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 .
Claim Interpretation
Claims 111-19 are not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they are all method claims.
Claims 1-10 are not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the recitations of “circuitry” provide sufficient structure to perform all claimed limitations.
Claim 205 is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it is an article of manufacture claim.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
An obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but an examined application claim is not patentably distinct from the reference claim(s) because the examined claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985). Anticipation is “the ultimate or epitome of obviousness” (In re Kalm, 154 USPQ 10 (CCPA 1967), also In re Dailey, 178 USPQ 293 (CCPA 1973) and In re Pearson, 181 USPQ 641 (CCPA 1974)).
Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-20 of U.S. Pat. 12,088,773 B2, referred as ‘773 patent hereinafter).
Although the conflicting claims are not identical, they are not patentably distinct from each other because each limitation of the instant claims 1-20 is fully defined by claims 1-20 of the ‘773 patent. For example, as to the instant claim 1, claim 1 of the ‘773 patent discloses a training server, comprising: a processor configured to: acquire an input color image in a Red, Green, Blue (RGB) color space of a field-of-view (FOV) of an agricultural field (see lines 2-5 (“circuit…acquire an input color image…agriculture field”)); detect one or more foliage regions in the input color image and generate an output binary mask image of foliage mask indicating the one or more foliage regions and a soil region (see lines 6-9); generate an augmented color image by combining pixels of the soil region adjusted for soil hue, with pixels of the one or more foliage regions unaltered from the acquired input color image in the RGB color space (see lines 19-23 (the adjusted RGB image includes the adjusted hue component as set forth in lines13-18); and utilize the generated augmented color image in training of a crop detection (CD) neural network model (see lines 24-25).
While claim 1 of the ‘773 patent includes additional limitations (i.e., recitation in lines 10-12) that are not set forth in the instant claim 1, the use of transitional term “comprising” in the instant claim 1 fails to preclude the possibility of additional elements. Therefore, instant claim 1 fails to define an invention that is patentably distinct from claim 1 of the ‘733 patent.
Furthermore, each of the limitations recited in instant claim 1 is anticipated by claim 1 of the ‘773 patent and anticipation is “the ultimate or epitome of obviousness.”
Regarding instant claim 2, claim 10 of the ‘773 patent discloses wherein in a training phase, the processor is further configured to cause the CD neural network model to learn a plurality of different types of soil based on the generated augmented color image (see lines 1-4).
Regarding instant claim 3, claim 10 of the ‘773 patent discloses wherein in a training phase, the processor is further configured to cause the CD neural network model to learn a range of color variation of soil based on the generated augmented color image (see lines 1-4).
Regarding instant claim 4, claim 3 of the ‘773 patent discloses wherein the FOV of input color image ranges from 1.75 to 2.25 meters of the agricultural field (see lines 1-4).
Regarding instant claim 5, claim 4 of the ‘773 patent discloses wherein the processor is further configured to utilize the generated augmented color image in training of a foliage detection (FD) neural network model (see lines 1-7).
Regarding instant claim 6, claim 5 of the ‘773 patent discloses a Foliage Image Processing (FIP) component, wherein the processor is further configured to execute the FIP component on the acquired input color image in the RGB color space for the generation of the output binary mask image of foliage mask (see lines 1-6).
Regarding instant claim 7, claim 1 of the ‘773 patent discloses wherein the processor is further configured to convert the input color image from the RGB color space to a Hue, Saturation, Lightness (HSV) color space to obtain an HSV image (see lines 10-12).
Regarding instant claim 8, claim 1 of the ‘773 patent discloses wherein the processor is further configured to modify a hue value of a first set of pixels of the HSV image corresponding to the soil region indicated by the output binary mask image of foliage mask to selectively adjust the hue component of the HSV image (see lines 13-15).
Regarding instant claim 9, claim 2 of the ‘773 patent discloses wherein the processor is further configured to add or subtract a randomly chosen integer value in the range of 1 to 50 from the hue value of each pixel of the first set of pixels for the selective adjustment of the hue component of the HSV image (see lines 1-8).
Regarding instant claim 10, claim 1 of the ‘773 patent discloses wherein the processor is further configured to convert the selectively adjusted HSV image back to the RGB color space to obtain a soil region-adjusted RGB image, wherein the pixels of the soil region adjusted for the soil hue is a part of the soil region adjusted RGB image (see lines 16-18).
Regarding instant claim 11, claim 6 of the ‘773 patent discloses wherein the output binary mask image of foliage mask comprises a first set of pixels with binary value “1” corresponding to the one or more foliage regions and a second set of pixels with binary value “0” corresponding to the soil region (see lines 1-5).
Regarding instant claim 12, claim 7 of the ‘773 patent discloses wherein the processor is further configured to invert the output binary mask image of foliage mask to obtain an inverted output binary mask image of foliage mask in which: the first set of pixels with the binary value “1” corresponding to the one or more foliage regions is re-assigned the binary value “0”, and the second set of pixels with binary value “0” corresponding to the soil region is re-assigned the binary value “1” to allow processing of the second set of pixels for selectively adjustment of the hue component of the HSV image (see lines 1-12).
