DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The preliminary amendment filed on 08/25/2025 has been entered and fully considered.
Claims 1, 8, and 15 have been amended.
Claims 1-20 are pending in Instant Application.
Priority
Examiner acknowledges Applicant’s claim to priority benefits of U.S. Patent Application No. 17/850,988, entitled “METHODS AND SYSTEMS FOR MODELING SOIL PROCESSES AND PROPERTIES”, filed on June 27, 2022; which is a continuation of U.S. Patent Application No. 17/327,055, entitled “METHODS AND SYSTEMS FOR MODELING SOIL PROCESSES AND PROPERTIES”, filed on May 21, 2021.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 06/26/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered if signed and initialed by the Examiner.
Double Patenting
A rejection based on double patenting of the "same invention" type finds its support in the language of 35 U.S.C. 101 which states that "whoever invents or discovers any new and useful process ... may obtain a patent therefor ..." (Emphasis added). Thus, the term "same invention," in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957); and In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the conflicting claims so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-20 are non-provisionally rejected on the ground of non-statutory non-obviousness-type double patenting as being unpatentable over claims 1-20 of Berg et al., U.S. Patent 12,310,275. Although the claims at issue are not identical, they are not patentably distant from each other because they are drawn to obvious variations.
In view of the above, since the subject matters recited in the claims 1-20 of the instant application were fully disclosed in and covered by the claims 1-20 of US patent 12,310,275, allowing the claims to result in an unjustified or improper timewise extension of the "right to exclude" granted by a patent.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 13 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Regarding claim 13, the claim states generating an electronic report where the parent claim, claim 8, already teaches this limitation. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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.
Claims 1-2, 5, 8-9, 12-13, 15-16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Rooney et al. (USPGPub 2022/0051118) in view of Birkland et al. (USPGPub 2021/0282310). As per claim 1, Rooney discloses a computer-implemented method of improving and quantifying sustainable growing practices within an agricultural field, comprising: receiving, via one or more processors, a machine data set corresponding to a plurality of growing medium data points within the agricultural field (see at least paragraph 0080; wherein obtains 302 sensor data for each of a plurality of physical locations in the physical site); determining, via one or more processors, a respective soil sustainability measurement (see at least paragraph 0070; wherein the measurements from the tip force sensor and the sleeve force sensor can be combined to provide a measure of soil strength); processing, via one or more processors, the respective soil sustainability measurement of at least one of the plurality of area using a trained machine learning model (see at least paragraph 0084; wherein the sensor data is provided 304 to one or more machine learning models configured to receive the sensor data that includes the sensor profiles, and to predict one or more characteristics of the growing medium at each of the physical locations) to generate a recommendation for improving soil health including an executable agricultural prescription configured to be executed by an agricultural implement to modify a state of the agricultural field by at least one of: (i) adjusting tillage depth or angle, (ii) applying variable rates of a chemical and/or seed, or (iii) incorporating a plurality of different crop residues (see at least paragraph 0132; wherein the system generates 308 a recommendation using the predicted characteristics, handled by the recommendation engine 120 of the system 100. Types of recommendations include agronomic planning recommendations for the design, planting, and harvesting of plants in a physical site, and dynamic decision support for different issues associated with the upkeep of plants, including irrigation, fertilization, fertility management, and pest control); generating, via one or more processors, an electronic report corresponding to the recommendation (see at least paragraph 0142; wherein the user interface can display measured and predicted characteristics from the system, as well as generated recommendations…see at least paragraph 0143; wherein the user interface 800 can be displayed on a user device, e.g., a laptop or mobile phone). Rooney does not explicitly mention determining, via one or more processors, for each of a plurality of hexagrids corresponding to the agricultural field, a respective soil sustainability measurement; and storing, via one or more processors, the executable agricultural prescription in a non- transitory memory. However Birkland does disclose: determining, via one or more processors, for each of a plurality of hexagrids corresponding to the agricultural field, a respective soil sustainability measurement (see at least paragraph 0069; wherein each zone (of corresponding surface roughness index) may comprise a cell of uniform size or uniform dimensions through the field or work site, such as a zone or region with a polygonal boundary (e.g., triangular, rectangular, hexagonal or pentagonal), or a region with another geometric shape); and storing, via one or more processors, the executable agricultural prescription in a non- transitory memory (see at least paragraph 0080; wherein the data maps 301 or underlying data for surface index values versus zones may be stored on a data storage device 24 of the data processing system 14 or uploaded to a central server or a cloud computing service (e.g., with data storage devices) for retrieval by a different data processing system 14 on another subsequent vehicle or subsequent implement for performing a subsequent agronomic task in a growing season. For instance, an earlier vehicle (e.g., survey vehicle or reconnaissance vehicle, such as vehicle 82 without implement 83) may provide a survey service for collecting that data maps 301 of surface roughness index zones (e.g., incidental to the method of FIG. 2B), while a subsequent vehicle and/or implement (e.g., a combination of vehicle 82 with implement 83) may perform a tillage or planting operation (e.g., in FIG. 2C), or spraying operation (e.g., sprayer implement, or boom with nozzles required); where the earlier vehicle electronics and subsequent vehicle electronics (e.g., systems 11, 111 or data processing systems (e.g., 14)) can exchange or share data (e.g., estimated in step S207 of FIG. 2B for retrieval in step S217 of FIG. 2C) via the data storage device 24, the central service or cloud, with appropriate consent for processing, exchanging or sharing the data). