DETAILED ACTION
Claims 1-28 are currently pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/13/26 has been entered.
Response to Arguments
Applicant’s arguments with respect to claims 8, 9, 11, 22, 23 and 25 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
It is noted that Applicant does not argue that the amendments to the claims overcome the rejections of claims 1-7, 10, 12-21, 24 and 26-28 under 35 U.S.C. 103.
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.
Claims 4, 11 and 25 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 recites the limitation "the spray object " in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claims 11 and 25 recite the limitation "the duration " in line 2. There is insufficient antecedent basis for this limitation in the claims.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 4-7, 10, 12-16, 18-21, 24 and 26-28 are rejected under 35 U.S.C. 103 as being unpatentable over Wu US Publication 2019/0150357 (hereafter “Wu”), Wiltshire US Publication 2020/0104719 (hereafter “Wiltshire”) and Polzounov et al. US Publication 2019/0362146 (hereafter “Polzounov”).
Referring to claim 1, Wu discloses a method of evaluating a treatment of an agricultural object, the method comprising:
obtaining with one or more image sensors coupled to an agricultural treatment system, at a first time period, a first set of images each comprising a plurality of pixels depicting a ground area and a first target agricultural object positioned in the ground area (paragraph 112, The procedure includes capturing the image, associating or overlaying a virtual grid 100 over the captured image, then check whether (dyed) spray went past a boundary “Boundary” associated with one of the Y-direction lines of the grid 100);
emitting a first fluid projectile of a first fluid at the first target agricultural object (paragraph 99, the planned crop row 12 is first or subsequently sprayed/spread with fertilizer after the herbicide spot spraying);
obtaining with the one or more image sensors at a second time period, a second set of images comprising a plurality of pixels depicting the emitted first fluid projectile and the ground area and the agricultural object (paragraph 52, In some embodiments, the images are from videos. For example, when the vehicle is traveling, the video is constantly filming. At very specific intervals or upon a command to send images, a particular image from the video is extracted for analysis);
identifying a first group of pixels that represent a first spray object (paragraph 112, The procedure includes capturing the image, associating or overlaying a virtual grid 100 over the captured image, then check whether (dyed) spray went past a boundary “Boundary” associated with one of the Y-direction lines of the grid 100);
identifying a second group of pixels that represent a spray projectile of the emitted first fluid projectile in an image of the second set of images (paragraph 170, In yet another embodiment, the calculated predicted spray drift are compared with the detected spray drift based on a color image (e.g. spray dye of the end units). If the predicted and detected spray drift pattern on the ground agree to within a predetermined threshold (e.g. 85 to 95%), and over-or-under spraying or drift is found, the spray nozzles or agricultural vehicle take corrective actions and/or alert the operator); and
indexing the identified first spray object and the identified spray projectile (paragraph 156, In some embodiments, some percentage of all of the data (every sensor, vehicle speed and position, and every image, pixel or array member, i.e. completely unbiased data) is logged so that a secondary or tertiary analysis aids in improving or corroborating (e.g. a cross check of) the initial on-vehicle, real time trigger analysis used to decide the real time action).
While Wu discloses identifying a first group of pixels that represent a first spray object, Wu does not disclose expressly the identification is based on a determined change in pixels between the first and second images.
Wiltshire discloses comparing a first image of the first set of images with a second image of the second set of images to determine a change in pixel values between at least the first image and at least the second image (paragraph 41, At block 312, the method 300 includes a classification module to classify each pair of patches according to the updated feature map and the resultant probability that is greater. For example, if the matching probability is greater than 50%, the non-match probability is less than 50%, and the classifier classifies the pair of adjacent patches as a match. Conversely, if the matching probability is less than 50%, the non-matching probability is higher than 50%, and the classifier classifies the pair of adjacent patches as a non-match); and
based on the determined change in pixel values as between the first image and second image, identifying a first group of pixels that represent a first object (paragraph 43, At block 314, the method 300 includes an identification module for identifying dissimilar patches based on the classification probability from block 312).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to determine a change in pixels between the first and second images. The motivation for doing so would have been to effectively identify differences between images to obtain additional information that is not easily detected from analyzing each image separately.
Wu and Wiltshire do not disclose expressly wherein the determined change in pixel values indicates a spray impact on a ground area, a spray impact on an agricultural object and/or a spray splat.
