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 .
Acknowledgments
Claims 1-17 are pending.
Applicant provide information disclosure statement.
Continuation
This application is a continuation application of abandoned U.S. application no. 17480099 filed on September 20, 2021. See MPEP §201.07. In accordance with MPEP §609.02 A. 2 and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Application. Also in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered ‘of record’ in the Parent Application are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the Parent Application need not be resubmitted in this application unless Applicants desire the information to be printed on a patent issuing from this application. See MPEP §609.02 A. 2. Finally, Applicants are reminded that the prosecution history of the Parent Application is relevant in this application. See e.g., Microsoft Corp. v. Multi-Tech Sys., Inc., 357 F.3d 1340, 1350, 69 USPQ2d 1815, 1823 (Fed. Cir. 2004) (holding that statements made in prosecution of one patent are relevant to the scope of all sibling patents).
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 1-17 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 than the judicial exception itself.
Regarding Step 1 of subject matter eligibility for whether the claims fall within a statutory category (See MPEP 2106.03), claims 1-17 are directed to non-transitory computer-readable medium, system, and method.
Regarding step 2A-1, Claims 1-17 recite a Judicial Exception. Exemplary independent claim 1 and similarly claims 7 and 13 recite the limitations of
Partitioning…the agricultural field into hexagonal grid cells; measuring…a set of agricultural inputs for each of the respective grid cells, wherein the set of agricultural inputs includes both direct costs and indirect costs; receiving…market data corresponding to a crop planted on the agricultural field; computing…an agricultural yield for each of the respective grid cells; computing…a gross revenue for each of the respective grid cells by analyzing a respective cost of each of the agricultural inputs, the market data and the respective agricultural yield of each of the respective grid cells; computing…a net revenue for each of the respective grid cells by subtracting at least the direct costs and the indirect costs from the respective gross revenue; …generating…an analysis report including the gross revenue, the agricultural yield, and the net revenue for each of the respective selected grid cells, wherein the generating includes processing the respective selected grid cells using a…model trained using labeled yield data and revenue data to generate one or more recommendations, each corresponding to a respective one of the respective grid cells; and transmitting the analysis report …wherein the analysis report includes recommendations for adding or subtracting one or more agricultural inputs based on the net revenue and as-applied data of the respective grid cells.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation cover concepts of partitioning, measuring, receiving, computing, processing, generating, and transmitting data. The claim limitations fall under the abstract idea grouping of mental process, because the limitations can be performed in the human mind, or by a human using a pen and paper. For example, but for the language of a system and non-transitory computer-readable medium, the claim language encompasses simply partitioning a field into cells, measuring inputs for the field, receiving market data, computing a field yield/gross revenue/net revenue, generating an analysis report, processing data using a model, generating recommendations, and transmitting that data. These steps are mere data manipulation steps that do not require a computer. For example, a user is able to partition a field mentally or with pen and paper. In addition, a user is able to compute a yield for the field as well as a gross and net revenue. A user is also able to determine recommendations for the field. The claimed invention is merely automating a manual process.
The claims also recite yield, net, and gross revenue. The claimed invention clearly teaches managing a field with respect to business variables. Applicant’s specification also recites economic analysis of a field as seen in para 0006. These make the claims fall in the abstract idea grouping of certain methods of organizing human activity (fundamental economic principles or practices; business relations). It is clear the limitations recite these abstract idea groupings, but for the recitations of generic computer components. The mere nominal recitations of generic computer components does not take the limitations out of the mental process and certain methods of organizing human activity grouping. The claims are focused on the combination of these abstract idea processes.
Regarding step 2A-2- This judicial exception is not integrated into a practical application, and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements of processor, graphical user interface, computing device, computing system, memories, machine learning model, and non-transitory computer readable medium.
These components are recited at a high level of generality, and merely automate the steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component.
The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer components or software. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Further, the claims do not provide for recite any improvements to the functioning of a computer, or to any other technology or technical field; applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; applying the judicial exception with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; or applying or using 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.
