DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the
first inventor to file provisions of the AIA .
Response to Arguments
The Amendment filed on March 24, 2026 has been entered. The examiner
acknowledges the amendments to claims 1, 8-20.
Rejections under 35 U.S.C. § 112: Applicant’s amendments have now rendered the
claims in compliance with the definiteness requirements of 35 U.S.C. § 112 and those rejections are withdrawn.
Rejections under 35 U.S.C. § 101: Applicant argues, page 12, that “evaluating underutilized land…” is not a mental process. Examiner disagrees, noting evaluation is one of the most fundamental and likely most common of mental processes. Applicant also argues achieving a practical application through integration by “deploying a robot to obtain local measurement…”. Examiner notes that the use of the robot in the invention is a general linkage of a robotic device (one technology) to a processor running software (another technology) and does not disclose the integration of the two entities. As a result, the application of the robot as an additional element does not constitute a practical application, and in the absence of a practical application, the execution of abstract ideas in software on a computer is not patentable. The rejections under 35 U.S.C. § 101 will not be withdrawn.
Rejections under 35 U.S.C. § 103: Applicant’s argues in the independent claims that modifying a virtual map…, sectioning the map into zones…, and robot-executable task plans… are not taught or suggested by prior art in combination. Examiner notes the land discovery and identification of land suitability of AbdelRahman, portioning and sub-portioning of land by characteristics of Perry, and Perry’s application of automation, such as drones, vehicles, or camera systems provide robot-executable plans for crop cultivation do teach the concepts. The Examiner concludes, that despite amendments, the independent claims, 1, 8, and 15, are still obvious in view of the prior art. Based on this conclusion, the rejections to the independent claims under 35 U.S.C. § 103 will not be withdrawn, and as claims 2-7, 9-14 and 16-20 ultimately depend on the independent claims, the objections to these claims will similarly not be withdrawn under 35 U.S.C. § 103.
Claim Rejections – 35 U.S.C. § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed
to non-statutory subject matter. The claims, 1-20 are directed to a judicial exception (i.e., law of nature, natural phenomenon, abstract idea) without providing significantly more.
Step 1
Step 1 of the subject matter eligibility analysis per MPEP § 2106.03, required the claims to be a process, machine, manufacture or a composition of matter. Claims 1-20 are directed to a process (method), machine (system), and product/article of manufacture, which are statutory categories of invention.
Step 2A
Claims 1-20 are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
Step 2A-Prong 1
The claims recite the following limitations that are directed to abstract ideas, which can be summarized as being directed to a method, the abstract idea, of evaluating underutilized land for purpose of agriculture and crop production.
Claim 1: A method for land discovery for potential crop growing is provided
the method comprising:
extracting features of a geographic area; (following rules or instructions, observation, evaluation, judgement, opinion),
identifying underutilized or unutilized land within the geographic area based on the extracted features; (following rules or instructions, observation, evaluation, judgement, opinion)
analyzing climatic conditions affecting the underutilized or unutilized land; (following rules or instructions, observation, evaluation, judgement, opinion)
obtaining local measurements at the identified underutilized or unutilized land; (following rules or instructions, observation, evaluation, judgement, opinion),
modifying a virtual map associated with the geographic area by annotating the virtual map based on the extracted features based on the analysis; (following rules or instructions, observation, evaluation, judgement, opinion),
sectioning the annotated virtual map into a plurality of zones; (following rules or instructions, observation, evaluation, judgement, opinion),
assigning at least one zone of the plurality of zones to the at least one robot based on zone characteristics comprising terrain type, feature density, or measured environmental conditions; (following rules or instructions, observation, evaluation, judgement, opinion),
evaluating crop growth viability for at least one crop at the underutilized or unutilized land, wherein the crop growth viability includes the analyzed climatic conditions and obtained local measurements; (following rules or instructions, observation, evaluation, judgement, opinion), and
orchestrating the use of underutilize or unutilized land based on the evaluation of the crop growth viability by implementing the cultivation of the at least one crop by generating task plans specifying land modifications, array layouts, and machine paths, (following rules or instructions, observation, evaluation, judgement, opinion).
