DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The preliminary Amendment filed 12 December 2024 has been entered and considered. Claims 1-20 have been amended. Claims 1-20 are all the claims pending in the application.
Priority
This application claims benefit of foreign priority under 35 U.S.C. 119(a)-(d) of Application No. EP22180679.7, filed in Europe on 06/23/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/12/2024 was considered by the examiner.
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.
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 18 is rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, because the claim purports to invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, but fails to recite a combination of elements as required by that statutory provision and thus cannot rely on the specification to provide the structure, material or acts to support the claimed function. As such, the claim recites a function that has no limits and covers every conceivable means for achieving the stated function, while the specification discloses at most only those means known to the inventor. Accordingly, the disclosure is not commensurate with the scope of the claim.
Claims 18-20 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.
Claims 18-20 are rejected because the claimed invention is not clearly a device nor a method. In Ex Parte Miyazaki, the BPAI held “that if a claim is amenable to two or more plausible claim constructions, the USPTO is justified in requiring the applicant to more precisely define the metes and bounds of the claimed invention by holding the claim unpatentable under 35 U.S.C. § 112, second paragraph, as indefinite.” Ex Parte Miyazaki, 89 USPQ2d 1207, 11-12 (Bd. Pat. App. & Int. 2008). Applicant may cancel the claims, rewrite the claims in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements. However, to the extent that they are asserted to be dependent claims, they appear to remove limitations from the parent as the seps are nt required to be performed.
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 1-2, 6-9, 12-16, and 18-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ritter et al. (U.S. Patent App. Pub No. 2019/0286905 A1, hereafter referred as Ritter).
Regarding Claim 1:
Ritter teaches a computer-implemented method for detecting patterns in agricultural fields (Ritter: Par. [0008]; a method for determining statistics of plant populations based on overhead optical measurements.), wherein the patterns comprise pattern elements, which are repeated periodically on one direction and gaps between the pattern elements which define a pattern geometry (Ritter: Par. [0014-0015] and Figs. 5-6; FIG. 5 illustrates detection and characterization of crop rows, in accordance with one or more implementations, FIG. 6 illustrates segregation of vegetation growing within rows from vegetation growing outside of rows, in accordance with one or more implementations.), the method comprising: receiving remote image data of an agricultural field comprising a plurality of pixels (Ritter: Par. [0058]; At an operation 905, output signals provided by one or more remote sensing devices mounted to an overhead platform may be received. In some implementations, the output signals may convey information related to one or more images of a land area where crops are grown.), wherein each pixel comprises at least one pixel value, wherein the at least one pixel value is representative of a reflectance or emittance of at least one wavelength band (Ritter: Par. [0021]; The output signals may convey information related to one or more images of a land area where crops are grown. In some implementations, the one or more images may include one or more spectral measurements. For example, the one or more images may include one or more of a color measurement, a multi-spectral measurement, a hyperspectral measurement, and/or other spectral measurements of a land area where crops are grown. In some implementations, the one or more remote sensing devices 24 may record two-dimensional images of the land area where crops are grown formed on a single or multiple focal plane arrays. For example, a color or multispectral measurement may be formed through multiple spectral filters applied to individual pixels in a single focal plane array, or through spectral filters applied to entire focal plane arrays in a multiple focal plane array configuration.); processing the pixel values for at least a subset of contiguous pixels in the received remote image data (Ritter: Par. [0028]; In some implementations, image revisions component 30 may be configured to register one or more pixels from the one or more channels to a common pixel space. The first channel may correspond to a first spectral range and the second channel may correspond to a second spectral range. For example, one or more pixels of the first channel and the second channel may be registered to a common pixel space such that the corresponding pixels of each channel provide measurements of a common area of the target scene. In some implementations, cross-channel registration may include two-dimensional cross-correlation and/or other techniques to determine the translation, rotation, scaling, and/or