The dependent claims have the same deficiencies as their parent claims as being directed towards an abstract idea, as the dependent claims merely narrow the scope of their parent claims. For example, the dependent claims further describe additional field variables such as an agricultural implement. In addition, the dependent claims further recite additional steps that happen to the data such as standardizing the data. In addition, the dependent claims further recite what the recommendations are for, such as improving profitability.
Regarding step 2B the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because claim 1 recites
Method, however method is not considered an additional element.
Claim 1 further recites processors, machine learning model, receiving a user selection of one or more of the grid cells via a graphical user interface, computing device, display,
Claim 7 recites computing system, processors, memories, machine learning model, computing device
Claim 13 recites non-transitory computer readable medium, computer, machine learning model, computing device
When looking at these additional elements individually, the additional elements are purely functional and generic the Applicant specification states general purpose computer configurations as seen in para 0023.
When looking at the additional elements in combination, the Applicant’s specification merely states general purpose computer configurations as seen in para 0023. The computer components add nothing that is not already present when the steps are considered separately. See MPEP 2106.05
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, recitations of generic computer structure to perform generic computer functions that are used to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself.
Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-17 are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Perry (US20190050948A1) in further view of Rowan (US20180132423A1) in further view of O’Mahony (20170227370).
Regarding claims 1, 7, and 13, Perry teaches
a computer-implemented method for providing a high-resolution analysis of an agricultural field, comprising (See abstract-A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production.) This teaches analysis of an agriculture crop that includes a method as seen here. (See para 0005-In another embodiment, a system executes a method for crop productivity optimization).
A computing system for providing a high-resolution analysis of an agricultural field, comprising: one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the computing system to (See abstract-A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production.) This teaches analysis of an agriculture crop with respect to a system. (See figure1)
A non-transitory computer readable medium containing program instructions that when executed, cause a computer to (See para 0182-The storage device 1008 is any non-transitory computer-readable storage medium, such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory 1006 holds instructions and data used by the processor 1002. The pointing device 1014 may be a mouse, track ball, or other type of pointing device, and is used in combination with the keyboard 1010 to input data into the computer 1000.) This shows a memory.
partitioning, via one or more processors, the agricultural field into…grid cells (See fig. 5) (See para 0164-FIG. 5 illustrates an example land plot 500 partitioned into one or more planting regions 515. The land plot 500 includes features such as a house 505 and a river 510 that may impact the crop production of neighboring planting regions. As shown in FIG. 5, one or more planting regions may be part of a single field (e.g., one field is split into four planting regions: a first planting region 515A, a second planting region 515B, a third planting region 515C, and a fourth planting region 515D). The one or more planting regions are associated with one or more sets of data describing the conditions, composition, and other characteristics that may impact crop production and that may vary from planting to region to planting region (even among planting regions within a same field).) This shows that the system partitions the crop into different cells to run analysis as seen in fig. 6. In another interpretation, the agriculture field is partitioned into different grid cell columns and rows by the databases as seen in fig. 2 and 3.
measuring, via one or more processors, a set of agricultural inputs for each of the respective grid cells Examiner interprets agricultural inputs to be any data related to the crop. (See para 0010- In an embodiment, accessing field information collected from the sensors includes one or more of: soil temperature, air temperature, soil moisture, leaf temperature, leaf wetness, and spectral data over multiple wave length bands reflected from or absorbed by ground. In an embodiment, field information collected from the sensors is used to compute additional field information, including one or more of: a ratio of soil to air temperature, a ratio of leaf to air temperature, a soil wetness index, a number of cumulative growing degree days, a chlorophyll content, evapotranspiration, a daily light integral, a daily minimum temperature, a daily mean temperature, a daily maximum temperature, and a change in the normalized difference vegetation index. ) This teaches that sensors measure the agricultural inputs.
