DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Applicant claims the benefit of US Provisional Application No.16/262,517, filed January 1, 2018. Claims 1-20 have been afforded the benefit of this filing date.
Information Disclosure Statement
The IDSs dated 4/11/2025 and 10/01/2025 have been considered and placed in the application file.
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.
Claim 9 is rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
According to MPEP 2143.03 (I), “If a claim is subject to more than one interpretation, at least one of which would render the claim unpatentable over the prior art, the examiner should reject the claim as indefinite under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph (see MPEP § 2175) and should reject the claim over the prior art based on the interpretation of the claim that renders the prior art applicable. (Ex parte Ionescu, 222 USPQ 537 (Bd. Pat. App. & Inter. 1984)”
Claim 9 recite “co-registering … to a high level of precision.” It is unclear what is considered a high level of precision according to the specification. However, for searching for limitations, the interpretation of co-registering to any standard has been used.
1st 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 6, 10, 11, and 20 are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2015 0310633 A1, (Nelan) in view of US Patent Publication 2015 0172534 A1, (Miyakawa et al.).
Claim 1
Regarding claim 1, Nelan teach a method comprising: capturing, by at least one sensor of an aerial vehicle, image data of a ground region; ("The logic 200 provides ground images (202), e.g., taken from the aerial camera and optionally stitched together and geo-referenced to provide a larger composite image over any desired extend of ground," par. 32) determining a canopy coverage data of the ground region based on the captured image data; ("the system 100 processes images that have less than a pre-defined threshold percentage of vegetation coverage," par. 34) generating a normalized difference vegetation index (NDVI) profile and an NDVI image data of the ground region based on the canopy coverage data; ("the logic 200 may process the clipped vegetation image (238) to produce an NDVI image file," par. 34) and displaying the NDVI image data ("The result image may be referred to as an NDVI image file (246). The NDVI image file may be saved as part of a map template database (248) and exported (e.g., to customers) as a geo-tagged PDF NDVI map," par. 49).
Nelan do not explicitly teach all of displaying, by an image management component, both the captured image data and other data concurrently, wherein a visual dividing line separates the captured image data from the other data, the visual dividing line being moveable by a user.
However, Miyakawa et al. teach displaying, by an image management component, both the captured image data and other image data concurrently, wherein a visual dividing line separates the captured image data from the other image data, the visual dividing line being moveable by a user ("in the example of FIG. 7, the filter list display area 212 and the filter list display area 213 are disposed on the right side and the upper side of the image display area 211, but, it is also possible to provide the filter list display area 212 and a filter list display area 214 on the right side and the left side of the image display area 211, as illustrated in FIG. 8(b). In this case, the finger 1 selects the filter A from the filter list display area 212 and the processing of the filter A is performed on an area 410 from a position at which the finger slides in the image display area 211 to a boundary position," par. 69).
Therefore, taking the teachings of Nelan and Miyakawa et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the aerial image capturing and NDVI profile generation as taught by Nelan to use the user-dividable display as taught by Miyakawa et al. The suggestion/motivation for doing so would have been that, “the image display device and the image display program according to the present invention, it is possible to perform, in an easily understandable manner by senses, the operation in which the image displayed on the screen at the time of shooting the image or at the time of editing the image is divided, in a movable manner, into arbitrary plural areas, the different types of filter processing are performed for the respective areas, and the filter effects are compared” as noted by the Miyakawa et al. disclosure in paragraph [0116], which also motivates combination because the combination would predictably have an additional utility as there is a reasonable expectation that the user could visually isolate, filter, and compare specific segments of the generated NDVI aerial profile in real time to better identify crop variations or field anomalies; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
The rejection of method claim 1 above applies mutatis mutandis to the corresponding limitations of system claim 20 while noting that the rejection above cites to both device and method disclosures. Claim 20 is mapped below for clarity of the record and to specify any new limitations not included in claim 1.
Claim 2
Regarding claim 2, Nelan and Miyakawa et al. teach the method of claim 1 as noted above.
Additionally, Nelan teach wherein the canopy coverage is configured to be determined based on a percent of the ground region that is covered by vegetation ("the system 100 processes images that have less than a pre-defined threshold percentage of vegetation coverage," par. 34).