warping to be applied to each channel such that one or more pixels from the one or more channels are registered to a common pixel space.), wherein processing the pixel value for the at least a subset of contiguous pixels comprises: applying a Fourier Transform to the pixel values of the subset of contiguous pixels (Ritter: Par. [0033]; Returning to FIG. 1, crop row attributes component 38 may be configured to perform a two-dimensional Fast Fourier Transform (FFT) on the one or more images or on the numerical combination of images from the one or more channels as determined previously to determine the spatial frequencies, orientation, and curvature of peak energy with respect to the one or more images.); processing output data from the Fourier Transform to determine an offset value, wherein the offset value represents distance of center of a pixel to a nearest pattern element (Ritter: Par. [0033]; In some implementations, crop row attributes component 38 may identify two local maxima of peak energy corresponding to crop row spacing (the lowest frequency local maxima) and individual plant spacing along rows (the highest frequency local maxima). An Inverse Fast Fourier Transform (IFFT) of the low frequency local maxima may provide the spatial separation of crop rows and their orientation relative to the one or more images, Par. [0042]; In some implementations, crop density determination component 40 may determine a pixel distance between each center position using the center position coordinates. In some implementations, crop density determination component 40 may provide a histogram of center to center spacing that may yield a strong peak at the nominal plant spacing.), the method further comprising: generating a mask function, wherein the mask function comprises set values for the processed pixels, wherein the set values are determined based on the offset value of the processed pixels; and determining the pixels containing pattern elements based on the mask function (Ritter: Par. [0037]; By way of a non-limiting example, FIG. 6 illustrates segregation of vegetation growing within rows from vegetation growing outside of rows, in accordance with one or more implementations. As depicted in FIG. 6, a crop row mask 610 is represented as a series of thick lines or curved lines, each fully encompassing one crop row. In FIG. 6, once the location and orientation of the crop rows have been determined, mask 610 is applied to segregate vegetation growing within the rows (e.g., the crop population) from vegetation 620 growing outside of the rows (e.g., non-crop population). In some implementations, the width of the crop row mask 610 may be determined by using a priori information about the crop and/or by dynamically determining the crop width from the image based on the statistical crop size, Par. [0041]; In some implementations, crop density determination component 40 may utilize blob detection techniques and/or other algorithms to identify and count each of the crop plants within the crop row mask. In some implementations, crop density determination component 40 may determine a centroid position of each blob. In some implementations, crop density determination component 40 may provide a list of plant center position coordinates. By way of a non-limiting example, FIG. 8 illustrates a map of plant center positions, in accordance with one or more implementations. FIG. 8 depicts plant centers 810 that are located within each image to determine spacing and count per area.).
In regards to Claim 2, Ritter further teaches the method according to claim 1, wherein the Fourier Transform outputs a two-dimensional array of complex values, and wherein the method further comprises: determining an index of maximal magnitude values of the two-dimensional array and designating the complex values with maximal magnitude value as spectral peaks in a frequency domain; based on amplitude, phase and frequencies of the spectral peaks in the frequency domain, determining a direction vector indicative of the direction of the pattern elements, wherein the direction vector is given by an argument of the peak index regarded as complex frequency; determining a step vector, defined as a normal vector to the direction vector, wherein a modulus is defined by the frequency of the determined pattern; and determining the offset value based on an argument of the peak value in relation to the pattern frequency (Ritter: Par. [0033]; In some implementations, crop row attributes component 38 may identify two local maxima of peak energy corresponding to crop row spacing (the lowest frequency local maxima) and individual plant spacing along rows (the highest frequency local maxima). An Inverse Fast Fourier Transform (IFFT) of the low frequency local maxima may provide the spatial separation of crop rows and their orientation relative to the one or more images, Par. [0042]; In some implementations, crop density determination component 40 may determine a pixel distance between each center position using the center position coordinates. In some implementations, crop density determination component 40 may provide a histogram of center to center spacing that may yield a strong peak at the nominal plant spacing).
