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 .
Status of Claims
This communication is a Final Office Action in response to Applicant’s amendment for application number 18/263,696 received on 10/28/2025.
In accordance with Applicant’s amendment, claims 1-14 are amended. Claims 1-14 are currently pending and have been examined.
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
The amendment filed on 10/28/2025 has been entered.
Applicant’s amendment necessitated the new ground(s) of rejection set forth in this Office Action.
Upon review of the amended claims, the §112(f) claim interpretations applied to the original claims are withdrawn.
Upon review of the amended claims, the §112(b) rejections previously applied are withdrawn. Examiner notes that the amendments introduce new §112(b) issues. See §112(b) rejections below.
Upon review of the amended claims, the §112(a) rejections previously applied are withdrawn.
Response to Arguments
Response to Specification Objections arguments – Applicant’s arguments 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 (Remarks at pgs. 8-9) with respect to the §101 rejections previously applied to the original claims have been considered and are unpersuasive.
Applicant argues (Remarks at pgs. 8-9) – “First, applicants respectfully submit that the amended claims of the present response are not drawn to an abstract idea based on a mental process. For example, amended claim 1 includes a step of receiving, from a portable electronic device, temporal zoning data of at least one agricultural worker. This is a specific activity that is performed by wireless communication hardware and cannot be a mental process. Another activity that cannot be performed by the human mind is constructing an image for a determined agricultural parcel from the temporal zoning data and the databases of agricultural activities and agricultural exploitation. Additional features of amended claim 1, including storing a schedule of at least two agricultural tasks, each agricultural task being characterized by a place identifier and a position in the schedule, in a database of agricultural activities, and storing at least one agricultural parcel characterized by a place identifier and a location in an agricultural exploitation database, are not mental processes. Accordingly, applicants respectfully submit that the present claims are not drawn to a mental process.“. In response, Examiner respectfully disagrees and reminds Applicant that, as can be seen in the Step 2A, Prong 1 analysis below, the additional elements recited in the claims are excluded from the decision whether the claims recite abstract limitations. The additional elements are analyzed in Step 2A, Prong 2 (i.e. to determine whether the additional elements integrate the judicial exception into a practical application), and Step 2B (i.e. to determine whether the additional elements add significantly more.).
Applicant further argues (Remarks at pg. 9) – “Additional features of amended claim 1, including storing a schedule of at least two agricultural tasks, each agricultural task being characterized by a place identifier and a position in the schedule, in a database of agricultural activities, and storing at least one agricultural parcel characterized by a place identifier and a location in an agricultural exploitation database, are not mental processes. Accordingly, applicants respectfully submit that the present claims are not drawn to a mental process.”. In response, Examiner respectfully disagrees and notes that, as currently recited, the steps could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper by one of ordinary skill in the art. For example, one of ordinary skill in the art would be able to reasonably memorize the following hypothetical schedule (reasonable under BRI of the claim limitations, as currently presented):
Mowing on Field A: 8am on Tuesday and Thursday
Plowing on Field B: 8am on Monday, Wednesday, and Friday
Therefore, the steps recite an abstract idea directed to “Mental Processes”.
Applicant further argues (Remarks at pg. 9) – In addition, the inventive materials of the amended claims amount to significantly more than any alleged abstract idea. For example, with respect to claim 1, constructing an image for a determined agricultural parcel from the temporal zoning data and the databases of agricultural activities and agricultural exploitation, and associating the constructed image with an agricultural task carried out in the agricultural parcel using a convolutional neural network are significant improvements in agricultural technology.
As described with respect to the second embodiment of the specification, a CNN can use the image to associate the image with an agricultural task, e.g., determine with a high degree of accuracy what tasks were performed by a worker, using temporal zoning data received from a portable electronic device of the worker. The automatic associations represent a significant advancement to conventional agricultural technology which generally requires the workers to manually log tasks.”. In response, Examiner respectfully disagrees and notes that the recited computing additional elements fail to integrate the abstract idea into a practical application because they amount to using generic computing elements 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 CNN, the limitations provide nothing more than mere instructions to implement an abstract idea on a generic computer, which does not integrate the judicial exception into a practical application. 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.