Nelan and Miyakawa et al. are combined as per claim 1.
Claim 6
Regarding claim 6, Nelan and Miyakawa et al. teach the method of claim 1 as noted above.
Additionally, Nelan teach transmitting the captured image data to a server having a processor and addressable memory via a network-connected computing device ("the system 100 may receive image data through the networks 134 (including, e.g., Internet connections) from many different sources, and may transmit processed image data, soil characteristics, or any other data through the networks 134 to many different destinations. Examples of sources and destinations include local and remote file servers," par. 24).
Nelan and Miyakawa et al. are combined as per claim 1.
Claim 10
Regarding claim 10, Nelan and Miyakawa et al. teach the method of claim 1 as noted above.
Additionally, Nelan teach wherein the plurality of sensors comprise at least one of: an RGB sensor, a LIDAR sensor, and one or more multi-spectral cameras ("The logic 200 provides ground images (202), e.g., taken from the aerial camera and optionally stitched together and geo-referenced to provide a larger composite image over any desired extend of ground," par. 32).
Nelan and Miyakawa et al. are combined as per claim 1.
Claim 11
Regarding claim 11, Nelan and Miyakawa et al. teach the method of claim 1 as noted above.
Additionally, Nelan teach filtering, by the image management component, the displayed image data based on a plurality of spectrums selected by the user ("The logic 200 may further apply a color ramp (244). The color ramp (244) adds color changes to the vegetation processed data to highlight the characteristics of the vegetation," par. 48).
Nelan and Miyakawa et al. are combined as per claim 1.
Claim 20
Regarding claim 20, Nelan teach a system comprising: at least one sensor of an aerial vehicle configured to capture image data of a ground region; ("The logic 200 provides ground images (202), e.g., taken from the aerial camera and optionally stitched together and geo-referenced to provide a larger composite image over any desired extend of ground," par. 32) a processor having addressable memory, wherein the processor is configured to: ("The system logic 114 may include one or more processors 116 and memories 120. The memory 120 stores, for example, control instructions 122 that the processor 116 executes to carry out desired functionality for the system 100, such as processing source image data 126 to generate processed image data," par. 26) determining a canopy coverage data of the ground region based on the captured image data; ("the system 100 processes images that have less than a pre-defined threshold percentage of vegetation coverage," par. 34) and generating a normalized difference vegetation index (NDVI) profile and an NDVI image data of the ground region based on the canopy coverage data; ("the logic 200 may process the clipped vegetation image (238) to produce an NDVI image file," par. 34) and displaying the NDVI image data ("The result image may be referred to as an NDVI image file (246). The NDVI image file may be saved as part of a map template database (248) and exported (e.g., to customers) as a geo-tagged PDF NDVI map," par. 49).
Nelan do not explicitly teach all of a display configured to display both the captured image data and other image data concurrently, wherein a visual dividing line is configured to separate the captured image data from the other image data, the visual dividing line being moveable by a user.
However, Miyakawa et al. teach a display configured to display both the captured image data and other image data concurrently, wherein a visual dividing line is configured to separate the captured image data from the other image data, the visual dividing line being moveable by a user ("in the example of FIG. 7, the filter list display area 212 and the filter list display area 213 are disposed on the right side and the upper side of the image display area 211, but, it is also possible to provide the filter list display area 212 and a filter list display area 214 on the right side and the left side of the image display area 211, as illustrated in FIG. 8(b). In this case, the finger 1 selects the filter A from the filter list display area 212 and the processing of the filter A is performed on an area 410 from a position at which the finger slides in the image display area 211 to a boundary position," par. 69).
Nelan and Miyakawa et al. are combined as per claim 1.
2nd Claim Rejections - 35 USC § 103
Claims 3, 4, and 5 are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2015 0310633 A1, (Nelan) and US Patent Publication 2015 0172534 A1, (Miyakawa et al.) in view of US Patent Publication 2015 0278640 A1, (Johnson et al.).
Claim 3
Regarding claim 3, Nelan and Miyakawa et al. teach the method of claim 1 as noted above.
