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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/16/2025 has been entered.
Response to Arguments
Applicant’s arguments, see Remarks, filed 04/16/2025, with respect to the rejection(s) of claims 1 and 20 under 35 U.S.C.103 in view of Ruff (US 20190057461 A1) and Morrison (US 11140813 B1) have been fully considered and are persuasive in light of the amendments. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Ruff (US 20190057461 A1), Wu (US 20210365037 A1), and Morrison (US 11140813 B1).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 20 is indefinite in that it fails to point out what is included or excluded by the claim language. Limitations C and D both contain the word “optionally” so it is unclear if the limitations are being claimed or not. For the purposes of examination, the limitations will be read broadly and will not be required in order for prior art to teach the claim as a whole.
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.
Claims 1, 3-5 and 7-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ruff (US 20190057461 A1) in view of Wu (US 20210365037 A1) and Morrison (US 11140813 B1).
Regarding claim 1,
Ruff teaches,
A computer-implemented method for generating an application map for treating a field with an agricultural equipment comprising the following steps: (Para. [0003] teaches “The present disclosure relates to digital computer modeling and tracking of agricultural fields. Specifically, the present disclosure relates to modeling benefits to an agricultural field of performing particular practices, identifying locations for implementing trials of the particular practices, and tracking the performance of the particular practices.”)
a) providing a land cover map relating to a field to be treated; (Para. [0079] teaches “Data types may include field boundaries, yield maps, as-planted maps, soil test results, as-applied maps, and/or management zones, among others.”)
b) receiving master data selected from the group consisting of: regulatory data, machine data, field data, elevation data; (Para. [0079] teaches “Data types may include field boundaries, yield maps, as-planted maps, soil test results, as-applied maps, and/or management zones, among others.” Para. [0086] teaches “The performance instructions 216 may be programmed to communicate via the network(s) 109 to back-end analytics programs executed at agricultural intelligence computer system 130 and/or external data server computer 108 and configured to analyze metrics such as yield, yield differential, hybrid, population, SSURGO zone, soil test properties, or elevation, among others.”)
c) determining buffer zones as a further layer to the land cover map based on the master data; (Para. [0182] teaches “The system 130 is therefore configured to identify certain boundaries or other problematic areas of the fields that will not participate in prescribed experiments, and further determine specific strips or squares, with buffer areas in between, that will participate in prescribed experiments.”)
Ruff does not explicitly teach,
wherein the buffer zones are automatically adjusted to comply with the regulatory data, machine data, field data, and/ or elevation data;
Wu teaches,
wherein the buffer zones are automatically adjusted to comply with the regulatory data, machine data, field data, and/ or elevation data; (Para. [0010] “an automatic identification method, wherein the automatic driving system acquires the image information around a vehicle in real time, and updates the area and the boundary of the farmland identified by the image identification system based on the acquired image information, so as to provide technical support for accurate motion of the vehicle.” Para. [0077] teaches “The area in the farmland is divided into at least one non spraying area 100c, at least one spraying area 200c and at least one farmland boundary area 300c. The non spraying area 100c represents the area where the crops have not been sprayed with pesticide, the spraying area 200c represents the area where the crops have been sprayed with pesticide, and the farmland boundary 300b is ridge that separating crops in the farmland, an outer boundary of the farmland, and the area where there are obstacles in the farmland.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ruff wherein the buffer zones are automatically adjusted to comply with the regulatory data, machine data, field data, and/ or elevation data such as that of Wu.
One of ordinary skill would have been motivated to modify Ruff, because according to para. [0002] of Wu “When the agricultural machinery is in motion on the farmland, it is necessary to adjust an operation route of the agricultural machinery according to an operation condition of the farmland”
The combination of Ruff and Wu does not explicitly teach,
d) initiating the generation of, and/or generating an application map specifying areas for treating the field with an agricultural equipment, wherein the application map is based on the buffer zones.
Morrison teaches,
d) generating an application map specifying one or more sensitive areas for treating the field with an agricultural equipment, wherein the application map is based on the buffer zones with one or more treatment measures via a guided agricultural equipment (Abstract teaches "Additional sensors (camera(s), lidar, IMU, GPS, etc.) may be fused with radar returns to generate maps having associated moisture content, surface temperature, ambient light levels, additional indications of vegetative health (as may be determined by machine learned algorithms), etc. Such vegetative health maps may be provided to a user who, in turn, may indicate additional areas for the vegetative health device to scan or otherwise used to recommend and/or perform treatments.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ruff and Wu generating an application map specifying one or more sensitive areas for treating the field with an agricultural equipment, wherein the application map is based on the buffer zones with one or more treatment measures via a guided agricultural equipment such as that of Morrison.