Even though Perry teaches agricultural inputs that are measured, it doesn’t teach direct and indirect costs that are measured, however Perry, in other section, does teach direct and indirect costs as agricultural inputs as seen here wherein the set of agricultural inputs includes both direct costs and indirect costs (See para 0041-0054- Cost databases describing seed prices, commodity prices, prices of products or treatments, machinery and repair costs, labor costs, fuel and electricity costs, land costs, insurance costs, storage costs, and transportation costs) This teaches direct costs such as product prices and seed costs. This also teaches indirect costs such as insurance costs.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the direct and indirect costs also be measured. This would make the system of Perry more sophisticated since it would not have to rely on historical data from databases to receive information. Perry would be able to calculate these prices with its own algorithm and this would be updated based on price changes.
Perry further teaches receiving, via one or more processors, market data corresponding to a crop planted on the agricultural field (See para 0041-0054- Cost databases describing seed prices, commodity prices, prices of products or treatments, machinery and repair costs, labor costs, fuel and electricity costs, land costs, insurance costs, storage costs, and transportation costs) This also teaches receiving market data such as commodity prices for the agricultural crop.
computing, via one or more processors, an agricultural yield for each of the respective grid cells (See para 0162-The output of the crop prediction models applied by the crop prediction module 425 may be formatted in a variety of ways. In some embodiments, the crop prediction module 425 outputs a measure of crop productivity for a particular set of farming operations. For example, the crop prediction module 425 can output a numerical value representing a predicted crop yield) This teaches determining a yield for the agricultural crops. This is with respect to grid cells as seen in figure 5.
In addition, Perry teaches grid cells…each of the agricultural inputs, the market data and the respective agricultural yield of each of the respective grid cells …indirect costs The system looks at agricultural inputs such as sensor data, product prices, and insurance costs. Insurance costs correspond to indirect costs. The system looks at market data such as commodity costs. The system also deals with agricultural yield. These variables are taught above.
However, Perry doesn’t teach that these values are used to determine a gross revenue and net revenue, however Rowan teaches computing, via one or more processors, a gross revenue…analyzing a respective cost (See para 0336-If input selects the DEKALB brand and the DKC60-6 hybrid for seeds, then the GUI may also display detailed information for the selected seed hybrid and brand in an information window 1740 under program control. The depicted example illustrates that for the DEKALB brand and the DKC60-6 seed hybrid, an estimated yield in bushels per acre is 205 (as shown in a text box 1742), a count of seed bags is 45 (as shown in a text box 1744), a seed cost per acre is $147 (as shown in a text box 1746), and an estimated gross revenue per acre is $685 (as shown in a text box 1748)) This teaches the system computes a gross revenue for the crop. This is done by analyzing prices/cost for the seed brand and seed hybrid as seen in fig. 17.
computing, via one or more processors, a net revenue for each of the respective grid cells by subtracting at least the direct costs…from the respective gross revenue (See para 0396-By focusing on minimizing the seed cost, new recommendations may provide suggestions for achieving the highest possible net revenue and may balance the value of additional yield against the cost of seed required to achieve that yield. ) This teaches the system determines a high net revenue. This is done by subtracting a minimized seed cost from the gross revenue to get a net revenue. Net revenue formula is the gross revenue minus any deductions such as seed price which is a direct cost. Perry already teaches indirect costs as seen above.
Perry and Rowan are analogous art because they are from the same problem-solving area of management of agricultural crops. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Perry’s invention by incorporating the method of Rowan because Perry would also be able to use direct and indirect costs to determine a net revenue and also determine a gross revenue. This information would be useful to the user of Perry who is trying to maximize profit from the crops. This would make the system of Perry more sophisticated since the machine learning prediction model would be able to determine these additional variables and display them to the user.
Perry further teaches in response to receiving a user selection of one or more of the grid cells via a graphical user interface, generating, via one or more processors, an analysis report…the agricultural yield…for each of the respective selected grid cells (See fig. 6)(See para 0108-Likewise, if the crop prediction system 125 receives a request for a crop production prediction from a grower client device 102 for a particular field, the crop prediction engine 155 can request information associated with the particular field via the database interface module 150) (See para 0135-The crop prediction module 425 receives a request to generate an optimized crop production prediction for a field (which can include multiple fields or plots of land, adjacent or otherwise) and applies one or more crop prediction models data associated with the field to determine a set of farming operations to optimize a crop production for the field. In one embodiment, the request is received from a grower via client device 102. In other embodiments, the request is received via a GUI generated by the interface module 130) This shows a grower selects a field/crop with respect to the partitioned crop grid cells and an analysis report is run on the crop as seen in item 615 is figure 6. This includes yield data.
