threshold"; par. 0067-0072: "ideal furrow characteristics"); and
comparing the optimal planting quality value to (i.e. considering/taking into account) the collected plurality of types of data associated with planting via the agricultural planting implement (par. 0154, 0155, 0177-0183).
Regarding claims 2-5, Hubner et al. disclose updating a setting of the agricultural planting implement based on the expected planting quality indicator; wherein the setting is updated automatically via the processor; displaying the expected planting quality indicator on a display; displaying a suggested change to one or more settings of the agricultural planting implement to improve the expected planting quality indicator (par. 0064, 0070, 0072, 0075, 0096).
Regarding claim 6, Hubner et al. disclose storing the expected planting quality indicator and the plurality of types of data to a memory (para. 0070, 0076).
Regarding claim 7, Hubner et al. disclose analyzing a plurality of expected planting quality indicators based upon the plurality of types of data to improve the agricultural planting implement (par. 0074, 0079).
Regarding claim 8, Hubner et al. disclose at least one of the plurality of types of data comprises ambient weather conditions (par. 0043, 0092).
Regarding claim 9, Hubner et al. disclose at least one of the plurality of types of data comprises GPS data (par. 0045, 0079).
Regarding claim 11, Hubner et al. disclose wherein the processor is part of a display (par. 0114-0115).
Regarding claim 12, Hubner et al. disclose the display is configured to display the expected planting quality value (par. 0033: "determine a metric indicative of job quality (e.g., furrow quality). This metric can be stored and/or displayed in numerous ways to the operator"; par. 0070: "The furrow quality metric can be displayed as a value, such as a score on a scale (e.g., 1-10, A-F, or percentage out of 100%, etc.). This value or rating code can then be displayed to the operator in real time").
Regarding claim 13, Hubner et al. disclose the display comprises a graphical user interface (Fig. 7, 13, par. 0062, 0070, 0072, interface 226, 450).
Regarding claim 14, Hubner et al. disclose a user can make a change to one or more settings of the agricultural planting implement, via the graphical user interface, based upon the expected planting quality value (par. 0064, 0072, 0075, 0096).
Regarding claim 15, Hubner et al. disclose the graphical user interface comprises a map, and wherein the expected planting quality value is shown relative to a location on the map (par. 0072).
Regarding claim 16, Hubner et al. disclose the collected plurality of types of data and the expected planting quality value are saved in the memory and/or the non-transitory computer readable medium as a data pair comprising a location and the expected planting quality value (par. 0070: "along with the geospatial location", 0076: "map")
Regarding claim 17, Hubner et al. disclose a system for estimating an expected planting quality value (par. 0064: "furrow quality prediction"; par. 0091: "likely furrow quality metric") of an agricultural planting implement (100, 101), the system comprising:
at least one processor (control system 208, including a processor 284 and a data capture logic 214) and at least one memory (implicit at least for the processing software; data storage 278) configured to implement a learning model ("machine learning", see blocks 528 and 534 in fig. 8 and par. 0094-0095), the learning model generated from training data (implicit for machine learning), wherein the learning model is trained with a method comprising the steps of:
reviewing a plurality of data types associated with planting via the agricultural planting implement (it is implicit to train a machine learning model with historic data of the types to be processed by the model; and that it keeps learning with new data bunches, par. 0094-0095); and
identifying a classifier in the form of an expected planting quality value ("furrow quality prediction", par. 0064; "likely furrow quality metric", par. 0091) that corresponds to an operational quality of planting based upon the plurality of data types (a furrow receives the planted seeds in a desired depth and soil environment and must be properly closed such that furrow characteristics represent an operational quality of planting);
and wherein the learning model is stored on one or more non-transitory computer readable media (implicit for the processor)
comprising instructions comprising:
collecting, in real time, data associated with planting via the agricultural planting implement (par. 0065, 0067, 0092-0093); and
generating and displaying the expected planting quality value for the collected data via a display (generating: par. 0073, 0094-0095; displaying: par. 0033: "determine a metric indicative of job quality (e.g., furrow quality). This metric can be stored and/or displayed in numerous ways to the operator"; par. 0070: "The furrow quality metric can be displayed as a value, such as a score on a scale (e.g., 1-10, A-F, or percentage out of 100%, etc.). This value or rating code can then be displayed to the operator in real time'').
Regarding claim 18, Hubner et al. disclose wherein the expected planting quality value is stored on the at least one memory and/or the one or more non-transitory computer readable media (par. 0070, 0072, 0076).
Regarding claim 19, Hubner et al. disclose wherein the instructions of the one or more non-transitory computer readable media further comprise generating and displaying, via the display, suggestions for improving the expected planting quality value (par. 0064, 0072, 0075, 0096).
Regarding claim 20, Hubner et al. disclose wherein the plurality of data types comprises GPS data (242, par. 0045, 0079), ambient weather conditions (par. 0043, 0092), and settings of the agricultural planting implement (par. 0092: "current operating parameter data (e.g., current depth settings, current seed delivery settings, current downforce exertion, current substance delivery settings, etc.)”).
Other Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Johnson et al. (US 2022/0142040 A1) disclose a system for controlling the operation of a residue removal device of a seed-planting implement may include a residue removal device configured to remove residue from a path of the seed-planting implement. The system may also include a sensor configured to capture data indicative of a residue characteristic associated with a portion of the field within a detection zone positioned forward of the residue removal device relative to a direction of travel of the seed-planting implement. Furthermore, the system may include a controller communicatively coupled to the sensor. As such, the controller may be configured to monitor the residue characteristic associated with the portion of the field within the detection zone based on data received from the sensor. Additionally, the controller may be further configured to control the operation of the residue removal device based on the monitored residue characteristic.
Van De Woestyne et al. (US 11,589,496) disclose a map generator generates a replanting map designating a particular area in a field in which it is recommended to add additional seeds. A function of the agricultural machine is then controlled based at least in part on the replanting map so as to facilitate planting additional seeds in the designated particular area.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN H LE whose telephone number is (571)272-2275. The examiner can normally be reached on Monday-Friday from 7:00am – 3:30pm Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A. Turner can be reached on (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOHN H LE/Primary Examiner, Art Unit 2857