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 .
Response to Arguments
Regarding the 35 USC 112(f) interpretation, Examiner has fully considered Applicant’s arguments and amendments. Examiner has withdrawn the 35 USC 112(f) interpretation in view of the amended claims. Accordingly, the 35 USC 112(f) interpretation has been withdrawn.
Regarding the 35 USC 112(b) rejection, Examiner has fully considered Applicant’s arguments and amendments. Regarding Applicant’s assertion of “Without acquiescing to the propriety of the rejections, Applicant hereby amends Claims 1- 2, 4-6, 8-9, and 11-13 to overcome the outstanding rejections, as indicated by the Office Action,” Examiner has withdrawn the 35 USC 112(b) rejections of the Non-Final Office Action because the claim amendments are sufficient to overcome the rejections. However, the claim amendments have introduced further issues for consideration under 35 USC 112(b). See the detailed rejection below.
Accordingly, dependent claims 8 and 11 are rejected under 35 USC 112(b).
Regarding the 35 USC 101 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “At Step 2A Prong One of the subject matter eligibility analysis, the Office Action alleges that the claims "constitut[e] an abstract idea based on 'Mental Processes."' Office Action (at p. 12). Applicant respectfully disagrees. The Office Action improperly "oversimplif[ies] the claims by looking at them generally and failing to account for the specific requirements of the claims."MPEP §2106.05(a) (internal quotation marks and citations omitted). Furthermore, these elements also do not recite "mental processes" at asserted by the Office Action, because the claims "[c]annot practically be performed in the human mind""with or without a physical aid." See MPEP § 2106.04. For example, amended independent Claim 1 recites: "A method for managing agricultural processes... [including]... automatically and autonomously performing a task by the at least one sensor platform with an absolute accuracy of at least +/- 2.5 cm and/or using RTK and/or PPK, thereby reducing a computing capacity requirement while increasing a data analysis efficiency of the at least one sensor platform." Amended independent Claim 14 recites similar features. At least these steps and features cannot be performed without the aid of a computer. Therefore, for at least these reasons, Claims 1-16 do not fall under the enumerated grouping of an abstract idea because they do not constitute a "mental process.",” Examiner respectfully asserts that the argued limitations are not part of the abstract limitations for consideration under Step 2A, Prong 1. Rather, the above argued limitations are part of the additional elements for consideration under Step 2A, Prong 2. Therefore, these limitations are not part of the abstract limitations identified under step 2A, Prong 1.
Regarding Applicant’s assertion of “Applicant respectfully submits that the claims are patent-eligible because the claims integrate the alleged abstract idea by using a computing system that includes various components in a "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." MPEP §2106.04(d). For example, amended independent Claim 1 recites: "A method for managing agricultural processes, comprising:"…,” Examiner respectfully disagrees. The additional elements of the independent claims, as drafted, are nothing more than mere use of a computer as a tool, or are nothing more than merely generally linking. Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Regarding Applicant’s assertion of “Amended independent Claim 14 recites similar features. At least these features provide a substantial improvement over prior methods of agricultural process management, particularly because: Defining a data collection path for the at least one sensor platform, preferably the at least one sensor of the sensor platform, preferably with an absolute accuracy of at least +/- 2.5 cm and/or using RTK and/or PPK, has the advantage that, in particular in case the data is collected using a mobile sensor platform, e.g. an air-borne unmanned autonomous vehicle (UAV), the path along which the mobile sensor platform moves while collecting data can be matched to the field management unit, in particular the field management sub-units, with high absolute accuracy, which means that less redundant or unnecessary data is acquired (leading to higher data analysis efficiency and less computing capacity requirements) and that complex corrections of offset or other errors can be reduced or eliminated.,” Examiner respectfully asserts that claim 14 does not recite a mobile sensor platform or UAV. Applicant is reminded that it is impermissible to import limitations from the specification into the claims. (See MPEP 2111.01(II)) In order for Applicant to be given the argued interpretation, the claims must positively recite the argued subject matter.
Even assuming arguendo, the present claims do not reflect the purported improvement under broadest reasonable interpretation. The use of sensors, as drafted, is nothing more than generally linking the use of the judicial exception to a particular technological environment. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Regarding Applicant’s assertion of “Applicant respectfully submits that the instant claims do involve more than performance of well-understood, routine, and conventional activities previously known to the industry. For example, independent Claim 1 recites a series of steps, notably "automatically and autonomously performing a task by the at least one sensor platform with an absolute accuracy of at least +/- 2.5 cm and/or using RTK and/or PPK, thereby reducing a computing capacity requirement while increasing a data analysis efficiency of the at least one sensor platform" that allows for a significant and non-conventional method for accurate data collection. Independent Claim 14 recites similar features. And as the Federal Circuit recently reaffirmed, "when the claim limitations involve more than performance of well-understood, routine, and conventional activities previously known to the industry"-as is the case here-"[t]he second step of the Alice test is satisfied." Berkheimer, 881 F.3d at 1367 (internal quotation marks and punctuation omitted) (citing Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat'l Ass'n, 776 F.3d 1343, 1347 (Fed. Cir. 2014)); see also Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-25 (2014).,” Examiner respectfully asserts that the present claims are not rejected using a Berkheimer analysis. Therefore, while Examiner has considered Applicant’s assertions in view of whether the claims are well-understood, routine, and conventional, Applicant’s assertion is deemed moot because the present claims are not rejected under such analysis.
Accordingly, the 35 USC 101 rejection has been maintained.
Regarding the 35 USC 103 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “As such, Albrecht and Sood fail to disclose or render obvious each and every limitation and feature of independent Claims 1 and 14, as well as Claims 2-5, 7-13, and 15 for at least their respective dependencies from Claim 1 or 14, or for further features recited therein. Accordingly, Applicant respectfully requests the rejections of Claims 1-5 and 7-15 under 35 U.S.C. 103 over Albrecht and Sood be withdrawn.,” Applicant’s arguments with respect to the previous prior art combination of the record have been considered but are moot because the new grounds of rejection does not rely on any reference applied in the prior art rejection for any teachings or matter specifically challenged in the argument. The claims are rejected under a new grounds of rejection, which was necessitated by amendment. Examiner has introduced the combination of Carballido and Genty to cure the deficiencies of the prior art combination of the record. See the detailed rejection below.
Accordingly, the 35 USC 103 rejection has been maintained.
Information Disclosure Statement
The information disclosure statements (IDS) filed on 07/18/2025, 11/25/2025, 12/15/2025, and 03/18/2026 have been fully considered.
Claim Objections
Claim 14 is objected to because of the following informalities: Examiner suggests amending the claim for the sake of clarity by reciting “at least one sensor platform [[with]]comprising a control unit.” Appropriate correction is required.
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.
Claims 8 and 11 are 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.
Regarding claims 8 and 11, the metes and bounds of the claims are rendered unclear due to the language of “for example.” The metes and bounds of the claims are rendered unclear because it is unclear whether the further limiting limitations are actually required by the claim. The language of “for example” implies that the further narrowing of the claim is merely exemplary and thus not required by the claim. However, this language does appear to further narrow the claim limitations. Therefore, the metes and bounds of the claim are rendered unclear due to the language of “for example.” See MPEP 2173.05(d).
For the sake of compact prosecution, Examiner is interpreting the claim limitations reciting “for example” as not being required by the claim.
Accordingly, claims 8 and 11 are rejected under 35 USC 112(b).
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 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-13 and 15-17 are directed to a method and claim 14 is directed to a system. Therefore, the claims are directed to patent eligible categories of invention.