In regards to Claim 6, Ritter further teaches the method according to claim 1, wherein the method further comprises adjusting the pixel values for the pixels containing a pattern element (Ritter: Par. [0029]; In some implementations, the combination may include a Difference Vegetation Index (Difference VI), a Ratio Vegetation Index (Ratio VI), a Chlorophyll Index, a Normalized Difference Vegetation Index (NDVI), a Photochemical Reflectance Index (PRI), and/or other combinations of channels. In some implementations, contrast adjustment component 32 may amplify the contrast of one or more high spatial frequency components corresponding to the combination. For example, a two-dimensional bandpass filter may be used to suppress signals of spatial frequencies lower than the crop plants or an edge sharpening filter may be used to increase the contrast of plant and non-plant boundaries in the images. By way of a non-limiting example, FIG. 3 illustrates segregation of vegetation from background clutter, in accordance with one or more implementations. In FIG. 3, a false color image 310 may be converted into a high contrast image 320 which segregates growing vegetation 330 from background clutter 340.).
In regards to Claim 7, Ritter further teaches the method according to claim 1, wherein receiving remote image data of an agricultural field further comprises the pixel values being representative of the reflectance or emittance of a plurality of wavelengths, wherein the method further comprises: determining a vegetation index based on the pixel values of the plurality of wavelengths; adjusting the vegetation index for the pixels containing a pattern element (Ritter: Par. [0029]; In some implementations, the combination may include a Difference Vegetation Index (Difference VI), a Ratio Vegetation Index (Ratio VI), a Chlorophyll Index, a Normalized Difference Vegetation Index (NDVI), a Photochemical Reflectance Index (PRI), and/or other combinations of channels. In some implementations, contrast adjustment component 32 may amplify the contrast of one or more high spatial frequency components corresponding to the combination. For example, a two-dimensional bandpass filter may be used to suppress signals of spatial frequencies lower than the crop plants or an edge sharpening filter may be used to increase the contrast of plant and non-plant boundaries in the images. By way of a non-limiting example, FIG. 3 illustrates segregation of vegetation from background clutter, in accordance with one or more implementations. In FIG. 3, a false color image 310 may be converted into a high contrast image 320 which segregates growing vegetation 330 from background clutter 340.); and determining a soil or crop status value of the agricultural field based on the adjusted vegetation index (Ritter: Par. [0044-0045]; In some implementations, crop density determination component 40 may determine statistics of the crop population including one or more of plant count per unit area, plant size, plant health, and/or other statistics, In some implementations, crop density determination component 40 may determine plant size statistics by determining a number of contiguous pixels which constitute individual plants. In some implementations, crop density determination component 40 may determine plant health characteristics using one or more of spectral combination methods. For example, combinations of spectral reflectance values may be used to infer conditions of plant health. Such combinations may include Difference Vegetation Index (Difference VI), a Ratio Vegetation Index (Ratio VI), a Chlorophyll Index, a Normalized Difference Vegetation Index (NDVI), a Photochemical Reflectance Index (PRI), and/or other combinations of channels.).
In regards to Claim 8, Ritter further teaches the method according to claim 7, wherein adjusting the vegetation index comprises determining a correction value based on the respective vegetation index of neighboring pixels to pixels comprising a pattern element and determining an adjusted vegetation index based on the correction value (Ritter: Par. [0029]; In some implementations, the combination may include a Difference Vegetation Index (Difference VI), a Ratio Vegetation Index (Ratio VI), a Chlorophyll Index, a Normalized Difference Vegetation Index (NDVI), a Photochemical Reflectance Index (PRI), and/or other combinations of channels. In some implementations, contrast adjustment component 32 may amplify the contrast of one or more high spatial frequency components corresponding to the combination. For example, a two-dimensional bandpass filter may be used to suppress signals of spatial frequencies lower than the crop plants or an edge sharpening filter may be used to increase the contrast of plant and non-plant boundaries in the images. By way of a non-limiting example, FIG. 3 illustrates segregation of vegetation from background clutter, in accordance with one or more implementations. In FIG. 3, a false color image 310 may be converted into a high contrast image 320 which segregates growing vegetation 330 from background clutter 340.).