One of ordinary skill would have been motivated to modify the combination of Ruff and Wu, because according to Col. 30 Ln(s). [30-38] of Morrison “Further, since the maps are updated from time to time, impact of such treatments can be determined for adjusting recommended treatments (increasing or decreasing watering, increasing or decreasing seed, fertilizer, pesticide, herbicide, etc.). By creating a three-dimensional vegetative health map of an area as described by the processes and systems described herein, landscapers, caretakers, superintendents, and managers can ensure that all areas of vegetation remain optimally healthy.”
Regarding claim 3,
The combination of Ruff, Wu, and Morrison teaches the computer-implemented method according to claim 1.
Ruff further teaches,
a) providing a land cover map relating to a field to be treated; (Para. [0079] teaches “Data types may include field boundaries, yield maps, as-planted maps, soil test results, as-applied maps, and/or management zones, among others.”)
b) receiving master data selected from the group consisting of: regulatory data, machine data, field data, and elevation data; (Para. [0079] teaches “Data types may include field boundaries, yield maps, as-planted maps, soil test results, as-applied maps, and/or management zones, among others.” Para. [0086] teaches “The performance instructions 216 may be programmed to communicate via the network(s) 109 to back-end analytics programs executed at agricultural intelligence computer system 130 and/or external data server computer 108 and configured to analyze metrics such as yield, yield differential, hybrid, population, SSURGO zone, soil test properties, or elevation, among others.”)
d) receiving validation information selected from the group consisting of: (i) validation information relating to the field to be treated or relating to the land cover map, (ii) validation information relating to master data selected from the group consisting of regulatory data, machine data, field data, elevation data, and (iii) validation information relating to preliminary buffer zones; (Para. [0106] teaches “The preconfigured agronomic model may have been cross validated to ensure accuracy of the model. Cross validation may include comparison to ground truthing that compares predicted results with actual results on a field, such as a comparison of precipitation estimate with a rain gauge or sensor providing weather data at the same or nearby location or an estimate of nitrogen content with a soil sample measurement”)
Ruff does not explicitly teach,
and e) initiating the generation of, and/or generating an application map specifying areas for treating the field with an agricultural equipment, wherein the application map is based on the buffer zones.
Morrison teaches,
and f) (312) initiating the generation of, and/or generating an application map specifying areas for treating the field with an agricultural equipment, wherein the application map is based on the buffer zones. (Abstract teaches "Additional sensors (camera(s), lidar, IMU, GPS, etc.) may be fused with radar returns to generate maps having associated moisture content, surface temperature, ambient light levels, additional indications of vegetative health (as may be determined by machine learned algorithms), etc. Such vegetative health maps may be provided to a user who, in turn, may indicate additional areas for the vegetative health device to scan or otherwise used to recommend and/or perform treatments.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ruff, Wu, and Morrison with initiating the generation of, and/or generating an application map specifying areas for treating the field with an agricultural equipment, wherein the application map is based on the buffer zones such as that of Morrison.
One of ordinary skill would have been motivated to modify the combination of Ruff, Wu, and Morrison, because according to Col. 30 Ln(s). [30-38] of Morrison “Further, since the maps are updated from time to time, impact of such treatments can be determined for adjusting recommended treatments (increasing or decreasing watering, increasing or decreasing seed, fertilizer, pesticide, herbicide, etc.). By creating a three-dimensional vegetative health map of an area as described by the processes and systems described herein, landscapers, caretakers, superintendents, and managers can ensure that all areas of vegetation remain optimally healthy.”
Regarding claim 4,
The combination of Ruff, Wu, and Morrison teaches the computer-implemented method according to claim 1.
Ruff further teaches,
wherein the method comprises the following steps:
a) providing a land cover map relating to a field to be treated; (Para. [0079] teaches “Data types may include field boundaries, yield maps, as-planted maps, soil test results, as-applied maps, and/or management zones, among others.”)
b) receiving master data selected from the group consisting of: regulatory data, machine data, field data, elevation data, (Para. [0079] teaches “Data types may include field boundaries, yield maps, as-planted maps, soil test results, as-applied maps, and/or management zones, among others.” Para. [0086] teaches “The performance instructions 216 may be programmed to communicate via the network(s) 109 to back-end analytics programs executed at agricultural intelligence computer system 130 and/or external data server computer 108 and configured to analyze metrics such as yield, yield differential, hybrid, population, SSURGO zone, soil test properties, or elevation, among others.”)