Step 2A, Prong 1: Independent claims 1 and 14 recite creating a layout of a field management unit and attributing characteristics to the field management unit, constituting an abstract idea based on “Mental Processes” related to concepts performed in the human mind including observation, evaluation, judgment, and opinion. Claim 1 recites limitations including “creating a field management unit by defining georeferenced boundaries of the field management unit; creating a layout of the field management unit by defining georeferenced field management sub-units within the field management unit; attributing site characteristics to the field management unit, in particular to the field management sub-units; attributing plant characteristics to the field management unit, in particular to the field management sub-units.” Claim 14 recites limitations including “data relating to a field management unit, the data including: georeferenced boundaries of the field management unit; a layout of the field management unit defining georeferenced field management sub-units within the field management unit; site characteristics attributed to the field management unit, in particular to the field management sub-units; and/or plant characteristics to the field management unit, in particular to the field management sub-units.” These limitations, as drafted, but for the recitation of “sensor platform,” is a process that covers performance of the limitations in the mind but for the recitation of generic computer components. That is, but for the “sensor platform” language, nothing in the claim elements preclude the steps from practically being performed in the human mind. For example, with the exception of the “sensor platform” language, the claim steps in the context of the claim encompass a user mentally or manually performing the steps of the claim.
Dependent claims 2-5, 7, and 9-10 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 6, 8, 11-13, and 15-17 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claims 1 and 14 do not integrate the judicial exception into a practical application. Claim 1 is a method that recites additional elements including “storing the field management unit, including its boundaries and its field management sub-units, in a geospatial database provided on a server,” and “exchanging data relating to the field management unit with at least one sensor platform.” Claim 14 recites limitations including “a database system containing data…” and “wherein the control unit is adapted to receive data relating to the field management unit from the database system.” These limitations are nothing more than mere use of a computer as a tool. Use of a computer or other machinery in its ordinary capacity for 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., mental processes) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Claim 1 further recites the additional elements of “wherein the at least one sensor platform comprises at least one sensor, and wherein the at least one sensor comprises a phenotypical sensor,” “and responsive to exchanging the data, automatically and autonomously performing a task by the at least one sensor platform with an absolute accuracy of at least +/- 2.5 cm and/or using RTK and/or PPK, thereby reducing a computing capacity requirement while increasing a data analysis efficiency of the at least one sensor platform.” Claim 14 recites limitations including “at least one sensor platform with a control unit, wherein the at least one sensor platform comprises a phenotypical sensor and is configured to run autonomously,” and “wherein the control unit is adapted to automatically initiate the performing of a task by the at least one sensor platform responsive to receiving the data relating to the field management unit.” These limitations, as drafted, are nothing more than generally linking the use of the judicial exception to a particular technological environment. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 2-5, 7, and 9-10 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claim 6 introduces the additional element of “further comprising: creating a georeferenced isolation area of the field management unit and storing the isolation area of the field management unit in the geospatial database, wherein preferably the isolation area is created: by calculation of an outer boundary of the isolation area by adding at least one buffer distance to the boundaries of the field management unit in a direction pointing away from the field management unit; and/or by calculation of a distance between the field management unit and another field management unit or another isolation area; and/or by calculation of an inner boundary of the isolation area by adding at least one buffer distance from the boundaries of the field management unit in a direction pointing towards the field management unit.” Dependent claim 12 introduces the additional element of “wherein the geospatial database is part of a database system comprising one or more further databases, wherein a data connection within the database system and/or between the geospatial database and one or more of the further databases of the database system and/or between the at least one sensor platform and the database system, in particular one or more of databases of the database system, is a direct and/or indirect data connection.” Dependent claim 15 introduces the additional element of “a system comprising: one or more processors; and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to perform the method according to claim 1.” Dependent claim 16 introduces the additional element of “a computer program product comprising a non-transitory computer readable medium comprising computer-readable instructions that, when loaded and run on a computer, cause the computer to perform a method according to claim 1.” These limitations are nothing more than mere use of a computer as a tool. Use of a computer or other machinery in its ordinary capacity for 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., mental processes) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 8 introduces the additional element of “wherein the at least one sensor platform comprises a mobile sensor platform, and comprises one or more of: a smart phone; a tablet; a mobile computer; a wearable computer, in particular a smartwatch and/or hands free device, such as smart glasses; an unmanned autonomous vehicle, in particular ground-borne and/or air- borne, for example a field robot and/or a drone; an agricultural machine, such as a tractor and/or a planter and/or a harvester and/or a sprayer; a helicopter; an airplane; and/or a non-geostationary satellite, and/or wherein the at least one sensor platform comprises a stationary sensor platform, and comprises one or more of: a weather station; a stationary sensor; a stationary measuring device; and/or a geostationary satellite.” Dependent claim 11 introduces the additional element of “wherein the at least one sensor further comprises one or more of: an environmental sensor, for example soil sensor and/ or soil water sensor, such as TDR and/or FDR and/or UMP, and/or GPR and/or EMI and/or ERT; a weather sensor, for example weather station and/or sensor for weather data; a mechanical sensor, for example scales and/or seed counters; an identification sensor, for example scanner and/or NFC sensor and/or RFID sensor; and/or an optical sensor, for example LIDAR and/or light curtain and/or NIRS.” Dependent claim 13 introduces the additional element of “wherein: the at least one sensor platform collects data, in particular imaging data, of the field management unit, in particular of the field management sub-units, by defining a data collection path for the at least one sensor platform; with an absolute accuracy of at least +/- 2.5 cm and/or using RTK and/or PPK; and/or by collecting a plurality of at least partly overlapping images per field management unit, in particular per field management sub-unit, with an absolute accuracy of at least +/- 2.5 cm and/or using RTK and/or PPK, such that a plurality of images, are collected per pixel.” Dependent claim 17 introduces the additional element of “wherein the phenotypical sensor comprises one or more of a RGB camera, a thermal camera, a hyperspectral camera, a multispectral camera, or a combination thereof.” These limitations, as drafted, are nothing more than generally linking the use of the judicial exception to a particular technological environment. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. These limitations do not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1 and 14 do not comprise anything significantly more than the judicial exception. Claim 1 is a method that recites additional elements including “storing the field management unit, including its boundaries and its field management sub-units, in a geospatial database provided on a server,” and “exchanging data relating to the field management unit with at least one sensor platform.” Claim 14 recites limitations including “a database system containing data…” and “wherein the control unit is adapted to receive data relating to the field management unit from the database system.” These limitations are nothing more than mere use of a computer as a tool. Use of a computer or other machinery in its ordinary capacity for 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., mental processes) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Claim 1 further recites the additional elements of “wherein the at least one sensor platform comprises at least one sensor, and wherein the at least one sensor comprises a phenotypical sensor,” “and responsive to exchanging the data, automatically and autonomously performing a task by the at least one sensor platform with an absolute accuracy of at least +/- 2.5 cm and/or using RTK and/or PPK, thereby reducing a computing capacity requirement while increasing a data analysis efficiency of the at least one sensor platform.” Claim 14 recites limitations including “at least one sensor platform with a control unit, wherein the at least one sensor platform comprises a phenotypical sensor and is configured to run autonomously,” and “wherein the control unit is adapted to automatically initiate the performing of a task by the at least one sensor platform responsive to receiving the data relating to the field management unit.” This limitation, as drafted, is nothing more than generally linking the use of the judicial exception to a particular technological environment. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This limitation is not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are nothing significantly more than the judicial exception.
