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 .
DETAILED ACTION
Claim status
Claims 1-20 are rejected under 35 USC § 112 Rejection.
Remarks
Applicant’s arguments filed (04/13/226), with respect to pending claims 1-20, have been fully considered and are directed to claims as amended. The arguments addressed to the 103 rejection is persuasive, but the claim amendments raise a new issue.
Arguments
The Applicant argues (Page 11, line 15 through page 12, line 14):
“The amended claims recite a performance model configured to "determine a difference between the expected performance characteristics and the target performance characteristics" and to "calculate a modified identification sensitivity based on the difference between the expected and target performance characteristics, wherein the modified identification sensitivity, when
implemented by the performance model and the farming machine, predicted to achieve the target performance characteristics for the farming machine." Kwak has been relied upon as the sole anticipatory reference for these limitations, but that reliance is misplaced. Kwak does not disclose, expressly or inherently, a system that determines a difference between expected and target performance characteristics and calculates a modified identification sensitivity based on that difference.
Kwak discloses an agricultural spraying machine that operates a plant classifier to detect target plant areas in images and generates a confidence metric representing the confidence in the classification of those target plant areas. The confidence metric is compared to a threshold, and if the confidence metric is above the threshold, the control system operates the spraying system in a first, precision spraying mode. If the confidence metric falls below the threshold, the system falls back to a second, broadcast spraying mode. Kwak further discloses that the threshold used in this comparison is based on a detection sensitivity identified at an earlier configuration step, and that changes to the detection sensitivity can increase or decrease the frequency of precision spraying mode operation. Critically, the detection sensitivity in Kwak is identified at the outset of operation (e.g., as a default setting or a user-selected setting) and is not derived from any calculation involving performance characteristics.”
The Examiner agree with Applicant assertions above. Therefore the previous 103 rejection, as applied to the amended claims, is withdrawn.
Claim Objections
Claims 1, 14, and 20 are objected to due to the following informalities: “predicted” should be “is predicted”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The new limitation of Claims 1, 14 and 20
”determine a difference between the expected performance characteristics and the target performance characteristics; and calculate a modified identification sensitivity based on the difference between the expected performance characteristics and target performance characteristics, wherein the modified identification sensitivity, when implemented by performance model and the farming machine, [is] predicted to achieve the target performance characteristics for the farming machine” is not described in the specification as originally filed.
Examiner note regarding the prior art of the record:
Regarding claim 1, Kwak (U.S. Patent Publication 2022/0138464 A1) teaches a method for adjusting performance characteristics of a farming machine as it travels through a field (Kwak: Abstract; [“…image processing module configured to determine a characteristic of the plant matter based on the image. A control system is configured to control one or more of the actuatable applicator mechanisms to apply the substance on the agricultural surface based on the determined characteristic…”]), the method comprising:
inputting images captured by the farming machine as it travels through the field into a performance model (Kwak: FIGS. 1-2; ¶¶39-57 [“Image capture system 224 includes one or more image capture components 260 configured to capture images of the field, and image processing components 238 are configured to process those images. Examples of an image processing component 238 include an image signal processor or image processing module (IPM)…spraying system 102 applies the substance to the field in a generally uniform pattern as agricultural spraying machine 100 traverses the field…targeted areas are identified using images acquired by image capture system 224 and processed by image processing components 238 to identify locations of crop plants and/or weed plants, to be sprayed, within those images…”]; FIGS. 5-1, 5-2, 5-3; ¶¶61-70 [“…the plant classifier is configured to classify portions of an image as representing non-crop plants or weeds, to be targeted for precision spraying operations. In another example, a crop classifier can be used to classify areas of an image as including crop plants, and spraying system 102 can be controlled to spray the other areas of the field having plants that are not classified as the target crop type. In either case, the classifier is utilized to identify areas of the field to be sprayed for the given spraying operation…the classifier is applied to the ROI in the image to detect target plant areas to be sprayed. The detection of the target plant