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 .
Specification
The disclosure is objected to because of the following informalities:
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required:
Claim 8 recites: The method according to claim 7, wherein the image processing step that improves the distinguishing of the fertilizer grains from the substrate comprises a filtering step, wherein the filtering step comprises a convolution step and/or wherein the filtering step comprises a dilation step and/or an erosion step and/or histogram adjustment and/or wherein the filtering step comprises a fast Fourier transform and/or threshold filtering. The four underlined “and/or” imply the specification supports filtering that could involve either-or one of the steps or all of the steps as one of the scenarios. Because there are four “and/or”, twelve scenarios could exist as recited. The specification does not appear to support said implied meaning as the claim suggests. If this is not the intended effect from the claim, it is advisable to amend the claim for clarity. For purpose of prior art application to the claim meaning, an “either/or” scenario is being construed until such time the claim has been clarified.
Appropriate correction is required.
Claim Objections
Claim 8 is objected to because of the following informalities:
Claim 8 recites four underlined “and/or” expressions which imply the specification supports filtering that could involve either-or one of the steps or all of the steps as one of the scenarios. Because there are four “and/or” expressions, twelve scenarios could exist as recited. The detailed specification does not appear to support all twelve scenarios.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 10, 12-13, 16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by DE102014108561A1 “DE”.
Regarding claim 1, DE teaches a method for determining a distribution of fertilizer grains. “method and device for determining a distribution of fertilizer grains” (DE [0001]). DE further teaches laying out at least one collecting device for fertilizer grains, specifically an adhesive mat/plate. “at least one adhesive mat/plate is laid out essentially transverse to the direction of travel” (DE [0014]) and “laid out essentially transversely with respect to the direction of travel” (DE [0034]). DE teaches spreading the fertilizer grains over the collecting device using a fertilizer spreader. “fertilizer grains are spread out” by the fertilizer spreader and captured by the adhesive mat/plate (DE [0014], [0034]). DE also teaches taking a picture of the collecting device with a camera. “a camera is present” and the adhesive mat/plate is “imaged in a plan view” (DE [0015]) and “imaged in overlapping camera images” (DE [0016]). DE teaches localizing the fertilizer grains in the picture and calculating an actual distribution. “the corresponding evaluation for determining the mixing ratio” (DE [0007]) and “calculate therefrom a local distribution of the fertilizer grains” (DE [0024]); “The collected fertilizer grains … are digitally imaged … and this is evaluated” (DE [0035]). DE further teaches the improvement step that improves localization in the picture. DE discusses various steps that improve localization improvements. For example, “imaging overlapping camera images…to form a virtual panoramic image” for image evaluation (DE [0016]); “image contrast in the camera images…for fertilizer grains of different brightness and/or or color” (DE [0022]). It should be noted the recited “improvement step” is overly broad and is construed based on broadest reasonable interpretation (BRI) which encompasses “any step” that could facilitate better localization evaluation.
Regarding claim 2, DE teaches capturing at least one circumstantial parameter before taking the picture. “the fertilizer to be collected in measuring dishes or to be deposited on adhesion surfaces and thus to be used for image evaluation” (DE [0007], [0031]. “fertilizer grains are spread out at predetermined actual setting values (machine parameters) of the fertilizer spreader and are thereby captured by the adhesive mat/plate” (DE [0034]). The recited “circumstantial parameter” as read based on broadest reasonable interpretation (BRI) could mean what is disclosed in DE as mapped.
Regarding claim 3, DE teaches adjusting at least one setting parameter based on the circumstantial parameter captured when taking a picture. “evaluation can be carried out in a simple manner by installing algorithms in the computer in order to calculate new setting values which ensure the best possible mixing ratio” (DE [0008]).
Regarding claim 4, DE teaches that the circumstantial parameter may comprise an environmental parameter and/or a fertilizer spreader parameter. “In accordance with these determined setting values, the fertilizer spreader can be set and/or readjusted in an optimized manner in accordance with the displayed values and/or by corresponding actuators which are actuated by the computer” (DE [0008]).
Regarding claim 5, DE teaches that the circumstantial parameter may comprise a camera parameter, and that instructions for improving the camera parameter are output. DE teaches “color and/or gray level in order to achieve a suitable image contrast in the camera images even for fertilizer grains, ′ of different brightness and/or color” (DE [0022]). “preferably imaged in overlapping camera images in such a way that the camera images can be combined for image evaluation to form a virtual panoramic image or the like” (DE [0016]). These activities necessitate setting camera parameter and how images are processed i.e. output.
Regarding claim 10, DE teaches a compensation step for contaminants detected on the collecting device. “The rods to and/or tongues and the carrier layer are preferably designed in such a way that the caught fertilizer grains can be knocked out of the collecting spaces again after the distribution of the fertilizer grains has been measured. Errors due to fertilizer residues during subsequent measurements can thus be avoided.” (DE [0025).