Dependent claims 2-5, 7, and 9-10 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claim 6 introduces the additional element of “further comprising: creating a georeferenced isolation area of the field management unit and storing the isolation area of the field management unit in the geospatial database, wherein preferably the isolation area is created: by calculation of an outer boundary of the isolation area by adding at least one buffer distance to the boundaries of the field management unit in a direction pointing away from the field management unit; and/or by calculation of a distance between the field management unit and another field management unit or another isolation area; and/or by calculation of an inner boundary of the isolation area by adding at least one buffer distance from the boundaries of the field management unit in a direction pointing towards the field management unit.” Dependent claim 12 introduces the additional element of “wherein the geospatial database is part of a database system comprising one or more further databases, wherein a data connection within the database system and/or between the geospatial database and one or more of the further databases of the database system and/or between the at least one sensor platform and the database system, in particular one or more of databases of the database system, is a direct and/or indirect data connection.” Dependent claim 15 introduces the additional element of “a system comprising: one or more processors; and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to perform the method according to claim 1.” Dependent claim 16 introduces the additional element of “a computer program product comprising a non-transitory computer readable medium comprising computer-readable instructions that, when loaded and run on a computer, cause the computer to perform a method according to claim 1.” These limitations are nothing more than mere use of a computer as a tool. Use of a computer or other machinery in its ordinary capacity for 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., mental processes) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claim 8 introduces the additional element of “wherein the at least one sensor platform comprises a mobile sensor platform, and comprises one or more of: a smart phone; a tablet; a mobile computer; a wearable computer, in particular a smartwatch and/or hands free device, such as smart glasses; an unmanned autonomous vehicle, in particular ground-borne and/or air- borne, for example a field robot and/or a drone; an agricultural machine, such as a tractor and/or a planter and/or a harvester and/or a sprayer; a helicopter; an airplane; and/or a non-geostationary satellite, and/or wherein the at least one sensor platform comprises a stationary sensor platform, and comprises one or more of: a weather station; a stationary sensor; a stationary measuring device; and/or a geostationary satellite.” Dependent claim 11 introduces the additional element of “wherein the at least one sensor further comprises one or more of: an environmental sensor, for example soil sensor and/ or soil water sensor, such as TDR and/or FDR and/or UMP, and/or GPR and/or EMI and/or ERT; a weather sensor, for example weather station and/or sensor for weather data; a mechanical sensor, for example scales and/or seed counters; an identification sensor, for example scanner and/or NFC sensor and/or RFID sensor; and/or an optical sensor, for example LIDAR and/or light curtain and/or NIRS.” Dependent claim 13 introduces the additional element of “wherein: the at least one sensor platform collects data, in particular imaging data, of the field management unit, in particular of the field management sub-units, by defining a data collection path for the at least one sensor platform; with an absolute accuracy of at least +/- 2.5 cm and/or using RTK and/or PPK; and/or by collecting a plurality of at least partly overlapping images per field management unit, in particular per field management sub-unit, with an absolute accuracy of at least +/- 2.5 cm and/or using RTK and/or PPK, such that a plurality of images, are collected per pixel.” Dependent claim 17 introduces the additional element of “wherein the phenotypical sensor comprises one or more of a RGB camera, a thermal camera, a hyperspectral camera, a multispectral camera, or a combination thereof.” These limitations, as drafted, are nothing more than generally linking the use of the judicial exception to a particular technological environment. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. These limitations are not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, do not comprise anything significantly more than the judicial exception.
Accordingly, claims 1-17 are rejected under 35 USC 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-5, 7-11, and 13-17 are rejected under 35 U.S.C. 103 as being unpatentable over Albrecht et al. (US 20190370966 A1) in view of Carballido et al. ("Comparison of positional accuracy between RTK and RTX GNSS based on the autonomous agricultural vehicles under field conditions." 2014) in view of Genty et al. (US 20190377946 A1).
Regarding claim 1, Albrecht teaches a method for managing agricultural processes (Fig. 5), comprising:
creating a field management unit by defining georeferenced boundaries of the field management unit (Fig. 2 and [0024] teach generating a boundary delineation map from data including remote sensing measurements in one or more aerial images and historical planting patterns, wherein the boundary delineation map can be generated from a plurality of satellite images, wherein [0025] teaches the boundary delineation map shows boundary delineations separating a plot of land from other plots of land, which appear as polygons of various shapes and sizes, wherein the farm boundaries can be delineated by detecting and evaluating the satellite data using a threshold algorithm, as well as in [0043] teaches the boundary identifier generates a boundary delineation map having a plurality of polygons corresponding to parcels/lots and/or other features within the satellite image, wherein each plot of land can appear as a polygon which is bounded by a finite chain of line segments closing in a loop to form a closed shape; see also: [0037-0042]),
creating a layout of the field management unit by defining georeferenced field management sub-units within the field management unit (Fig. 2 and [0024] teach generating a boundary delineation map from data including remote sensing measurements in one or more aerial images and historical planting patterns, wherein the boundary delineation map can be generated from a plurality of satellite images, wherein [0025] teaches the boundary delineation map shows boundary delineations separating a plot of land from other plots of land, which appear as polygons of various shapes and sizes, wherein the farm boundaries can be delineated by detecting and evaluating the satellite data using a threshold algorithm, wherein [0026] teaches any open polygons, such as shapes that are not completely closed, within the boundary delineation map can be closed such that the boundary delineation map includes multiple plots of land known as parcels, wherein the farms may be identified as substantially rectangular parcels or a subset of well-defined features that, when combined, form a substantially rectangular parcel, wherein the boundary delineations can be extracted from satellite imagery and such information can be used to update land maps/surveys, as well as in [0043] teaches the boundary identifier generates a boundary delineation map having a plurality of polygons corresponding to parcels/lots and/or other features within the satellite image, wherein each plot of land can appear as a polygon which is bounded by a finite chain of line segments closing in a loop to form a closed shape; see also: [0037-0042]),
storing the field management unit, including its boundaries and its field management sub-units, in a geospatial database provided on a server ([0031] teaches extracted data can be stored in the memory, wherein the extracted data includes boundary delineations, as well as in [0059] teaches the farm boundaries and boundary delineation map are stored in a database, as well as in Fig. 8 and [0094] teaches the workloads layer includes the boundary delineation, wherein [0091] teaches the hardware and software layer includes storage devices, database software, and more, as well as in [0059] teaches the cropped shapes may be representative of the updated landownership values and define new farm boundary contours can be used to update existing databases; see also: [0027, 0043, 0093]),
attributing site characteristics to the field management unit, in particular to the field management sub-units ([0059] teaches updating landowner ship data using the boundary delineation map, wherein previous landownership information, including boundary delineations and/or property lines, can be updated with the boundary delineation map, wherein these cropped shapes may be representative of the updated landownership values and define new farm boundary contours that can be used to update existing databases, as well as in [0043] teaches the boundary identifier generates a boundary delineation map that includes identifying whether various parcel belongs to a same owner, as well as in [0036] teaches determining farm boundary delineations to provide land ownership information; see also: [0032-0034, 0046-0050]);
attributing plant characteristics to the field management unit, in particular to the field management sub-units ([0049] teaches the system includes a vegetation identifier that employs image recognition methods and NDVI values to determine crop types within each of the parcels, wherein the crop types for each parcel can be determined ever month and stored in the memory as historical planting patterns, wherein the vegetation identifier can determine that a particular parcel, defined by one or more boundary delineations, is planted with soybean and corn in an alternating cycle, wherein [0027] teaches extracting information from satellite data in order to make determinations including the presence of vegetation, the types of vegetation, similarity of crop types between parcels, boundary delineations, and more; see also: [0030, 0033, 0047, 0055]).
However, Albrecht does not explicitly teach exchanging data relating to the field management unit with at least one sensor platform, wherein the at least one sensor platform comprises at least one sensor, and wherein the at least one sensor comprises a phenotypical sensor; and responsive to exchanging the data, automatically and autonomously performing a task by the at least one sensor platform with an absolute accuracy of at least +/- 2.5 cm and/or using RTK and/or PPK, thereby reducing a computing capacity requirement while increasing a data analysis efficiency of the at least one sensor platform.