areas at block 438 can be based on location within the image (block 440) and/or based on color within the image (block 442). Also, as noted above, the ROI can be normalized for changes in height, as represented at block 444. Again, ROI normalization can include adjusting the portion of the image being analyzed for plant detection, to normalize the ROI for the distance of the camera to the field surface when the camera acquired the image. Of course, the detection at block 438 can be performed in other ways, as represented by block 446…it is noted that in one example image processing component 238 can obtain crop location data to augment the detection of the target plant areas. For example, crop location data can be obtained from a prior planting operation, or otherwise, and indicate the locations where crop seeds were planted, which can be indicative of the crop rows, the crop spacing within rows, etc.…the image processing performed by image processing component 238 can analyze RGB color vectors from a pixel clustering algorithm to determine whether an area of the image that represents a plant indicates a crop plant or a non-crop plant or otherwise identifies the plant as a target plant type to be sprayed for the particular spraying operation. The target plant areas detected in the image can then be correlated to the respective areas of the field…”]);
identify plants in the images using an original identification sensitivity for the performance model (Kwak: FIGS. 1-2; ¶¶39-57; FIGS. 5-1, 5-2, 5-3; ¶¶61-70 {See above.}),
determine expected performance characteristics of the farming machine when treating plants identified in the images using the original identification sensitivity (Kwak: FIGS. 1-2; ¶¶39-57; FIGS. 5-1, 5-2, 5-3; ¶¶61-70 {See above.}),
access target performance characteristics for the farming machine (Kwak: FIGS. 1-2; ¶¶39-57; FIGS. 5-1, 5-2, 5-3; ¶¶61-70 {See above.}; FIG. 3-2; ¶¶71-77 [“…operational characteristics of the machine are obtained at block 450. For example, operational characteristics can include indications of the machine speed and/or boom height. For example, higher machine speeds and boom heights can result in lower confidence metrics due to lower image capture quality. Also, image processing data, such as data from an image processing module can be obtained. For example, image processing data can include image processing fault data,…targeted areas are identified using images acquired by image capture system 224 and processed by image processing components 238 to identify locations of crop plants and/or weed plants, to be sprayed, within those images…if the confidence metric is above the threshold, operation proceeds to block 456 in which control system 206 controls spraying system in a first spraying mode, that is based on the image data. In the present example, the first spraying mode provides image-based precision spraying, wherein selected nozzles 108 are operated to spray one or more discrete dispersal areas at block 458…If the confidence metric is below the threshold at block 455, operation proceeds to block 460 in which control system 206 controls spraying system 102 to operate in a second spraying mode…), and
determine a modified identification sensitivity for the performance model expected to achieve the target performance characteristics for the farming machine (Kwak: FIGS. 3-2, 5-1, 5-2, 5-3; ¶¶71-76 [“…operational characteristics of the machine are obtained at block 450…operational characteristics can include indications of the machine speed and/or boom height…image processing data, such as data from an image processing module can be obtained. For example, image processing data can include image processing fault data, diagnostic information…confidence metric, generated at block 448, is compared to a threshold. The threshold can be pre-defined, user-defined, or defined in other ways…the threshold is based on the detection sensitivity identified at block 412….if the confidence metric is above the threshold, operation proceeds to block 456 in which control system 206 controls spraying system in a first spraying mode…the first spraying mode provides image based precision spraying, wherein selected nozzles 108 are operated to spray one or more discrete dispersal areas at block 458. An example of image-based precision spraying is illustrated above with respect to FIG. 3-2. 3-2. If the confidence metric is below the threshold at block 455, operation proceeds to block 460 in which control system 206 controls spraying system 102 to operate in a second spraying mode…it is based on the operational characteristics obtained at block 450 that affected the confidence metric generation (e.g., reasons why the confidence metric was below the threshold)…Referring again to block 454, it can be seen that changes to the detection sensitivity can increase or decrease operation in the first mode. That is, increases in the detection sensitivity can increase the confidence in the classification resulting in more frequent precision spraying mode operation. Conversely, decreases in the detection sensitivity can decrease the confidence in the classification resulting in less frequent precision spraying mode operation.”]);