Polzounov discloses wherein the determined change in pixel values indicates a spray impact on a ground area, a spray impact on an agricultural object and/or a spray splat (paragraph 108-110, In this example, the verification mechanism 150 is mounted to the rear of the farming machine 100 such that the area of the field is imaged after the spray nozzles pass over the area and treat the cotton plants. The accessed image 210 also includes information representing a treated area where certain spray areas 1040 have been sprayed by the spray nozzles. In this case, the information indicating a plant treatment is illustrated as dampened soil 1210. Returning to FIG. 11, the control system applies 1130 a model 800 to identify pixels in the accessed post-image 212 representing a treated area).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to analyze a spray area. The motivation for doing so would have been to verify that intended areas have been sprayed. Therefore, it would have been obvious to combine Wiltshire and Polzounov with Wu to obtain the invention as specified in claim 1.
Referring to claims 2 and 16, Wu discloses Polzounov discloses determining a quantity of fluid that impacted the ground area and/or that impacted the agricultural object by identifying a group of pixels that comprise the agricultural object, and another group of pixels that depict the spray impact on the ground and the spray impact on the agricultural object (paragraph 109, The accessed image 210 also includes information representing a treated area where certain spray areas 1040 have been sprayed by the spray nozzles. In this case, the information indicating a plant treatment is illustrated as dampened soil 1210 [determining that an area is not damped is determining a quantity of fluid that impacted the ground i.e. zero fluid]).
Referring to claim 4, Wu discloses determining an inaccurate application of the emitted first fluid projectile to the first target agricultural object by determining whether the spray object lines up with the first target agricultural object and applying a second corrected fluid projectile to the first target agricultural object (paragraph 170, In yet another embodiment, the calculated predicted spray drift are compared with the detected spray drift based on a color image (e.g. spray dye of the end units). If the predicted and detected spray drift pattern on the ground agree to within a predetermined threshold (e.g. 85 to 95%), and over-or-under spraying or drift is found, the spray nozzles or agricultural vehicle take corrective actions and/or alert the operator).
Referring to claims 5 and 19, Wu discloses determining the first group of pixels of the first spray object is the spray impact on the agricultural object (paragraph 168, The image of the spray cones that are dyed or illuminated is analyzed (e.g. pattern color and intensity) to determine the average spray envelope of the cone, then correlated with a calibrated envelope threshold value with a known spray envelope (e.g. 90-95% droplets within an envelope) with respect to droplets, using the color/intensity image captured for each particular angle of view of the spray cone);
determining a second group of pixels is a second spray object of a spray impact on a ground area about the agricultural object (paragraph 168, Yet another embodiment project the vectors and perform calculations along the two axes parallel and perpendicular to the geometrical boundaries of the field in order to assess if the spray has gone beyond the boundary or if the spray pattern on the ground indicate uneven coverage (“skipping”)); and
based on the group of pixels representing the spray impact on the agricultural object and the second group of pixels of the spray impact on the ground area, determining a quantity of the first fluid projectile that likely comprises the spray object (paragraph 170, In yet another embodiment, the calculated predicted spray drift are compared with the detected spray drift based on a color image (e.g. spray dye of the end units). If the predicted and detected spray drift pattern on the ground agree to within a predetermined threshold (e.g. 85 to 95%), and over-or-under spraying or drift is found, the spray nozzles or agricultural vehicle take corrective actions and/or alert the operator).
Referring to claims 6 and 20, Wu discloses based on the group of pixels representing the spray object, determining a spray coverage percentage based on an average of the multiple fluid projectiles identified to have actually been sprayed upon their intended target object (paragraph 170, In yet another embodiment, the calculated predicted spray drift are compared with the detected spray drift based on a color image (e.g. spray dye of the end units). If the predicted and detected spray drift pattern on the ground agree to within a predetermined threshold (e.g. 85 to 95%), and over-or-under spraying or drift is found, the spray nozzles or agricultural vehicle take corrective actions and/or alert the operator).
Referring to claims 7 and 21, Wu discloses based on the group of pixels representing the spray object, identifying a line of the spray object, wherein the spray object is the spray projectile of the emitted first fluid projectile (paragraph 112, High powered light on the spray nozzles or ends of the boom or each boom arm or section reflects back from the spray droplets and the image captured of the spray shows the path of travel of the light and if there is a curvature in the travel of the droplets, indicating that the spray is going out of an acceptable or predicted area).
