Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/29/2025 has been entered.
Status of Claims
This communication is a Non-Final Office Action in response to Applicant’s RCE for application number 18/263,696 received on 05/31/2026.
In accordance with Applicant’s amendment, claims 1-5, and 7-14 are amended, currently pending and have been examined. Claim 6 has been canceled.
Priority
Applicants claim for the benefit of a prior-filed application under 35 U.S.C. 119 and/or 35 U.S.C. 120 is acknowledged.
Response to Amendment
Applicant’s amendment necessitated the new ground(s) of rejection set forth in this Office Action.
Regarding the §112(b) rejections previously applied to claim 12, upon review of the amended claims, the rejection is withdrawn.
Regarding the objection to pars. 15, and 42 of the Specification previously applied, upon review of the Specification amendment and Applicant’s arguments, the objections are withdrawn.
Response to Arguments
Response to Specification Objections arguments – Applicant’s arguments for the objections of par. 17 of the Specification have been considered and are not persuasive. While Applicant is citing to the publication of this application, US 20240311722 A1, Examiner notes that the originally filed specification of 07/31/2023 has the typographical errors. Therefore, the objections are maintained. Examiner suggests amending the instant disclosure to fix the typographical errors.
Response to §101 arguments – Applicant’s arguments with respect to the §101 rejections previously applied to the claims are primarily raised in support of the amendments. The amendments and supporting arguments are believed to be fully addressed in the updated §101 rejections below.
Accordingly, the §101 rejections previously applied are maintained and updated to address the amendments (See §101 rejections section below for further details).
Response to §103 arguments – Applicant’s arguments with respect to the §103 rejections previously applied to the claims have been considered and are unpersuasive.
Applicant argues (Remarks at pgs. 11-12): “Regarding the database of agricultural activities, the present rejection alleges that Fig.5 and paragraph [0034] of Hicks disclose this feature: a The database 440 may include a list of tasks (e.g., plowing, mowing) that are associated with different combinations of equipment and a list of activities (e.g., plowing field 4) associated the combination of tasks and defined area of interest.
This feature of Hicks simply discloses that the database 440 includes a list of tasks. The feature of the present application is different, indeed the database of the agricultural activities includes agricultural activities, each including different tasks assigned to a location and a position in the activity's calendar. It should therefore be noted that an agricultural activity is a set of tasks distributed at different times in a calendar. See, e.g., [0085] of the published application ("An agricultural activity is defined as a set of agricultural tasks. The agricultural tasks of an agricultural activity are distributed according to a schedule."). The task is therefore characterized both by the place where it is carried out but also by its position in the calendar of the agricultural activity.”. In response, Examiner respectfully disagrees and notes that Hicks discloses a database that, among other data, stores data related to a list of tasks including “plowing field 4”. Hicks also discloses storing information related to the time of activities in a list of tasks (e.g., plowing, mowing), and location. Therefore, Hicks teaches the limitation, as currently recited, to store, for each agricultural activity, a schedule of at least two agricultural tasks, each agricultural task being characterized by a place identifier and a position in the schedule.
Applicant argues (Remarks at pg. 12): “Regarding the use of temporal zoning data, claim 1 features: "receiving, from a portable electronic device of the at least one agricultural worker, temporal zoning data of the at least one agricultural worker". The present rejection alleges that this feature is disclosed by Hicks [0054]: "The wireless device 120 may include a GPS module 210 that is used to determine the location (e.g., latitude and longitude) of the worker 360." Even if it is possible to know the worker's position at a given time with a GPS, time zoning data, as can be seen in Figure 13 of the present application, allows the user's entire movements to be traced over time. Implementing such features from GPS data would require additional data processing, which is not disclosed in Hicks. In response, Examiner respectfully disagrees and notes that, as disclosed in par. [0025], Hicks discloses: “The location of the worker may be utilized in the determination of an activity”. Therefore, the temporal zoning data (e.g., location of the worker) is associated with an agricultural task.
Applicant argues (Remarks at pg. 15): “The feature of claim 1: “The feature of claim 1: "associating the constructed image with an agricultural task carried out in the agricultural parcel using a convolutional neural network" is neither disclosed by Hicks nor by Vollmar. Stueve does not cure the deficiencies of Hicks and Vollmar.”. In response, Examiner respectfully disagrees and notes that Vollmar teaches using an artificial neural network (par. [0029]) to determine a treatment from a set of data, including an image (par. [0054]). However, Vollmar doesn’t teach identifying a task (e.g., treatment) using a CNN. Stueve teaches using a CNN to process data (par. [0133]), to among other things, identify a specific context or action (par. [0136]), therefore making it obvious for one of ordinary skill in the art to combine the references.
Applicant argues (Remarks at pgs. 16-18): “Further regarding claim 6, the Office Action states the claimed feature "the constructed image comprises a trace formed by the temporal zoning data and at least one geometric primitive parameterized from the temporal zoning data and said agricultural activity and exploitation databases; at least one intensity of the image being determined from the temporal zoning data and the agricultural activity and exploitation databases" is taught by Fig. 15 and Fig. 16 of Vollmar and broadly interpreting the term "image intensity". The present rejection alleges that a person skilled in the art could reasonably interpret "intensity of the image" as being equivalent to using visual representations to encode additional information on the image, which would be disclosed by Figures 15 and 16 of Vollmar. Applicants respectfully disagree. First, it is correct that the present application provides for encoding information on the image using visual representations as in paragraph [0158]. However, this characteristic is very far from what is disclosed in Figures 15 and 16 of Vollmar. Indeed, the latter are not intended to encode data; on the contrary, the objective is for the user to be able to easily and unambiguously understand the constructed field map. Whereas in the present application, the interpretation of the encoded data is not necessarily accessible to a user and their interpretation is not necessarily easy, because the constructed images are interpreted by a neural network. Moreover, "intensity of the image" does not simply mean that there is a use of visual representation to encode of information on an image, but it represents one of the attributes used to encode its information. By varying the intensity (e.g., light) of elements that are encoded on at least one of the layers of the image, it is possible to encode information on this at least one layer. There is no mention of such a feature in the cited references. For at least this reason, Applicants respectfully submit that the rejection of claim 6 is improper and should be withdrawn.”. In response, Examiner respectfully disagrees and notes that Vollmar teaches the discussed limitations of Applicant’s claim in at least pars [0089 – 0090], where Vollmar discloses “overlaying the treated geographic area representation (represented with a first visual indicator, such as a first color) over the representation of the geographic region to be treated (represented with a second visual indicator, such as a second color)”, or “determining the current magnitude supplied to the solenoid for each of a set of geographic locations within the geographic region to be treated (e.g., based on the timestamp of the measurement and the location estimated for the treatment timestamp), assigning a different visual indicator (e.g., different color) to each current magnitude range, and generating a virtual map of the geographic region, mapping the visual indicator for the current magnitude corresponding to the respective geographic location.”. With respect to Applicant’s argument regarding the accessibility of the information discussed above, this is irrelevant to the analysis because these features are not recited or required by the claim. For example, the claims do not recite or require “the encoded data is not necessarily accessible to a user and their interpretation is not necessarily easy”. Applicant’s argument lacks merit because is relies on limitations not required by the claims and it would be improper to import such limitations from the Specification. See Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875, 69 USPQ2d 1865, 1868 (Fed. Cir. 2004). See also, CollegeNet, Inc. v. Apply Yourself Inc., 418 F.3d 1225, 1231 (Fed. Cir. 2005) (while the specification can be examined for proper context of a claim term, limitations from the specification will not be imported into the claims). Furthermore, the phrase “their interpretation is not necessarily easy” is a relative phrase, such that it would be impossible for one of ordinary skill in the art to determine the level of difficulty required, therefore such a limitation would render the claim indefinite.