From the same or similar field of endeavor, Carballido teaches exchanging data relating to the field management unit with at least one sensor platform (Pg. 362-363 teach the autonomous agricultural tractor was retrofitted for autonomous agricultural operations, wherein the RTK system includes rover sensors and GNSS data, wherein the sensors include manual override sensors, steering angle sensors, and terrain compensation sensing, wherein the autonomous tractor unit further includes a GNSS antenna, camera, and fuel cell, wherein the equipment is set up to use in the field, wherein the base station includes an RTK-GNSS receiver and WiFi Router, wherein the autonomous tractor can transmit data to the receiver of the base station; see also: Pgs. 364-365),
and responsive to exchanging the data, automatically and autonomously performing a task by the at least one sensor platform with an absolute accuracy of at least +/- 2.5 cm and/or using RTK (Pg. 361 teaches dynamic tests between rows demonstrated real time satellite corrections (RTK) of 2.55 cm, wherein the autonomous vehicle requires a combination of several techniques in order to achieve a very high location accuracy, wherein as the resolution improves, it increases the number of plant-specific management tasks, wherein Pg. 362-363 teach the autonomous agricultural tractor was retrofitted for autonomous agricultural operations, wherein the RTK system includes rover sensors and GNSS data, wherein the sensors include manual override sensors, steering angle sensors, and terrain compensation sensing, wherein the autonomous tractor unit further includes a GNSS antenna, camera, and fuel cell, wherein the equipment is set up to use in the field, wherein the base station includes an RTK-GNSS receiver and WiFi Router, wherein the autonomous tractor can transmit data to the receiver of the base station, wherein with the 2 cm accuracy, the RTK systems are the most accurate solution for GNSS applications, wherein field tests were performed along rows of a plot and the location of the tractor was tracked; see also: Pgs. 364-365),
thereby reducing a computing capacity requirement while increasing a data analysis efficiency of the at least one sensor platform (Pg. 361 teaches dynamic tests between rows demonstrated real time satellite corrections (RTK) of 2.55 cm, wherein the autonomous vehicle requires a combination of several techniques in order to achieve a very high location accuracy, wherein as the resolution improves, it increases the number of plant-specific management tasks, wherein the autonomous vehicle can increase efficiency, productivity, and safety in all field activities, wherein the fleet automated systems can lower the control system cost, increase production flexibility, and reduce the number of devices aboard each fleet, wherein Pg. 362-363 teach the autonomous agricultural tractor was retrofitted for autonomous agricultural operations, wherein the RTK system includes rover sensors and GNSS data, wherein the sensors include manual override sensors, steering angle sensors, and terrain compensation sensing, wherein the autonomous tractor unit further includes a GNSS antenna, camera, and fuel cell, wherein the equipment is set up to use in the field, wherein the base station includes an RTK-GNSS receiver and WiFi Router, wherein the autonomous tractor can transmit data to the receiver of the base station, wherein with the 2 cm accuracy, the RTK systems are the most accurate solution for GNSS applications, wherein field tests were performed along rows of a plot and the location of the tractor was tracked, wherein Pg. 365 teaches the RMS error of the tractor using the base station signals via RTK GNSS was approximately 4 times less than the RMS using RTX correction signals; see also: Pg. 364).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Albrecht to incorporate the teachings of Carballido to include exchanging data relating to the field management unit with at least one sensor platform, wherein the at least one sensor platform comprises at least one sensor, and responsive to exchanging the data, automatically and autonomously performing a task by the at least one sensor platform with an absolute accuracy of at least +/- 2.5 cm and/or using RTK and/or PPK, thereby reducing a computing capacity requirement while increasing a data analysis efficiency of the at least one sensor platform. One would have been motivated to do so in order to provide a high degree of accuracy when tracking autonomous vehicles using real-time base station corrections (Carballido, Pg. 361). By incorporating the teachings of Carballido, one would have been able to provide centimeter-level real time position accuracy through the use of real-time extended positioning (Carballido, Pg. 362).
However, the combination of Albrecht and Carballido does not explicitly teach and wherein the at least one sensor comprises a phenotypical sensor.
From the same or similar field of endeavor, Genty teaches and wherein the at least one sensor comprises a phenotypical sensor ([0040] teaches the plant phenotype output may include plant phenotype information for each of the one or more plants in the indoor farm, wherein the plant phenotype information may be derived from one or more imaging sensors in the system using computer vision and computer based image analysis, wherein [0026] teaches the autonomous system receives the generated image data, wherein the image data can be RGB imaging, infrared imaging, multi-spectral imaging, and fluorescent imaging; see also: [0020, 0039]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Albrecht and Carballido to incorporate the teachings of Genty to include and wherein the at least one sensor comprises a phenotypical sensor. One would have been motivated to do so in order to improve the growing of crops through the use of intelligent, variable spectrum sensors (Genty, [0024]). By incorporating the teachings of Genty, one would have been able to derive plant phenotype information using imaging sensors that perform computer vision (Genty, [0040]).
Regarding claim 2, the combination of Albrecht, Carballido, and Genty teaches all the limitations of claim 1 above.
Albrecht further teaches further comprising: selecting one out of two or more field management units based on site characteristics and/or plant characteristics attributed to the respective field management units and/or based on selection criteria ([0047-0048] teach if adjacent parcels exhibit similar management practices at the same timestamp over a predetermined period of time, the boundary identifier can determine that such parcels are under common ownership, wherein [0027] teaches determinations can be made about the presence of vegetation, similarity of crop types between parcels, boundary delineations, and more, wherein [0025] teaches the boundary delineation map shows boundary delineations separating a plot of land from other plots of land, which appear as polygons of various shapes and sizes, wherein in areas where a similar value for pixels exist, a segmentation algorithm can be applied to place all pixels in the same category, and thus larger areas sharing the same characteristics can be defined to provide a boundary delineation, wherein [0030] teaches extracting distinct changes in the types of crops planed on each parcel, wherein pixels having the same characteristics is indicative of the same/similar crop types, and thus can be extracted to determine a total area having a specific planted crop type; see also: [0029-0031, 0050-0051, 0055]).
Regarding claim 3, the combination of Albrecht, Carballido, and Genty teaches all the limitations of claim 1 above.
Albrecht further teaches wherein steps are carried out for a plurality of field management units (Fig. 5 and [0060] teach a farm management plan can be generated for each parcel including updated boundary delineations and maps to reconfigure land ownership data, wherein the management plan can provide new recommendations that can be made regarding management practices, as well as in [0050] teaches generating one or more farm management plans for each parcel; see also: [0052, 0061]).
Regarding claim 4, the combination of Albrecht, Carballido, and Genty teaches all the limitations of claim 1 above.
Albrecht further teaches further comprising: attributing treatment information to the field management unit, in particular to the field management sub-units ([0054] teaches information associated with the target area, such as the geographic area of land, is obtained including historical planting patterns, including crop types and associated planting schedules, production costs including fertilizer and seeds, historical supply/demand information for particular crop types, availability of resources, and more, as well as in [0038] teaches the historical data can be received for particular parcels of land over time including prior management practices of the target geographic area including tilling, irrigation, planting patterns, and more, wherein [0039] teaches the historical data includes requirements of fertilizer related to crop types and crop rotation; see also: [0043]), and/or
attributing observation information to the field management unit, in particular to the field management sub-units ([0030-0031] teach extracting management practices of a particular parcel of land can be monitored and extracted from images to further define historical planting patterns and boundary delineations, wherein the NDVI can be measured/extracted from each parcel within the image at predetermined intervals over a period of time; see also: [0032-0034]), and/or
attributing administrative information to the field management unit, in particular to the field management sub-units ([0030-0031] teach extracting management practices of a particular parcel of land can be monitored and extracted from images to further define historical planting patterns and boundary delineations; see also: [0032-0034]).
However, Albrecht does not explicitly teach wherein one or more of the treatment information, the observation information, the effected planting information, the harvest information, the post-harvest information, and the administrative information is collected and/or attributed with an absolute accuracy of at least +/- 2.5 cm and/or using RTK and/or PPK.