Applicant further argues (Remarks at pg. 9) – “Other conventional approaches, such as that of JP 41708792, rely on complex human parameters such as speed and efficiency, which are subject to high degrees of variability, and are limited to a small number of tasks. In contrast, embodiments of the present application draw upon databases of agricultural activities and agricultural exploitation to construct an image, and the image is associated with an agricultural task carried out in a parcel using a CNN. Such features tremendously enhance the scope, efficiency, and accuracy of tracking agricultural activities.”. In response, Examiner respectfully disagrees and reminds Applicant that whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Therefore, the rejections are maintained.
Response to §103 arguments – Applicant’s arguments (Remarks at pgs. 10-18) with respect to the §101 rejections previously applied to the original claims have been considered and are unpersuasive.
Applicant argues (Remarks at pg. 10) – “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. 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 the cited figure (Fig. 5) illustrates an example diagram of information stored in a database 440 utilized in an activity tracking system, including: beacon identification information 510, defined area information 520, task/activity information 530 and user (worker) information (Tasks, Locations, Time) 540.
Applicant further argues (Remarks at pg. 10) – “The present rejection alleges that this feature is discloses 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. Implemented such features from GPS data would require additional data processing, which is not disclosed in Hicks.”. In response, Examiner respectfully notes that under BRI, one of ordinary skill in the art would reasonably interpret temporal zoning data of the at least one agricultural worker as data that identifies the location of the worker at different points in time, which is taught by Hicks in at least par. [0054] as cited in the office action (OA) dated 05/29/2025: “[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.”.
Applicant further argues (Remarks at pg. 10) – “The present rejection alleges that paragraph Hicks [0054] discloses this feature: "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.". However, in this paragraph, only the position of the user or the equipment that the worker uses appears to be used. Whereas in claim 1, determining which task is being carried out uses not only the time zoning data but also the agricultural activity database to know the progress of the agricultural activity and finally the agricultural exploitation database.”. In response, Examiner respectfully disagrees and notes that the location of the worker and the equipment, as well as the area of interest 700, and the transportation activity are all elements that can be interpreted as being considered in par. [0054] of Hicks. Furthermore, regarding applicant’s argument “to know the progress of the agricultural activity”, this item irrelevant to the analysis because this feature is not recited or required by the claim. For example, the claim does not recite or require observing or storing activity progress. 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).
Applicant further argues (Remarks at pg. 12) – “However, paragraph [0022] of the present application states: "Preferably, 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.". In response, Examiner notes that this item irrelevant to the analysis because this feature is not recited or required by the claim. For example, the claim does not recite or require observing or storing activity progress. 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).
Applicant further argues (Remarks at pg. 13) – “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 an automatically generated activity tracking record for a set of fields in Fig. 10 (map of the field). Vollmar also teaches in par. [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. The generated maps disclosed by Vollmar, which include records of activities, would be reasonably interpreted by one of skill in the art as associating the constructed image with an agricultural task carried out in the agricultural parcel. Furthermore, Stueve discloses using a CNN to generate a map in an agricultural environment to, making it analogous art to Applicant’s invention. Therefore, it would’ve been obvious to one of skill in the art at the time of Applicant’s invention to combine Hicks with Vollmar’s and Stueve’s feature(s).
Applicant further argues (Remarks at pg. 13) – “Further regarding claim 2, as explained previously, the use of a database of agricultural activities is a novel feature of the present application, therefore the feature "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" is a novel feature.
For at least this reason, Applicants respectfully submit that the rejection of claim 2 is improper and should be withdrawn.
Further regarding claim 5, as explained previously, the use of a database of agricultural activities is a novel feature of the present application, therefore the feature "updating the database of agricultural activities from at least the interpreted location data"is a novel feature.
For at least this reason, Applicants respectfully submit that the rejection of claim 5 is improper and should be withdrawn. “. In response, Examiner notes that Applicant’s conclusory statements of the claims (as currently presented), are contradicted by Examiner’s findings of existing prior art as shown in the rejections in the OA dated 05/29/2025, as well as the rejections below.
Applicant further argues (Remarks at pgs. 15-16) – “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 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 notes that these items are irrelevant to the analysis because these features are not recited or required by the claim. For example, the claim does not recite or require limiting access to a user, or varying the intensity of elements that are encoded, or define “intensity of the image” in the way Applicant suggest in the this argument. 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).