Applicant argues (Remarks at pg. 20): “Further regarding claim 9, the Office Action alleges that training the convolutional neural network with images associated with at least one of: an agricultural task from the database of agricultural activities of an agricultural exploitation of the user or external agricultural exploitations, is taught by paragraph [0133] of Stueve which discloses a CNN trained to generate maps from geospatial image data for providing treatment maps. However, the neural network of the patent application operates in a different way. Paragraph [0150] of the present application discloses: "Indeed, convolutional neural networks achieve excellent results in image classification. The objective is to transform the collected data into an image representative of these data and then train a neural network to classify the images constructed according to the most likely agricultural task to be associated with an image on the basis of the agricultural activity model."[0220]: "Once trained, the neural network classifier is able to classify each image: for each input image of the classifier, it assigns this image the most likely agricultural task." Thus, it is clear that the neural network in the present application does not generate crop maps like those of Stueve. For at least this reason, Applicants respectfully submit that the rejection of claim 9 is improper and should be withdrawn.”. In response, Examiner respectfully disagrees and notes that as discussed above, Stueve teaches training a machine learning algorithm (at least par. [0051], [Fig. 7], [Fig. 9]), including a CNN (at least par. [0101] and [Fig. 9]), with source data including treatment application (at least par. [0082]). Therefore, Stueve teaches Claim 9.
Accordingly, the §103 rejections previously applied are maintained and updated to address the amendments (See §103 rejections section below for further details).
Specification
The disclosure is objected to because of the following informalities (grammatical errors of the typographical kind – underlined below):
Paragraph 17: “…according to the temporal zoning data data and said databases…”
Appropriate correction is required.
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 1-5, and 7-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as further set forth in MPEP 2106.
Step 1: The claimed invention is analyzed to determine if it falls outside one of the four statutory categories of invention. See MPEP 2106.03
Claim(s) 1-5, 7-10 and 14 is/are directed to a method (i.e., Process), and claim(s) 11-13 is/are directed to a system (i.e., Machine). Therefore, the claims are directed to patent eligible categories of invention. Accordingly, the claims satisfy Step 1 of the eligibility inquiry.
Step 2A, Prong 1: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether they recite a judicial exception. See MPEP 2106.04
Independent claims 1, and 11 recite a method and a system for interpreting location data. As drafted, the limitations recited by the independent claims fall under the “Mental Processes” abstract idea group by setting forth activities that could be performed mentally by a human (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Independent claim 1 recites a computerized method for interpreting location data of at least one agricultural worker with the following limitations: “storing, in a database of agricultural activities, for each agricultural activity, a schedule of at least two agricultural tasks, each agricultural task being characterized by a place identifier and a position in the schedule; storing, in an agricultural exploitation database, at least one agricultural parcel characterized by a place identifier and a location; receiving, from a portable electronic device of the at least one agricultural worker, temporal zoning data of the at least one agricultural worker; and associating the temporal zoning data with an agricultural task using the temporal zoning data and the agricultural activity and agricultural exploitation databases by: constructing an image for a determined agricultural parcel from the temporal zoning data and the databases of agricultural activities and agricultural exploitation, wherein the constructed image comprises a trace formed by the temporal zoning data and at least one geometric primitive parameterized according to the temporal zoning data and said databases of agricultural activities and agricultural exploitation; at least one intensity of the image being determined from the temporal zoning data and the agricultural activity and exploitation databases, wherein the at least one intensity encodes a previous agricultural task carried out in the determined agricultural parcel as determined from the schedule stored in the database of agricultural activities, and associating the constructed image with an agricultural task carried out in the agricultural parcel using a convolutional neural network trained to classify the constructed image according to a most likely agricultural task on the basis of an agricultural activity model specific to the agricultural exploitation.”. But for the additional elements recited in the claim limitations – underlined – to be analyzed under steps 2A, prong 2, and 2B, the steps in the claim could be accomplished mentally, such as by human observation, evaluation, judgement, opinion, or with the help of pen and paper.
Independent claim 11 recites a computerized system with the following limitations: “a database of agricultural activities comprising, for each agricultural activity, a schedule of at least two agricultural tasks, each agricultural task being characterized by a place identifier and a position in the schedule; an agricultural exploitation database comprising at least one agricultural parcel characterized by a place identifier and a location; a convolutional neural network trained to classify a constructed image according to a most likely agricultural task on the basis of an agricultural activity model specific to the agricultural exploitation; at least one memory; and at least one processor coupled with the at least one memory and configured to cause the system to: receive temporal zoning data from a portable electronic device of at least one agricultural worker; and associate the temporal zoning data with an agricultural task using the temporal zoning data and the agricultural activity and agricultural exploitation databases by: constructing an image for a determined agricultural parcel from the temporal zoning data and the databases of agricultural activities and agricultural exploitation, wherein the constructed image comprises a trace formed by the temporal zoning data and at least one geometric primitive parameterized according to the temporal zoning data and said databases of agricultural activities and agricultural exploitation; at least one intensity of the image being determined from the temporal zoning data and the agricultural activity and exploitation databases, wherein the at least one intensity encodes a previous agricultural task carried out in the determined agricultural parcel as determined from the Schedule stored in the database of agricultural activities, and wherein the system associates the constructed image with an agricultural task carried out in the agricultural parcel using the convolutional neural network.”. But for the additional elements recited in the claim limitations – underlined – to be analyzed under steps 2A, prong 2, and 2B, the steps in the claim could be accomplished mentally, such as by human observation, evaluation, judgement, opinion, or with the help of pen and paper.