From the same or similar field of endeavor, Carballido further teaches wherein one of the observation information is collected and/or attributed with an absolute accuracy of at least +/- 2.5 cm and/or using RTK (Pg. 361 teaches dynamic tests between rows demonstrated real time satellite corrections (RTK) of 2.55 cm, wherein the autonomous vehicle requires a combination of several techniques in order to achieve a very high location accuracy, wherein as the resolution improves, it increases the number of plant-specific management tasks, wherein Pg. 362-363 teach the autonomous agricultural tractor was retrofitted for autonomous agricultural operations, wherein the RTK system includes rover sensors and GNSS data, wherein the sensors include manual override sensors, steering angle sensors, and terrain compensation sensing, wherein the autonomous tractor unit further includes a GNSS antenna, camera, and fuel cell, wherein the equipment is set up to use in the field, wherein the base station includes an RTK-GNSS receiver and WiFi Router, wherein the autonomous tractor can transmit data to the receiver of the base station, wherein with the 2 cm accuracy, the RTK systems are the most accurate solution for GNSS applications, wherein field tests were performed along rows of a plot and the location of the tractor was tracked; see also: Pgs. 364-365).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Albrecht, Carballido, and Genty to incorporate the further teachings of Carballido to include wherein one or more of the treatment information, the observation information, the effected planting information, the harvest information, the post-harvest information, and the administrative information is collected and/or attributed with an absolute accuracy of at least +/- 2.5 cm and/or using RTK and/or PPK. One would have been motivated to do so in order to provide a high degree of accuracy when tracking autonomous vehicles using real-time base station corrections (Carballido, Pg. 361). By incorporating the teachings of Carballido, one would have been able to provide centimeter-level real time position accuracy through the use of real-time extended positioning (Carballido, Pg. 362).
Regarding claim 5, the combination of Albrecht, Carballido, and Genty teaches all the limitations of claim 1 above.
Albrecht further teaches further comprising: attributing time information to the field management unit, in particular to one or more of the field management sub-units ([0030-0031] teach extracting management practices of a particular parcel of land can be monitored and extracted from images to further define historical planting patterns and boundary delineations, wherein the NDVI can be measured/extracted from each parcel within the image at predetermined intervals over a period of time; see also: [0032-0034]),
the site characteristics ([0059] teaches updating landowner ship data using the boundary delineation map, wherein previous landownership information, including boundary delineations and/or property lines, can be updated with the boundary delineation map, wherein these cropped shapes may be representative of the updated landownership values and define new farm boundary contours that can be used to update existing databases, as well as in [0043] teaches the boundary identifier generates a boundary delineation map that includes identifying whether various parcel belongs to a same owner, as well as in [0036] teaches determining farm boundary delineations to provide land ownership information; see also: [0032-0034, 0046-0050]),
the plant characteristics ([0049] teaches the system includes a vegetation identifier that employs image recognition methods and NDVI values to determine crop types within each of the parcels, wherein the crop types for each parcel can be determined ever month and stored in the memory as historical planting patterns, wherein the vegetation identifier can determine that a particular parcel, defined by one or more boundary delineations, is planted with soybean and corn in an alternating cycle, wherein [0027] teaches extracting information from satellite data in order to make determinations including the presence of vegetation, the types of vegetation, similarity of crop types between parcels, boundary delineations, and more; see also: [0030, 0033, 0047, 0055]),
treatment information ([0054] teaches information associated with the target area, such as the geographic area of land, is obtained including historical planting patterns, including crop types and associated planting schedules, production costs including fertilizer and seeds, historical supply/demand information for particular crop types, availability of resources, and more, as well as in [0038] teaches the historical data can be received for particular parcels of land over time including prior management practices of the target geographic area including tilling, irrigation, planting patterns, and more, wherein [0039] teaches the historical data includes requirements of fertilizer related to crop types and crop rotation; see also: [0043]),
observation information ([0030-0031] teach extracting management practices of a particular parcel of land can be monitored and extracted from images to further define historical planting patterns and boundary delineations, wherein the NDVI can be measured/extracted from each parcel within the image at predetermined intervals over a period of time; see also: [0032-0034]), and
administrative information ([0030-0031] teach extracting management practices of a particular parcel of land can be monitored and extracted from images to further define historical planting patterns and boundary delineations; see also: [0032-0034]).
Regarding claim 7, the combination of Albrecht, Carballido, and Genty teaches all the limitations of claim 1 above.
Albrecht further teaches wherein: the field management unit comprises a polygon ([0025] teaches the boundary delineation map shows boundary delineations separating a plot of land from other plots of land, which appear as polygons of various shapes and sizes, wherein [0043] teaches each plot of land can appear as a polygon; see also: [0026, 0058]); and/or
at least one of the field management sub-units, several of the field management sub-units, or all of the field management sub-units comprise polygons ([0025] teaches the boundary delineation map shows boundary delineations separating a plot of land from other plots of land, which appear as polygons of various shapes and sizes, wherein [0026] teaches any open polygons are closed such that the boundary delineation map includes multiple plots of land, which are parcels, as well as in [0043] teaches the boundary delineation map has a plurality of polygons corresponding to parcels/lots; see also: [0058]).
Regarding claim 8, the combination of Albrecht, Carballido, and Genty teaches all the limitations of claim 1 above.
However, Albrecht does not explicitly teach wherein the at least one sensor platform comprises a mobile sensor platform, and comprises one or more of: a smart phone; a tablet; a mobile computer; a wearable computer, in particular a smartwatch and/or hands free device, such as smart glasses; an unmanned autonomous vehicle, in particular ground-borne and/or air- borne, for example a field robot and/or a drone; an agricultural machine, such as a tractor and/or a planter and/or a harvester and/or a sprayer; a helicopter; an airplane; and/or a non-geostationary satellite, and/or wherein the at least one sensor platform comprises a stationary sensor platform, and comprises one or more of: a weather station; a stationary sensor; a stationary measuring device; and/or a geostationary satellite.
From the same or similar field of endeavor, Carballido further teaches wherein the at least one sensor platform comprises a mobile sensor platform, and comprises one or more of: an unmanned autonomous vehicle, in particular ground-borne; an agricultural machine, such as a tractor (Pg. 362-363 teach the autonomous agricultural tractor was retrofitted for autonomous agricultural operations, wherein the RTK system includes rover sensors and GNSS data, wherein the sensors include manual override sensors, steering angle sensors, and terrain compensation sensing, wherein the autonomous tractor unit further includes a GNSS antenna, camera, and fuel cell, wherein the equipment is set up to use in the field, wherein the base station includes an RTK-GNSS receiver and WiFi Router, wherein the autonomous tractor can transmit data to the receiver of the base station, wherein with the 2 cm accuracy, the RTK systems are the most accurate solution for GNSS applications, wherein field tests were performed along rows of a plot and the location of the tractor was tracked; see also: Pgs. 361, 364-365).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Albrecht, Carballido, and Genty to incorporate the further teachings of Carballido to include wherein the at least one sensor platform comprises a mobile sensor platform, and comprises one or more of: a smart phone; a tablet; a mobile computer; a wearable computer, in particular a smartwatch and/or hands free device, such as smart glasses; an unmanned autonomous vehicle, in particular ground-borne and/or air- borne, for example a field robot and/or a drone; an agricultural machine, such as a tractor and/or a planter and/or a harvester and/or a sprayer; a helicopter; an airplane; and/or a non-geostationary satellite, and/or wherein the at least one sensor platform comprises a stationary sensor platform, and comprises one or more of: a weather station; a stationary sensor; a stationary measuring device; and/or a geostationary satellite. One would have been motivated to do so in order to provide a high degree of accuracy when tracking autonomous vehicles using real-time base station corrections (Carballido, Pg. 361). By incorporating the teachings of Carballido, one would have been able to provide centimeter-level real time position accuracy through the use of real-time extended positioning (Carballido, Pg. 362).