Nelan do not explicitly teach all of determining an anomaly level of the ground region based on the captured image data, wherein the anomaly level is configured to be determined based on a percent of the ground region that have anomalies.
However, Johnson et al. teach determining an anomaly level of the ground region based on the captured image data, ("information regarding stand determination can be received by, for example, Stand Analyzer and Alert Generator 110 from, for example, a user, such as user 130 (via a user interface, such as user interface 125), a database, such as database 135, a data feed, such as data feed 115, and an in-season data gatherer, such as in-season data gatherer 140, via a communication network, such as communication network 105. Example received information can relate to target areas for the stand determination and alert system, a UAV event and the data generated," par. 83) wherein the anomaly level is configured to be determined based on a percent of the ground region that have anomalies ("the stand analysis is performed by the Stand Analyzer and Alert Generator 110, the results are then analyzed against predefined triggers. Such triggers for the Stand Analyzer and Alert Generator 110 can change depending on, for example, the time of year, the type of crop, and the stage of the crop in its growth cycle. For example, early in the growing season the triggers can be at a level such that the user is notified with a higher sensitivity to stand deficiency because replanting certain areas in the field can be a viable option if deficient stand population and consistency are discovered early," par. 84).
Therefore, taking the teachings of Nelan, Miyakawa et al., and Johnson et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the aerial image capturing and NDVI profile generation as taught by Nelan and the user-dividable display as taught by Miyakawa et al. to use the anomaly determining system as taught by Johnson et al. The suggestion/motivation for doing so would have been that, “Stand Analyzer and Alert Generator 110, in some examples, can be configured to generate a stand alert by receiving input from user 130, data feed 115, commercial and/or public data sources 120, in-season data gatherer 140, and/or accessing data stored in database 135. Stand Analyzer and Alert Generator 110 can use historical crop information in order to, for example, determine stand based on past field or seed stand performance and/or practices such as planting or tillage that have impacted stand” as noted by the Johnson et al. disclosure in paragraph [0061], which also motivates combination because the combination would predictably have an additional utility as there is a reasonable expectation that integrating automated crop performance analysis with a split-screen display allows a user to simultaneously cross-reference real-time visual anomalies with historical stand alerts, thereby accelerating the detection and diagnosis of field-level cultivation issues; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 4
Regarding claim 4, Nelan, Miyakawa et al., and Johnson et al. teach the method of claim 3 as noted above.
Nelan do not explicitly teach all of sending a message when the determined anomaly level exceeds a set amount.
However, Johnson et al. teach sending a message when the determined anomaly level exceeds a set amount ("The alert content can depend on the embodiment and can differ for many reasons including, for example, the preference of the user, type of crop, or severity of the stand issues. Another example of message content is a notification to “check a field” or maintain surveillance on a field on a “watch list.”," par. 94).
Nelan, Miyakawa et al., and Johnson et al. are combined as per claim 3.
Claim 5
Regarding claim 5, Nelan, Miyakawa et al., and Johnson et al. teach the method of claim 3 as noted above.
Nelan do not explicitly teach all of wherein the anomaly level is configured to be determined based on a percent of the canopy coverage that have anomalies.
However, Johnson et al. teach wherein the anomaly level is configured to be determined based on a percent of the canopy coverage that have anomalies ("the stand analysis is performed by the Stand Analyzer and Alert Generator 110, the results are then analyzed against predefined triggers. Such triggers for the Stand Analyzer and Alert Generator 110 can change depending on, for example, the time of year, the type of crop, and the stage of the crop in its growth cycle. For example, early in the growing season the triggers can be at a level such that the user is notified with a higher sensitivity to stand deficiency because replanting certain areas in the field can be a viable option if deficient stand population and consistency are discovered early," par. 84).
Nelan, Miyakawa et al., and Johnson et al. are combined as per claim 3.
3rd Claim Rejections - 35 USC § 103
Claims 7 and 9 are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2015 0310633 A1, (Nelan) and US Patent Publication 2015 0172534 A1, (Miyakawa et al.) in view of US Patent Publication 2015 0286868 A1, (Flores et al.).
Claim 7
Regarding claim 7, Nelan and Miyakawa et al. teach the method of claim 6 as noted above.