Additional limitations employ the method for identifying any manmade features, natural features, and signage features, (following rules or instructions, observation, evaluation, judgement, opinion – claim 2), where imaging includes pictures taken from space, (following rules or instructions, observation, evaluation, judgement, opinion – claim 3), where local measurements at the identified land include soil quality, (following rules or instructions, observation, evaluation, judgement, opinion – claim 4), where evaluating crop viability is based on growing conditions for at least one crop, (following rules or instructions, observation, evaluation, judgement, opinion – claim 5), determining modifications to the land to meet the requisite growing conditions and a stipulated minimum profit for a periodic crop yield, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion- claim 6), where implementing cultivation includes one of tilling, planting or harvesting, (following rules or instructions, observation, evaluation, judgement, opinion- claim 7).
Each of these claimed limitations employ: organizing human activity in the form of fundamental economic principles and practices based on mitigating risk or calculating costs, following rules or instructions, performing mental processes including observation, evaluation, judgement, and opinion.
Claims 8-20 recite similar abstract ideas as those identified with respect to claims 1-7.
Thus, the concepts set forth in claims 1-20 recite abstract ideas.
Step 2A-Prong 2
As per MPEP § 2106.04, while the claims 1-20 recite additional limitations which are hardware or software elements such as AI, imaging, multimedia related to climate conditions, a robot, virtual image sectioning, satellite imaging, computer-readable storage media, non-transitory computer-readable storage media, computer processors, and transmitting robot-executable plans, these limitations are not sufficient to qualify as a practical application being recited in the claims along with the abstract ideas since these elements are invoked as tools to apply the instructions of the abstract ideas in a specific technological environment. The mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP § 2106.05 (f) & (h)).
Evaluated individually, the additional elements do not integrate the identified abstract ideas into a practical application. Evaluating the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
The claims do not amount to a “practical application” of the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, claims 1-20 are directed to abstract ideas.
Step 2B
Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The analysis above describes how the claims recite the additional elements beyond those identified above as being directed to an abstract idea, as well as why identified judicial exception(s) are not integrated into a practical application. These findings are hereby incorporated into the analysis of the additional elements when considered both individually and in combination.
For the reasons provided in the analysis in Step 2A, Prong 1, evaluated individually, the additional elements do not amount to significantly more than a judicial exception. Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Evaluating the claim limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. In addition to the factors discussed regarding Step 2A, prong two, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely amount to instructions to implement the identified abstract ideas on a computer.
Therefore, since there are no limitations in the claims 1-20 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, the claims are directed to non-statutory subject matter and are rejected under 35 U.S.C. § 101.
Claim Rejections 35 U.S.C. §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 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.
Claims 1, 3-8, 10-15, 17-20 are rejected under 35 U.S.C. § 103 as being unpatentable
over AbdelRahman, “Assessment of land suitability using a soil-indicator-based approach in a geomatics environment,” in Scientific Reports, in view of Perry, (US 20190050948 A1), “Machine Learning in Agricultural Planting Growing and Harvesting Contexts.”
Regarding Claim 1, AbdelRahman teaches, A method for AI land discovery for potential
crop growing is provided, (The study aims to develop new approach for soil suitability evaluation, [ ], the findings of the study will help narrow the area to the suitable sites that may further be sustainably used for annual and/or perennial crops. The proposed approach has high potential in applications for assessing land conditions and can facilitate optimal planning for agricultural use, [Abstract]), the method comprising:
extracting features from imaging of a geographic area; (The numerous maps utilized in this study were created using ENVI 5.1, ERDAS Imagine 14, Global Mapper software, and Arc GIS 10.5. IDRISI 19.0.2 and satellite image (Landsat 8), Tiff format, USGS website, Table 2) [p.4]),
identifying underutilized or unutilized land within the geographic area based on the
extracted features; (Using GIS and modelling tools, the land suitability model was created. Using a parametric method, the lands were categorized. The parametric technique uses many ratings to define characteristics of the land and climate. According to the Sys table, the determining factors for land suitability in this method are ranked between a minimum and maximum value (often between 0 and 100)11. A feature will receive a score of 100 if it is very influential and 0 otherwise. These rankings are displayed using formula (3)’s letters A,
B, C, etc. Equation (3) was used to calculate various features and land indices, and A, B, C …is parameters rank influencing the land suitability, [p.6]).