The dependent claims further narrow the abstract idea and introduce the following additional elements for consideration:
From claim 10: computer program comprising code instructions
From claim 12: at least one communicating portable electronic device of an agricultural worker
From claim 13: at least one communicating portable electronic device of an agricultural machine
Dependent claims 2-5, 7-9, and 14 further narrow the abstract idea and do not introduce further additional elements for consideration.
Step 2A, Prong 2: An evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the judicial exception into a practical application of the exception. See MPEP 2106.04(d).
Regarding the computing additional elements, namely in a database of agricultural activities, in an agricultural exploitation database, from a portable electronic device of the at least one agricultural worker, at least one memory, and at least one processor coupled with the at least one memory and configured to cause the system to from claims 1 and 11, these additional elements have been evaluated but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements (based on Examiner’s interpretation set forth in Claim Interpretation section above) or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h).
With respect to the limitations for using a convolutional neural network trained from claims 1 and 11, these limitations fail to integrate the abstract idea into a practical application because they provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
With respect to the computer program comprising code instructions from claim 10, this additional element fails to integrate the abstract idea into a practical application because it provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Regarding the at least one communicating portable electronic device of an agricultural worker from claim 12, and at least one communicating portable electronic device of an agricultural machine from claim 13, these additional elements have been evaluated but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements (based on Examiner’s interpretation set forth in Claim Interpretation section above) or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h).
Dependent claims 2-5, 7-9, and 14 recite the same abstract ideas (“mental processes”) as the independent claims along with further steps/details falling under the scope of the abstract idea itself, along with the same or substantially same additional elements addressed.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
Step 2B: The claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for "inventive concept." See MPEP 2106.05.
Regarding the computing additional elements, namely in a database of agricultural activities, in an agricultural exploitation database, from a portable electronic device of the at least one agricultural worker, at least one memory, and at least one processor coupled with the at least one memory and configured to cause the system to from claims 1 and 11, these additional elements have been evaluated, but fail to add significantly more to the claims because they amount to using generic computing elements (computer hardware) or instructions/software to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (network computing environment) and does not amount to significantly more than the abstract idea itself. Therefore, the computing additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
With respect to the limitations for using a convolutional neural network trained from claims 1 and 11, these limitations fail to add significantly more to the abstract idea because the provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
With respect to the computer program comprising code instructions from claim 10, this additional element fails to add significantly more to the abstract idea because the provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Regarding the at least one communicating portable electronic device of an agricultural worker from claim 12, and at least one communicating portable electronic device of an agricultural machine from claim 13, these additional elements have been evaluated, but fail to add significantly more to the claims because they amount to using generic computing elements (computer hardware) or instructions/software to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (network computing environment) and does not amount to significantly more than the abstract idea itself. Therefore, the computing additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Dependent claims 2-5, 7-9, and 14 recite the same abstract ideas (“mental processes”) as the independent claims along with further steps/details falling under the scope of the abstract idea itself, along with the same or substantially same additional elements addressed.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, and 9-14 are rejected under 35 U.S.C. 103 as being unpatentable over Hicks (US 20190050946 A1, hereinafter “Hicks”), in view of Vollmar et al. (US 20160147962 A1, hereinafter “Vollmar”), in further view of Stueve et al. (US 20200364843 A1, hereinafter “Stueve”).
Regarding claims 1/11: Hicks teaches a computerized method for interpreting data ([0044] method 600), and a computerized system ([0084] all methods described herein can also be stored on a computer readable storage to control a computer) with the following limitations:
storing, in a database of agricultural activities, ([0012] FIG. 5 illustrates an example diagram of information stored in a database utilized in an activity tracking system)for each agricultural activity, a schedule of at least two agricultural tasks, each agricultural task being characterized by a place identifier and a position in the schedule; ([0033] The database 440 is where information pertaining to the activity tracking system is maintained.; [0034] The database 440 may include a list of tasks (e.g., plowing, mowing) that are associated with different combinations of equipment and a list of activities (e.g., plowing field 4) associated the combination of tasks and defined area of interest.; [0035] The database 440 may include information that is gathered during the operation of the activity tracking system. The information may be provided by the apps running on the wireless devices 120 that communicate with the server 160. The information stored in the database 440 may include, for example, the beacons detected, the time and location for the beacon detection, entry/exit from the area defined by geo-fences and times associated therewith, the activities associated with the workers and the times associated with the activities, and location of the worker at defined intervals (e.g., every 5 minutes).; [0036] The parameters (e.g., number, type, configuration) of the database 440 may vary based on implementation. One skilled in the art would understand the various ways to establish the database(s) 440 and select an appropriate set up.; [0043] The data collected may also include tasks associated with the worker, areas of interest that the worker enters and the times the worker enters and exits, and activities associated with the worker and the times associated therewith.; [0050] The activity tracking app may communicate with the server 160 to determine that the beacons 310, 340 are associated with the tractor 300 and the mower 330 respectively based on the beacon identifications. The worker 360 can then be assigned the tractor 300 and the mower 330 and may optionally be associated with the task of mowing. As noted above, since task association at this point is simply identifying tasks that may be performed based on the equipment available this association may be excluded. Rather, the equipment in addition to a defined area may be utilized to associate an activity (the actual performing of a task). The assignment of the equipment 300, 330 to the worker 360 and the optional determination of task may be recorded in the database 440. Information recorded may include, for example, equipment, worker, time, location, and possibly task.);
storing, in an agricultural exploitation database, at least one agricultural parcel characterized by a place identifier and a location; ([0036] The parameters (e.g., number, type, configuration) of the database 440 may vary based on implementation. One skilled in the art would understand the various ways to establish the database(s) 440 and select an appropriate set up.; [0039] FIG. 5 illustrates an example diagram of information stored in a database 440 utilized in an activity tracking system. The database 440 may include beacon identification information 510, defined area information 520, task/activity information 530 and user (worker) information 540. The beacon identification information 510 may include, for example, the identification for each of the beacons that are defined for the system and information about the equipment that the beacon is associated therewith. The information may simply be the type of equipment or may be the type of equipment and parameters associated therewith. For example, beacon 1234 may be associated with a specific tractor (tractor 7) and the tractor may be capable of pulling a plow, mower and baler but not a seeder.; [0045] Different combinations of areas of interest and equipment (or optionally tasks previously determined based on equipment) may be associated with activities 650. For example, the combination of the worker having a tractor and plow (or determination of a plowing task) and field 1 as the area of interest may be associated with an activity of plowing field 1.);