c) receiving validation information relating to the field to be treated or relating to the land cover map, (Para. [0106] teaches “The preconfigured agronomic model may have been cross validated to ensure accuracy of the model. Cross validation may include comparison to ground truthing that compares predicted results with actual results on a field, such as a comparison of precipitation estimate with a rain gauge or sensor providing weather data at the same or nearby location or an estimate of nitrogen content with a soil sample measurement”)
d) initiating the determination of, and/or determining buffer zones as a further layer to the land cover map based on the master data and based on the validation information; (Para. [0182] teaches “The system 130 is therefore configured to identify certain boundaries or other problematic areas of the fields that will not participate in prescribed experiments, and further determine specific strips or squares, with buffer areas in between, that will participate in prescribed experiments.”)
Ruff does not explicitly teach,
and e) initiating the generation of, and/or generating an application map specifying areas for treating the field with an agricultural equipment, wherein the application map is based on the buffer zones.
Morrison teaches,
and e) initiating the generation of, and/or generating an application map specifying areas for treating the field with an agricultural equipment, wherein the application map is based on the buffer zones. (Abstract teaches "Additional sensors (camera(s), lidar, IMU, GPS, etc.) may be fused with radar returns to generate maps having associated moisture content, surface temperature, ambient light levels, additional indications of vegetative health (as may be determined by machine learned algorithms), etc. Such vegetative health maps may be provided to a user who, in turn, may indicate additional areas for the vegetative health device to scan or otherwise used to recommend and/or perform treatments.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ruff, Wu, and Morrison with initiating the generation of, and/or generating an application map specifying areas for treating the field with an agricultural equipment, wherein the application map is based on the buffer zones such as that of Morrison.
One of ordinary skill would have been motivated to modify the combination of Ruff, Wu, and Morrison, because according to Col. 30 Ln(s). [30-38] of Morrison “Further, since the maps are updated from time to time, impact of such treatments can be determined for adjusting recommended treatments (increasing or decreasing watering, increasing or decreasing seed, fertilizer, pesticide, herbicide, etc.). By creating a three-dimensional vegetative health map of an area as described by the processes and systems described herein, landscapers, caretakers, superintendents, and managers can ensure that all areas of vegetation remain optimally healthy.”
Regarding claim 5,
The combination of Ruff, Wu, and Morrison teach the computer-implemented method according to claim 1.
wherein the method comprises the following steps: a) providing a land cover map relating to a field to be treated; (Para. [0079] teaches “Data types may include field boundaries, yield maps, as-planted maps, soil test results, as-applied maps, and/or management zones, among others.”)
b) receiving master data selected from the group consisting of: regulatory data, machine data, field data, elevation data; (Para. [0079] teaches “Data types may include field boundaries, yield maps, as-planted maps, soil test results, as-applied maps, and/or management zones, among others.” Para. [0086] teaches “The performance instructions 216 may be programmed to communicate via the network(s) 109 to back-end analytics programs executed at agricultural intelligence computer system 130 and/or external data server computer 108 and configured to analyze metrics such as yield, yield differential, hybrid, population, SSURGO zone, soil test properties, or elevation, among others.”)
c) receiving validation information relating to master data selected from the group consisting of regulatory data, machine data, field data, elevation data; (Para. [0106] teaches “The preconfigured agronomic model may have been cross validated to ensure accuracy of the model. Cross validation may include comparison to ground truthing that compares predicted results with actual results on a field, such as a comparison of precipitation estimate with a rain gauge or sensor providing weather data at the same or nearby location or an estimate of nitrogen content with a soil sample measurement”)
d) initiating the determination of, and/or determining buffer zones as a further layer to the land cover map based on the master data and based on the validation information; (Para. [0182] teaches “The system 130 is therefore configured to identify certain boundaries or other problematic areas of the fields that will not participate in prescribed experiments, and further determine specific strips or squares, with buffer areas in between, that will participate in prescribed experiments.”) Ruff does not explicitly teach,
and e) initiating the generation of, and/or generating an application map specifying areas for treating the field with an agricultural equipment, wherein the application map is based on the buffer zones.