analyzing multimedia related to climatic conditions affecting the underutilized
or unutilized land; (Crop requirements were matched with land attributes to assess the study area’s crop production potential; the weights created by the AHP technique were applied to the reclassified thematic maps/layers of each variable soil, topography, agro-climatic map, and land use map. After doing a weighted overlay analysis with spatial analyst tools (GIS), the weighted maps/layers were overlaid, and a suitability map was created, [p.6]),
evaluating crop growth viability for at least one crop at the underutilized or
unutilized land, wherein the crop growth viability includes, (the methodological procedures used to evaluate sites for suitability of different crops, Figure 3, [p.4]), the analyzed climatic conditions, (The Climatic Quality Index (CQI) was calculated utilizing variables that affect plant water availability, such as rainfall, air temperature, and aridity, as well as climate dangers that may limit plant growth, [p.8]), and obtained local measurements; (Analytical tools and supporting materials used in the current study are: Both primary data (soil analysis data) and secondary data (environmental data) (Table 2), [p.4]),
evaluating crop growth viability for at least one crop at the underutilized or unutilized land, wherein the crop growth viability includes the analyzed climatic conditions and obtained local measurements; and implementing the cultivation of the at least one crop, (the methodological procedures used to evaluate sites for suitability of different crops, Figure 3, [p.4], the Climatic Quality Index (CQI) was calculated utilizing variables that affect plant water availability, such as rainfall, air temperature, and aridity, as well as climate dangers that may limit plant growth, [p.8], and analytical tools and supporting materials used in the current study are: Both primary data (soil analysis data) and secondary data (environmental data) (Table 2), [p.4], and (the method herein given can be simply re-applied over large areas to evaluate the suitability of the land and estimate the crop output in order to assist the development and implementation of sustainable agricultural operations, [p.19]). The new methodology considering using the suitability of water for growing the selected crops, the proposed methodology was found to be efficient for all crops, wheat, Sugar Beets, [p.9], Alfalfa, [p.11], potatoes, barley, [p.12]).
While Abdelrahman teaches land discovery, viability, and implementation using sensors, he does not teach robotic sensors; however Perry teaches, deploying at least one robot to obtain local measurements at the identified underutilized or unutilized land; (The accessed field information can be collected from one or more of: sensors located at the first portion of land, satellites, aircraft, unmanned aerial vehicles, land-based vehicles, and land-based camera systems, [0017],
modifying a virtual map associated with the geographic area by annotating the virtual
map based on the extracted features based on the analysis; (The prediction model can map characteristics described by the field information to the selected set of farming operations, [0018]),
virtually sectioning the annotated virtual map into a plurality of zones;
assigning at least one zone of the plurality of zones to the at least one robot based on zone characteristics comprising terrain type, feature density, or measured environmental conditions; (The geographic database 135 stores and maintains data describing geographic characteristics of portions of land (such as fields, plots, sub-portions of the same, and the like) that may impact crop production. As used herein, a “portion of land” refers to any amount of land in any shape or size. For instance, a “portion of land” can refer to a grower's entire property, a field, a plot of land, a planting region, a zone or management zone, and the like. Likewise, a portion of land can include one or more “sub-portions” of land, which refers to a subset of the portion of land of any shape or size, [0080], and information describing geographic characteristics that may impact crop production are associated with a plot index that uniquely identifies a particular field or plot of land associated with the characteristics. As shown in FIG. 2, the geographic database 135 associates each uniquely identified plot with one or more sets of associated data (“County,” “Avg. Rainfall,” and “Avg. Temp”). Although the example database of FIG. 2 organizes geographic information by plot of land, in other embodiments, the geographic database 135 can be organized in other ways, for instance by land category (field, mountain, city, etc.), by land owner, or by any other suitable characteristic. [0081],
orchestrating the use of underutilize or unutilized land based on the evaluation of the crop growth viability by implementing the cultivation of the at least one crop by generating and transmitting robot-executable task plans specifying land modifications, array layouts, and
machine paths to the at least one robot, (In various embodiments, the crop prediction module 425 outputs one or more farming operations in the set of farming operations directly to farming equipment (e.g., a smart tractor or sprinkler system) for implementation, such as a route to traverse, a crop treatment to apply, or an instruction to capture images of an identified portion of a field. In some embodiments, the interface 130 can modify a user interface displayed by an external device (such as the grower client device 102) to display (for instance) a set of farming operations to perform and an expected crop yield that will result from performing the displayed set of farming operations, [0163]).