Regarding claim 9, the combination of Albrecht, Carballido, and Genty teaches all the limitations of claim 1 above.
Albrecht further teaches wherein managing the agricultural processes comprises one or more of: planning ([0018] teaches generating farm management plans to support production management);
administrating ([0030-0031] teach extracting management practices of a particular parcel of land can be monitored and extracted from images to further define historical planting patterns and boundary delineations; see also: [0032-0034]);
monitoring ([0031] teaches management practices can be monitored from images; see also: [0032-0034]);
documenting ([0027] teaches storing satellite data for the geographic area; see also: [0024]);
analyzing ([0035] teaches data analysis can be performed, as well as in [0037] teaches analyzing the satellite data to determine boundary delineations);
evaluating ([0055-0057] teach evaluating the images in order to generate one or more line segments and comparing management practices; see also: [0052]); and/or
wherein the agricultural processes comprise one or more of:
harvesting ([0032] teaches identifying whether management practices are occurring at the same time in adjacent parcels of land that exhibit similar manage practices, wherein the management practices include harvesting, as well as in [0052] teaches identifying high yield and the number of invoices for renting harvesting equipment; see also: [0050]);
treatment with fertilizer ([0052] teaches identifying the amount of fertilizer used in a given field, as well as in [0060] teaches generating recommendations related to the application of fertilizer).
Regarding claim 10, the combination of Albrecht, Carballido, and Genty teaches all the limitations of claim 1 above.
Albrecht further teaches wherein the field management sub-units comprise one or more of
plots ([0026] teaches the boundary delineation map can include multiple plots of land, or parcels, wherein farms can be identified as a parcel from satellite imagery; see also: [0027-0035]);
in particular of individual plant material ([0026-0027] teach the boundary delineation map can include multiple plots of land, or parcels, wherein farms can be identified as a parcel from satellite imagery, wherein data can be extracted from the satellite data including the presence of vegetation, types of vegetation, and more; see also: [0028-0035]),
and/or wherein the site characteristics comprise one or more of: field crop history ([0031] teaches defining historical planting patterns and boundary delineations, wherein this data can be stored in the memory; see also: [0033]); farmer ([0059] teaches updating landowner ship data using the boundary delineation map, wherein previous landownership information, including boundary delineations and/or property lines, can be updated with the boundary delineation map, wherein these cropped shapes may be representative of the updated landownership values and define new farm boundary contours that can be used to update existing databases, as well as in [0043] teaches the boundary identifier generates a boundary delineation map that includes identifying whether various parcel belongs to a same owner, as well as in [0036] teaches determining farm boundary delineations to provide land ownership information; see also: [0032-0034, 0046-0050]);
wherein the plant characteristics comprise one or more of: year ([0049] teaches the system includes a vegetation identifier that employs image recognition methods and NDVI values to determine crop types within each of the parcels, wherein the crop types for each parcel can be determined ever month and stored in the memory as historical planting patterns, wherein the vegetation identifier can determine that a particular parcel, defined by one or more boundary delineations, is planted with soybean and corn in an alternating cycle, wherein [0027] teaches extracting information from satellite data in order to make determinations including the presence of vegetation, the types of vegetation, similarity of crop types between parcels, boundary delineations, and more, wherein [0031] teaches the parcel images can be extracted for each month over a period of ten years; see also: [0030, 0033, 0047, 0055]); – crop ([0049] teaches the system includes a vegetation identifier that employs image recognition methods and NDVI values to determine crop types within each of the parcels, wherein the crop types for each parcel can be determined ever month and stored in the memory as historical planting patterns, wherein the vegetation identifier can determine that a particular parcel, defined by one or more boundary delineations, is planted with soybean and corn in an alternating cycle, wherein [0027] teaches extracting information from satellite data in order to make determinations including the presence of vegetation, the types of vegetation, similarity of crop types between parcels, boundary delineations, and more; see also: [0030, 0033, 0047, 0055]).
Regarding claim 11, the combination of Albrecht, Carballido, and Genty teaches all the limitations of claim 1 above.
However, Albrecht does not explicitly teach wherein the at least one sensor further comprises one or more of: an environmental sensor, for example soil sensor and/ or soil water sensor, such as TDR and/or FDR and/or UMP, and/or GPR and/or EMI and/or ERT; a weather sensor, for example weather station and/or sensor for weather data;
From the same or similar field of endeavor, Genty further teaches wherein the at least one sensor further comprises one or more of: an optical sensor, for example NIRS ([0002] teaches the system includes an internet of things for monitoring and optimizing plant growth and quality using optical sensors, wherein [0026-0029] teach the image sensors include infrared sensors that can measure the reflectance of spectra of plants within the visible and/or near infrared range for measuring; see also: [0060, 0068]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Albrecht, Carballido, and Genty to incorporate the further teachings of Genty to include wherein the at least one sensor further comprises one or more of: an environmental sensor, for example soil sensor and/ or soil water sensor, such as TDR and/or FDR and/or UMP, and/or GPR and/or EMI and/or ERT; a weather sensor, for example weather station and/or sensor for weather data;
Regarding claim 13, the combination of Albrecht, Carballido, and Genty teaches all the limitations of claim 1 above.
However, Albrecht does not explicitly teach wherein: the at least one sensor platform collects data, in particular imaging data, of the field management unit, in particular of the field management sub-units, by defining a data collection path for the at least one sensor platform; with an absolute accuracy of at least +/- 2.5 cm and/or using RTK and/or PPK; and/or by collecting a plurality of at least partly overlapping images per field management unit, in particular per field management sub-unit, with an absolute accuracy of at least +/- 2.5 cm and/or using RTK and/or PPK, such that a plurality of images, are collected per pixel.
From the same or similar field of endeavor, Genty further teaches wherein: the at least one sensor platform collects data, in particular imaging data, of the field management unit, in particular of the field management sub-units, by defining a data collection path for the at least one sensor platform ([0026] teaches the image sensors in the autonomous plant growth optimization system can monitor plants in the farming facility, wherein the imaging systems may be used for a plurality of plants, such as all plants in a particular row, or all plants within a particular quadrant, as well as in [0033] teaches the various environmental and image sensors may be used for all plants within a particular row or quadrant, wherein [0020] teaches image capturing sensors that generate image data for use in extracting phenotypic features; see also: [0026]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Albrecht, Carballido, and Genty to incorporate the further teachings of Genty to include wherein: the at least one sensor platform collects data, in particular imaging data, of the field management unit, in particular of the field management sub-units, by defining a data collection path for the at least one sensor platform. One would have been motivated to do so in order to improve the growing of crops through the use of intelligent, variable spectrum sensors (Genty, [0024]). By incorporating the teachings of Genty, one would have been able to derive plant phenotype information using imaging sensors that perform computer vision (Genty, [0040]).
However, the combination does not explicitly teach with an absolute accuracy of at least +/- 2.5 cm and/or using RTK and/or PPK.
From the same or similar field of endeavor, Carballido further teaches with an absolute accuracy of at least +/- 2.5 cm and/or using RTK (Pg. 361 teaches dynamic tests between rows demonstrated real time satellite corrections (RTK) of 2.55 cm, wherein the autonomous vehicle requires a combination of several techniques in order to achieve a very high location accuracy, wherein as the resolution improves, it increases the number of plant-specific management tasks, wherein Pg. 362-363 teach the autonomous agricultural tractor was retrofitted for autonomous agricultural operations, wherein the RTK system includes rover sensors and GNSS data, wherein the sensors include manual override sensors, steering angle sensors, and terrain compensation sensing, wherein the autonomous tractor unit further includes a GNSS antenna, camera, and fuel cell, wherein the equipment is set up to use in the field, wherein the base station includes an RTK-GNSS receiver and WiFi Router, wherein the autonomous tractor can transmit data to the receiver of the base station, wherein with the 2 cm accuracy, the RTK systems are the most accurate solution for GNSS applications, wherein field tests were performed along rows of a plot and the location of the tractor was tracked; see also: Pgs. 364-365).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Albrecht, Carballido, and Genty to incorporate the further teachings of Carballido to include with an absolute accuracy of at least +/- 2.5 cm and/or using RTK and/or PPK. One would have been motivated to do so in order to provide a high degree of accuracy when tracking autonomous vehicles using real-time base station corrections (Carballido, Pg. 361). By incorporating the teachings of Carballido, one would have been able to provide centimeter-level real time position accuracy through the use of real-time extended positioning (Carballido, Pg. 362).
