DETAILED ACTION
This final office action is responsive to application 17/960,926 with applicant’s amendments and request for reconsideration as submitted 27 Jan. 2026.
Claim status is currently pending and under examination for claims 1-20 of which independent claims are 1 and 10; amended claims are 1-2, 7-8, 10-11, 15 and 20; no claims are currently in condition for allowance.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Remarks
Applicant’s responsive remarks filed 01/27/26 are considered together with amendments and with the result as follows:
The rejection of claims 8 and 11 under 35 U.S.C. 112(b) as lacking antecedent basis is withdrawn, as necessitated by applicant’s amendments made to the claims.
The rejection of claims 1-20 under 35 U.S.C. 101 as being directed to an abstract idea without significantly more is maintained herein. Applicant’s remarks have been fully considered but are not persuasive for the following reasons. Principally, the claims as they stand broadly cover functionalities that do not align with comments, e.g. scale of data being millions or thousands has no nexus with claims as written. The broadest reasonable interpretation is simply a plurality of data points, e.g. image(s).
Looking at the abstract idea, it is clear that feedback is from human user: [0005] “user feedback” thus serves as the basis for the operations. It is not clear why user feedback cannot be part of a mental process for labeling data to be fed into a model for subsequent training. Training itself is not treated as the abstract idea. Rather, steps are performed to process data prior to be used for training. In other words, pre-processing or data cleaning to sort information that is to be provided to a model. Such functionality may automatically include a user’s bias, no bias mitigation or technical solution is specifically detailed.
The absence of practical application is apparent from the only non-data elements of the claim: source devices connected to computing network, this is a standard computer with internet connection under the broadest reasonable interpretation. Practical application might be suggested if there were some farm tractor or agricultural embodiment, but this is not suggested at the level of the independent claim. Additional elements simply point to networked devices and training.
The training is not significantly more because particularity is insufficient to give model a particular structure. Numerous examples of trained models with particular structure are noted throughout this office action, e.g. WheatNet or DeepCorn noted in conclusion which could be used as an available selected model. Rather, in the instant claims, the model is merely ‘associated with’ the ‘development’ of a model using the abstract idea. An improvement to the abstract idea is still an abstract idea itself, no benchmarks or unexpected results are put forward as evidence to substantiate a particular improvement when looking at the claim as an ordered combination. The noted challenge of data collection can be crowdsourcing data for labeling and subsequent operations with resources as already addressed. Accordingly, eligibility is not satisfied at this time the rejection is maintained.
Applicant’s remarks regarding the prior art have been considered, but they are moot in view of the new grounds of rejection as necessitated by applicant’s amendments. Updated search reveals newly relied upon primary reference Rude in place of Capota to remedy the amended breadth of claim scope. An updated rejection under 35 U.S.C. 103 is detailed below.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In determining whether the claims are subject matter eligible, the examiner applies guidance set forth under MPEP 2106. The response to remarks above are incorporated herein.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—all claims fall within one of the four statutory categories: claims 1-9 are a method or process, and claims 10-20 are a system or machine. Thus, the analysis should proceed per MPEP 2106.03.
Step 2A, prong one: Does the claim recite an abstract idea, law of nature or natural phenomenon? Yes—the claims, under the broadest reasonable interpretation, recites an abstract idea. In this case, claims fall within the enumerated grouping of abstract idea being “Mental Processes”, but for the recitation of generic computer components. In particular, claims recite:
“in association with development of at least a first model for classifying at least a first type of subject, generating notifications to each of a plurality of potential feedback source
(Mental Process, judgment or opinion)
“automatically tagging input messages comprising responsive images […] with source metadata and further as being in association with the notification” (Mental Process, evaluation or judgment)
“correlating the images […], as components of a first data set for the at least first model, with the at least first type of subject and tagged metadata” (Mental Process, judgment or evaluation)
Focus of the claims concern model development feedback based on generating notifications, tagging images with metadata and correlating images with subjects and metadata. These are processes that can be performed mentally. A human developer may classify upon identifying or observing images of a type for labeling and comparatively correlate to attribute metadata. Doing so as feedback for classifying may entail a user verifying information. Accordingly, the claims are drawn to mental processes as the abstract idea.