However Perry doesn’t teach gross and net revenue, however Rowan teaches the gross revenue…and the net revenue (See para 0336-If input selects the DEKALB brand and the DKC60-6 hybrid for seeds, then the GUI may also display detailed information for the selected seed hybrid and brand in an information window 1740 under program control. The depicted example illustrates that for the DEKALB brand and the DKC60-6 seed hybrid, an estimated yield in bushels per acre is 205 (as shown in a text box 1742), a count of seed bags is 45 (as shown in a text box 1744), a seed cost per acre is $147 (as shown in a text box 1746), and an estimated gross revenue per acre is $685 (as shown in a text box 1748)) (See para 0396-By focusing on minimizing the seed cost, new recommendations may provide suggestions for achieving the highest possible net revenue and may balance the value of additional yield against the cost of seed required to achieve that yield. )
Perry and Rowan are analogous art because they are from the same problem-solving area of management of agricultural crops. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Perry’s invention by incorporating the method of Rowan because Perry would also be able to use direct and indirect costs to determine a net revenue and also determine a gross revenue. This information would be useful to the user of Perry who is trying to maximize profit from the crops. This would make the system of Perry more sophisticated since the machine learning prediction model would be able to determine these additional variables and display them to the user.
Perry further teaches wherein the generating includes processing the respective selected grid cells using a machine learning model (See para 0109- The crop prediction engine 155 trains and applies crop prediction models by performing one or more machine learning operations to determine predictions for crop production and corresponding sets of farming operations that result in the predicted crop production.) This teaches machine learning is used with respect to the crops such as those in figure 5.
trained using labeled yield data…to generate one or more recommendations, each corresponding to a respective one of the respective grid cells; (See para 0109- The crop prediction engine 155 trains and applies crop prediction models by performing one or more machine learning operations to determine predictions for crop production and corresponding sets of farming operations that result in the predicted crop production.) (See para 0115- The training module 410 trains crop prediction models by performing machine learning operations on training data accessed by the crop prediction system 125, for instance from the geographic database 135 and the agricultural database 140.)(See para 0176- A crop prediction system 125 accesses crop growth information 805 from one or more sources. The crop growth information can include geographic information, agricultural information, and crop production information.) This shows the machine learning model is trained with labeled yield data/crop production information. This is used to generated farming operation recommendation to help the crop.
However it is not clear that the machine learning is trained with revenue data, however Rowan teaches revenue data (See para 0336-If input selects the DEKALB brand and the DKC60-6 hybrid for seeds, then the GUI may also display detailed information for the selected seed hybrid and brand in an information window 1740 under program control. The depicted example illustrates that for the DEKALB brand and the DKC60-6 seed hybrid, an estimated yield in bushels per acre is 205 (as shown in a text box 1742), a count of seed bags is 45 (as shown in a text box 1744), a seed cost per acre is $147 (as shown in a text box 1746), and an estimated gross revenue per acre is $685 (as shown in a text box 1748))
Perry and Rowan are analogous art because they are from the same problem-solving area of management of agricultural crops. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Perry’s invention by incorporating the method of Rowan because Perry would also be able to use revenue data in the machine learning process. This information would make the machine learning process more robust since it would take into account revenue data when determining farming operation recommendations. This would allow the user of Perry to customize the crop optimization to be focused on revenue.