In regards to Claim 9, Ritter further teaches the method according to claim 8, wherein determining the correction value based on the respective vegetation index of neighboring pixels to pixels comprising a pattern element further comprises excluding neighboring pixels comprising a pattern element (Ritter: Par. [0029]; In some implementations, the combination may include a Difference Vegetation Index (Difference VI), a Ratio Vegetation Index (Ratio VI), a Chlorophyll Index, a Normalized Difference Vegetation Index (NDVI), a Photochemical Reflectance Index (PRI), and/or other combinations of channels. In some implementations, contrast adjustment component 32 may amplify the contrast of one or more high spatial frequency components corresponding to the combination. For example, a two-dimensional bandpass filter may be used to suppress signals of spatial frequencies lower than the crop plants or an edge sharpening filter may be used to increase the contrast of plant and non-plant boundaries in the images. By way of a non-limiting example, FIG. 3 illustrates segregation of vegetation from background clutter, in accordance with one or more implementations. In FIG. 3, a false color image 310 may be converted into a high contrast image 320 which segregates growing vegetation 330 from background clutter 340.).
In regards to Claim 12, Ritter further teaches the method according to claim 7, wherein the method comprises determining an agricultural practice based on the determined soil or crop status value and the agricultural practice is at least one of: applying a fertilizer, applying a fertigation product, applying a pesticide product, and irrigation (Ritter: Par. [0003]; For example, timely and accurate knowledge of the emergent plant density and size distribution and their spatial variances across the field may enable growers and agronomists to a) determine more accurately how the emergent crop population differs from the planned population and where replanting may be necessary; b) detect poor germination areas which can then be investigated for bad seed, bad soil, or other disadvantageous conditions; c) detect malfunctioning planting implements for corrective action; d) more accurately and selectively apply inputs such as fertilizers, fungicides, herbicides, pesticides, and other inputs; e) more thoroughly understand how combinations of seed types, planting densities, soil chemistries, irrigation, fertilizers, chemicals, etc. contribute to crop populations which optimize production yields.).
In regards to Claim 13, Ritter further teaches the method according to claim 1, wherein the set values being determined based on the offset value of the processed pixels comprises comparing the offset value of the processed pixel to a predetermined value (Ritter: Par. [0041]; In some implementations, crop density determination component 40 may utilize blob detection techniques and/or other algorithms to identify and count each of the crop plants within the crop row mask. In some implementations, crop density determination component 40 may determine a centroid position of each blob. In some implementations, crop density determination component 40 may provide a list of plant center position coordinates. By way of a non-limiting example, FIG. 8 illustrates a map of plant center positions, in accordance with one or more implementations. FIG. 8 depicts plant centers 810 that are located within each image to determine spacing and count per area.).
In regards to Claim 14, Ritter further teaches the method according to claim 13, wherein comparing the offset value of the processed pixel to the predetermined value comprises adjusting the predetermined value based on farm and/or field data (Ritter: Par. [0041]; In some implementations, crop density determination component 40 may utilize blob detection techniques and/or other algorithms to identify and count each of the crop plants within the crop row mask. In some implementations, crop density determination component 40 may determine a centroid position of each blob. In some implementations, crop density determination component 40 may provide a list of plant center position coordinates. By way of a non-limiting example, FIG. 8 illustrates a map of plant center positions, in accordance with one or more implementations. FIG. 8 depicts plant centers 810 that are located within each image to determine spacing and count per area.).
In regards to Claim 15, Ritter further teaches the method according to claim 2, wherein comparing the offset value of the processed pixel to a predetermined value further comprises determining a pixel orientation and adjusting the predetermined value based on the orientation of the processed pixel and at least one of the direction vector and the step vector (Ritter: Par. [0034]; In some implementations, crop row attributes component 38 may perform a Hough transform to provide the location of each row in the one or more images along with individual row orientation, spacing, and curvature. By way of a non-limiting example, FIG. 5 illustrates detection and characterization of crop rows, in accordance with one or more implementations. In FIG. 5, crop rows 510, crop row spacing 520 in pixel coordinates, and crop row orientation 530 relative to the one or more remote sensing devices have been determined. In some implementations, crop row attributes component 38 may determine a spacing of one or more crop rows in pixels. In some implementations, crop row attributes component 38 may determine the pixel's Ground Sample Dimension using externally provided (e.g., by external resources 16) row spacing and the row spacing in pixels.).