Regarding claim 14, Albrecht teaches a system for managing agricultural processes (Figs. 1 & 4), comprising:
a database system containing data relating to a field management unit ([0031] teaches extracted data can be stored in the memory, wherein the extracted data includes boundary delineations, as well as in [0059] teaches the farm boundaries and boundary delineation map are stored in a database, as well as in Fig. 8 and [0094] teaches the workloads layer includes the boundary delineation, wherein [0091] teaches the hardware and software layer includes storage devices, database software, and more, as well as in [0059] teaches the cropped shapes may be representative of the updated landownership values and define new farm boundary contours can be used to update existing databases; see also: [0027, 0043, 0093]), the data including:
georeferenced boundaries of the field management unit ([0031] teaches extracted data can be stored in the memory, wherein the extracted data includes boundary delineations, as well as in [0059] teaches the farm boundaries and boundary delineation map are stored in a database, as well as in Fig. 8 and [0094] teaches the workloads layer includes the boundary delineation, wherein [0091] teaches the hardware and software layer includes storage devices, database software, and more, as well as in [0059] teaches the cropped shapes may be representative of the updated landownership values and define new farm boundary contours can be used to update existing databases; see also: [0027, 0043, 0093]);
a layout of the field management unit defining georeferenced field management sub-units within the field management unit ([0031] teaches extracted data can be stored in the memory, wherein the extracted data includes boundary delineations, as well as in [0059] teaches the farm boundaries and boundary delineation map are stored in a database, as well as in Fig. 8 and [0094] teaches the workloads layer includes the boundary delineation, wherein [0091] teaches the hardware and software layer includes storage devices, database software, and more, as well as in [0059] teaches the cropped shapes may be representative of the updated landownership values and define new farm boundary contours can be used to update existing databases; see also: [0027, 0043, 0093]);
site characteristics attributed to the field management unit, in particular to the field management sub-units ([0059] teaches updating landowner ship data using the boundary delineation map, wherein previous landownership information, including boundary delineations and/or property lines, can be updated with the boundary delineation map, wherein these cropped shapes may be representative of the updated landownership values and define new farm boundary contours that can be used to update existing databases, as well as in [0043] teaches the boundary identifier generates a boundary delineation map that includes identifying whether various parcel belongs to a same owner, as well as in [0036] teaches determining farm boundary delineations to provide land ownership information; see also: [0032-0034, 0046-0050]);
and/or plant characteristics to the field management unit, in particular to the field management sub-units ([0049] teaches the system includes a vegetation identifier that employs image recognition methods and NDVI values to determine crop types within each of the parcels, wherein the crop types for each parcel can be determined ever month and stored in the memory as historical planting patterns, wherein the vegetation identifier can determine that a particular parcel, defined by one or more boundary delineations, is planted with soybean and corn in an alternating cycle, wherein [0027] teaches extracting information from satellite data in order to make determinations including the presence of vegetation, the types of vegetation, similarity of crop types between parcels, boundary delineations, and more; see also: [0030, 0033, 0047, 0055]).
However, Albrecht does not explicitly teach and at least one sensor platform with a control unit, wherein the at least one sensor platform comprises a phenotypical sensor and is configured to run autonomously, wherein the control unit is adapted to receive data relating to the field management unit from the database system, and wherein the control unit is adapted to automatically initiate the performing of a task by the at least one sensor platform responsive to receiving the data relating to the field management unit.
From the same or similar field of endeavor, Carballido teaches and at least one sensor platform with a control unit (Pg. 362-363 teach the autonomous agricultural tractor was retrofitted for autonomous agricultural operations, wherein the RTK system includes rover sensors and GNSS data, wherein the sensors include manual override sensors, steering angle sensors, and terrain compensation sensing, wherein the autonomous tractor unit further includes a GNSS antenna, camera, and fuel cell, wherein the equipment is set up to use in the field, wherein the base station includes an RTK-GNSS receiver and WiFi Router, wherein the autonomous tractor can transmit data to the receiver of the base station; see also: Pgs. 364-365),
wherein the at least one sensor platform is configured to run autonomously (Pg. 362-363 teach the autonomous agricultural tractor was retrofitted for autonomous agricultural operations, wherein the RTK system includes rover sensors and GNSS data, wherein the sensors include manual override sensors, steering angle sensors, and terrain compensation sensing, wherein the autonomous tractor unit further includes a GNSS antenna, camera, and fuel cell, wherein the equipment is set up to use in the field, wherein the base station includes an RTK-GNSS receiver and WiFi Router, wherein the autonomous tractor can transmit data to the receiver of the base station; see also: Pgs. 364-365),
wherein the control unit is adapted to receive data relating to the field management unit from the database system (Pg. 362-363 teach the autonomous agricultural tractor was retrofitted for autonomous agricultural operations, wherein the RTK system includes rover sensors and GNSS data, wherein the sensors include manual override sensors, steering angle sensors, and terrain compensation sensing, wherein the autonomous tractor unit further includes a GNSS antenna, camera, and fuel cell, wherein the equipment is set up to use in the field, wherein the base station includes an RTK-GNSS receiver and WiFi Router, wherein the autonomous tractor can transmit data to the receiver of the base station; see also: Pgs. 364-365), and
wherein the control unit is adapted to automatically initiate the performing of a task by the at least one sensor platform responsive to receiving the data relating to the field management unit (Pg. 361 teaches dynamic tests between rows demonstrated real time satellite corrections (RTK) of 2.55 cm, wherein the autonomous vehicle requires a combination of several techniques in order to achieve a very high location accuracy, wherein as the resolution improves, it increases the number of plant-specific management tasks, wherein Pg. 362-363 teach the autonomous agricultural tractor was retrofitted for autonomous agricultural operations, wherein the RTK system includes rover sensors and GNSS data, wherein the sensors include manual override sensors, steering angle sensors, and terrain compensation sensing, wherein the autonomous tractor unit further includes a GNSS antenna, camera, and fuel cell, wherein the equipment is set up to use in the field, wherein the base station includes an RTK-GNSS receiver and WiFi Router, wherein the autonomous tractor can transmit data to the receiver of the base station, wherein with the 2 cm accuracy, the RTK systems are the most accurate solution for GNSS applications, wherein field tests were performed along rows of a plot and the location of the tractor was tracked; see also: Pgs. 364-365).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Albrecht to incorporate the teachings of Carballido to include and at least one sensor platform with a control unit, wherein the at least one sensor platform is configured to run autonomously, wherein the control unit is adapted to receive data relating to the field management unit from the database system, and wherein the control unit is adapted to automatically initiate the performing of a task by the at least one sensor platform responsive to receiving the data relating to the field management unit. One would have been motivated to do so in order to provide a high degree of accuracy when tracking autonomous vehicles using real-time base station corrections (Carballido, Pg. 361). By incorporating the teachings of Carballido, one would have been able to provide centimeter-level real time position accuracy through the use of real-time extended positioning (Carballido, Pg. 362).
However, the combination of Albrecht and Carballido does not explicitly teach wherein the at least one sensor platform comprises a phenotypical sensor.