Morrison teaches,
and e) initiating the generation of, and/or generating an application map specifying areas for treating the field with an agricultural equipment, wherein the application map is based on the buffer zones. (Abstract teaches "Additional sensors (camera(s), lidar, IMU, GPS, etc.) may be fused with radar returns to generate maps having associated moisture content, surface temperature, ambient light levels, additional indications of vegetative health (as may be determined by machine learned algorithms), etc. Such vegetative health maps may be provided to a user who, in turn, may indicate additional areas for the vegetative health device to scan or otherwise used to recommend and/or perform treatments.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ruff, Wu, and Morrison with initiating the generation of, and/or generating an application map specifying areas for treating the field with an agricultural equipment, wherein the application map is based on the buffer zones such as that of Morrison.
One of ordinary skill would have been motivated to modify the combination of Ruff, Wu, and Morrison, because according to Col. 30 Ln(s). [30-38] of Morrison “Further, since the maps are updated from time to time, impact of such treatments can be determined for adjusting recommended treatments (increasing or decreasing watering, increasing or decreasing seed, fertilizer, pesticide, herbicide, etc.). By creating a three-dimensional vegetative health map of an area as described by the processes and systems described herein, landscapers, caretakers, superintendents, and managers can ensure that all areas of vegetation remain optimally healthy.”
Regarding claim 7,
The combination of Ruff, Wu, and Morrison teaches the computer-implemented method according to claim 1.
Ruff further teaches,
wherein the land cover map is generated based on a least one of the following categories of data: general map data, remote sensing data and user map data. (Para. [0061] teaches “In alternative embodiments, the user may specify identification data by accessing field identification data (provided as shape files or in a similar format) from the U. S. Department of Agriculture Farm Service Agency or other source via the user device and providing such field identification data to the agricultural intelligence computer system.”)
Regarding claim 8,
The combination of Ruff, Wu, and Morrison teaches the computer-implemented method according to claim 1.
Ruff does not explicitly teach,
wherein the land cover map is generated via a land cover classification algorithm.
Morrison further teaches,
wherein the land cover map is generated via a land cover classification algorithm. (Col. 4 Ln(s). [57-66] teach “Segmentation is the process of extracting useful information from sensor data (e.g., separating different objects from each other and a background in image data). Classification is the process by which sensor data is associated with a label. For example, image and/or radar data may be segmented based on different objects in the environment and subsequently classified, such that the segmented and classified data indicates the existence of plants, golf balls, rocks, pipes, branches, leaves, or any other object detectable by any of the sensor data.” Col. 5 Ln(s). [18-20] teach “Such segmented and classified data may be used in both generating the three-dimensional map, as well as localizing the vegetative health device to the map.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ruff, Wu, and Morrison with wherein the land cover map is generated via a land cover classification algorithm such as that of Morrison.
One of ordinary skill would have been motivated to modify the combination of Ruff, Wu, and Morrison, because a machine learning algorithm will be able to classify the land quicker and more efficiently than other known methods.
Regarding claim 9,
The combination of Ruff, Wu, and Morrison teaches the computer-implemented method according to claim 1.
Ruff further teaches,
wherein the master data include regulatory data, machine data, and field data. (Para. [0079] teaches “Data types may include field boundaries, yield maps, as-planted maps, soil test results, as-applied maps, and/or management zones, among others.” Para. [0086] teaches “The performance instructions 216 may be programmed to communicate via the network(s) 109 to back-end analytics programs executed at agricultural intelligence computer system 130 and/or external data server computer 108 and configured to analyze metrics such as yield, yield differential, hybrid, population, SSURGO zone, soil test properties, or elevation, among others.”)
Regarding claim 10,
The combination of Ruff, Wu, and Morrison teaches the computer-implemented method according to claim 1.
Ruff further teaches,
wherein the master data include machine data. (Para. [0079] teaches “Data types may include field boundaries, yield maps, as-planted maps, soil test results, as-applied maps, and/or management zones, among others.” Para. [0086] teaches “The performance instructions 216 may be programmed to communicate via the network(s) 109 to back-end analytics programs executed at agricultural intelligence computer system 130 and/or external data server computer 108 and configured to analyze metrics such as yield, yield differential, hybrid, population, SSURGO zone, soil test properties, or elevation, among others.”)
Regarding claim 11,
The combination of Ruff, Wu, and Morrison teaches the computer-implemented method according to claim 1.
Ruff further teaches,
wherein the master data include regulatory data. (Para. [0079] teaches “Data types may include field boundaries, yield maps, as-planted maps, soil test results, as-applied maps, and/or management zones, among others.” Para. [0086] teaches “The performance instructions 216 may be programmed to communicate via the network(s) 109 to back-end analytics programs executed at agricultural intelligence computer system 130 and/or external data server computer 108 and configured to analyze metrics such as yield, yield differential, hybrid, population, SSURGO zone, soil test properties, or elevation, among others.”)