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—a practical application is not integrated by the judicial exception because the additional elements are as follows:
“training of supervised learning models… training the at least first model at the central computing network” generally linking the use of the judicial exception to a particular technological environment or field of use
“establishing a respective feedback connection between each of the plurality of potential feedback source devices and a data storage network” MPEP 2106.05(f)(g) merely uses a computer as a tool to perform an abstract idea, e.g. computer with Internet connection, or insignificant extra-solution activity added to the judicial exception
images “to be received at a central computing network” again, images “received at the central computing network via the feedback connection” and once more, images “received via the respective feedback connection” MPEP 2106.05(g) adding insignificant extra-solution activity, e.g. mere data gathering
Balance of the claim concerns training with network-connected devices. The trained model fails to present any weights, hyperparameters or loss function giving structure to a particular model. Rather, off-the-shelf models may be trained such as a fine-tuning for the update of a black-box. The claimed invention is closer to feature engineering or data wrangling over specified inputs to be received. This can be performed at the back-end of a computer network, e.g. wizard of oz. The receiving via network connection is read in light of the specification which describes [0024] “Internet” and/or [0027] “cloud… generic hardware processor.” The claims do not present any real-world application and simply use networked devices to receive data. Receiving data via networked devices points to distributed training which is not new and does not provide a concrete use-case like employing computer vision models on an agricultural sprayer. Accordingly, the claims remain directed to an abstract idea and the additional elements fail to integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No—the claims do not include additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea in to a practical application, the additional elements are identified with respect to MPEP 2106.05 and are as follows:
“training of supervised learning models… training the at least first model at the central computing network” generally linking the use of the judicial exception to a particular technological environment or field of use. Particularly, the trained model fails to effect a particular transformation or detail a technical solution that meaningfully limits the claim under MPEP 2106.05(c)(e).
“establishing a respective feedback connection between each of the plurality of potential feedback source devices and a data storage network” MPEP 2106.05(f)(g) merely uses a computer as a tool to perform an abstract idea, or insignificant extra-solution activity. Particularly, said extra-solution activity is a well-understood, routine and conventional activity under MPEP 2106.05(d)(II)(i) “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: i. Receiving or transmitting data over a network, e.g., using the Internet” Further, the claimed devices are generic computing devices which do not qualify as a particular machine under MPEP 2106.05(b).
images “to be received at a central computing network” again, images “received at the central computing network via the feedback connection” and once more, images “received via the respective feedback connection” MPEP 2106.05(g) adding insignificant extra-solution activity, e.g. mere data gathering. Particularly, said extra-solution activity is a well-understood, routine and conventional activity under MPEP 2106.05(d)(II)(i) “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as an insignificant extra-solution activity: i. Receiving or transmitting data over a network, e.g., using the Internet”
Significantly more is not evident from the balance of the claim. Training being distributed among networked devices, e.g. training via internet, is not an inventive concept as of effective filing date ~2022. Establishing connections between devices to receive data is a well-understood, routine and conventional function of generic computers, and the training lacks particularity. If the claim language provides only a result-oriented solution, with insufficient detail for how a computer accomplishes it, then the claims do contain an inventive concept. Taken alone, their additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination does not elevate the claim as a whole to show inventive concept. In view of the foregoing, the claims are found as not patent eligible.
This rejection applies to independent claims 1 as well as independent claim 10 in which a system performs limitations of claim 1 and further recites a computing network. The computing network is an additional element that amount to mere use of computers as a tool to perform the abstract idea under MPEP 2106.05(f). A network of computing devices in communication is tantamount to using the Internet as already pointed out. This does not integrate the abstract idea into a practical application or amount to significantly more.
Dependent claims when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that they are not directed to the abstract idea, or that they include additional elements which integrate the abstract idea into a practical application or amount to significantly more.