Perry further teaches and transmitting the analysis report to a computing device for display to the user, wherein the analysis report includes recommendations for adding or subtracting one or more agricultural inputs based on… and as-applied data of the respective grid cells. (See para 0168- The crop prediction system 125 transmits the identified set of farming operations corresponding to the optimized predicted crop production to the grower client device 102 to modify a user interface of the grower client device to display the identified set of farming operations. The grower 610 in turn can perform the identified set of farming operations on the field 605A.) This shows transmitting the report with the farming recommendations to the display device of the user. The recommendations can include adding agricultural inputs such as additional crop treatment (i.e. treatment costs see para 0121-0135). This is based on as applied data such as historical data of the grid cells with respect to items 114, 116, 112, 135, and 140.
However the recommendations are not based on a net revenue, however Rowan teaches net revenue (See para 0396-By focusing on minimizing the seed cost, new recommendations may provide suggestions for achieving the highest possible net revenue and may balance the value of additional yield against the cost of seed required to achieve that yield. )
Perry and Rowan are analogous art because they are from the same problem-solving area of management of agricultural crops. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Perry’s invention by incorporating the method of Rowan because Perry would also be able to use net revenue data in the machine learning process. This information would make the machine learning process more robust since it would take into account net revenue data when determining farming operation recommendations. This would allow the user of Perry to customize the crop optimization to be focused on net revenue.
In addition, even though Perry teaches grid cells, it doesn’t teach hexagonal grid cells, however O’Mahony teaches hexagonal grid cells (See fig. 6)
Perry and O’Mahony are analogous art because they are from the same problem-solving area of dividing an area in zones for analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Perry’s invention by incorporating the method of O’Mahony because Perry would also be able to generate hexagonal cells in addition to the land partitioning as already taught in fig. 5. This would make the system of Perry more sophisticated since it would provide for another analysis type when determining recommendations. Hexagonal analysis is also better for spatial analysis of the crops.
Regarding claims 2, 8, and 14, Perry further teaches
wherein measuring the set of agricultural inputs for each of the respective grid cells includes receiving as-applied data from an agricultural implement. (See para 0010- In an embodiment, accessing field information comprises collecting the field information from one or more sensors located at the first portion of land. In an embodiment, accessing field information collected from the sensors includes one or more of: soil temperature, air temperature, soil moisture, leaf temperature, leaf wetness, and spectral data over multiple wave length bands reflected from or absorbed by ground. In an embodiment, field information collected from the sensors is used to compute additional field information, including one or more of: a ratio of soil to air temperature, a ratio of leaf to air temperature, a soil wetness index, a number of cumulative growing degree days, a chlorophyll content, evapotranspiration, a daily light integral, a daily minimum temperature, a daily mean temperature, a daily maximum temperature, and a change in the normalized difference vegetation index. In an embodiment, the one or more sensors include thermometers, barometers, weather detection sensors, soil composition sensors, soil moisture sensors, hygrometers, pyranometers, pyrheliometers, spectrometers, spectrophotometers, spectrographs, spectral analyzers, refractometers, spectroradiometers, radiometers, electrical conductivity sensors, and pH sensors.) This shows receiving as-applied data (i.e. data that applied to the crop) from an agriculture implement such as a sensor in the crop.
Regarding claims 3, 9, and 15, Perry further teaches
further comprising: standardizing the as-applied data from the agricultural implement. Perry teaches standardizing data by way of the normalization module. (See para 0103- The normalization module 145 receives data in a variety of formats from the external databases 112, sensor data sources 114, image data sources 116, or other data sources and normalizes the data for storage in the geographic database 135 and the agricultural database 140 and use by the crop prediction engine 155. ) (See para 0104- For a particular type of data, the normalization module 145 selects a common format, normalizes received data of the particular type into the common format, and stores the normalized data within the geographic database 135 and the agricultural database 140)
Regarding claims 4, 10, and 16, Perry, Rowan, and O’Mahony teach the limitations of claims 1, 7, and 13 however Perry further teaches
displaying a plurality of the grid cells in a graphical user interface (See fig. 6) This shows the different grid cells are shown with respect to a mobile device to the grower. The grower has a user interface (See para 0135- In other embodiments, the request is received via a GUI generated by the interface module 130 )
and causing, in response to a user selection of one of the plurality of grid cells, at least one of the set of agricultural inputs, the gross revenue, the agricultural yield, and the net revenue corresponding to the selected one of the plurality of grid cells to be displayed in the graphical user interface. (See fig. 6) With respect to the grower request of selecting a grid cell to analyze, a yield is displayed to the grower.