Referring to claim 10, Wu discloses determining a change in pixels (colors/luminosity/etc.) that are above a threshold value of a ground area and/or the target agricultural object in the first image as compared to the second image (paragraph 170, In yet another embodiment, the calculated predicted spray drift are compared with the detected spray drift based on a color image (e.g. spray dye of the end units). If the predicted and detected spray drift pattern on the ground agree to within a predetermined threshold (e.g. 85 to 95%), and over-or-under spraying or drift is found, the spray nozzles or agricultural vehicle take corrective actions and/or alert the operator).
Referring to claims 12 and 26, Wu discloses based on the first group of pixels representing the first spray object, determining a first emission pattern of the first fluid projectile (paragraph 170, In yet another embodiment, the calculated predicted spray drift are compared with the detected spray drift based on a color image (e.g. spray dye of the end units). If the predicted and detected spray drift pattern on the ground agree to within a predetermined threshold (e.g. 85 to 95%), and over-or-under spraying or drift is found, the spray nozzles or agricultural vehicle take corrective actions and/or alert the operator); and
indexing the first spray object as the first emission pattern (paragraph 52, To synchronize different image sensor (e.g. cameras), a single image from the different videos are picked off based upon a particular time stamp on the image) (paragraph 156, In some embodiments, some percentage of all of the data (every sensor, vehicle speed and position, and every image, pixel or array member, i.e. completely unbiased data) is logged so that a secondary or tertiary analysis aids in improving or corroborating (e.g. a cross check of) the initial on-vehicle, real time trigger analysis used to decide the real time action).
Referring to claims 13 and 27, Wu discloses based on the first group of pixels representing the first spray object, determining a first treatment pattern (paragraph 170, In yet another embodiment, the calculated predicted spray drift are compared with the detected spray drift based on a color image (e.g. spray dye of the end units). If the predicted and detected spray drift pattern on the ground agree to within a predetermined threshold (e.g. 85 to 95%), and over-or-under spraying or drift is found, the spray nozzles or agricultural vehicle take corrective actions and/or alert the operator); and
indexing the first spray object as the first treatment pattern (paragraph 52, To synchronize different image sensor (e.g. cameras), a single image from the different videos are picked off based upon a particular time stamp on the image) (paragraph 156, In some embodiments, some percentage of all of the data (every sensor, vehicle speed and position, and every image, pixel or array member, i.e. completely unbiased data) is logged so that a secondary or tertiary analysis aids in improving or corroborating (e.g. a cross check of) the initial on-vehicle, real time trigger analysis used to decide the real time action).
Referring to claims 14 and 28, Wu discloses based on the first group of pixels representing a first spray object, indexing the agricultural object as being treated (paragraph 66, Detect and assess the amount of residue on the ground in order for a cultivator or planter to adjust the pressure on its implements) (paragraph 156, In some embodiments, some percentage of all of the data (every sensor, vehicle speed and position, and every image, pixel or array member, i.e. completely unbiased data) is logged so that a secondary or tertiary analysis aids in improving or corroborating (e.g. a cross check of) the initial on-vehicle, real time trigger analysis used to decide the real time action).
Referring to claim 15, Wu discloses a system for treating agricultural objects, the system comprising:
a first treatment unit, first treatment unit having at least one spraying head configured to emit a fluid projectile, the spraying head moveable about an Θ position and a Ψ position (paragraph 169, Example spray nozzles have adjustable partitioning doors, valves, or “blinders” that are remotely controlled to close or open to adjust the spray fan angle in the side-side and/or fore-aft direction);
one or more image sensors configured to obtain 3-dimensional image data (paragraph 52, Images from adjacent or near adjacent image sensor units 50 are stitched together to map the terrain or crop row 12 to form a 3D image); and
one or more processors, the one more processors configured to:
obtain with one or more image sensors at a first time period, a first set of images each comprising a plurality of pixels depicting a ground area and a first target agricultural object positioned in the ground area (paragraph 112, The procedure includes capturing the image, associating or overlaying a virtual grid 100 over the captured image, then check whether (dyed) spray went past a boundary “Boundary” associated with one of the Y-direction lines of the grid 100);
instruct, via a controller, the emitting a first fluid projectile of a first fluid at the first target agricultural object (paragraph 99, he planned crop row 12 is first or subsequently sprayed/spread with fertilizer after the herbicide spot spraying);
obtain with the one or more image sensors at a second time period, a second set of images comprising a plurality of pixels depicting the emitted first fluid projectile and the ground area and the agricultural object paragraph 52, In some embodiments, the images are from videos. For example, when the vehicle is traveling, the video is constantly filming. At very specific intervals or upon a command to send images, a particular image from the video is extracted for analysis);
identify first group of pixels that represent a first spray object (paragraph 112, The procedure includes capturing the image, associating or overlaying a virtual grid 100 over the captured image, then check whether (dyed) spray went past a boundary “Boundary” associated with one of the Y-direction lines of the grid 100);
identify a second group of pixels that represent a spray projectile of the emitted first fluid projectile in an image of the second set of images (paragraph 170, In yet another embodiment, the calculated predicted spray drift are compared with the detected spray drift based on a color image (e.g. spray dye of the end units). If the predicted and detected spray drift pattern on the ground agree to within a predetermined threshold (e.g. 85 to 95%), and over-or-under spraying or drift is found, the spray nozzles or agricultural vehicle take corrective actions and/or alert the operator); and
index the identified first spray object and the identified spray projectile (paragraph 156, In some embodiments, some percentage of all of the data (every sensor, vehicle speed and position, and every image, pixel or array member, i.e. completely unbiased data) is logged so that a secondary or tertiary analysis aids in improving or corroborating (e.g. a cross check of) the initial on-vehicle, real time trigger analysis used to decide the real time action).