Dependent claims 2 and 15 disclose initiating a campaign for development of classifier for subjects under classifying conditions and where images associated with the campaign are correlated. This is considered part of the abstract idea of mental processes including judgment and evaluation. Correlation is an evaluation and campaign development for classifying with classifying conditions can be judged by a human with physical aid such as a template for categorizing. There is no indication of what the conditions or subjects are required to be, they simply serve to embellish the abstract idea. There are no additional elements.
Dependent claims 3 and 16 disclose broadcasting notifications to a network of devices in accordance with the campaign including a request for images. Broadcasting notifications and requesting images are considered additional elements which amount to adding insignificant extra-solution activity to the judicial exception under MPEP 2106.05(g). Particularly, said insignificant extra-solution activity is a well-understood, routine and conventional activity under MPEP 2106.05(d)(II)(i). Therefore, the additional elements do not integrate the judicial exception into a practical application or amount to significantly more.
Dependent claims 4-6 and 17-19 disclose weather conditions associated with devices that are selected. This can be considered part of the abstract idea as mental process including observation exemplified as observing storm clouds will likely rain on devices in proximity of the observed weather. Crowdsourcing such observations can be shared among human peers. There are no additional elements.
Dependent claims 7 and 20 disclose automatically verifying images as relating to subject types and only correlating verified images as components of first data with first type of subject and tagged metadata. This is considered part of the abstract idea being mental process of evaluation or judgment. Verifying is checking compliance with criteria such as the color of a vehicle. The receiving is an insignificant extra-solution activity under MPEP 2106.05(g) which is a well-understood, routine and conventional activity under MPEP 2106.05(d)(II)(i). As such, the claim is not integrated into a practical application or amount to significantly more.
Dependent claims 8 and 11 disclose a display, user interface and image sensors for image capture and transmission via feedback link. The limitation is considered as additional elements such that display and user interface amount to general computer elements under MPEP 2106.05(f), and with insignificant extra-solution activity of transmitting image data captured via feedback link being under MPEP 2106.05(g). The transmitting is a well-understood, routine and conventional activity falling under MPEP 2106.05(d)(II)(i), and the display, user interface and sensors are general computer elements. There are no screenshots to detail the display interface and sensor can be a conventional camera. Accordingly, the additional elements do not integrate the judicial exception into a practical application or amount to significantly more.
Dependent claims 9 and 14 discloses wherein tagged metadata for each image comprises alternatives of image sensor type, or location of devices, or data and time of image capture. This is considered part of the abstract idea being mental process as observation or opinion as it embellishes tagged metadata. For example, labeling a black and white photo as being from a relic sensor, or annotating where a photo was taken, or historical period of time when photo was taken or captured. The limitation does not demonstrate an inventive concept.
Dependent claim 12 discloses wherein a subset of devices comprise work vehicles. This is an additional element which is considered generally linking the use of the judicial exception to a particular technological environment or field of use under MPEP 2106.05(h). There is no indication what work is being performed and a vehicle by itself does not meaningfully limit the claim that which is established in the field. Therefore, the additional element does not integrate the judicial exception into a practical application or amount to significantly more.
Dependent claim 13 disclose wherein a subset of devices comprise mobile computing devices. This is an additional element which amount to mere use of a computer to perform an abstract idea under MPEP 2106.05(f). The mobile computing device is recited at a high level of generality and fails the test of particular machine under MPEP 2106.05(b). Therefore, the additional element does not integrate the judicial exception into a practical application or amount to significantly more.
Taken alone, their additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination does not elevate the claim as a whole to show inventive concept.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 7-8, 10-13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over:
Rude et al., PCT WO2022/246548A1 hereinafter Rude, in view of
Bhate et al., US PG Pub No 2023/0376805A1 hereinafter Bhate, in view of
Bourdev et al., US PG Pub No 2015/0036919A1 hereinafter Bourdev.