From the same or similar field of endeavor, Genty teaches wherein the at least one sensor platform comprises a phenotypical sensor ([0040] teaches the plant phenotype output may include plant phenotype information for each of the one or more plants in the indoor farm, wherein the plant phenotype information may be derived from one or more imaging sensors in the system using computer vision and computer based image analysis, wherein [0026] teaches the autonomous system receives the generated image data, wherein the image data can be RGB imaging, infrared imaging, multi-spectral imaging, and fluorescent imaging; see also: [0020, 0039]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Albrecht and Carballido to incorporate the teachings of Genty to include wherein the at least one sensor platform comprises a phenotypical sensor. One would have been motivated to do so in order to improve the growing of crops through the use of intelligent, variable spectrum sensors (Genty, [0024]). By incorporating the teachings of Genty, one would have been able to derive plant phenotype information using imaging sensors that perform computer vision (Genty, [0040]).
Regarding claim 15, the combination of Albrecht, Carballido, and Genty teaches all the limitations of claim 1 above.
Albrecht further teaches a system comprising: one or more processors ([0095-0096] teach a computer system with a memory storing instructions for use by the processor to carry out aspects of the present invention; see also: [0019-0023]); and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to perform the method according to claim 1 ([0095-0096] teach a computer system with a memory storing instructions for use by the processor to carry out aspects of the present invention; see also: [0019-0023]).
Regarding claim 16, the combination of Albrecht, Carballido, and Genty teaches all the limitations of claim 1 above.
Albrecht further teaches a computer program product comprising a non-transitory computer readable medium comprising computer-readable instructions that, when loaded and run on a computer, cause the computer to perform a method according to claim 1 ([0095-0097] teach the computer program product may include a computer readable storage medium having computer readable program instructions thereon for causing a processor to carry out the invention).
Regarding claim 17, the combination of Albrecht, Carballido, and Genty teaches all the limitations of claim 1 above.
However, Albrecht does not explicitly teach wherein the phenotypical sensor comprises one or more of a RGB camera, a thermal camera, a hyperspectral camera, a multispectral camera, or a combination thereof.
From the same or similar field of endeavor, Genty further teaches wherein the phenotypical sensor comprises one or more of a RGB camera, a thermal camera, a multispectral camera, or a combination thereof ([0002] teaches the system includes an internet of things for monitoring and optimizing plant growth and quality using optical sensors, wherein [0026-0029] teach the image sensors include RGB imaging, multi-spectral imaging, and infrared sensors that can measure the reflectance of spectra of plants within the visible and/or near infrared range for measuring, wherein the IR imaging system may include thermographic imaging for extracting plant information; see also: [0060, 0068]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Albrecht, Carballido, and Genty to incorporate the further teachings of Genty to include wherein the phenotypical sensor comprises one or more of a RGB camera, a thermal camera, a hyperspectral camera, a multispectral camera, or a combination thereof. One would have been motivated to do so in order to improve the growing of crops through the use of intelligent, variable spectrum sensors (Genty, [0024]). By incorporating the teachings of Genty, one would have been able to derive plant phenotype information using imaging sensors that perform computer vision (Genty, [0040]).
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over Albrecht et al. (US 20190370966 A1) in view of Carballido et al. ("Comparison of positional accuracy between RTK and RTX GNSS based on the autonomous agricultural vehicles under field conditions." 2014) in view of Genty et al. (US 20190377946 A1) in view of Briggs (US 20200380438 A1).
Regarding claim 6, the combination of Albrecht, Carballido, and Genty teaches all the limitations of claim 1 above.
However, Albrecht does not explicitly teach further comprising: creating a georeferenced isolation area of the field management unit and storing the isolation area of the field management unit in the geospatial database, wherein
From the same or similar field of endeavor, Briggs teaches further comprising: creating a georeferenced isolation area of the field management unit and storing the isolation area of the field management unit in the geospatial database ([0006] teaches an automated system for planning placement of plants including determining an exclusion zone for each plant to be planted, as well as in [0043] teaches the exclusion zone is an area surrounding a plant that represents the amount of space a plant occupies or is expected to occupy at a given growth stage, and within which, other plants should not be planted so that the plant has sufficient room to grow, wherein [0045] teaches the exclusion zones can be stored in the data structure; see also: [0044, 0046-0047]),
wherein the isolation area is created: by calculation of a distance between the field management unit and another field management unit or another isolation area ([0006] teaches an automated system for planning placement of plants including determining an exclusion zone for each plant to be planted, as well as in [0043] teaches the exclusion zone is an area surrounding a plant that represents the amount of space a plant occupies or is expected to occupy at a given growth stage, and within which, other plants should not be planted so that the plant has sufficient room to grow, wherein [0045] teaches the exclusion zones can be stored in the data structure, wherein [0060-0061] teach determining whether there is any overlap between the exclusion zones, and whether the overlap is greater than an allowable or permissible threshold; see also: [0044, 0046-0048]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Albrecht, Carballido, and Genty to incorporate the teachings of Briggs to include further comprising: creating a georeferenced isolation area of the field management unit and storing the isolation area of the field management unit in the geospatial database, wherein preferably the isolation area is created: by calculation of an outer boundary of the isolation area by adding at least one buffer distance to the boundaries of the field management unit in a direction pointing away from the field management unit; and/or by calculation of a distance between the field management unit and another field management unit or another isolation area; and/or by calculation of an inner boundary of the isolation area by adding at least one buffer distance from the boundaries of the field management unit in a direction pointing towards the field management unit. One would have been motivated to do so in order to maximize or optimize the planning of the placement of plants and the stimulation of plants already planted based on plant spacing and environmental conditions (Briggs, [0003]). By incorporating the teachings of Briggs, one would have been able to maximize the spacing between plants in order to create an improved planting plan including exclusion zones (Briggs, [0049-0050]).
Claim(s) 12 is rejected under 35 U.S.C. 103 as being unpatentable over Albrecht et al. (US 20190370966 A1) in view of Carballido et al. ("Comparison of positional accuracy between RTK and RTX GNSS based on the autonomous agricultural vehicles under field conditions." 2014) in view of Genty et al. (US 20190377946 A1) in view of Sood et al. (US 20200202458 A1).
Regarding claim 12, the combination of Albrecht, Carballido, and Genty teaches all the limitations of claim 1 above.
However, Albrecht does not explicitly teach wherein the geospatial database is part of a database system comprising one or more further databases, wherein a data connection within the database system and/or between the geospatial database and one or more of the further databases of the database system and/or between the at least one sensor platform and the database system, in particular one or more of databases of the database system, is a direct and/or indirect data connection.
From the same or similar field of endeavor, Sood teaches wherein the geospatial database is part of a database system comprising one or more further databases, wherein a data connection within the database system one or more of the further databases of the database system, in particular one or more of databases of the database system, is a direct data connection ([0065] teaches the data management layer comprises a collection of databases including hierarchical databases, relational databases, flat file databases, and more, wherein [0119] teaches receiving historical data in publicly available agricultural databases, as well as in [0145] teaches a third-party online weather database, wherein [0060] teaches the network can include wireline or wireless links including terrestrial or satellite links, wherein the various elements may have direct communication links; see also: [0056, 0149]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Albrecht, Carballido, and Genty to incorporate the teachings of Sood to include wherein the geospatial database is part of a database system comprising one or more further databases, wherein a data connection within the database system and/or between the geospatial database and one or more of the further databases of the database system and/or between the at least one sensor platform and the database system, in particular one or more of databases of the database system, is a direct and/or indirect data connection. One would have been motivated to do so in order to optimize subfield yields by understanding yield performance at a subfield level, thus allowing growers to accurately vary seeding rates (Sood, [0006]). By incorporating the teachings of Sood, one would have been able to provide yield prediction at a granular subfield level for observations over different geo-locations within a field (Sood, [0005]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Regan et al. (US 20190200535 A1) discloses an automated planting system including sensors that detect phenotype
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SARA GRACE BROWN/Primary Examiner, Art Unit 3625