Applicant further argues (Remarks at pg. 16) – “Vollmar's fig 16 only indicates on a map of the field, the areas that have been treated or not, the information is then directly accessible, whereas claim 7 of the present application provides for encoding information on layers of the image which does not allow direct access to the information. In addition, as seen previously, Hicks, Vollmar and Stueve do not disclose the presence of an agricultural activity database, it is then appropriate that the characteristic: "the at least one piece of information being derived from or deduced from temporal zoning databases and from agricultural activity and agricultural exploitation databases" is also new considering the cited references.
For at least this reason, Applicants respectfully submit that the rejection of claim 7 is improper and should be withdrawn.”. In response, Examiner respectfully disagrees and notes that as detailed in the rejections below, Hicks teaches an agricultural activities database (([0034] The database 440 may include a list of tasks (e.g., plowing, mowing)). Furthermore, the rest of items in this argument are irrelevant to the analysis because these features are not recited or required by the claim. For example, the claim does not recite or require limiting access to a user. 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).
Applicant further argues (Remarks at pg. 17) – “However, the image of the Hicks and Vollmar association is a map of the field, it is therefore not obvious to represent on a map information other than spatial information such as temporal information or information on the weather for example. Nothing is indicated in Vollmar so that this can be the case and nothing encourages the skilled person to do so in Hicks and Vollmar.
For at least this reason, Applicants respectfully submit that the rejection of claim 8 is improper and should be withdrawn.”. In response, Examiner respectfully disagrees and notes that Vollmar, in at least Fig. 15, and as recognized by Applicant in their argument (Remarks at pg. 17), 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. One of skill in the art would reasonably interpret this teaching from Vollmar as equivalent to “a trace formed by the temporal zoning data”, which is one of the alternatives in the Markush Group recited by claim 8.
Applicant further argues (Remarks at pg. 16) – “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.".”. In response, Examiner notes that this item (par. [0150] of Applicant’s Specification) is irrelevant to the analysis because the features are not recited or required by the claim. 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).
Therefore, the rejections are maintained.
Specification
The disclosure is objected to because of the following informalities (grammatical errors of the typographical kind – underlined below):
Paragraph 15: “According to one embodiment, the method further comprises udating the database of agricultural activities from at least the interpreted location data.”
Paragraph 17: “…according to the temporal zoning data data and said databases…”
Paragraph 42: “Agricultural” means any activity relating to the cultivation of plants or the raising of animals, including forestry, oyster farming, myciculture, shellfish farming, mussel farming, or heliciculture.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 12 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Regarding claim 12: The claim recites the claim language of “…the computerized interpretation module”. However, the term “the computerized interpretation module” lacks antecedent basis, and also renders the claim ambiguous (i.e., it's not just a typographical error). 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 1-14 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) 1-10, 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, claims 1-14 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 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; (But for the additional elements – underlined – recited in this limitation, the step for “storing a schedule” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
storing, in an agricultural exploitation database, at least one agricultural parcel characterized by a place identifier and a location; (But for the additional elements – underlined – recited in this limitation, the step for “storing a place identifier and a location” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
receiving, from a portable electronic device of the at least one agricultural worker, temporal zoning data of the at least one agricultural worker; (But for the additional elements – underlined – recited in this limitation, the step for “receiving temporal zoning data” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper. Additionally, even if considered as an additional element, this step amounts to insignificant extra-solution activity as mere data gathering.);
and associating the temporal zoning data with an agricultural task using the temporal zoning data and the agricultural activity and agricultural exploitation databases by: (But for the additional elements – underlined – recited in this limitation, the step for “associating the temporal zoning data with an agricultural task” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
constructing an image for a determined agricultural parcel from the temporal zoning data and the databases of agricultural activities and agricultural exploitation, (But for the additional elements – underlined – recited in this limitation, the step for “constructing an image” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
and associating the constructed image with an agricultural task carried out in the agricultural parcel using a convolutional neural network. (But for the additional elements – underlined – recited in this limitation, the step for “associating the constructed image with an agricultural task” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.).
Independent claim 11 recites a system with limitations that are substantially similar to the limitations of independent claim 1. Therefore, the same analysis applies. The additional elements beyond the abstract idea for consideration under Step 2A, Prong 2, and Step 2B recited by the independent claims are: database of agricultural activities, agricultural exploitation database, portable electronic device, convolutional neural network, at least one memory, and at least one processor coupled with the at least one memory.