With respect to claim 1, Rude teaches:
A method of expediting distributed feedback for training of supervised learning models {Rude [0027] “Fig. 1, a method and system” where [0054] “feedback may be used to retrain the ML” model selection [0058] e.g. CNN and/or SegNet, expediting is [0060] “processing time of the deployment model may be reduced to balance a speed and a performance”}, the method comprising:
in association with development of at least a first model for classifying at least a first type of subject, generating notifications to each of a plurality of potential feedback source devices requesting responsive images to be received at a central computing network and comprising the at least first type of subject {Rude at [0052-54] “images with a leaf area of 0% to 10% may be selected for uploading to the cloud server… cloud server 211 may generate a report from the ML process. The report may comprise image data… images from these management zones along with the feedback may be used to retrain the ML” using “segmentation at the image pixel level. The process involves manual labelling of weed pixels in a number of images for the purposes of training the model” Fig 1:211 server receives the uploaded images from camera 202 mounted on farm vehicles 200 [0024-26] are the source devices. The labeling conveys supervised learning, classifier models are selected e.g. SegNet (segmentation network), ResNet, U-net and/or CNN [0058]. The generated report is the notification which includes alerts [0049]. See Figs 1-3};
establishing a respective feedback connection between each of the plurality of potential feedback source devices and a data storage network associated with the at least a first model {Rude Fig 2:112 “wireless data connection” e.g. [0034-35] “communication channel, such as a cellular modem 208 to a cloud server 211 located remotely from the farm vehicle 200” Figs 1-2 arrows show connection, devices illustrated Fig 1 such that farm vehicles 200 (source devices) are connected to server 211 and [0060] “trained model may be deployed” based on feedback [0054] comprising label/tag [0053,59]}, and
However, Rude does not disclose the following limitation which is met by Bhate:
automatically tagging input messages comprising responsive images received at the central computing network via the feedback connection with source metadata and further as being in association with the notification {Bhate [0127] “Auto-labeling is the process of automatically annotating data, e.g., adding metadata” see Fig 7:723 with cloud, auto-labeling/annotation is tagging automatically, and [0133] “labeling process may include an image” e.g. [0128] “sensor derived auto-labeling, a sensor annotates the data. For example, an image” and/or [0080] “traffic sign classification model may take images… with confidence scores lower than 70%, this can trigger an alert” alert is notification. See also [0058], Figs 1-4};
training the at least first model at the central computing network {Bhate discloses [0059] “models…trained in the cloud” similar at [0078] and/or [0043] “cloud-based model training” shown Fig 1:130 cloud comprises 153 training}
Bhate is directed to distributed training of classifier models thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to automatically label/tag per Bhate in combination for a motivation that “automatic annotation/labeling process 205 can serve to label the data in real-time, allowing for immediate use of the data without having to wait for a manual labeling” [0050] similarly at [0129] “not requiring human labeling can be gathered and labeled for use by online learning without involving a human labeler.”
Further, training via cloud provides advantages of “leverage… massive computing resources” [0024] and “cloud 130 may be configured to continually deliver updated models” [0056] e.g. “deployment of an improved model” [0107] with “cloud online model FNV AI/ML framework that allows for integration of ML models developed independently from each other, any yet still allows these models to improve.”
However, the combination Rude and Bhate does not expressly disclose “correlating” which is disclosed by Bourdev:
correlating the images received via the respective feedback connections, as components of a first data set for the at least first model, with the at least first type of subject and tagged metadata {Bourdev [0004,09] “Correlation of each image in the sample set with an image class is scored based on one or more social cues… cues may comprise one or more image tags” i.e. [0029] “metadata tag” and/or “attribute”, again at [0071] “image class correlation module 308 may analyze a variety of social cues, including but not limited to… metadata” subjects are objects such as cat, car, person etc. [0036]. The model feedback connection is [0047] “classifier 208 may include a return connection (e.g., via a feedback loop)” see also Figs 3, 6-8 }.