Regarding claim 12,
The combination of Ruff, Wu, and Morrison teaches the computer-implemented method according to claim 2.
Ruff further teaches,
wherein the validation information includes at least one of the following information: 1) information that all data relating to the field to be treated or all objects shown in the land cover map is correct; 2) information that specific data relating to the field to be treated have to be corrected; 3) information that specific objects in the land cover map have to be either added, or modified, or deleted. (Para. [0106] teaches “The preconfigured agronomic model may have been cross validated to ensure accuracy of the model. Cross validation may include comparison to ground truthing that compares predicted results with actual results on a field, such as a comparison of precipitation estimate with a rain gauge or sensor providing weather data at the same or nearby location or an estimate of nitrogen content with a soil sample measurement”)
Regarding claim 13,
The combination of Ruff, Wu, and Morrison teaches the computer-implemented method according to claim 2.
Ruff further teaches,
wherein the validation information includes at least one of the following information:
1) information that all master data are correct;
2) information that specific master data have to be corrected;
3) information that specific master data have to be either added, or modified, or deleted. (Para. [0237] teaches “alternatively, the agricultural intelligence computing system may send instructions that, if executed, cause a field implement to correct planting or applications in the testing location.”)
Regarding claim 14,
The combination of Ruff, Wu, and Morrison teaches the computer-implemented method according to claim 2.
wherein the validation information includes at least one of the following information:
1) information that all preliminary buffer zones and/or all objects shown in the preliminary buffer zones are correct;
2) information that specific preliminary buffer zones and/or specific objects shown in the preliminary buffer zones have to be corrected;
3) information that specific preliminary buffer zones and/or specific objects shown in the preliminary buffer zones have to be either added, or modified, or deleted. (Para. [0237] teaches “alternatively, the agricultural intelligence computing system may send instructions that, if executed, cause a field implement to correct planting or applications in the testing location.”)
Regarding claim 15,
The combination of Ruff, Wu, and Morrison teaches the computer-implemented method according to claim 2.
Ruff further teaches,
wherein the validation information is inputted by a user. (Para. [0021] teaches “FIG. 10 depicts an example graphical user interface for defining selected locations.”)
Regarding claim 16,
The combination of Ruff, Wu, and Morrison teaches the computer-implemented method according to claim 2.
Ruff further teaches,
wherein the validation information relating to the field to be treated or relating to the land cover map is inputted by a sensor or machine which is capable of automatically or semi-automatically obtaining data relating to the field to be treated or recognizing objects shown in the land cover map. (Para. [0053] teaches “Examples of agricultural apparatus 111 include tractors, combines, harvesters, planters, trucks, fertilizer equipment, aerial vehicles including unmanned aerial vehicles, and any other item of physical machinery or hardware, typically mobile machinery, and which may be used in tasks associated with agriculture. In some embodiments, a single unit of apparatus 111 may comprise a plurality of sensors 112 that are coupled locally in a network on the apparatus; controller area network (CAN) is example of such a network that can be installed in combines, harvesters, sprayers, and cultivators. Application controller 114 is communicatively coupled to agricultural intelligence computer system 130 via the network(s) 109 and is programmed or configured to receive one or more scripts that are used to control an operating parameter of an agricultural vehicle or implement from the agricultural intelligence computer system 130.” Para. [0076] teaches “Additionally, field manager computing device 104 may automatically send field data 106 when one or more of the data values becomes available to field manager computing device 104.”)
Regarding claim 17,
The combination of Ruff, Wu, and Morrison teaches the computer-implemented method according to claim 2.
Ruff further teaches,
wherein the validation information relating to the master data is inputted sensor or machine which is capable of automatically or semi-automatically obtaining master data. (Para. [0053] teaches “Examples of agricultural apparatus 111 include tractors, combines, harvesters, planters, trucks, fertilizer equipment, aerial vehicles including unmanned aerial vehicles, and any other item of physical machinery or hardware, typically mobile machinery, and which may be used in tasks associated with agriculture. In some embodiments, a single unit of apparatus 111 may comprise a plurality of sensors 112 that are coupled locally in a network on the apparatus; controller area network (CAN) is example of such a network that can be installed in combines, harvesters, sprayers, and cultivators. Application controller 114 is communicatively coupled to agricultural intelligence computer system 130 via the network(s) 109 and is programmed or configured to receive one or more scripts that are used to control an operating parameter of an agricultural vehicle or implement from the agricultural intelligence computer system 130.” Para. [0076] teaches “Additionally, field manager computing device 104 may automatically send field data 106 when one or more of the data values becomes available to field manager computing device 104.”)