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Birkland with the teachings as in Rooney. The motivation for doing so would have been to reduce fuel consumption of the vehicle and over-application of crop inputs, see Birkland paragraph 105. As per claims 2, 9, and 16, Rooney discloses wherein receiving, via the one or more processors, the machine data set corresponding to the plurality of growing medium data points within the agricultural field includes receiving the machine data from a cloud server via a computer network (see at least paragraph 0060; wherein the sensor processing engine 105 can be implemented on the cloud, and communicatively connected to receiving units, e.g., radio transmitters, configured to receive and transmit data from the sensor units to the sensor processing engine 105). As per claims 5, 12, and 19, Rooney discloses further comprising: providing input into the machine learning model including a soil strength, a rate of soil change or an aggregate sustainability of at least one of the plurality of growing medium data points within the agricultural field (see at least paragraph 0070; wherein the measurements from the tip force sensor and the sleeve force sensor can be combined to provide a measure of soil strength). As per claim 8, Rooney discloses a computing system comprising: one or more processors (see at least paragraph 0151; wherein a computer program can be based on general or special-purpose microprocessors, quantum computers, or any combination, or any other kind of central processing unit); and one or more memories having stored thereon instructions that, when executed by the one or more processors (see at least paragraph 0151; wherein a central processing unit will receive instructions and data from a read-only memory or a random-access memory or both), cause the computing system to: receive, via the one or more processors, a machine data set corresponding to a plurality of growing medium data points within an agricultural field (see at least paragraph 0080; wherein obtains 302 sensor data for each of a plurality of physical locations in the physical site); determine a respective soil sustainability measurement (see at least paragraph 0070; wherein the measurements from the tip force sensor and the sleeve force sensor can be combined to provide a measure of soil strength); process the respective soil sustainability measurement of at least one of the plurality of area using a trained machine learning model (see at least paragraph 0084; wherein the sensor data is provided 304 to one or more machine learning models configured to receive the sensor data that includes the sensor profiles, and to predict one or more characteristics of the growing medium at each of the physical locations) to generate a recommendation for improving soil health including an executable agricultural prescription configured to be executed by an agricultural implement to modify a state of the agricultural field by at least one of: (i) adjusting tillage depth or angle, (ii) applying variable rates of a chemical and/or seed, or (iii) incorporating a plurality of different crop residues (see at least paragraph 0132; wherein the system generates 308 a recommendation using the predicted characteristics, handled by the recommendation engine 120 of the system 100. Types of recommendations include agronomic planning recommendations for the design, planting, and harvesting of plants in a physical site, and dynamic decision support for different issues associated with the upkeep of plants, including irrigation, fertilization, fertility management, and pest control); and generate, via one or more processors, an electronic report corresponding to the recommendation (see at least paragraph 0142; wherein the user interface can display measured and predicted characteristics from the system, as well as generated recommendations…see at least paragraph 0143; wherein the user interface 800 can be displayed on a user device, e.g., a laptop or mobile phone). Rooney does not explicitly mention determine, for each of a plurality of hexagrids corresponding to the agricultural field, a respective soil sustainability measurement; and store the executable agricultural prescription in the one or more memories. However Birkland does disclose: determine, for each of a plurality of hexagrids corresponding to the agricultural field, a respective soil sustainability measurement (see at least paragraph 0069; wherein each zone (of corresponding surface roughness index) may comprise a cell of uniform size or uniform dimensions through the field or work site, such as a zone or region with a polygonal boundary (e.g., triangular, rectangular, hexagonal or pentagonal), or a region with another geometric shape); and store the executable agricultural prescription in the one or more memories (see at least paragraph 0080; wherein the data maps 301 or underlying data for surface index values versus zones may be stored on a data storage device 24 of the data processing system 14 or uploaded to a central server or a cloud computing service (e.g., with data storage devices) for retrieval by a different data processing system 14 on another subsequent vehicle or subsequent implement for performing a subsequent agronomic task in a growing season. For instance, an earlier vehicle (e.g., survey vehicle or reconnaissance vehicle, such as vehicle 82 without implement 83) may provide a survey service for collecting that data maps 301 of surface roughness index zones (e.g., incidental to the method of FIG. 2B), while a subsequent vehicle and/or implement (e.g., a combination of vehicle 82 with implement 83) may perform a tillage or planting operation (e.g., in FIG. 2C), or spraying operation (e.g., sprayer implement, or boom with nozzles required); where the earlier vehicle electronics and subsequent vehicle electronics (e.g., systems 11, 111 or data processing systems (e.g., 14)) can exchange or share data (e.g., estimated in step S207 of FIG. 2B for retrieval in step S217 of FIG. 2C) via the data storage device 24, the central service or cloud, with appropriate consent for processing, exchanging or sharing the data). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Birkland with the teachings as in Rooney. The motivation for doing so would have been to reduce fuel consumption of the vehicle and over-application of crop inputs, see Birkland paragraph 105. As per claim 13, Rooney discloses the one or more memories having stored thereon further instructions that when executed, cause the computing system to: generate an electronic report (see at least paragraph 0142; wherein the user interface can display measured and predicted characteristics from the system, as well as generated recommendations…see at least paragraph 0143; wherein the user interface 800 can be displayed on a user device, e.g., a laptop or mobile phone). As per claim 15, Rooney discloses non-transitory computer readable medium containing program instructions that when executed, cause a computer to: receive, via one or more processors, a machine data set corresponding to a plurality of growing medium data points within an agricultural field (see at least paragraph 0080; wherein obtains 302 sensor data for each of a plurality of physical locations in the physical site); determine a respective soil sustainability measurement (see at least paragraph 0070; wherein the measurements from the tip force sensor and the sleeve force sensor can be combined to provide a measure of soil strength); process the respective soil sustainability measurement of at least one of the plurality of area using a trained machine learning model (see at least paragraph 0084; wherein the sensor data is provided 304 to one or more machine learning models configured to receive the sensor data that includes the sensor profiles, and to predict one or more characteristics of the growing medium at each of the physical locations) to generate a recommendation for improving soil health including an executable agricultural prescription configured to be executed by an agricultural implement to modify a state of the agricultural field by at least one of: (i) adjusting tillage depth or angle, (ii) applying variable rates of a chemical and/or seed, or (iii) incorporating a plurality of different crop residues (see at least paragraph 0132; wherein the system generates 308 a recommendation using the predicted characteristics, handled by the recommendation engine 120 of the system 100. Types of recommendations include agronomic planning recommendations for the design, planting, and harvesting of plants in a physical site, and dynamic decision support for different issues associated with the upkeep of plants, including irrigation, fertilization, fertility management, and pest control); and generate, via one or more processors, an electronic report corresponding to the recommendation (see at least paragraph 0142; wherein the user interface can display measured and predicted characteristics from the system, as well as generated recommendations…see at least paragraph 0143; wherein the user interface 800 can be displayed on a user device, e.g., a laptop or mobile phone). Rooney does not explicitly mention determine, for each of a plurality of hexagrids corresponding to the agricultural field, a respective soil sustainability measurement; and store the executable agricultural prescription in the computer readable medium. However Birkland does disclose: determine, for each of a plurality of hexagrids corresponding to the agricultural field, a respective soil sustainability measurement (see at least paragraph 0069; wherein each zone (of corresponding surface roughness index) may comprise a cell of uniform size or uniform dimensions through the field or work site, such as a zone or region with a polygonal boundary (e.g., triangular, rectangular, hexagonal or pentagonal), or a region with another geometric shape); and store the executable agricultural prescription in the computer readable medium (see at least paragraph 0080; wherein the data maps 301 or underlying data for surface index values versus zones may be stored on a data storage device 24 of the data processing system 14 or uploaded to a central server or a cloud computing service (e.g., with data storage devices) for retrieval by a different data processing system 14 on another subsequent vehicle or subsequent implement for performing a subsequent agronomic task in a growing season. For instance, an earlier vehicle (e.g., survey vehicle or reconnaissance vehicle, such as vehicle 82 without implement 83) may provide a survey service for collecting that data maps 301 of surface roughness index zones (e.g., incidental to the method of FIG. 2B), while a subsequent vehicle and/or implement (e.g., a combination of vehicle 82 with implement 83) may perform a tillage or planting operation (e.g., in FIG. 2C), or spraying operation (e.g., sprayer implement, or boom with nozzles required); where the earlier vehicle electronics and subsequent vehicle electronics (e.g., systems 11, 111 or data processing systems (e.g., 14)) can exchange or share data (e.g., estimated in step S207 of FIG. 2B for retrieval in step S217 of FIG. 2C) via the data storage device 24, the central service or cloud, with appropriate consent for processing, exchanging or sharing the data). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Birkland with the teachings as in Rooney. The motivation for doing so would have been to reduce fuel consumption of the vehicle and over-application of crop inputs, see Birkland paragraph 105.