Bourdev is directed to trained classifier models thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to perform correlation Bourdev in combination to arrive at the invention as claimed for a motivation “image class correlation module 308 may also analyze whether a plurality of images tags are synonymous with one another” (e.g. similarity analysis) [0062], which “may prove particularly advantageous to recognizing trademarked images or logos” [0068], and/or more generally to “identify relationships between different objects” [0110].
With respect to claim 7, the combination of Rude, Bhate and Bourdev teaches the method of claim 1, comprising
automatically verifying images received at the central computing network via the feedback connection as relating to the at least first type of subject, based at least in part on the source metadata and the corresponding notification {Bhate Fig 7:711 “Auto Validation” i.e. [0044-45] “verification and validation” for model which includes images that can be extracted as a feature vector [0080,73]. Fig 9:939 shows validation set received in a distributed environment, meta-data is shown Fig 4:413 and notification is alert [0078,80]}, and
A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to automatically validate/verify per Bhate in combination for the motivation “performance can be tracked in real time and contemplated against expected values” [0039] and vetting performance can result in rollback or reversion [0107].
However, the following limitation is taught by Bourdev:
only correlating verified images in the data storage, as components of the first data set, for the at least first model, with the at least first type of subject and the tagged metadata {Bourdev [0074] “only the 200 top scoring images to the classifier 208 as the training set of images” top-k ranking is described for the correlation module 308, such that verification can be a ranked score of top images, tagged metadata is disclosed [0029]}.
A person having ordinary skill in the art to only correlate images of top-k rank per Bourdev in combination to arrive at the invention as claimed for a motivation of “selecting a top scoring subset of the sample set… top scoring subset of the evaluation set” [0088-89].
With respect to claim 8, the combination of Rude, Bhate and Bourdev teaches the method of claim 1, wherein
the plurality of potential feedback source devices each comprise an onboard display unit, a user interface, and one or more image sensors, and wherein user engagement of at least one specified portion of the user interface responsive to the generated notification causes an image to be captured by at least one of the one or more image sensors and then transmitted via the respective feedback connection {Rude Fig 1 illustrates vehicles 200 described [0024-26] corresponding to source devices, display with user interface is e.g. Fig 1:209,212 described [0024,27], [0049]; Fig 1:202 camera is image sensor for transmitting/uploading to server 211 via connection indicated by arrows, see e.g. [0027] “image data may be automatically uploaded via cellular data transfer 208 to a field specific folder on a cloud server 211” which generates reports [0054] and/or alerts [0049]}.
With respect to claim 10, the rejection of claim 1 is incorporated. The difference in scope being a system to perform limitations similar to claim 1 method, and further recites a central computing network. Rude discloses [0002] “system for capturing images for analysis by machine learning processes” shown Fig 1 where cloud server is networked to computer mounted to farm vehicles. The remainder of this claim is rejected for the rationale as applied to claim 1.
With respect to claim 11, the combination of Rude, Bhate and Bourdev teaches the system of claim 10, and further teaches the limitation of claim 8. Therefore, the rejection of claim 8 is applied to claim 11.
With respect to claim 12, the combination of Rude, Bhate and Bourdev teaches the system of claim 11, wherein
a subset of the plurality of potential feedback source devices comprises one or more work vehicles {Rude Fig 1:200 described [0024-26] “wide variety of agricultural vehicles, most often a sprayer …especially a sprayer” where sprayer is a particular subset of agricultural vehicle(s), further discloses “autonomous farm vehicles”}.
With respect to claim 13, the combination of Rude, Bhate and Bourdev teaches the system of claim 11, wherein
a subset of the plurality of potential feedback source devices comprises one or more mobile computing devices {Rude Fig1:200-201 shows farm vehicle with on-board CPU computer, [0024-28]}.
With respect to claim 20, the combination of Rude, Bhate and Bourdev teaches the system of claim 10, and further teaches the limitation of claim 7. Therefore, the rejection of claim 7 is applied to claim 20.
Claims 2, 4-6, 9, 14-15 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Rude, Bhate, and Bourdev in view of Capota et al., US PG Pub No 2020/0410288A1 hereinafter Capota.