Regarding claim 12, DE teaches guiding a user through the imaging process, including visually and/or acoustically. “fertilizer grains…are digitally imaged… and this is evaluated in the computing unit. By calculating an actual distribution of the fertilizer grains…it is checked whether a desired distribution quality of the mixed fertilizer is achieved with the actual setting values of the fertilizer spreader. If this is not the case, the actual setting values of the fertilizer spreader are specifically corrected / optimized” (DE [000035]).
Regarding claim 13, DE teaches that information about the collecting device is taken into account when calculating the actual distribution of fertilizer grains. Rejected as stated in claim 12 above. DE teaches “By calculating an actual distribution of the fertilizer grains…it is checked whether a desired distribution quality of the mixed fertilizer is achieved with the actual setting values of the fertilizer spreader. If this is not the case, the actual setting values of the fertilizer spreader are specifically corrected / optimized” (DE [000035]).
Regarding claim 16, which is a system claim the scope of which corresponds to the method of claims 1, 12, 13. Therefore, the rejections based on DE for those claims are fully incorporated herein. Fig. 1 of DE illustrates a system for taking a picture in a method for determining an actual distribution of fertilizer grains, including a camera/mobile device and a computing unit. “a camera is present” and “a computing unit is integrated into the mobile radio device” (DE [0015]); “The collected fertilizer grains … are digitally imaged in a plan view … and this is evaluated” (DE [0035]). DE further teaches that images may be combined into a panoramic image and used to calculate local distribution. “combined for image evaluation to form a virtual panoramic image” (DE [0016]); “calculate therefrom a local distribution of the fertilizer grains” (DE [0024]). DE further teaches a screen, a processor, and a memory. “a camera is present, which is preferably integrated into a mobile radio device, such as a mobile telephone.” (CN [0015]; It is inherent that a smartphone/mobile device comprises a screen, processor, memory) that store instructions (DE [0006] “evaluation program”) to visually and/or acoustically guide a user (see rejection of claim 12).
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.
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 6-9, 11, 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over DE102014108561A1 “DE” in view of CN107516311A “CN”.
Regarding claim 6, DE does not expressly teach recognizing a region of interest and cropping the picture to the region of interest. However, CN teaches “inputting RGB images through a CCD camera, carrying out image segmentation, and extracting single corn kernel images i.e. cropping (CN [0038]). Accordingly, it would have been obvious to incorporate CN’s segmentation and image-processing improvements into DE’s fertilizer-distribution imaging method to improve localization accuracy and distribution analysis.
Regarding claim 7, DE does not teach an image processing step that improves distinguishing the fertilizer grains from the background. CN teaches “an excellent segmentation algorithm can separate a detection object from a background and then perform image analysis” (CN [0023]). Accordingly, it would have been obvious to incorporate CN’s segmentation and image-processing improvements into DE’s fertilizer-distribution imaging method to improve localization accuracy and distribution analysis.
Regarding claim 8, by virtue of the claim’s interpretation as stipulated in the aforementioned objection to the specification, DE does not expressly teach a filtering step. CN further teaches a filtering step and the specific filtering techniques recited in the claim. CN teaches “feature extraction” i.e. filtering (CN [0025]). CN further teaches a convolution step. “The corn kernel damage judgment method adopts a deep learning method based on a convolutional neural network to judge the damage of the corn kernels” (CN [0026]). Accordingly, it would have been obvious to incorporate CN’s segmentation and image-processing improvements into DE’s fertilizer-distribution imaging method to improve feature extraction step.
Regarding claim 9, DE teaches that the improvement step may be adapted taking into account the at least one circumstantial parameter. Rejected as stated in claim 2 above. CN also teaches a training step for future steps of the method. “1. training network model” (CN [0042) and “acquiring images, manufacturing a training set and a testing set, and dividing the images into a complete type and a damaged type according to the actual state of corn kernels” (CN [0043).
Regarding claim 11, DE does not teaches that image processing is parallelized. CN further teaches “the time consumption needs to be reduced by adopting a parallelization method. After the parallelization process, the required time is only 0.05s, and the acceleration ratio reaches 20 times.” (CN [0054-0055]). Accordingly, it would have been obvious to incorporate CN’s segmentation and image-processing improvements into DE’s fertilizer-distribution imaging method to improve image processing speed.
Regarding claim 14, CN teaches pixel-wise image segmentation using a neural network. Rejected as discussed in claims 6, 8 and 9 above. CN further teaches using the OTSU method which is a technique that separates the foreground and background based on pixel intensity i.e., pixel-wise segmentation ([0044-0045]).
Regarding claim 15, CN teaches filtering using a specially trained neural network. Rejected as discussed in claims 8 and 9 above. CN further teaches “Deep learning can automatically extract features by utilizing a convolution network, so that extraction of artificial features is avoided, and a large amount of feature extraction work is saved.” (CN [0027].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-11087153-B2; US-20210319539-A1; US-20130089304-A1
Contact Information
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Vu Le can be reached at 2-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/VU LE/Supervisory Patent Examiner, Art Unit 2668
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