Dependent claims 2-10, and 12-14 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 database of agricultural activities, agricultural exploitation database, portable electronic device, at least one memory, and at least one processor coupled with the at least one memory, 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 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). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (generic computing environment).
With respect to the convolutional neural network, the limitations for and associating the constructed image with an agricultural task carried out in the agricultural parcel using a convolutional neural network, they provide nothing more than mere instructions to implement an abstract idea on a generic computer, which does not integrate the judicial exception into a practical application. 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.
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 database of agricultural activities, agricultural exploitation database, portable electronic device, at least one memory, and at least one processor coupled with the at least one memory, these additional element(s) has/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 (engine) 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, the internet, online) and does not amount to significantly more than the abstract idea itself. Applicant’s specification recites the computing additional elements at a high level of generality. 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). Additionally, use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
With respect to the convolutional neural network, the limitations for and associating the constructed image with an agricultural task carried out in the agricultural parcel using a convolutional neural network, they provide nothing more than mere instructions to implement an abstract idea on a generic computer, which does not add significantly more to the abstract idea. 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.
Furthermore, even if the receiving temporal zoning data steps are interpreted as additional elements, these activities at most amount to insignificant extra-solution activity (i.e., mere data gathering), which does not add significantly more to the abstract idea, as noted in MPEP 2106.05(g). Additionally, the receiving temporal zoning data extra-solution activity has been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)).
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. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to amount to significantly more than the abstract idea itself. The ordered combination of elements in the claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to 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 1-14 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; ([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.; [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.);
storing, in an agricultural exploitation database, at least one agricultural parcel characterized by a place identifier and a location; ([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: ([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,
and associating the constructed image with an agricultural task carried out in the agricultural parcel using a convolutional neural network.
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.);
and associating the constructed image with an agricultural task carried out in the agricultural parcel (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.).
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 to generate parcel maps and associated with tasks carried out.
Vollmar doesn’t explicitly teach:
using a convolutional neural network.
Stueve teaches:
using a convolutional neural network. ([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.
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 doesn’t teach:
associating the agricultural worker's temporal zoning data with the agricultural task (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.).
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 6: Hicks doesn’t teach:
wherein 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 the 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.
Volmar teaches:
wherein the constructed image comprises a trace formed by the temporal zoning data ([Fig. 10] teaches a map showing agricultural fields with geometric primitives (e.g., circles) identifying treated areas.);
and at least one geometric primitive parameterized from the temporal zoning data and the agricultural activity and exploitation databases; ([Fig. 15] teaches a set of treatment maps with traces of a time-ordered (i.e., temporal) treatment session.
at least one intensity of the image being determined from the temporal zoning data and the agricultural activity and exploitation databases. ([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. Examiner notes that one of ordinary skill in the art would reasonably interpret the “intensity of the image” from Applicant’s disclosure to be equivalent to using visual representations to encode additional information pertaining to the field, as disclosed in paragraph [158] of Applicant’s specification: For example, a white circle pattern whose diameter indicates the time elapsed since the last task carried out in the determined agricultural parcel can be integrated into the layer encoding the information “previous task carried out in the determined agricultural parcel”.).
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 delineate a treated geographic area (Vollmar; [0020]). By incorporating the additional teachings of Vollmar, one would’ve been able to encode information into a layer of the images/maps generated.
Regarding claim 7: Hicks doesn’t teach:
encoding at least one piece of information on one layer of a plurality of layers forming the image.
the at least one piece of information being derived from or deduced from temporal zoning databases 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 forming the 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).);
the at least one piece of information being derived from or deduced from temporal zoning databases 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.
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 trace formed by the temporal zoning data, ([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), 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.
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.
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 the 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 the user or external agricultural exploitations ([0133] discloses a CNN trained to generate maps from geospatial image data for providing treatment maps), an agricultural task entered by the user, a previously determined agricultural task. Examiner notes that one of ordinary skill in the art would reasonably interpret geospatial data to be inclusive of, and equivalent to: user location, machine location, field maps, or weather maps, among other data used in Applicant’s disclosure.).
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 computerized interpretation module. ([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.).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached on (571)270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/G.J.T./Examiner, Art Unit 3625
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625