Nelan teach storing the captured image data on the server ("the system 100 may receive image data through the networks 134 (including, e.g., Internet connections) from many different sources, and may transmit processed image data, soil characteristics, or any other data through the networks 134 to many different destinations. Examples of sources and destinations include local and remote file servers," par. 24).
Nelan do not explicitly teach all of generating captured image metadata based on the stored captured image data; and filtering, by the image management component, the captured image data based on the generated captured image metadata.
However, Flores et al. teach generating captured image metadata based on the stored captured image data; and filtering, by the image management component, the captured image data based on the generated captured image metadata ("the processor 24 may be configured to use the travel path metadata to suppress false alarms for detections that are not on a travel path. Once a moving object of interest 46 is detected, the method performs the steps of FIG. 6B which uses travel path metadata to filter out false alarms and enhance missed detections," par. 32).
Therefore, taking the teachings of Nelan, Miyakawa et al., and Flores et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the aerial image capturing and NDVI profile generation as taught by Nelan and the user-dividable display as taught by Miyakawa et al. to use the image filtering based on metadata and image co-registration as taught by Flores et al. The suggestion/motivation for doing so would have been that, “Since the images 44a, 44b, 44c are geo-registered, the detected moving object of interest 46 has precise geo-coordinates” as noted by the Flores et al. disclosure in paragraph [0037], which also motivates combination because the combination would predictably have a higher efficiency as there is a reasonable expectation that aligning the aerial images with precise geo-coordinates allows the user-dividable display to seamlessly track, filter, and compare specific geographic regions without manual realignment errors; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 9
Regarding claim 9, Nelan, Miyakawa et al., and Flores et al. teach the method of claim 7 as noted above.
Nelan do not explicitly teach all of co-registering, by the image management component, the captured image data with at least one pre-loaded image to a high level of precision.
However, Flores et al. teach co-registering, by the image management component, the captured image data with at least one pre-loaded image to a high level of precision ("the processor 24 may be configured to register the first image 44a to the reference map," par. 25).
Nelan, Miyakawa et al., and Flores et al. are combined as per claim 7.
4th Claim Rejections - 35 USC § 103
Claim 8 is rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2015 0310633 A1, (Nelan), US Patent Publication 2015 0172534 A1, (Miyakawa et al.), and US Patent Publication 2015 0286868 A1, (Flores et al.) in view of US Patent Publication 2017 0108123 A1, (Baurer et al.).
Claim 8
Regarding claim 8, Nelan, Miyakawa et al., and Flores et al. teach the method of claim 7 as noted above.
Nelan do not explicitly teach all of adding at least one of: a season date range for the ground region, one or more crop types for the ground region, one or more tags to the stored image data, and one or more notes to the stored image data via a dashboard component.
However, Baurer et al. teach generating captured image metadata based on the stored captured image data; and filtering, by the image management component, the captured image data based on the generated captured image metadata ("Using the display depicted in FIG. 5, a user computer can input a selection of a particular field and a particular date for the addition of event," par. 36).
Therefore, taking the teachings of Nelan, Miyakawa et al., Flores et al., and Baurer et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the aerial image capturing and NDVI profile generation as taught by Nelan, the user-dividable display as taught by Miyakawa et al., and the image filtering based on metadata and image co-registration as taught by Flores et al. to use the user-display and image tags as taught by Baurer et al. The suggestion/motivation for doing so would have been that, “The user computer may then select a location on the timeline for a particular field in order to indicate an application of nitrogen on the selected field. In response to receiving a selection of a location on the timeline for a particular field, the data manager may display a data entry overlay, allowing the user computer to input data pertaining to nitrogen applications, planting procedures, soil application, tillage procedures, irrigation practices, or other information relating to the particular field. For example, if a user computer selects a portion of the timeline and indicates an application of nitrogen, then the data entry overlay may include fields for inputting an amount of nitrogen applied, a date of application, a type of fertilizer used, and any other information related to the application of nitrogen” as noted by the Flores et al. disclosure in paragraph [0036], which also motivates combination because the combination would predictably have a higher efficiency as there is a reasonable expectation that organizing the image data using the specific tagging and user-display frameworks would allow the data manager to process, retrieve, and cross-reference multi-temporal field data with significantly less computational overhead; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
5th Claim Rejections - 35 USC § 103
Claims 12, 15, 16, 17, and 19 are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2015 0310633 A1, (Nelan) and US Patent Publication 2015 0172534 A1, (Miyakawa et al.) in view of US Patent Publication 2013 0335566 A1, (Coulter et al.).