In regards to Claim 16, Ritter further teaches the method according to claim 1, wherein detecting the patterns may comprise at least one of detecting vehicle tracks of agricultural machines and detecting row crops (Ritter: Par. [0022]; In some implementations, the one or more images may be of sufficient spatial resolution to detect individual plants within the crop population. In some implementations, the one or more images may be of sufficient spectral resolution to resolve spectral differences between growing vegetation and background clutter. In some implementations, the measurements may be of sufficient resolution such that the ground resolved distance (GRD) is smaller than a characteristic dimension of one or more target plants in the land area.).
In regards to Claim 18, Ritter further teaches a data processing apparatus comprising means for carrying out the method of claim 1 (Ritter: Par. [0008]; a method for determining statistics of plant populations based on overhead optical measurements, Par. [0052-0053]; Server(s) 12 may include electronic storage 14, one or more processors 22, and/or other components, Electronic storage 14 may comprise non-transitory storage media that electronically stores information.).
In regards to Claim 19, Ritter further teaches a non-transitory computer-readable storage medium comprising instructions which, when executed by a computer system, cause the computer to carry out the method of claim 1 (Ritter: Par. [0008]; a method for determining statistics of plant populations based on overhead optical measurements, Par. [0052-0053]; Server(s) 12 may include electronic storage 14, one or more processors 22, and/or other components, Electronic storage 14 may comprise non-transitory storage media that electronically stores information.).
In regards to Claim 20, Ritter further teaches a computer program product comprising instructions stored in a non-transitory computer-readable storage medium which, when the program is executed by a computer system, cause the computer to carry out the method of claim 1 (Ritter: Par. [0008]; a method for determining statistics of plant populations based on overhead optical measurements, Par. [0052-0053]; Server(s) 12 may include electronic storage 14, one or more processors 22, and/or other components, Electronic storage 14 may comprise non-transitory storage media that electronically stores information.).
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.
Claims 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Ritter et al. (U.S. Patent App. Pub No. 2019/0286905 A1, hereafter referred as Ritter) in view of Shamir et al. (U.S. Patent App. Pub No. 2014/0099000 A1, hereafter referred as Shamir).
In regards to Claim 3, Ritter fails to further teach the method according to claim 2, wherein processing the pixel value for the at least a subset of contiguous pixels in the received remote image data further comprises: applying a window function prior to the applying of the Fourier Transform centered in the at least a subset of contiguous pixels in the received remote image data, hereby adjusting the pixel values; wherein a window width of the window function is chosen according to a predetermined parameter.
Shamir, like Ritter, is directed to the determination of patterns in agricultural fields by means of remote imagery. Shamir does teach wherein processing the pixel value for the at least a subset of contiguous pixels in the received remote image data further comprises: applying a window function prior to the applying of the Fourier Transform centered in the at least a subset of contiguous pixels in the received remote image data, hereby adjusting the pixel values; wherein a window width of the window function is chosen according to a predetermined parameter (Shamir: Par. [0064-65]; Such input allowed the starting point to be identified, e.g., the maximal value in the FFT image is the starting point. As detailed herein, once the starting point is identified, the patch center may be exactly identified. According to some embodiments, the patch center may be identified by using a search window of .+-.N degrees in orientation and .+-.M % of frequency around the starting point. Such a procedure identifies the patch center as well as an area around the patch center that may be included in the patch. According to some embodiments, N may be up to 30 degrees. According to some embodiments, M may be up to 30%, The patch may be identified according to the patch center by utilizing any appropriate frequency classification/segmentation method, such as search known as Peak Search and Major Frequency Search. For example, the following operations may be used for identifying the patch from the patch center (as with other embodiments disclosed herein, different operations or combinations of operations are used): a. defining a search window around the patch center; b. calculating a median value of the pixels in the search window; c. filtering out (e.g., removing) pixels in the search window having a value less than the median value and marking those pixels as "non-patch").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ritter to utilize the windowing technique, as taught by Shamir, to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods for improvement to yield predictable results. As taught by Shamir, the proposed modification allows for the exact identification of a patch center in images having significant variation in frequency and orientation of lines, e.g., low uniformity of plantation rows (Shamir: Par. [0076]).