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Rooney et al. (USPGPub 2022/0051118), in view of Birkland et al. (USPGPub 2021/0282310), and further in view of Acedo et al. (USPGPub 2022/0240432). As per claims 3, 10, and 17, Rooney and Birkland do not explicitly mention decorating the machine data by encoding the machine data in a spatial data format. However Acedo does disclose: decorating the machine data by encoding the machine data in a spatial data format (see at least paragraph 0151; wherein generating digital objects (e.g., in visual data formats, in audio data formats, in haptic data formats, encoding information) or instructions for generating digital objects). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Acedo with the teachings as in Rooney and Birkland. The motivation for doing so would have been to improve productivity or maintain health of the agriculture site, see Acedo paragraph 0017.
Claims 4, 6, 11, 14, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rooney et al. (USPGPub 2022/0051118), in view of Birkland et al. (USPGPub 2021/0282310), and further in view of Barhai et al. (USPGPub 2022/0295691). As per claims 4, 11, and 18, Rooney and Birkland do not explicitly mention wherein processing the respective soil sustainability measurement includes maximizing a yield function and/or minimizing a sustainability function. However Barhai does disclose: wherein processing the respective soil sustainability measurement includes maximizing a yield function and/or minimizing a sustainability function (see at least paragraph 0009; wherein a recommendation generation module configured to generate a set of nutrients recommendations corresponding to one or more additional nutrients required in the water-based solution for maximum crop yield based on the received nutrient information). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Barhai with the teachings as in Rooney and Birkland. The motivation for doing so would have been to provide an improved system and method for managing nutrient concentrate in water-based solutions, in order to address the aforementioned issues, see Barhai paragraph 0007. As per claims 6, 14, and 20, Rooney and Birkland do not explicitly mention optimizing the executable agricultural prescription to balance maximum yield and soil health for sustainability of the agricultural field. However Barhai does disclose: optimizing the executable agricultural prescription to balance maximum yield and soil health for sustainability of the agricultural field (see at least paragraph 0009; wherein a recommendation generation module configured to generate a set of nutrients recommendations corresponding to one or more additional nutrients required in the water-based solution for maximum crop yield based on the received nutrient information). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Barhai with the teachings as in Rooney and Birkland. The motivation for doing so would have been to provide an improved system and method for managing nutrient concentrate in water-based solutions, in order to address the aforementioned issues, see Barhai paragraph 0007.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Rooney et al. (USPGPub 2022/0051118), in view of Birkland et al. (USPGPub 2021/0282310), and further in view of Sasamoto et al. (USPGPub 2020/0305338). As per claim 7, Rooney and Birkland do not explicitly mention generating, via one or more processors, one or more map layers based on the machine data set corresponding to the plurality of growing medium data points within the agricultural field. However Sasamoto does disclose: generating, via one or more processors, one or more map layers based on the machine data set corresponding to the plurality of growing medium data points within the agricultural field (see at least paragraph 0122; wherein the layer map screen M4 is configured to display an agricultural field map F11, a harvest map F12, a taste map F13, a growth map F14). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Sasamoto with the teachings as in Rooney and Birkland. The motivation for doing so would have been to improve the reliability of data of the work result data for a work plan, see Sasamoto paragraph 0107.
Relevant Art
The prior art made of record and not relied upon are considered pertinent to applicant’s disclosure: USPGPub 2022/0256834 – Provide generating an application map (20) for treating a field with an agricultural equipment comprising the following steps: providing (S10) a field map (10) of a field to be treated; determining (S20) areas in the field map (10) with a weed and/or pest infestation by using an image classification algorithm; and generating (S30) an application map (20) specifying areas for treating the field with an agricultural equipment, wherein the application map (20) is based on the determined areas infested by weed and/or pest infestation; wherein the method further comprises the step of providing boundary data with respect to the field. USPGPub 2021/0224927 – Provide a crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAHMOUD S ISMAIL whose telephone number is (571)272-1326. The examiner can normally be reached M - F: 8:00AM- 4:00PM.
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/MAHMOUD S ISMAIL/Primary Examiner, Art Unit 3662
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