Regarding instant claim 13, claim 8 of the ‘773 patent discloses wherein the processor, in a training phase, is further configured to apply a plurality of different image level augmentation operations on a first set of input color images of the agricultural field or another agricultural field in a first training dataset to obtain a second set of augmented color images greater in number than the first set of input color images, and wherein a combination of the second set of augmented color images and the first set of input color images in form of a modified training dataset is further used for the training of the CD neural network model (see lines 1-11).
Regarding instant claim 14, claim 9 of the ‘773 patent discloses wherein the processor, in the training phase, is further configured to apply a dataset level augmentation in addition to the plurality of different image level augmentation operations (see lines 1-4).
Regarding instant claim 15, claim 11 of the ‘773 patent discloses a method of augmenting agricultural image data, the method comprising: in a training server: acquiring an input color image in a Red, Green, Blue (RGB) color space of a field-of-view (FOV) of an agricultural field (see lines 3-5); detecting one or more foliage regions in the input color image and generate an output binary mask image of foliage mask indicating the one or more foliage regions and a soil region 9see lines 6-9); generating an augmented color image by combining pixels of the soil region adjusted for soil hue, with pixels of the one or more foliage regions unaltered from the acquired input color image in the RGB color space (see lines 20-24 (adjusted RGB image includes adjust hue according to lines 13-15); and utilizing the generated augmented color image in training of a crop detection (CD) neural network model (see lines 25-29).
While claim 11 of the ‘773 patent includes additional limitations (i.e., recitation in lines 10-12) that are not set forth in the instant claim 1, the use of transitional term “comprising” in the instant claim 1 fails to preclude the possibility of additional elements. Therefore, instant claim 1 fails to define an invention that is patentably distinct from claim 11 of the ‘733 patent.
Regarding instant claim 16, claim 20 of the ‘773 patent discloses causing the CD neural network model to learn a plurality of different types of soil based on the generated augmented color image in a training phase (see lines 1-4).
Regarding instant claim 17, claim 20 of the ‘773 patent discloses causing the CD neural network model to learn a range of color variation of soil based on the generated augmented color image in a training phase (see lines 1-4).
Regarding instant claim 18, claim 13 of the ‘773 patent discloses utilizing the generated augmented color image in training of a foliage detection (FD) neural network model (see lines 1-6).
Regarding instant claim 19, claim 11 of the ‘773 patent discloses converting the input color image from the RGB color space to a Hue, Saturation, Lightness (HSV) color space to obtain an HSV image (see lines 10-12); and modifying a hue value of a first set of pixels of the HSV image corresponding to the soil region indicated by the output binary mask image of foliage mask to selectively adjust the hue component of the HSV image (see lines 17-19).
Regarding instant claim 20. A computer program product for augmenting agricultural image data, the computer program product comprising a non-transitory computer-readable storage medium having program instructions embodied therewith, the program instructions are executable by a system to cause the system to execute operations, the operations comprising: acquiring an input color image in a Red, Green, Blue (RGB) color space of a field-of-view (FOV) of an agricultural field; detecting one or more foliage regions in the input color image and generate an output binary mask image of foliage mask indicating the one or more foliage regions and a soil region; generating an augmented color image by combining pixels of the soil region adjusted for soil hue, with pixels of the one or more foliage regions unaltered from the acquired input color image in the RGB color space; and utilizing the generated augmented color image in training of a crop detection (CD) neural network model.
Regarding instant claim 20, the advanced statements as applied to claims 1 and 15 above are incorporated hereinafter. While neither claims 1 and 11 of the ‘733 patent disclose a computer program product, such computer program product is inherently included in claim 1 and 11 of the ‘733 patent in order to carry out the functions of the circuitry.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pothen et al. (WO 2023/242793 Al) teaches an agriculture device for removing weeds between the crop plant (see figure 1 and abstract) comprising a control circuit (i.e., control circuitry 112 of figure 1) to acquire an input image a field-of-view (POV) of an agricultural field (i.e., 502 of figure 5 and page 22 lines 8-12 ("At step 502, the method 500 includes capturing, by the at least one image-capture unit 108 of the agricultural device 102, a field-of-view (POV) of a defined area of an agricultural 10 field"); and page 2 lines 17-20 ("image capture unit configured to capture a field-of-view (POV) of a defined area of the agricultural field and a weeding arrangement") and to detect one or more foliage regions in the input image (i.e., page 22 lines 8- 12 ("The captured POV of the defined area provides the clear and detailed images of the crop plants and the weeds, which further enables the agricultural device 102 to clearly distinguish the crop plants from the weeds").
Kiepe et al (U.S. Pat. App. Pub. No. 2019/0220666 Al) teaches a method and system for recognizing weed in a natural environment (abstract; para. [0001]; figure 1 and para. [0042]) comprising a control circuit (para. [0038]: computer), a camera for capturing weed image (figures 1-2 and para. [0070]), color space conversion (para. [0031] (transforming image from RGB to HSY color model)), and an automated machine-based process to recognize a specific type of weed from the weed image (para. [0051]).
Kuramoto et al. (U.S. Pat. App. Pub. No. 2021/0397888 Al) teaches a diagnostic assistance system (para. [0062] and figure 2) comprising a control circuit (para. [0028] (CPU), a camera for acquiring an input image a field-of-view (POV) of an agricultural field (para. [0066]), identifying vegetation region or ground region (para. [0117]), and training crop detection neural network model (paras. [0068] and [0151]).
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DMD
5/2026
/DUY M DANG/Primary Examiner, Art Unit 2662
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