In regards to Claim 4, Ritter as modified by Shamir further teaches the method according to claim 3, wherein the window function is a Gaussian Window, and wherein the method further comprises: interpolating the complex values of the two-dimensional array adjacent to the maximal magnitude value of the two-dimensional array of elements with a parabolic function, and determining a maximum value of the interpolating parabolic function; wherein the offset value is adjusted based on the maximum value of the interpolating parabolic function (Shamir: Par. [0064-65]; Such input allowed the starting point to be identified, e.g., the maximal value in the FFT image is the starting point. As detailed herein, once the starting point is identified, the patch center may be exactly identified. According to some embodiments, the patch center may be identified by using a search window of .+-.N degrees in orientation and .+-.M % of frequency around the starting point. Such a procedure identifies the patch center as well as an area around the patch center that may be included in the patch. According to some embodiments, N may be up to 30 degrees. According to some embodiments, M may be up to 30%, The patch may be identified according to the patch center by utilizing any appropriate frequency classification/segmentation method, such as search known as Peak Search and Major Frequency Search. For example, the following operations may be used for identifying the patch from the patch center (as with other embodiments disclosed herein, different operations or combinations of operations are used): a. defining a search window around the patch center; b. calculating a median value of the pixels in the search window; c. filtering out (e.g., removing) pixels in the search window having a value less than the median value and marking those pixels as "non-patch"; obvious rendition of windowing).
In regards to Claim 5, Ritter as modified by Shamir further teaches the method according to claim 3, wherein processing the Fourier Transform outputs further comprises applying a mask to the Fourier Transform output data, wherein the mask is configured to remove the complex values outside a predetermined frequency range (Shamir: Par. [0050]; Removing all frequencies found outside the patch, thereby obtaining a masked frequency domain that is an FFT image including only the patch frequencies; Par. [0114]; As shown by block 740, the method may include generating a masked frequency domain, thereby removing all frequencies outside the identified patch. As shown by block 750, the method may include generating a reverse transformed image. As detailed herein, such a reverse transform of the masked frequency domain may provide a spatial domain image comprising only physical data corresponding to the chosen frequency patch. Since the patch may correspond to plant rows, the reverse transform may provide an image including only such rows.).
Allowable Subject Matter
Claims 10-11 and 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Claim 10 recites, refining the received remote image data to a predefined resolution, wherein the predefined resolution is finer than an original resolution of the received remote image data and the pixel values of the refined remote image data are determined based on the values of the coarser pixels of the remote image data; and determining the offset value for the refined pixels of the remote image data; wherein the mask function is a refined mask function generated based on said predefined resolution, wherein the set values of the refined mask function are determined based on the offset value of the refined pixels of the remote image data; wherein the refined pixels of the remote image data comprising a pattern element are determined based on the refined mask function; and wherein the vegetation index is adjusted for the refined pixels of the remote image data comprising a pattern element. The cited art of record does not teach or suggest such a combination of features.
Claim 11 would be allowable by virtue of its dependency on Claim 10.
Claim 17 recites, wherein the method further comprises determining a Moire pattern correction based on the detected pattern for the pixel containing pattern elements. The cited art of record does not teach or suggest such a combination of features.
Because the cited art of record, alone or in combination, does not teach or suggest each and every feature of dependent Claims 10-11 and 17, these claims would be allowable.
Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tenhunen et al. (NPL: Automatic detection of cereal rows by means of pattern recognition techniques) teaches identifying plants growing in rows and between rows by developing a method for locating the rows in drone photographs taken from a weedless field.
Delenne et al. (NPL: From pixel to vine parcel: A complete methodology for vineyard delineation and characterization using remote-sensing data) teaches a frequency analysis approach used for a coarse parcel delineation and a new algorithm is proposed for row detection and adjustment.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RENAE BITOR whose telephone number is (703)756-5563. The examiner can normally be reached Monday to Friday: 8:00 - 5:30 but off the 1st Friday of the biweek.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, GREG MORSE can be reached on (571)272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RENAE A BITOR/Examiner, Art Unit 2663
/GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698