Regarding claim 18,
The combination of Ruff, Wu, and Morrison teaches the computer-implemented method according to claim 2.
Ruff further teaches,
wherein the validation information relating to the preliminary buffer zone is inputted sensor or machine which is capable of automatically or semi-automatically obtaining data relating to the preliminary buffer zone. (Para(s). [0051-0052] teach “The field manager computer device 104 is programmed or configured to provide field data 106 to an agricultural intelligence computer system 130 via one or more networks 109. Examples of field data 106 include (a) identification data (for example, acreage, field name, field identifiers, geographic identifiers, boundary identifiers, crop identifiers, and any other suitable data that may be used to identify farm land, such as a common land unit (CLU), lot and block number, a parcel number, geographic coordinates and boundaries,”)
Regarding claim 19,
The combination of Ruff, Wu, and Morrison teach the computer-implemented method according to claim 1.
Ruff further teaches,
Agricultural equipment configured to be controlled by control data and/or a control map provided by a method according to claim 1. (Para. [0053] teaches “Application controller 114 is communicatively coupled to agricultural intelligence computer system 130 via the network(s) 109 and is programmed or configured to receive one or more scripts that are used to control an operating parameter of an agricultural vehicle or implement from the agricultural intelligence computer system 130.”)
Regarding claim 20,
Ruff teaches,
A computer system for generating an application map for treating a field with an agricultural equipment comprising: (Para. [0003] teaches “The present disclosure relates to digital computer modeling and tracking of agricultural fields. Specifically, the present disclosure relates to modeling benefits to an agricultural field of performing particular practices, identifying locations for implementing trials of the particular practices, and tracking the performance of the particular practices.”)
1) a first interface component configured to provide a land cover map relating to a field to be treated, (Para. [0079] teaches “Data types may include field boundaries, yield maps, as-planted maps, soil test results, as-applied maps, and/or management zones, among others.”)
2) a second interface component configured to receive master data selected from the group consisting of: regulatory data, machine data, field data, elevation data; (Para. [0079] teaches “Data types may include field boundaries, yield maps, as-planted maps, soil test results, as-applied maps, and/or management zones, among others.” Para. [0086] teaches “The performance instructions 216 may be programmed to communicate via the network(s) 109 to back-end analytics programs executed at agricultural intelligence computer system 130 and/or external data server computer 108 and configured to analyze metrics such as yield, yield differential, hybrid, population, SSURGO zone, soil test properties, or elevation, among others.”)
5) a second system module configured to determine buffer zones as a further layer to the land cover map based on the master data and based on the validation information; (Para. [0182] teaches “The system 130 is therefore configured to identify certain boundaries or other problematic areas of the fields that will not participate in prescribed experiments, and further determine specific strips or squares, with buffer areas in between, that will participate in prescribed experiments.”)
Ruff does not explicitly teach,
wherein the buffer zones are automatically adjusted to comply with the regulatory data. machine data. field data. and/or elevation data; 6) third system module configured to generate an application map specifying one or more sensitive areas for treating the field based on the buffer zones with one or more treatment measures via a guided agricultural equipment,
Wu teaches,
wherein the buffer zones are automatically adjusted to comply with the regulatory data, machine data, field data, and/ or elevation data; (Para. [0010] “an automatic identification method, wherein the automatic driving system acquires the image information around a vehicle in real time, and updates the area and the boundary of the farmland identified by the image identification system based on the acquired image information, so as to provide technical support for accurate motion of the vehicle.” Para. [0077] teaches “The area in the farmland is divided into at least one non spraying area 100c, at least one spraying area 200c and at least one farmland boundary area 300c. The non spraying area 100c represents the area where the crops have not been sprayed with pesticide, the spraying area 200c represents the area where the crops have been sprayed with pesticide, and the farmland boundary 300b is ridge that separating crops in the farmland, an outer boundary of the farmland, and the area where there are obstacles in the farmland.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ruff wherein the buffer zones are automatically adjusted to comply with the regulatory data, machine data, field data, and/ or elevation data such as that of Wu.