Claim 12
Regarding claim 12, Nelan teach a method comprising: defining a ground region for capturing one or more images; ("a ground image 500 before clipping (e.g., the output of (202)). The ground image includes a field of interest 502. FIG. 6 shows a field boundary clipping window 600 that will be applied against the field of interest 502 to select the field of interest 502 for soil image processing," par. 56) determining a canopy coverage data of the defined ground region based on the received image data; ("the system 100 processes images that have less than a pre-defined threshold percentage of vegetation coverage," par. 34) generating a normalized difference vegetation index (NDVI) profile and an NDVI image data of the defined ground region based on the canopy coverage data; ("the logic 200 may process the clipped vegetation image (238) to produce an NDVI image file," par. 34) and displaying the NDVI image data ("The result image may be referred to as an NDVI image file (246). The NDVI image file may be saved as part of a map template database (248) and exported (e.g., to customers) as a geo-tagged PDF NDVI map," par. 49).
Nelan do not explicitly teach all of receiving image data of the defined ground region from an aerial vehicle and one or more satellite images,; and displaying, by an image management component, both the received image data and the other image data concurrently, wherein a visual dividing line being moveable by a user.
However, Miyakawa et al. teach displaying, by an image management component, both the received image data and the other image data concurrently, wherein a visual dividing line being moveable by a user ("in the example of FIG. 7, the filter list display area 212 and the filter list display area 213 are disposed on the right side and the upper side of the image display area 211, but, it is also possible to provide the filter list display area 212 and a filter list display area 214 on the right side and the left side of the image display area 211, as illustrated in FIG. 8(b). In this case, the finger 1 selects the filter A from the filter list display area 212 and the processing of the filter A is performed on an area 410 from a position at which the finger slides in the image display area 211 to a boundary position," par. 69).
Additionally, Coulter et al. teach receiving image data of the defined ground region from an aerial vehicle and one or more satellite images ("sensor station matching (e.g., returning a sensor, such as a camera, to the same absolute spatial position and viewing the same scene with about the same viewing geometry) and precise spatial co-registration of a series of multi-temporal airborne or satellite remotely sensed images (images collected without being in direct contact with features of interest; normally earth observation from aircraft and satellite platforms) on a frame-by-frame basis," par. 4).
Therefore, taking the teachings of Nelan, Miyakawa et al., and Coulter et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the aerial image capturing and NDVI profile generation as taught by Nelan and the user-dividable display as taught by Miyakawa et al. to use the satellite imagery and co-location techniques as taught by Coulter et al. The suggestion/motivation for doing so would have been that, “When a temporal series of images are collected with approximately the same viewing geometry, precise (even pixel-level) spatial co-registration may be attained with ultra-high spatial resolution imagery (e.g., 3 inch spatial resolution) using simple techniques such as point matching and image transformation” as noted by the Coulter et al. disclosure in paragraph [0004], which also motivates combination because the combination would predictably have a greater accuracy as there is a reasonable expectation that aligning multi-temporal imagery through pixel-level spatial co-registration would reduce spatial displacement and errors when generating and comparing the NDVI profiles; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 15
Regarding claim 15, Nelan, Miyakawa et al., and Coulter et al. teach the method of claim 12 as noted above.
Nelan teach wherein the aerial vehicle includes a vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) ("the individual digital images may be obtained by images taken from an aerial camera. In one implementation, the aerial camera is mounted to a helicopter, drone, or airplane," par. 28).
Nelan, Miyakawa et al., and Coulter et al. are combined as per claim 12.
Claim 16
Regarding claim 16, Nelan, Miyakawa et al., and Coulter et al. teach the method of claim 12 as noted above.