receiving, from a portable electronic device of the at least one agricultural worker, temporal zoning data of the at least one agricultural worker; ([0043] The user (worker) information 540 may include data about the worker as well as data collected by the wireless device 120. The worker data may include, for example, name, employee identification, job title, pay grade, normal hours worked, authorized areas of interest and wireless device identification. The data collected may include the beacon identifications detected by the worker's wireless device 120, along with, for example, the time and location the beacons were detected and the time and location when the beacons were out of contact. The data collected may also include tasks associated with the worker, areas of interest that the worker enters and the times the worker enters and exits, and activities associated with the worker and the times associated therewith. The data collected may also include questions that were answered by the worker (e.g., type of seed used).; [0054] The wireless device 120 may include a GPS module 210 that is used to determine the location (e.g., latitude and longitude) of the worker 360);
and associating the temporal zoning data with an agricultural task using the temporal zoning data and the agricultural activity and agricultural exploitation databases by: ([0025] Based on the equipment that is detected in close proximity to the worker, the server 160 may make a determination as to what task the worker may be doing. The wireless device 120 may be equipped with GPS functionality to determine a location of the worker. The location of the worker may be utilized in the determination of an activity (e.g., task at specific location) being performed. A work order may be presented to the worker based on the activity determination.; [0054] The wireless device 120 may include a GPS module 210 that is used to determine the location (e.g., latitude and longitude) of the worker 360 (and associated equipment 300, 330). When the worker 360 is on their way to the area of interest 700 the activity tracking app may communicate with the server 160 and a transportation activity may be initiated.; [0042] The task/activity information 530 may include, for example, activities that are associated with different combinations of tasks and specific areas of interest. For example, the mowing task in defined area field 1 may be associated with mowing field 1. The various activities may also include specific questions that may be presented to the worker upon a determination that the activity is being performed. For example, if the activity is seeding field 1, the question may pertain to the type of seed being used or the amount of seed. If the activity is mowing field 2, the question may pertain to the height of the crop prior to mowing.; [0043] The user (worker) information 540 may include data about the worker as well as data collected by the wireless device 120. The worker data may include, for example, name, employee identification, job title, pay grade, normal hours worked, authorized areas of interest and wireless device identification. The data collected may include the beacon identifications detected by the worker's wireless device 120, along with, for example, the time and location the beacons were detected and the time and location when the beacons were out of contact. The data collected may also include tasks associated with the worker, areas of interest that the worker enters and the times the worker enters and exits, and activities associated with the worker and the times associated therewith. The data collected may also include questions that were answered by the worker (e.g., type of seed used).).
Hicks doesn’t explicitly teach:
constructing an image for a determined agricultural parcel from the temporal zoning data and the databases of agricultural activities and agricultural exploitation, wherein the constructed image comprises a trace formed by the temporal zoning data and at least one geometric primitive parameterized according to the temporal zoning data and said databases of agricultural activities and agricultural exploitation; at least one intensity of the image being determined from the temporal zoning data and the agricultural activity and exploitation databases, wherein the at least one intensity encodes a previous agricultural task carried out in the determined agricultural parcel as determined from the schedule stored in the database of agricultural activities,
and associating the constructed image with an agricultural task carried out in the agricultural parcel using a convolutional neural network trained to classify the constructed image according to a most likely agricultural task on the basis of an agricultural activity model specific to the agricultural exploitation.
Vollmar teaches:
constructing an image for a determined agricultural parcel from the temporal zoning data and the databases of agricultural activities and agricultural exploitation, ([0019] FIG. 15 discloses a time-ordered set of treatment maps generated for a treatment record set.; [Claim 18] The method of claim 5, wherein automatically generating a treatment summary based on the set of sensor measurements comprises generating a virtual map representing a treated portion of a geographic region encompassed within the predefined geofence, based on a subset of the set of sensor measurements and recordation locations for each of the subset of sensor measurements.; [Claim 20] The method of claim 18, wherein automatically generating a treatment summary further comprises: generating a different virtual map for each of a set of timestamps, each virtual map generated based on sensor measurements recorded prior the timestamp; time-ordering the virtual maps according to the respective timestamp; receiving a playback request from a second user device; and in response to receipt of the playback request, sequentially displaying the virtual maps in time-order on the second user device.; [0090] generating a treatment map can include: assigning a sensor measurement, recorded during a treatment session, to the respective geographic recordation location; assigning a visual indicator to the sensor measurement; and mapping the visual indicator to the virtual geographic location representing to the geographic recordation location on a virtual representation of the geographic region. For example, the sensor measurement can be the current supplied to a solenoid valve controlling fertilizer application, wherein generating a treatment map includes: determining the current magnitude supplied to the solenoid for each of a set of geographic locations within the geographic region to be treated (e.g., based on the timestamp of the measurement and the location estimated for the treatment timestamp), assigning a different visual indicator (e.g., different color) to each current magnitude range, and generating a virtual map of the geographic region, mapping the visual indicator for the current magnitude corresponding to the respective geographic location.);
wherein the constructed image comprises a trace formed by the temporal zoning data and at least one geometric primitive parameterized according to the temporal zoning data and said databases of agricultural activities and agricultural exploitation; ([0035] a geofencing module that functions to determine the predefined geofences. The geofences preferably delineate the boundaries of a field, but can alternatively represent the boundaries of any other suitable geographic region. The geofences are preferably each associated with one or more user accounts, but can alternatively be associated with any other suitable set of entities. In a first variation, the geofences are received from a user account (e.g., at a map or other user interface). In a second variation, the geofences are automatically determined. In one embodiment of the second variation, the geofences can be automatically determined from political records (e.g., county records, property records, etc.). In a second embodiment of the second variation, the geofences can be automatically determined from remote monitoring data, such as satellite images, based on long-term differences and/or similarities between adjacent geographic units. In a third embodiment of the second variation, the geofences can be automatically determined based on other user accounts' geofences. However, the geofences can be otherwise determined. The geofencing module can be run or updated: once; every year (e.g., after crop harvest, after crop yield is determined, etc.); every time the method is performed; every time a new geofence is received from a user account; or at any other suitable frequency; [0089] In a first example, generating the treatment map includes: determining the treated geographic area and virtually comparing a virtual representation of the treated geographic area with a virtual representation of the geographic region to be treated (example shown in FIG. 16). Determining the treated geographic area can include: determining a starting location, determining a traversal path, and determining an end location (e.g., the location of the last sensor measurement), wherein the treated geographic area is the area covered, starting from the starting location, following the traversal path, and ending at the end location.);