One of ordinary skill would have been motivated to modify Ruff, because according to para. [0002] of Wu “When the agricultural machinery is in motion on the farmland, it is necessary to adjust an operation route of the agricultural machinery according to an operation condition of the farmland”
The combination of Ruff and Wu does not explicitly teach,
6) third system module configured to generate an application map specifying one or more sensitive areas for treating the field based on the buffer zones with one or more treatment measures via a guided agricultural equipment,
Morrison teaches,
6) third system module configured to generate an application map specifying one or more sensitive areas for treating the field based on the buffer zones with one or more treatment measures via a guided agricultural equipment, (Abstract teaches "Additional sensors (camera(s), lidar, IMU, GPS, etc.) may be fused with radar returns to generate maps having associated moisture content, surface temperature, ambient light levels, additional indications of vegetative health (as may be determined by machine learned algorithms), etc. Such vegetative health maps may be provided to a user who, in turn, may indicate additional areas for the vegetative health device to scan or otherwise used to recommend and/or perform treatments.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ruff and Wu with a sixth interface component configured to initiate the generation of, and/or a third system module configured to generate an application map specifying areas for treating the field with an agricultural equipment, wherein the application map is based on the buffer zones such as that of Morrison.
One of ordinary skill would have been motivated to modify the combination of Ruff and Wu, because according to Col. 30 Ln(s). [30-38] of Morrison “Further, since the maps are updated from time to time, impact of such treatments can be determined for adjusting recommended treatments (increasing or decreasing watering, increasing or decreasing seed, fertilizer, pesticide, herbicide, etc.). By creating a three-dimensional vegetative health map of an area as described by the processes and systems described herein, landscapers, caretakers, superintendents, and managers can ensure that all areas of vegetation remain optimally healthy.”
Claims 2, 6, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Ruff (US 20190057461 A1), Wu (US 20210365037 A1), and Morrison (US 11140813 B1) as applied to claim 1 above, and further in view of Nourse (US 8711181 B1).
Regarding claim 2,
The combination of Ruff, Wu, and Morrison teaches the computer-implemented method according to claim 1.
Ruff further teaches,
comprising the following steps:
a) providing a land cover map relating to a field to be treated; (Para. [0079] teaches “Data types may include field boundaries, yield maps, as-planted maps, soil test results, as-applied maps, and/or management zones, among others.”)
b) receiving master data selected from the group consisting of: regulatory data, machine data, field data, elevation data; (Para. [0079] teaches “Data types may include field boundaries, yield maps, as-planted maps, soil test results, as-applied maps, and/or management zones, among others.” Para. [0086] teaches “The performance instructions 216 may be programmed to communicate via the network(s) 109 to back-end analytics programs executed at agricultural intelligence computer system 130 and/or external data server computer 108 and configured to analyze metrics such as yield, yield differential, hybrid, population, SSURGO zone, soil test properties, or elevation, among others.”)
d) initiating the determination of, and/or determining buffer zones as a further layer to the land cover map based on the master data and based on the validation information; (Para. [0182] teaches “The system 130 is therefore configured to identify certain boundaries or other problematic areas of the fields that will not participate in prescribed experiments, and further determine specific strips or squares, with buffer areas in between, that will participate in prescribed experiments.”)
Ruff does not explicitly teach,
c) initiating the determination of, and/or determining preliminary buffer zones as a further layer to the land cover map based on the master data;
and e) initiating the generation of, and/or generating an application map specifying areas for treating the field with an agricultural equipment, wherein the application map is based on the buffer zones.
Morrison teaches,
and e) initiating the generation of, and/or generating an application map specifying areas for treating the field with an agricultural equipment, wherein the application map is based on the buffer zones. (Abstract teaches "Additional sensors (camera(s), lidar, IMU, GPS, etc.) may be fused with radar returns to generate maps having associated moisture content, surface temperature, ambient light levels, additional indications of vegetative health (as may be determined by machine learned algorithms), etc. Such vegetative health maps may be provided to a user who, in turn, may indicate additional areas for the vegetative health device to scan or otherwise used to recommend and/or perform treatments.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ruff, Wu, and Morrison with initiating the generation of, and/or generating an application map specifying areas for treating the field with an agricultural equipment, wherein the application map is based on the buffer zones such as that of Morrison.
One of ordinary skill would have been motivated to modify the combination of Ruff, Wu, and Morrison, because according to Col. 30 Ln(s). [30-38] of Morrison “Further, since the maps are updated from time to time, impact of such treatments can be determined for adjusting recommended treatments (increasing or decreasing watering, increasing or decreasing seed, fertilizer, pesticide, herbicide, etc.). By creating a three-dimensional vegetative health map of an area as described by the processes and systems described herein, landscapers, caretakers, superintendents, and managers can ensure that all areas of vegetation remain optimally healthy.”