It would have been obvious before the earliest effective filing date of this application to modify AbdelRahman’s land suitability/discovery method with the teachings of Perry in the use of robotic features with the motivation to directly couple with one or more image data sources 116 and can receive image data without human intervention, [0070]. The claimed invention is a combination of existing elements and one of ordinary skill in the art would recognize that elements would continue to perform the same functions as they did separately and produce predictable results, in this case delivering required data to the processor and program.
Regarding claim 3, the method of claim 2, wherein the imaging includes satellite imaging of the geographic area, AbdelRahman teaches, (The main source for mapping vegetation and plant cover classifications was satellite photographs, [p.8]).
Regarding claim 4, The method of claim 1, wherein the local measurements obtained by the at least one deployed robot at the identified underutilized or unutilized land includes soil quality. AbdelRahman does not teach, Perry teaches, Sensor data sources 114 may additionally be located at fixed points (e.g., a sensor placed at a designated point on a field) or may be coupled to moving objects (e.g., an autonomous, manned, or remotely controlled land or aerial vehicle), [0055]).
AbdelRahman and Perry are both considered to be analogous to the claimed invention because both are in the field of agricultural analysis for crop production. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the soil evaluation techniques of AbdelRahman with the measuring techniques of Perry to enable a system to select a set of farming operations that maximize crop productivity, Perry. [0005].
Regarding claim 5, The method of claim 1, wherein the evaluating crop growth viability for the at least one crop at the underutilized or unutilized land is based at least in part on requisite crop growing conditions for the at least one crop. AbdelRahman teaches, (Although FAO methodology for assessing the suitability of a particular site to produce a particular crop under a specific agricultural production system based on agro-climatic conditions i.e. heat and
humidity, and on agricultural conditions i.e. soil and morphology. However, by using the new methodology considering using the suitability of water for growing the selected crops, the proposed methodology was found to be efficient for all crops (Figs. 10, 11, 12, 13 and 14) as shown from the R-Square calculation in Table 15.
Regarding claim 6, the method of claim 5, further comprising, determining modifications to the underutilized or unutilized land in order to meet at least one of the requisite growing conditions and a stipulated minimum profit for a periodic crop yield. AbdelRahman does not teach, Perry teaches, (By generating crop production predictions and identifying farming operations to perform for particular portions of land, the crop prediction system 125 allows growers to quickly access information to optimize crop production. [ ] As used here, “crop production” can refer to any measure associated with the planting, growing, and/or harvesting of crops, including but not limited to crop yield for a current year or for multiple seasons, current or future profit, expected planting/growing/harvesting costs, soil health over one or more season, carbon sequestration, production at a particular date or
within a range of dates, composition profiles of crops and the like, [0111], and FIG. 3 illustrates an example agricultural database 140 for a machine learning crop prediction system. In the example database of FIG. 3, information describing agricultural factors that may impact crop production are associated with a plot index that uniquely identifies a particular field or plot of land and a crop variety. The agricultural database 140 identifies one or more sets of data (“Plant Date,” “Field Treatments,” and “Harvest Date”) associated with a plot index and crop
variant [ ] in practice, an agricultural database can include specific details of applied crop treatments (such as the specific treatment applied, the method of application, the application rate, date and location, etc.), [0091].
AbdelRahman and Perry are both considered to be analogous to the claimed invention because both are in the field of agricultural analysis for crop production. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the soil evaluation techniques of AbdelRahman with the measuring techniques of Perry to enable a system to select a set of farming operations that maximize crop productivity, Perry. [0005].
Regarding claim 7, The method of claim 6, wherein the implementing the cultivation of the at least one crop includes instructions to the at least one robot to perform at least one of the determined modifications, tilling, planting, and harvesting. AbdelRahman does not teach, Perry teaches, (The grower client devices 102, broker client devices 104, crop recipient client devices 106, and agronomist client devices 108 are computing devices capable of receiving user input, displaying information to a user, and transmitting and/or receiving data via the network 120. Hereafter, a “client device” can refer to any of the grower client device 102, broker client device 104, crop recipient client device 106, and agronomist client device 108. In one embodiment, a client device may be any device having computer functionality, such as a personal digital assistant (PDA), a mobile telephone, a smartphone, a tablet computer, a desktop or laptop computer, a server, a workstation, smart farming equipment (such as a smart tractor, sprinkler system, and the like), unmanned aerial and ground-based vehicles both remotely controlled and autonomous, or another suitable device. A client device is configured to communicate with the crop prediction system 125 via the network, [0033], and the grower client device 102 communicates with the crop prediction system 125 via the network 120 to request and receive crop prediction information, such as predictions of crop production, selections of crops to plant, and farming operations that, when performed, optimize crop
Productivity, [0034]).