inputting additional images captured by the farming machine as it continues to travel through the field into the performance model, the performance model identifying a plant in the field using the modified identification sensitivity (Kwak: FIGS. 3-2, 5-1, 5-2, 5-3; ¶¶78-84 [“…other cameras 130 can be calibrated and utilized for image capture and control of corresponding nozzles 108. At block 472, user interface generator 231 generates a user interface to operator 228 (or other user, such as remote user 218). The user interface can display a video feed from one or more of cameras 260, as represented by block 473…camera feeds from multiple cameras 260 can be stitched at block 475…the video feed can be displayed with overlays with display elements representing configuration of the imaging system and/or spraying system, as represented by block 476. For example, an overlay can include display elements that represent the camera ROI (block 477), sensitivity (block 478), spray length/width (block 479) and can include other display elements 480 as well…an ROI display element identifies the nozzle(s) that are mapped to the particular ROI, as represented by block 481. At block 482, the user interface display includes user input mechanisms that are configured to receive input from operator 228 (or other user) to control the imaging system and/or spraying system…Based on the user input, the configuration of the imaging system and/or spraying system is adjusted at block 489. For example, a configuration adjustment can include changing the ROI at block 490, changing the sensitivity at block 491, changing the spray length and/or width at block 492, or other adjustments to the configuration at block 493. To adjust the configuration at block 489, control system 206 generates corresponding control signals to perform the configuration action…”]); and
treating the plant in the field using a treatment mechanism of the farming machine (Kwak: FIGS. 1-2; ¶¶39-57; FIGS. 5-1, 5-2, 5-3; ¶¶61-70 {See above.}).
Bechtel (US Pub.20120050074A1) teaches (para [0115], where Light (e.g., bright spots or peaks) aggregation (e.g., grouping) can be performed by creating a binary image in the neighborhood of both candidate peaks using an adjustable threshold (e.g., 0.5 or 0.25), where pixel values greater than the threshold are represented as ones and pixels equal to or below the threshold are represented as zeroes, according to one embodiment. This can also be done on a single color channel (e.g., green) such that color differences do not affect the calculation;
Para [0078], where data may be used to further differentiate lighting conditions to indicate probable or directly measured color content of the illumination source and selection of color detection thresholds may be based in part on image data from images of the sky. Additionally, the color of pixels from images of lane markings identified by features such as position, size, and orientation, as well as of general color may be used to adjust color detection thresholds to increase inclusion of these colors).
Barros et al., (US Pub.20120163709A1) teaches (Abstract, where a per-pixel penalty value can be the difference between a color value for that pixel and a predetermined range of color values, based on corresponding pixels in other images. The per-pixel penalty value can be determined for each component color and then optionally summed together. The threshold penalty values can be adjusted to provide for greater, or less, sensitivity to differences among the images).
The prior art of record does not teach or fairly suggest a method for adjusting performance characteristics of a farming machine having the steps of:
“determine a difference between the expected performance characteristics and the target performance characteristics; and calculate a modified identification sensitivity based on the difference between the expected performance characteristics and target performance characteristics, wherein the modified identification sensitivity, when implemented by performance model and the farming machine, predicted to achieve the target performance characteristics for the farming machine”.
Regarding Claims 14 and 20, each claim recites limitations found within Claim 1, and is distinguished under the same rationale applied to Claim 1.
The prior art of record does not teach or fairly suggest a farming machine having the steps of:
“determine a difference between the expected performance characteristics and the target performance characteristics; and calculate a modified identification sensitivity based on the difference between the expected performance characteristics and target performance characteristics, wherein the modified identification sensitivity, when implemented by performance model and the farming machine, predicted to achieve the target performance characteristics for the farming machine”.
Claims 2-13 and 15-19 are not rejected over prior art as being dependent from base claims 1 and 14 respectively.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KALERIA KNOX whose telephone number is (571)270-5971. The examiner can normally be reached M-F 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached at (571)2722302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KALERIA KNOX/
Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857
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