While Wu discloses identifying a first group of pixels that represent a first spray object, Wu does not disclose expressly the identification is based on a determined change in pixels between the first and second images.
Wiltshire discloses compare the first image with the second image to determine a change in pixels between at least a first image of the first set of images and at least a second image of the second set of images (paragraph 41, At block 312, the method 300 includes a classification module to classify each pair of patches according to the updated feature map and the resultant probability that is greater. For example, if the matching probability is greater than 50%, the non-match probability is less than 50%, and the classifier classifies the pair of adjacent patches as a match. Conversely, if the matching probability is less than 50%, the non-matching probability is higher than 50%, and the classifier classifies the pair of adjacent patches as a non-match); and
based on the determined change in pixel values as between the first and second images, identify first group of pixels that represent a first object (paragraph 43, At block 314, the method 300 includes an identification module for identifying dissimilar patches based on the classification probability from block 312).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to determine a change in pixels between the first and second images. The motivation for doing so would have been to effectively identify differences between images to obtain additional information that is not easily detected from analyzing each image separately.
Wu and Wiltshire do not disclose expressly wherein the determined change in pixel values indicates a spray impact on a ground area, a spray impact on an agricultural object and/or a spray splat.
Polzounov discloses wherein the determined change in pixel values indicates a spray impact on a ground area, a spray impact on an agricultural object and/or a spray splat (paragraph 108-110, In this example, the verification mechanism 150 is mounted to the rear of the farming machine 100 such that the area of the field is imaged after the spray nozzles pass over the area and treat the cotton plants. The accessed image 210 also includes information representing a treated area where certain spray areas 1040 have been sprayed by the spray nozzles. In this case, the information indicating a plant treatment is illustrated as dampened soil 1210. Returning to FIG. 11, the control system applies 1130 a model 800 to identify pixels in the accessed post-image 212 representing a treated area).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to analyze a spray area. The motivation for doing so would have been to verify that intended areas have been sprayed. Therefore, it would have been obvious to combine Wiltshire and Polzounov with Wu to obtain the invention as specified in claim 15.
Referring to claim 18, Wiltshire discloses wherein the performing image segmentation comprises:
aligning the first image and the second image using features or common pixel patterns in the images (paragraph 37, As the UAV flies over a scene, a plurality of digital images of the scene having the same resolution is captured by the imaging subsystem 12, namely the optics 16, and are aligned according with the imagery in the field of view 11 by an alignment module, block 304); and
generating a pixel mask of the first spray object (paragraph 43, The mask 602 identifies a change in the corresponding patch locations and a degree of change between the input images 400 and 500. The degree of change can include information such as color mapping based on a corresponding classification confidence level associated with the corresponding patch. The mask 602 can then be overlaid on top of the latter of two input images, e.g. image 500, as shown in reference image 600 of FIG. 6).
Referring to claim 24, Wu discloses determining a change in pixels (colors/luminosity/etc.) that are above a threshold value (paragraph 170, In yet another embodiment, the calculated predicted spray drift are compared with the detected spray drift based on a color image (e.g. spray dye of the end units). If the predicted and detected spray drift pattern on the ground agree to within a predetermined threshold (e.g. 85 to 95%), and over-or-under spraying or drift is found, the spray nozzles or agricultural vehicle take corrective actions and/or alert the operator).