Regarding claims 5, 11, and 17, Perry, Rowan, and O’Mahony teach the limitations of claims 1, 7, and 13 however Perry further teaches
Determining…a plurality of the grid cells of the agricultural field, that the agricultural field should not be planted. One of the field recommendations the system determines is not to plant as seen here (See para 0012- The second set of operation can include one or more of: a seeding rate operation, a seeding date range operation, an operation to not plant a crop)
However this is not based on a net revenue, however Rowan teaches based on the respective net revenue (See para 0396-By focusing on minimizing the seed cost, new recommendations may provide suggestions for achieving the highest possible net revenue and may balance the value of additional yield against the cost of seed required to achieve that yield. )
Perry and Rowan are analogous art because they are from the same problem-solving area of management of agricultural crops. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Perry’s invention by incorporating the method of Rowan because Perry would also be able to use net revenue data in the machine learning process. This information would make the machine learning process more robust since it would take into account net revenue data when determining farming operation recommendations. This would allow the user of Perry to customize the crop optimization to be focused on net revenue.
Regarding claims 6 and 12, Perry, Rowan, and O’Mahony teach the limitations of claims 1 and 7, however Perry further teaches
further comprising: analyzing the respective set of agricultural inputs, the respective agricultural yield…to determine a recommendation for improving profitability of the grid cells. (See figure 1) The crop prediction system analyzes agricultural inputs such as data from external databases, agricultural/geographic databases and sensors. These include yield data as seen here (See para 0138- the field parameters are retrieved from the geographic database 135, the agricultural database 140, or an external source (such as an entity of FIG. 1). Continuing with the previous example, after the farmer requests prediction information, the crop prediction engine 155 can access historic sunlight information for the field portion from the geographic database 135, crop yield information for similar fields from the agricultural database 140, and an expected future rainfall for the field portion from a weather database. ) This information is used to improve profit as seen here (See para 0162- For example, the crop prediction module 425 can output a numerical value representing a predicted crop yield; a probability distribution (e.g., a 20% chance of a decrease in crop production and an 80% chance of an increase in productivity); a map overlay representing an area of land including a planting region corresponding to a request showing management zones of application of one or more farming operations and a predicted productivity corresponding to the management zones; a predicted cost/profit ratio per acre; a yield or profitability delta relative to similar or nearby groups of fields; a yield or profitability delta relative to historical practices or productivity of the same planting region; and the like.)
However this is not based on a net revenue, however Rowan teaches the respective net revenue (See para 0396-By focusing on minimizing the seed cost, new recommendations may provide suggestions for achieving the highest possible net revenue and may balance the value of additional yield against the cost of seed required to achieve that yield. )
Perry and Rowan are analogous art because they are from the same problem-solving area of management of agricultural crops. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Perry’s invention by incorporating the method of Rowan because Perry would also be able to use net revenue data in the machine learning process. This information would make the machine learning process more robust since it would take into account net revenue data when determining farming operation recommendations. This would allow the user of Perry to customize the crop optimization to be focused on net revenue.
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Basso (US20170270616A1) Discloses methods and related systems for precision crop modeling and management using the same.
Xu (US20180046735A1) Discloses computer systems that are useful in agriculture and climatology. The disclosure is also in the technical field of computer systems that are programmed or configured to generate management zones for agricultural fields based on digital historical yield map data, pipelined data processing, and computer-implemented data recommendations for use in agriculture.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MUSTAFA IQBAL whose telephone number is (469)295-9241. The examiner can normally be reached Monday Thru Friday 9:30am-7:30 CST.
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, Beth Boswell can be reached at (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MUSTAFA IQBAL/Primary Examiner, Art Unit 3625