Nelan teach wherein the received image data comprises multi-spectral images of the pre-defined ground region, and wherein the multi-spectral images comprise at least one of: red, green, blue, infra-red, and ultra-violet spectrums ("The camera film may be, for instance, Kodak Aerochrome III Film 1443. However, other films with varying infrared-sensitivity, false-color reversal, resolving power, grain, and other characteristics may also be used," par. 28).
Nelan, Miyakawa et al., and Coulter et al. are combined as per claim 12.
Claim 17
Regarding claim 17, Nelan, Miyakawa et al., and Coulter et al. teach the method of claim 12 as noted above.
Nelan teach processing the image data and storing the processed image data ("The memory 120 stores, for example, control instructions 122 that the processor 116 executes to carry out desired functionality for the system 100, such as processing source image data," par. 26).
Nelan do not explicitly teach all of prior to generating the NDVI profile and the NDVI image data: associating each received image data from the one or more satellites with a respective latitude and longitude; co-locating each received image data from the aerial vehicle with the received image data from the one or more satellites.
However, Coulter et al. teach prior to generating the NDVI profile and the NDVI image data: associating each received image data from the one or more satellites with a respective latitude and longitude; co-locating each received image data from the aerial vehicle with the received image data from the one or more satellites ("The approach may use repeat pass imagery collected from multiple sensor stations in the sky or in space. Global navigation satellite systems (e.g., Global Positioning System or "GPS") may be utilized to guide an aircraft or satellite platform along a specific flight path and the navigation system may also be used to trigger imaging sensors at predetermined sensor stations during each repeat pass. Using this methodology, the imaging sensor may be returned to the same physical locations (e.g., same X, Y, and Z coordinates representing location and altitude above ground), and the effect is that the imaging sensor is fixed mounted in that location taking video images with intermittent frequency. Multi-temporal imagery collected from the same sensor stations can be co-registered (aligned) automatically and utilized to mimic a video image sequence," par. 15).
Nelan, Miyakawa et al., and Coulter et al. are combined as per claim 12.
Claim 19
Regarding claim 19, Nelan, Miyakawa et al., and Coulter et al. teach the method of claim 12 as noted above.
Nelan teach wherein the canopy coverage is configured to be determined based on a percent of the ground region that is covered by vegetation ("the system 100 processes images that have less than a pre-defined threshold percentage of vegetation coverage," par. 34).
Nelan, Miyakawa et al., and Coulter et al. are combined as per claim 12.
6th Claim Rejections - 35 USC § 103
Claims 13 and 14 are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2015 0310633 A1, (Nelan), US Patent Publication 2015 0172534 A1, (Miyakawa et al.), and US Patent Publication 2013 0335566 A1, (Coulter et al.) in view of US Patent Publication 2015 0278640 A1, (Johnson et al.).
Claim 13
Regarding claim 13, Nelan, Miyakawa et al., and Coulter et al. teach the method of claim 12 as noted above.
Nelan do not explicitly teach all of determining an anomaly level of the ground region based on the captured image data, wherein the anomaly level is configured to be determined based on a percent of the ground region that have anomalies.
However, Johnson et al. teach determining an anomaly level of the ground region based on the captured image data, ("information regarding stand determination can be received by, for example, Stand Analyzer and Alert Generator 110 from, for example, a user, such as user 130 (via a user interface, such as user interface 125), a database, such as database 135, a data feed, such as data feed 115, and an in-season data gatherer, such as in-season data gatherer 140, via a communication network, such as communication network 105. Example received information can relate to target areas for the stand determination and alert system, a UAV event and the data generated," par. 83) wherein the anomaly level is configured to be determined based on a percent of the ground region that have anomalies ("the stand analysis is performed by the Stand Analyzer and Alert Generator 110, the results are then analyzed against predefined triggers. Such triggers for the Stand Analyzer and Alert Generator 110 can change depending on, for example, the time of year, the type of crop, and the stage of the crop in its growth cycle. For example, early in the growing season the triggers can be at a level such that the user is notified with a higher sensitivity to stand deficiency because replanting certain areas in the field can be a viable option if deficient stand population and consistency are discovered early," par. 84).