at least one intensity of the image being determined from the temporal zoning data and the agricultural activity and exploitation databases, ([0090] In a second example, generating a treatment map can include: assigning a sensor measurement, recorded during a treatment session, to the respective geographic recordation location; assigning a visual indicator to the sensor measurement; and mapping the visual indicator to the virtual geographic location representing to the geographic recordation location on a virtual representation of the geographic region. For example, the sensor measurement can be the current supplied to a solenoid valve controlling fertilizer application, wherein generating a treatment map includes: determining the current magnitude supplied to the solenoid for each of a set of geographic locations within the geographic region to be treated (e.g., based on the timestamp of the measurement and the location estimated for the treatment timestamp), assigning a different visual indicator (e.g., different color) to each current magnitude range, and generating a virtual map of the geographic region, mapping the visual indicator for the current magnitude corresponding to the respective geographic location.);
wherein the at least one intensity encodes a previous agricultural task carried out in the determined agricultural parcel as determined from the schedule stored in the database of agricultural activities, ([0089] In a first example, generating the treatment map includes: determining the treated geographic area and virtually comparing a virtual representation of the treated geographic area with a virtual representation of the geographic region to be treated (example shown in FIG. 16). Determining the treated geographic area can include: determining a starting location, determining a traversal path, and determining an end location (e.g., the location of the last sensor measurement), wherein the treated geographic area is the area covered, starting from the starting location, following the traversal path, and ending at the end location. The starting location can be the location at which the trigger event is detected, the location at which the sensor measurements indicate a treatment has begun, or be any other suitable treatment initiation location. The traversal path can be determined from the accelerometer, velocity, gyroscope, or other sensor measurements. The end location can be the location at which the treatment ended. However, the treated geographic area can be otherwise determined. Virtually comparing a virtual representation of the treated geographic area with a virtual representation of the geographic region to be treated can include: overlaying the treated geographic area representation (represented with a first visual indicator, such as a first color) over the representation of the geographic region to be treated (represented with a second visual indicator, such as a second color); subtracting the locations of the treated geographic area from the locations of the geographic region; or otherwise comparing the treated geographic area with the geographic region to be treated.;
and associating the constructed image with an agricultural task carried out in the agricultural parcel using a convolutional neural network trained to classify the constructed image according to a most likely agricultural task on the basis of an agricultural activity model specific to the agricultural exploitation. ([Fig. 10] automatically generated activity tracking record for a set of fields; [0019] FIG. 15 is a specific example of a time-ordered set of treatment maps for a treatment record set., the treatment record set including treatment records for a treatment session.; [0029] The treatment parameter determination module can be a: probabilistic module, heuristic module, deterministic module, clustering module, unsupervised machine learning module (e.g., artificial neural network, association rule learning, hierarchical clustering, cluster analysis, outlier detection), supervised learning module, semi-supervised learning module, deep learning module, or any other suitable module leveraging any other suitable machine learning method or combination thereof.; [0054] Recording a set of sensor measurements over a treatment time period S300 functions to record a set of data as the treatment is being performed, wherein the performed treatment can be identified based on the recorded data. The data, or a combination thereof, can be used to identify a specific treatment, a treatment category, or any other suitable treatment parameter. The data can include sensor data, data received from the user (e.g., cultivar inputs, field inputs, treatment inputs, etc.), device use information (e.g., patterns of user device activation), application use information (e.g., patterns of application use, functions of each application used, etc.), crop plan information for the field (e.g., a schedule of planned treatments for the field, a schedule of previously performed treatments for the field, etc.), or any other suitable data. Examples of the sensor measurement can include acceleration, velocity, other orientation sensor measurements, light, sound, images, a location identifier).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Hicks with Vollmar’s feature(s) listed above. One would’ve been motivated to do so in order to identify the treatment, generate maps of the field (Vollmar; [0028]). By incorporating the teachings of Vollmar, one would’ve been able to use temporal zoning data and agricultural activities data to generate parcel maps and associate them with tasks carried out.
Vollmar doesn’t explicitly teach:
and associating the constructed image with an agricultural task carried out in the agricultural parcel using a convolutional neural network
Stueve teaches:
and associating the constructed image with an agricultural task carried out in the agricultural parcel using a convolutional neural network ([0101] In some embodiments, the measurement of crop field parameters utilizes one or more machine vision algorithms. Various machine vision algorithms are within the scope of the present invention. Illustrative machine vision algorithms utilizing a convolutional neural network (CNN) architecture are described below in reference to FIG. 7 below.; [0133] FIG. 7 shows an exemplary CNN module that may be utilized for implementing various machine vision algorithms described herein. In FIG. 7, one or more input layers 702 are connected via a multiplicity of hidden layers 704 to one or more output layers 706. This neural network architecture may receive geospatial image data 701 and may be trained to generate 708 a crop vigor map.; [0136] As noted, embodiments of devices and systems (and their various components) described herein can employ artificial intelligence (AI) to facilitate automating one or more features described herein. The components can employ various AI-based schemes for carrying out various embodiments/examples disclosed herein. To provide for or aid in the numerous determinations (e.g., determine, ascertain, infer, calculate, predict, prognose, estimate, derive, forecast, detect, compute) described herein, components described herein can examine the entirety or a subset of the data to which it is granted access and can provide for reasoning about or determine states of the system, environment, etc. from a set of observations as captured via events and/or data. Determinations can be employed to identify a specific context or action, or can generate a probability distribution over states, for example. The determinations can be probabilistic—that is, the computation of a probability distribution over states of interest based on a consideration of data and events.). Examiner's Note: Please see the 35 USC 103 combination above (Vollmar) for teachings pertaining to the unbolded language.
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Hicks with Stueve’s feature(s) listed above. One would’ve been motivated to do so in order to generate a crop vigor map 311 and for estimating crop parameters 321 such as crop row spacing, canopy closure, and so forth, from geospatial image data 301 (Stueve; [0132]). By incorporating the teachings of Stueve, one would’ve been able to use a convolutional neural network to generate images for a field.
Regarding claim 2: Hicks doesn’t teach:
applying at least one of a classifier relating to the agricultural activities defined for the agricultural exploitation and a classifier relating to the agricultural activities defined for a set of agricultural exploitations.