The combination of Ruff, Wu, and Morrison does not explicitly teach,
c) initiating the determination of, and/or determining preliminary buffer zones as a further layer to the land cover map based on the master data;
Nourse teaches,
c) initiating the determination of, and/or determining preliminary buffer zones as a further layer to the land cover map based on the master data; (Col 2 Ln(s). [45-50] teach “pre-fetch map data tiles to be requested from a remote map database and stored on the client device for eventual rendering of the visual display in response to a subsequent user request; requesting, from the remote map database, the pre-fetch map data tiles, wherein the map database stores map data in the form of a plurality of map data tiles, and the pre-fetch map data tiles are a sub-set of the plurality of map data tiles;”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ruff, Wu, and Morrison with initiating the determination of, and/or determining preliminary buffer zones as a further layer to the land cover map based on the master data such as that of Nourse.
One of ordinary skill would have been motivated to modify the combination of Ruff, Wu, and Morrison, because according to Col. 2 Ln(s). [23-26] “As a result, there is a need to have more intelligent mechanisms for downloading map data, in particular map data tiles, to sufficiently satisfy the needs of the user, while doing so in a manner that addresses network bandwidth and memory conditions.” Therefore, including the pre-fetch map data would reduce the amount of memory needed to perform the computer implemented method of Ruff and Morrison.
Regarding claim 6,
The combination of Ruff, Wu, and Morrison teaches the computer-implemented method according to claim 1.
Ruff further teaches,
comprising the following steps:
a) providing a land cover map relating to a field to be treated; (Para. [0079] teaches “Data types may include field boundaries, yield maps, as-planted maps, soil test results, as-applied maps, and/or management zones, among others.”)
b) receiving master data selected from the group consisting of: regulatory data, machine data, field data, elevation data; (Para. [0079] teaches “Data types may include field boundaries, yield maps, as-planted maps, soil test results, as-applied maps, and/or management zones, among others.” Para. [0086] teaches “The performance instructions 216 may be programmed to communicate via the network(s) 109 to back-end analytics programs executed at agricultural intelligence computer system 130 and/or external data server computer 108 and configured to analyze metrics such as yield, yield differential, hybrid, population, SSURGO zone, soil test properties, or elevation, among others.”)
d) receiving validation information relating to preliminary buffer zones; (Para. [0106] teaches “The preconfigured agronomic model may have been cross validated to ensure accuracy of the model. Cross validation may include comparison to ground truthing that compares predicted results with actual results on a field, such as a comparison of precipitation estimate with a rain gauge or sensor providing weather data at the same or nearby location or an estimate of nitrogen content with a soil sample measurement”)
e) initiating the determination of, and/or determining buffer zones as a further layer to the land cover map based on the master data and based on the validation information; (Para. [0182] teaches “The system 130 is therefore configured to identify certain boundaries or other problematic areas of the fields that will not participate in prescribed experiments, and further determine specific strips or squares, with buffer areas in between, that will participate in prescribed experiments.”)
Ruff does not explicitly teach,
c) initiating the determination of, and/or determining preliminary buffer zones as a further layer to the land cover map based on the master data;
and f) initiating the generation of, and/or generating an application map specifying areas for treating the field with an agricultural equipment, wherein the application map is based on the buffer zones.
Morrison teaches,
and f) initiating the generation of, and/or generating an application map specifying areas for treating the field with an agricultural equipment, wherein the application map is based on the buffer zones. (Abstract teaches "Additional sensors (camera(s), lidar, IMU, GPS, etc.) may be fused with radar returns to generate maps having associated moisture content, surface temperature, ambient light levels, additional indications of vegetative health (as may be determined by machine learned algorithms), etc. Such vegetative health maps may be provided to a user who, in turn, may indicate additional areas for the vegetative health device to scan or otherwise used to recommend and/or perform treatments.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ruff, Wu, and Morrison with initiating the generation of, and/or generating an application map specifying areas for treating the field with an agricultural equipment, wherein the application map is based on the buffer zones such as that of Morrison.
One of ordinary skill would have been motivated to modify the combination of Ruff, Wu, and Morrison, because according to Col. 30 Ln(s). [30-38] of Morrison “Further, since the maps are updated from time to time, impact of such treatments can be determined for adjusting recommended treatments (increasing or decreasing watering, increasing or decreasing seed, fertilizer, pesticide, herbicide, etc.). By creating a three-dimensional vegetative health map of an area as described by the