With respect to claim 2, the combination of Rude, Bhate and Bourdev teaches the method of claim 1, but does not expressly disclose “campaign.” Capota discloses comprising:
initiating at least a first campaign for development of the at least first model for classifying the at least first type of subject under one or more defined classifying conditions {Capota Fig 6:A110 [0008] “initiating, by the campaign server, a campaign” detailed [0102-03] “During campaign initiation, a model is selected to be deployed on the participating devices, the model is contained within a model repository” including [0025] “classification models… classification include object identification” where object is subject, conditions are e.g. [0131] “recognize, for example, a traffic sign in rainy conditions”};
Capota is directed to distributed machine learning with classification model training thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to specify campaigns per Capota in combination for a motivation “The purpose of the campaign is to achieve a specific improvement to a product or learning/algorithm improvement through an artificial-intelligence process” [0037] and/or “Campaign Management system enables these actions to persist and be communicated to the devices participating in each campaign” [0106].
However, Capota does not explicitly recite “correlated” which is met by Bourdev:
wherein the images received at the central computing network via the respective feedback connections in association with the at least first campaign are correlated with the at least first type of subject and tagged source metadata comprising at least one of the one or more defined classifying conditions {Bourdev [0058] “image class correlation module 308 may receive one or more of the sample set of images, along with associated contextual cues, from the contextual cue extraction module 304, and receive a specified image class… correlation module 308 may select a training set” training set selection defines data conditions for training classifier Fig 3:208, similar at Figs 6-8. Claimed subjects are objects [0036,0108], metadata includes attributes e.g. visual attributes [0071,74], conditions can be visual pattern template [0077,83]}.
A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to correlate per Bourdev in combination to arrive at the invention as claimed for reasons similar to those identified in claim 1 and because it enables selection of training set of images to be classified [0058,46].
With respect to claim 4, the combination of Rude, Bhate, Bourdev and Capota teaches the method of claim 2, comprising
selecting each of the plurality of potential feedback source devices from a network of available feedback source devices based on identified lighting and/or weather conditions associated with each of the network of available feedback source devices as corresponding to the at least one of the one or more defined classifying conditions {Capota Fig 6:A120 “Select participating devices” where [0131] “device 122 trains the model using… Labeled data is used for supervised learning” i.e. classifying particularly [0131] “light conditions… rainy conditions” and/or [0113] “weather conditions” similar at [0096,72] feature data includes weather for on-device weather predictions}.
With respect to claim 5, the combination of Rude, Bhate, Bourdev and Capota teaches the method of claim 4, wherein
the lighting and/or weather conditions associated with each of the network of available feedback source devices are identified by reference to respective position data with respect to a time of day and/or determined weather conditions {Capota [0072,70] “feature data 308-312 also include real-time sensor data… weather” within “geographic database 123” Fig 4 shows 308-312 within 123, geographic is positional [0132-131] “global positioning system (GPS) or positional sensor” preceded by “light conditions… rainy conditions” for labeled training data, weather estimation at [0096]}.
With respect to claim 6, the combination of Rude, Bhate, Bourdev and Capota teaches the method of claim 5, wherein
the respective weather conditions for each of the network of available feedback source devices are determined via one or more third party weather applications and/or databases {Capota [0096] “cloud-based weather service, downloaded to the devices, in areas with high accuracy in order to predict the weather in areas of poor accuracy/coverage of the cloud-based service” is a third party weather application}.
With respect to claim 9, the combination of Rude, Bhate and Bourdev teaches the method of claim 8, wherein
the tagged source metadata for each image comprises a type of image sensor capturing the image, and/or a location of the feedback source devices, and/or a data and time at which the image is captured {Capota [0049] “metadata contained in the campaign server” as [0053] “location attribute …Sensor Versions, Manufacturer, Ownership attributes or extended attributes attached over time to devices. The device profile may be shared in whole or in part with the campaign server” Fig 7:133 shows campaign catalog interface that includes a date-timestamp for last communication and device ID}.