Vollmar teaches:
applying at least one of a classifier relating to the agricultural activities defined for the agricultural exploitation and a classifier relating to the agricultural activities defined for a set of agricultural exploitations. ([0030] The treatment identification module functions to determine an identifier for the agricultural treatment based on sensor measurements were recorded during the agricultural treatment (recorded treatment). The treatment identifier can be a treatment type (e.g., a non-unique or shared identifier), a user-specified treatment identifier (e.g., a unique identifier), or be any other suitable identifier for the treatment; [0024] treatment services (e.g., harvesting crops, spraying crops, analyzing crops, etc.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Hicks with Vollmar’s feature(s) listed above. One would’ve been motivated to do so in order to determine the probability of the recorded treatment being a given treatment type based on the percentages of other fields (or entities) performing the given treatment type within a predetermined time duration from the treatment time (Vollmar; [0030]). By incorporating the additional teachings of Vollmar, one would’ve been able to apply the classifiers related to agricultural activities.
Regarding claim 3/13: Hicks teaches:
temporal zoning data of at least one agricultural machine received from a portable electronic device communicating from the agricultural machine are provided, ([0076] It should be noted that in addition to activity tracking that the activity tracking system may also be utilized for tracking location of equipment. For example, the system records the location and time associated with when the wireless device 120 detects a beacon signal from the beacon located on equipment and the time and location when the beacon signal is lost. This data can be used to define where the equipment was moved from and to and can be utilized to determine last known location of equipment.);
and the temporal zoning data of the agricultural worker is associated with the agricultural task further using temporal zoning data of the agricultural machine. ([0078] The activity tracking system has been discussed as the server 160 having the configuration information (e.g., beacons identified, equipment associated with beacons, geo-fences defined for locations of interest, activities associated with equipment (or tasks) and locations of interest and communications with worker 360 associated with activities) stored therein. The mobile device 120 communicates location and beacons detected with the server 160 and the server 160 determines, for example, the equipment associated with the worker 360, when the worker 360 is within a location of interest, the activities being performed by the worker 360.).
Regarding claim 4: Hicks teaches:
associating the temporal zoning data of the at least one agricultural worker with the agricultural task ([Abstract] Beacons that transmit identification signals are located on equipment that workers may utilize during the course of their jobs. A server may define parameters about the equipment the beacons are located on and geo-fences around areas of interest. A worker carries a wireless device that is capable of receiving the beacon signals and determining location of the worker. The wireless device has an activity tracking app running thereon that can communicate with the server. A determination is made as to the equipment assigned to the worker based on the beacon signals received by the worker and if the worker is within an area of interest based on their location. Activities can be initiated for the worker based on the defined area and the equipment (e.g., if worker is assigned a tractor and a mower and is within field 1, the activity the worker is assigned is mowing field 1).; [0078] The activity tracking system has been discussed as the server 160 having the configuration information (e.g., beacons identified, equipment associated with beacons, geo-fences defined for locations of interest, activities associated with equipment (or tasks) and locations of interest and communications with worker 360 associated with activities) stored therein. The mobile device 120 communicates location and beacons detected with the server 160 and the server 160 determines, for example, the equipment associated with the worker 360, when the worker 360 is within a location of interest, the activities being performed by the worker 360 and communications with the worker 360 associated with the activity). However, such an arrangement requires that the mobile device 120 and the server 160 are able to communicate via the data network 150 or via a wireless connection (e.g., Wi-Fi) to an access point (e.g., router) 130 for a provider network 140.).
Hicks doesn’t teach:
further comprises using weather data relating to the agricultural exploitation obtained from a weather database.
Vollmar teaches:
further comprises using weather data relating to the agricultural exploitation obtained from a weather database. ([0018] FIG. 14 is a specific example of a dashboard for a user account, including automatically generated and/or user-generated, previously performed treatments, instantaneous and forecasted weather associated with the geographic locations of the fields; [0027] The data used by the modules preferably includes the set of sensor measurement values recorded for the recorded treatment (e.g., the instantaneous treatment being analyzed), auxiliary data (e.g., third party data, weather data, etc.) …).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Hicks with Vollmar’s feature(s) listed above. One would’ve been motivated to do so in order to determine an identifier for the agricultural treatment (Vollmar; [0030]). By incorporating the additional teachings of Vollmar, one would’ve been able to use weather data related to agricultural activities.
Regarding claim 5: Hicks teaches:
further comprising updating the database of agricultural activities from at least the interpreted location data. ([0035] The information stored in the database 440 may include, for example, the beacons detected, the time and location for the beacon detection, entry/exit from the area defined by geo-fences and times associated therewith, the activities associated with the workers and the times associated with the activities, and location of the worker at defined intervals (e.g., every 5 minutes).).
Regarding claim 9: Hicks doesn’t teach:
further comprising training the convolutional neural network with images associated with at least one of: an agricultural task from the database of agricultural activities of an agricultural exploitation of a user or external agricultural exploitations, an agricultural task entered by the user, a previously determined agricultural task.