Claims 3 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Wu US Publication 2019/0150357, Wiltshire US Publication 2020/0104719 and Polzounov et al. US Publication 2019/0362146 as applied to claims 1 and 15 above, and further in view of Humpal et al. US Publication 2022/0192174 (hereafter “Humpal”).
Referring to claims 3 and 17, Wu discloses when the agricultural treatment system obtained the first set and/or second set of images by the one or more image sensors, but does not disclose expressly creating a homography matrix.
Humpal discloses creating a homography matrix to account for motion when the agricultural treatment system obtained the first set and/or second set of images by the one or more image sensors; and
applying the homography matrix to account for a point of view change between the first image and the second image and using the homography matrix to account for an image misalignment of the first image and the second image (paragraph 130, Homography is used to calibrate the image sensors 122 to accommodate the fact that the image pixel points and real-world coordinates are different when image sensor position changes).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to apply a homography matrix. The motivation for doing so would have been to accommodate using sensors of different heights to detect an object in both images. Therefore, it would have been obvious to combine Humpal with Wu to obtain the invention as specified in claims 3 and 17.
Claims 8, 9, 22 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Wu US Publication 2019/0150357, Wiltshire US Publication 2020/0104719 and Polzounov et al. US Publication 2019/0362146 as applied to claims 7 and 21 above, and further in view of Takatsu US Patent 6,430,319 (hereafter “Takatsu”).
Referring to claims 8 and 22, Wu discloses identifying pixels in the second image depicting the emitted first fluid projectile, wherein the second image depicts both the emitted first fluid projectile and the identified first spray object (paragraph 112, FIG. 8 depicts another example Master Application, which is used to detect spray drift and the spray pattern landing on the ground of a crop field), but does not disclose expressly line fitting the identified pixels to determine the spray line of the spray projectile.
Takatsu discloses line fitting the identified pixels to determine the spray line of the spray projectile (col. 1, lines 48-59, Based on the foregoing principle, a trajectory linking N points in an image is drawn in the ρ-θ space, and the cross point of the trajectory is obtained in order to define a straight line fitted to the set of points in the image).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to fit a line to determine a trajectory. The motivation for doing so would have been to increase the accuracy and to simply the calculations required for determining the direction of an object. Therefore, it would have been obvious to combine Takatsu with Wu to obtain the invention as specified in claims 8 and 22.
Referring to claims 9 and 23, Takatsu discloses performing a Hough line detection operation on the first group of pixels to identify the spray line of the spray projectile (col. 1, lines 48-59, Based on the foregoing principle, a trajectory linking N points in an image is drawn in the ρ-θ space, and the cross point of the trajectory is obtained in order to define a straight line fitted to the set of points in the image. This processing is referred to as the Hough transform).
Claims 11 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Wu US Publication 2019/0150357, Wiltshire US Publication 2020/0104719 and Polzounov et al. US Publication 2019/0362146 as applied to claims 1 and 17 above, and further in view of Nevenka et al. US Publication 2003/0108334 (hereafter “Nevenka”).
Referring to claims 11 and 25, Wu discloses wherein the second time period is temporally later than the first time period (paragraph 173, The example sensors capture information periodically (e.g. camera, one image at a time) and/or capture information continuously (e.g. video, continuous images). Even in some embodiments where the sensors are operated continuously, specific frames are grabbed during the data acquisition of the sensor data), but does not disclose expressly that the duration between the first time period and second time period is less than 60 seconds.
Nevenka discloses that the duration between the first time period and second time period is less than 60 seconds (paragraph 68, It should be noted that the process of grabbing and analyzing frames is preferably performed at pre-defined intervals for each recording device. For instance, when a recording device begins recording data, keyframes can be grabbed every 30 seconds).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to grab an image every 30 seconds. The motivation for doing so would have been to efficiently perform detection while also reducing the memory and processing involved. Furthermore, selecting the interval of uploading images is a matter of design choice. Therefore, it would have been obvious to combine Nevenka with Wu to obtain the invention as specified in claims 11 and 25.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER K HUNTSINGER whose telephone number is (571)272-7435. The examiner can normally be reached Monday - Friday 8:30 - 5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benny Q Tieu can be reached at 571-272-7490. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PETER K HUNTSINGER/Primary Examiner, Art Unit 2682
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