With respect to claim 14, the combination of Rude, Bhate and Bourdev teaches the system of claim 11, further combination with Capota teaches the limitation of claim 9. Therefore, the rejection of claim 9 is applied to claim 14.
With respect to claim 15, the combination of Rude, Bhate and Bourdev teaches the system of claim 10, further combination with Capota teaches the limitation of claim 2. Therefore, the rejection of claim 2 is applied to claim 15.
With respect to claim 17, the combination of Rude, Bhate, Bourdev and Capota teaches the system of claim 15, and further teaches the limitation of claim 4. Therefore, the rejection of claim 4 is applied to claim 17.
With respect to claim 18, the combination of Rude, Bhate, Bourdev and Capota teaches the system of claim 17, and further teaches the limitation of claim 5. Therefore, the rejection of claim 5 is applied to claim 18.
With respect to claim 19, the combination of Rude, Bhate, Bourdev and Capota teaches the system of claim 18, and further teaches the limitation of claim 6. Therefore, the rejection of claim 6 is applied to claim 19.
Claims 3 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over, Rude, Bhate, Bourdev and Capota in view of Lee et al., US PG Pub No 2022/0148014A1 hereinafter Lee.
With respect to claim 3, the combination of Rude, Bhate, Bourdev and Capota teaches the method of claim 2. Lee teaches comprising
broadcasting notifications to a network of available feedback source devices comprising each of the plurality of potential feedback source devices, in accordance with the first campaign, wherein the broadcast notifications including a request for images comprising the at least first type of subject under the at least one of the one or more defined classifying conditions {Lee see Fig 7:704, 4:404 “Send notification to first subset of users” and/or [0022] “transmit notifications” is broadcasting, devices of a network are shown Fig 3:150,102,103 for campaign 305, again at Fig 7:702, 6:608. Further, [0039] “upload images” provides images which are necessarily of some subject, and discloses “request” [0056] as well as “rulesets for automatic classification” [0061] similar at [0091,94]}.
Lee is directed to generating notifications with machine learning algorithms for classifying thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to send/transmit notifications per Lee in combination to arrive at the invention as claimed for a motivation “improve the user experience” [0057] by providing “notifications tailored to that specific user in order to try to better elicit a desired user action” [0024] and/or “enable automatic generation of recommended parameters for a messaging campaign… enable distribution of messages in accordance with a defined campaign” [0073,84].
With respect to claim 16, the combination of Rude, Bhate, Bourdev and Capota teaches the system of claim 15, further combination with Lee teaches the limitation of claim 3. Therefore, the rejection of claim 3 is applied to claim 16.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Badrinarayanan et al., “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labeling” arXiv: 1505.07293v1 see Fig 1 soft-max classifier
Iowa State Univ. (applicant’s locale):
Khaki et al., WheatNet & DeepCorn, arXiv: 2103.09408v2 & 2007.10521v2
Esfandiari et al., “Distributed Deep Learning for Persistent Monitoring of agricultural Fields” NeurIPS, Fig 1
Nagasubramanian et al., “Plant phenotyping with limited annotation: Doing more with less” self-supervised pre-training with supervised fine-tuning
Jiang et al., “Crop and weed classification based on AutoML” Figs 1-2 see [P.47 Last¶] “images are uploaded to a data server…for models training” AutoML
Berg et al., US Patent No 11,185,007B1 Advanced Agrilytics see Fig 1:154,106,130 being tractor, remote training, and camera-based sprayer, respectively.
Tufail et al., “Identification of Tobacco Crop Based on Machine Learning for a Precision Agricultural Sprayer” see Figs 7-9, feature selection and detailed tractor
Halbersberg et al., “Transfer Learning of Photometric Phenotypes using Metadata” ICLR arXiv: 2004.00303v1 discloses metadata embeddings, Fig 2
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.
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/CHASE P. HINCKLEY/Examiner, Art Unit 2124
/MIRANDA M HUANG/Supervisory Patent Examiner, Art Unit 2124
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