Stueve teaches:
further comprising training the convolutional neural network with images associated with at least one of: an agricultural task from the database of agricultural activities of an agricultural exploitation of a user or external agricultural exploitations ([Fig. 7] MV Module (E.G., CONVOLUTIONAL NEURAL NETWORK (CNN)); [Fig. 9]; [0051] FIG. 9 shows an example flow diagram for training the machine learning algorithm, in accordance with example embodiments of the disclosure.; [0101] In some embodiments, the measurement of crop field parameters utilizes one or more machine vision algorithms. Various machine vision algorithms are within the scope of the present invention. Illustrative machine vision algorithms utilizing a convolutional neural network (CNN) architecture are described below in reference to FIG. 7 below.; [0133] discloses a CNN trained to generate maps from geospatial image data for providing treatment maps; [0081] In one embodiment, the risk model (108, 130) may use a machine learning module, as described below. For example, the machine learning module may use an ensemble of random forest regressors, where the predictor variables are the crop vigor map (102, 116) and other remotely sensed or estimated crop field parameters (120, 128), in conjunction with macroclimate weather data 122. The dependent variable may be the pest of interest (e.g., white mold severity in soybeans). Model training is performed, where ground truth data comprising the coordinates and corresponding disease severity from a field at the end of the season may be used for modeling with in-season crop vigor indices, or other remotely sensed crop field parameters and macroclimate conditions at the time of imagery collection. This process quantifies the relationship between pest severity and in-season variables of crop vigor or other remotely sensed crop parameters and macroclimate conditions. This procedure should be repeated for the pest and crop of interest, as different relationships are expected, resulting in unique crop- and pest-dependent models. It is important to note that training with data from coordinates with all pest outbreak levels (e.g., no disease, light disease, moderate disease, and severe disease) is preferred. Further detailed discussion of the risk model is given below (e.g., see FIGS. 2, 3, 8).; [0082] FIG. 2 outlines one embodiment of a process according to the present disclosure, which illustrates a process to predict a pest susceptibility of a crop field. Several inputs and input parameters 210 are automatically determined from source data 205. The source data 205 comprises microclimate data and geospatial image data, such as aerial, satellite, and drone images, as well as sensor and machine data collected remotely or on the crop field. In addition, source data may also include crop stage models. The process generates a set of inputs relative to crop vigor 210 and a set of input parameters 211 from the source data 205. The crop vigor inputs 210 are extracted from the geospatial image data and include a crop vigor index applying to the whole crop field (i.e., field-scale crop vigor index) or a set of geolocated crop vigor indices forming a fine-scale crop vigor map for the crop field. The input parameters 211 comprise microclimate data (e.g., air temperature, relative humidity, wind speed, solar insolation and/or sun exposure) relative to the crop field. The input parameters 211 may also comprise agronomic data (e.g., row spacing, irrigation status, crop stage, and canopy closure) at various times (e.g., planting, treatment application) as well. A risk model 220 is used to predict the susceptibility of an outbreak of one or more crop pests (e.g., white mold in soybeans) based upon the crop vigor 210 and input parameters 211. An output 230 of the process is a measure of pest susceptibility at a fine scale (i.e., pest susceptibility map) or at a field scale (e.g., pest susceptibility index). A treatment plan 231 for the crop field may also be generated from the generated pest susceptibility, where the treatment plan comprises the implementation of agricultural management techniques (e.g., applications of specific chemicals such as fungicides) to prevent the outbreak, or control the propagation, of crop pest(s). In one embodiment, an output treatment plan 231 may depend on whether the field-scale (i.e., macro) and/or the fine-scale (i.e., micro) conditions for a pest outbreak are met, leading to (a) full treatment of the crop field if macro conditions are met and micro conditions are met throughout the field; (b) targeted treatment of certain portions of the crop field if macro conditions are not met and micro conditions are met for those portions, or (c) no treatment if macro conditions are not met or micro conditions are not met throughout the crop field. The process of FIG. 2 therefore converts the crop vigor index map and other input parameters (210, 211) into a prescription for an appropriate agricultural management procedure, such as spraying pesticide (e.g., herbicide, insecticide, fungicide, insect repellent, animal repellent) in parts of the field that are most likely to experience pest outbreaks. Agricultural management procedures also include applying fertilizer and plant nutrients. The output pest susceptibility values 230 can be broken down into zones for targeting different treatments (e.g., spray rates) based on the product being applied, the equipment available for application, and the risk a grower/agronomist is willing to take. an agricultural task entered by the user, a previously determined agricultural task.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Hicks with Stueve’s feature(s) listed above. One would’ve been motivated to do so in order to allows measurement and quantification of crop field parameters, such as row spacing and canopy closure, across the entire field, and thus provides a more accurate measure across an entire field or specific portions of the field (Stueve; [0100]). By incorporating the additional teachings of Stueve, one would’ve been able to train a CNN with data about agricultural activities performed by a user.
Regarding claim 10: Hicks teaches:
A computer program comprising program code instructions for carrying out the steps of the method according to claim 1 when the program is executed on a computer ([0084] all methods described herein can also be stored on a computer readable storage to control a computer.).
Regarding claim 12: Hicks teaches:
further comprising at least one communicating portable electronic device of an agricultural worker adapted to communicate temporal zoning data of the agricultural worker to the at least one processor. ([0028] A worker 360 has a wireless device 120 (e.g., smart phone, tablet, smart watch) that is used during the course of a work day; [0035] The database 440 may include information that is gathered during the operation of the activity tracking system. The information may be provided by the apps running on the wireless devices 120 that communicate with the server 160. The information stored in the database 440 may include, for example, the beacons detected, the time and location for the beacon detection, entry/exit from the area defined by geo-fences and times associated therewith, the activities associated with the workers and the times associated with the activities, and location of the worker at defined intervals (e.g., every 5 minutes).).
Regarding claim 14: Hicks teaches:
further comprising: applying a classifier relating to the agricultural activities defined for the agricultural exploitations; ([0055] The transportation activity may be recorded in the database 440. Information recorded may include, for example, worker, equipment, location, time when the transportation activity is initiated and a time when the transportation activity is stopped (e.g., completed, new activity begins).);
applying a classifier relating to the agricultural activities defined for a set of agricultural exploitations; ([0034] The database 440 may include a list of tasks (e.g., plowing, mowing) that are associated with different combinations of equipment and a list of activities (e.g., plowing field 4) associated the combination of tasks and defined area of interest.);
and selecting an agricultural task based on the results of the two classifiers. ([0020] The app running on the wireless device 120 may access the server 160 to verify that the beacon is associated therewith, determine equipment that the beacon is located on, and determine actions to be taken based thereon; [0038] When the activity tracking system is executed by the processor 410 the activity tracking system may enable wireless devices 120 to access the server 160 to, for example, determine (a) equipment assigned to a worker based on beacons detected; (b) worker being within areas of interest; and (c) the tasks and the activities a worker is performing.).
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Hicks (US 20190050946 A1, hereinafter “Hicks”), in view of Vollmar et al. (US 20160147962 A1, hereinafter “Vollmar”), in further view of Stueve et al. (US 20200364843 A1, hereinafter “Stueve”) as applied to claim 1 above, in further view of Dai (US 20210027088 A1, hereinafter “Dai”).
Regarding claim 7: Hicks doesn’t teach:
encoding at least one piece of information on one layer of a plurality of layers superimposed to form the constructed image.
the at least one piece of information being derived from or deduced from temporal zoning data and from agricultural activity and agricultural exploitation databases.