Therefore, taking the teachings of Nelan, Miyakawa et al., Coulter et al., and Johnson et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the aerial image capturing and NDVI profile generation as taught by Nelan, the user-dividable display as taught by Miyakawa et al., and the satellite imagery and co-location techniques as taught by Coulter et al. to use the anomaly determining system as taught by Johnson et al. The suggestion/motivation for doing so would have been that, “Stand Analyzer and Alert Generator 110, in some examples, can be configured to generate a stand alert by receiving input from user 130, data feed 115, commercial and/or public data sources 120, in-season data gatherer 140, and/or accessing data stored in database 135. Stand Analyzer and Alert Generator 110 can use historical crop information in order to, for example, determine stand based on past field or seed stand performance and/or practices such as planting or tillage that have impacted stand” as noted by the Johnson et al. disclosure in paragraph [0061], which also motivates combination because the combination would predictably have an additional utility as there is a reasonable expectation that integrating automated crop performance analysis with a split-screen display allows a user to simultaneously cross-reference real-time visual anomalies with historical stand alerts, thereby accelerating the detection and diagnosis of field-level cultivation issues; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 14
Regarding claim 14, Nelan, Miyakawa et al., Coulter et al., and Johnson et al. teach the method of claim 13 as noted above.
Nelan do not explicitly teach all of sending a message when the determined anomaly level exceeds a set amount.
However, Johnson et al. teach sending a message when the determined anomaly level exceeds a set amount ("The alert content can depend on the embodiment and can differ for many reasons including, for example, the preference of the user, type of crop, or severity of the stand issues. Another example of message content is a notification to “check a field” or maintain surveillance on a field on a “watch list.”," par. 94).
Nelan, Miyakawa et al., Coulter et al., and Johnson et al. are combined as per claim 13.
7th Claim Rejections - 35 USC § 103
Claim 18 is rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2015 0310633 A1, (Nelan), US Patent Publication 2015 0172534 A1, (Miyakawa et al.), and US Patent Publication 2013 0335566 A1, (Coulter et al.) in view of US Patent Publication 2018 0108123 A1, (Baurer et al.).
Claim 18
Regarding claim 18, Nelan, Miyakawa et al., and Coulter et al. teach the method of claim 12 as noted above.
Nelan do not explicitly teach all of adding at least one of: a season date range for the defined ground region, one or more crop types for the defined ground region, one or more tags to the received image data, and one or more notes to the received image data via a dashboard component.
However, Baurer et al. teach adding at least one of: a season date range for the defined ground region, one or more crop types for the defined ground region, one or more tags to the received image data, and one or more notes to the received image data via a dashboard component ("Using the display depicted in FIG. 5, a user computer can input a selection of a particular field and a particular date for the addition of event," par. 36).
Therefore, taking the teachings of Nelan, Miyakawa et al., Coulter et al., and Baurer et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the aerial image capturing and NDVI profile generation as taught by Nelan, the user-dividable display as taught by Miyakawa et al., and the satellite imagery and co-location techniques as taught by Coulter et al. to use the user-display and image tags as taught by Baurer et al. The suggestion/motivation for doing so would have been that, “The user computer may then select a location on the timeline for a particular field in order to indicate an application of nitrogen on the selected field. In response to receiving a selection of a location on the timeline for a particular field, the data manager may display a data entry overlay, allowing the user computer to input data pertaining to nitrogen applications, planting procedures, soil application, tillage procedures, irrigation practices, or other information relating to the particular field. For example, if a user computer selects a portion of the timeline and indicates an application of nitrogen, then the data entry overlay may include fields for inputting an amount of nitrogen applied, a date of application, a type of fertilizer used, and any other information related to the application of nitrogen” as noted by the Flores et al. disclosure in paragraph [0036], which also motivates combination because the combination would predictably have a higher efficiency as there is a reasonable expectation that organizing the image data using the specific tagging and user-display frameworks would allow the data manager to process, retrieve, and cross-reference multi-temporal field data with significantly less computational overhead; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Patent Publication 2016 0202227 A1 to Mathur et al. discloses farm data models used to analyze sensor inputs, detect local issues, and deliver targeted corrective recommendations directly to farmers.
Conclusion
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/Karsten F. Lantz/Examiner, Art Unit 2664
Date: 9/1/2026
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664
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