AbdelRahman and Perry are both considered to be analogous to the claimed invention because both are in the field of agricultural analysis for crop production. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the soil evaluation techniques of AbdelRahman with the measuring techniques of Perry to enable a system to select a set of farming operations that maximize crop productivity, Perry. [0005].
Claims 8, 10-14, are rejected for reasons corresponding to those of claims 1, 3-7. The addition of a computer-readable storage media and non-transitory computer readable media capable of performing the method of claims 1, 3-7, does not change the reasons for rejection under 35 U.S.C. § 103 based on the referenced prior art. Perry teaches, a non-transitory computer-readable storage medium [ ] that holds instructions used by the processor, [0182].
Claims 15, 17-20 are rejected for reasons corresponding to those of claims 1, 3-7. The addition of one or more computer processors and computer readable storage media storing program instructions does not change the reasons for rejection under 35 U.S.C. § 103 based on the referenced prior art. Perry teaches, at least one processor, [ ] and a memory storage device, [0181].
Claims 2, 9, 16, are rejected under 35 U.S.C. § 103 as being unpatentable over
AbdelRahman, “Assessment of land suitability using a soil-indicator-based approach in a geomatics environment,” in view of Perry, (US 20190050948 A0, “Machine Learning in Agricultural Planting Growing and Harvesting Contexts,” in further view of Yu, (US 20170286805 A1), “Systems and Methods for Identifying Entities Directly from Imagery.”
Regarding claim 2, The method of claim 1, wherein the extracting features from the imaging of the geographic area includes identifying any manmade features, natural features, AbdelRahman does not teach, Perry teaches, (The image data sources 116 are one or more sources of image data that can be used by machine learning operation of the crop prediction system 125 to train crop prediction models, to apply crop prediction models to predict future crop production, and to identify farming operations that optimize future crop production [ ] to provide the collected images in association with information identifying the field, capturing the image and any infrastructure (such as a vehicle, stand, machinery, and the like), [0070],
AbdelRahman and Perry are both considered to be analogous to the claimed invention because both are in the field of agricultural analysis for crop production. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the soil evaluation techniques of AbdelRahman with the measuring techniques of Perry to enable a system to select a set of farming operations that maximize crop productivity, Perry. [0005].
and signage features, AbdelRahman does not teach, Yu teaches, (the Convolutional Neural Network (CNN) can be configured to extract features from the image(s) without transcribing text, for instance, located on storefront signage depicted in the image(s), [0025]).
AbdelRahman and Yu are both considered to be analogous to the claimed invention because both are in the field of image analysis application. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the land evaluation techniques of AbdelRahman with the precision imagery techniques of Yu to improve on OCR identification reliability, Yu, [0004].
Claim 9 is rejected for reasons corresponding to those of claim 2. The addition of a computer-readable storage media and non-transitory computer readable media capable of performing the method of claim 2, does not change the reasons for rejection under 35 U.S.C. § 103 based on the referenced prior art. Perry teaches, a non-transitory computer-readable storage medium [ ] that holds instructions used by the processor, [0182].
Claim 16 is rejected for reasons corresponding to those of claim 2. The addition of one or more computer processors and computer readable storage media storing program instructions does not change the reasons for rejection under 35 U.S.C. § 103 based on the referenced prior art. Perry teaches, at least one processor, [ ] and a memory storage device, [0181].
Conclusion
THIS ACTION IS MADE FINAL. 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.
The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
Any inquiry concerning this communication or earlier communications from the
examiner should be directed to MICHAEL BOROWSKI whose telephone number is (703)756-1822. The examiner can normally be reached M-F 8-4:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O’Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MB/
Patent Examiner, Art Unit 3624
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624
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