Vollmar teaches:
encoding at least one piece of information on one layer of a plurality of layers superimposed to form the constructed image. (([Fig. 16] shows an example of a region to be treated (e.g., field) with parallel lines delineating an area of the field that has been previously treated (i.e., encoding the treatment of the area treated).; [Claim 18] The method of claim 5, wherein automatically generating a treatment summary based on the set of sensor measurements comprises generating a virtual map representing a treated portion of a geographic region encompassed within the predefined geofence, based on a subset of the set of sensor measurements and recordation locations for each of the subset of sensor measurements.; [Claim 20] The method of claim 18, wherein automatically generating a treatment summary further comprises: generating a different virtual map for each of a set of timestamps, each virtual map generated based on sensor measurements recorded prior the timestamp; time-ordering the virtual maps according to the respective timestamp; receiving a playback request from a second user device; and in response to receipt of the playback request, sequentially displaying the virtual maps in time-order on the second user device.);
the at least one piece of information being derived from or deduced from temporal zoning data and from agricultural activity and agricultural exploitation databases. ([0026] In a specific example, the method can include: determining whether a user or agricultural equipment is in a user's field (i.e., temporal location); in response to user or equipment location within the field, determining whether sensor measurements should be recorded; in response to determination that the sensor measurements should be recorded, recording sensor measurements (e.g., by the user device, agricultural equipment, or other data logger); in response to sensor measurement recordation, determining a set of treatment parameters characterizing the recorded treatment based on the sensor measurements; and creating and storing a record of the performed treatment.
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Hicks with Vollmar’s feature(s) listed above. One would’ve been motivated to do so in order to populate a schedule of past treatments, update the parameters of a previously scheduled treatment in a crop plan, generate recommendations for future treatments (Vollmar; [0026]). By incorporating the additional teachings of Vollmar, one would’ve been able to generate information to be encoded in an image.
Vollmar doesn’t explicitly teach:
a plurality of layers superimposed to form the constructed image.
Dai teaches:
a plurality of layers superimposed to form the constructed image. ([0110] In specific implementation, the step that an operation route of a mobile device in the corresponding area to be operated is determined based on the acquired boundary of the area to be operated includes that: the acquired boundary data of the area to be operated is superimposed into an image of the area to be operated, the image of the area to be operated being an image of an area where the area to be operated is located in the original image; and an operation route of a mobile device in the area to be operated is determined based on the image of the area to be operated, superimposed with the boundary data of the area to be operated.; [0111] the location information of the boundary of the farmland area may be obtained by a traditional visual boundary detection algorithm (for example, an edge detection method such as Canny operator). The information of boundary points may be stored in an (x, y) format. x represents the number of pixels from a boundary point to a left boundary of an image, and y represents the number of pixels from the boundary point to an upper boundary. Then, the boundary points of the farmland boundary are superimposed into the original image. Specifically in the present embodiment, the obtained boundary points of the farmland boundary are superimposed into the original farmland image as shown in FIG. 2 to obtain a farmland image as shown in FIG. 9. In FIG. 9, the boundary lines between the white narrow areas and the black wider areas are the farmland boundaries (that is, the boundary of the area to be operated) superimposed on the original farmland image). Examiner's Note: Please see the 35 USC 103 combination below for teachings pertaining to the unbolded language.
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Hicks with Dai’s feature(s) listed above. One would’ve been motivated to do so in order to superimpose farmland boundary data into the original farmland image (Dai; [0111]). By incorporating the additional teachings of Dai, one would’ve been able to superimpose layers of information to form a constructed image.
Regarding claim 8: Hicks doesn’t teach:
wherein the at least one piece of information is at least one of: the trace formed by the temporal zoning data, the agricultural parcel determined according to the temporal zoning data and the agricultural exploitation database, a trace of the agricultural parcel, a previous task carried out in the determined agricultural parcel, a time elapsed since the last task performed, a type of agricultural parcel, a user using the portable system, a machine using the portable system, a weather forecast, an activity carried out on other agricultural exploitations.
Vollmar teaches:
wherein the at least one piece of information is at least one of: the agricultural parcel determined according to the temporal zoning data and the agricultural exploitation database, (Fig. 16] field; [Abstract] a field identifier; [Claim 18] The method of claim 5, wherein automatically generating a treatment summary based on the set of sensor measurements comprises generating a virtual map representing a treated portion of a geographic region encompassed within the predefined geofence, based on a subset of the set of sensor measurements and recordation locations for each of the subset of sensor measurements.; [Claim 20] The method of claim 18, wherein automatically generating a treatment summary further comprises: generating a different virtual map for each of a set of timestamps, each virtual map generated based on sensor measurements recorded prior the timestamp; time-ordering the virtual maps according to the respective timestamp; receiving a playback request from a second user device; and in response to receipt of the playback request, sequentially displaying the virtual maps in time-order on the second user device.), a trace of the agricultural parcel, ([Fig. 15] discloses a set of treatment maps with traces of a time-ordered (i.e., temporal) treatment session obtained from tracking agricultural activities at the field, including user location, and equipment location, among other data), a previous task carried out in the determined agricultural parcel, ([Abstract] an identifier for the past agricultural treatment), a time elapsed since the last task performed, ([Fig. 7] activity schedule: past and future), a type of agricultural parcel ([0030] soil data for the field), a user using the portable system, ([Abstract] wherein the user device is associated with a user account; [Fig. 7] user device, [0026] determining whether a user or agricultural equipment is in a user's field), a machine using the portable system, ([Fig. 7] equipment; [0024] The farmer can additionally own and be associated with agricultural equipment; [0026] determining whether a user or agricultural equipment is in a user's field), a weather forecast, ([0018] forecasted weather associated with the geographic locations of the fields), an activity carried out on other agricultural exploitations. ([0026] The record can be used to populate a schedule of past treatments).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Hicks with Vollmar’s feature(s) listed above. One would’ve been motivated to do so in order to identify fields that have been treated recently (Vollmar; [0026]). By incorporating the additional teachings of Vollmar, one would’ve been able to encode information in an image.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
McCann et al. (WO 2020172756 A1), which discloses systems and methods of using an unmanned aerial or land vehicle (e.g. drone) for agricultural and/or pest control applications.
K. L. -M. Ang and J. K. P. Seng, "Big Data and Machine Learning With Hyperspectral Information in Agriculture," in IEEE Access, vol. 9, pp. 36699-36718, 2021, which discloses a comprehensive review of representative studies to provide insights into significant research efforts in agriculture using Big data, machine learning and deep learning with the focus on frameworks or architectures, information processing and analytics with hyperspectral and multispectral data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIEL J TORRES CHANZA whose telephone number is (571)272-3701. The examiner can normally be reached Monday thru Friday 8am - 5pm ET.
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/G.J.T./Examiner, Art Unit 3625
/SARA GRACE BROWN/Primary Examiner, Art Unit 3625
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