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 .
Election/Restrictions
Claims 9-11 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected group, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 05 August 2026.
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, 5 ,and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Reigber et al (“Classifying polarimetric SAR data by combining expectation methods with spatial context") in view of well-known prior art (MPEP 214.03).
Referring to claim 1:
Reigber et al disclose image analysis comprising:
calculating similarity between a representative distribution representing a cluster and a distribution of pixel values (section 2.1: then-look sample covariance matrix C at each pixel in polarimetric SAR image based on the complex scattering vector k forms a distribution of pixel values; eqns. 1-2) in the cluster (par. 4 and first part of par. 5 in section 2.2: the similarity calculation corresponds to the expectation step in EM, where the algorithm calculates the probability that each pixel belongs to a specific class, given its covariance and a set of class centers),
calculating a distribution for a plurality of pixels in each cluster as the representative distribution (second part of par. 5 in section 2.2: the representative
calculation corresponds to the maximization step in EM, where the class centers are updated to maximize the likelihood of the observed data), and
assigning pixels in an input image to one of a plurality of the clusters with reference to the similarity (last par. in section 2.2: "Since the EM algorithm does not assign pixels to a single class, the colour in figure 1(d) was chosen according to the most likely class of each pixel"; eqn. 4; Fig. 1d).
While Reigber et al do not disclose a memory storing software instructions, and one or more processors configured to execute the software instructions to perform the above image analysis, it is understood and well-known to those of ordinary skill in the art to implement such image analysis on a computer. Therefore, it would have been prima facie obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have provided a memory storing software instructions, and one or more processors configured to execute the software instructions to perform the image analysis disclosed by Reigber et al in the most practical manner with a reasonable expectation of success.
Referring to claim 2:
Reigber et al disclose limiting the search range for similarity calculation based on a pixel representing the cluster and determining the position of the pixel representing the cluster (par. 1-2 in section 2.3).
Referring to claim 5:
Reigber et al disclose a similarity of the distribution of pixel values to belong to one or another cluster is "synthesized" (first par. in section 2.2).
Referring to claim 7:
Reigber et al disclose calculating similarities between clusters and segmenting an image based on the similarity between clusters (PLR in par. 1-2 in section 2.3 and the EM-PLR combination in par. 2-3 in section 2.4 are regarded as segmentation; Fig. 2; EM-PLR in Fig. 6d compared to EM in Fig. 6c).
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Reigber et al in view of well-known prior art as applied to claim 1 above, and further in view of Tran et al ("Initialization of Markov Random Field Clustering of Large Remote Sensing Images").
Referring to claims 3-4:
Reigber et al do not disclose the detection of outliers and isolated pixels, which are to be excluded. However, Tran et al disclose for a pixel value that is regarded as an outlier from the distribution, or for a pixel having a different distribution compared to neighboring pixels in the input image determined to be an isolated pixel, excluding the outlier or isolated pixel from the determination of whether or not the outlier or isolated pixel has a distribution similar to the representative distribution representing the cluster (section 111.2 "Model Outliers"; penultimate par. in section IV; Fig. 9).
It would have been prima facie obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Reigber et al in view of Tran et al to have provided the detection of outliers and isolated pixels, and exclusion thereof from the determination of whether or not the outlier or isolated pixel has a distribution similar to the representative distribution representing the cluster in order to achieve faster computational analysis.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Reigber et al in view of well-known prior art as applied to claim 1 above, and further in view of Novak ("Advances in SAR Change Detection")
Referring to claim 6:
While Reigber et al do not disclose the calculation of a complex correlation between two images for each cluster. However, Novak discloses coherent change detection (CCD) and non-coherent change detection (NCCD) algorithms using high-resolution synthetic aperture radar (SAR) imagery (section 2; Fig. 6).
Reigber et al state "Classification results make SAR data easier to interpret and can serve as a starting point for automated analysis techniques that apply to homogeneous regions of the observed scene" (abstract). Given the problem of downstream comparison of homogeneous regions in the observed scene, it would have been prima facie obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Reigber et al in view of Novak to use the CCD algorithm in downstream processing to compare homogeneous regions in different images of the observed scene and thereby solve the stated problem, achieving improved sensitivity to small structural and surface disturbances and isolating actual scene changes by suppressing noise as a result of the CCD algorithm’s reliance on complex cross-correlation.
Allowable Subject Matter
Claim 8 is 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. Referring to claim 8, the prior art searched and of record neither anticipates nor suggests the limitations added in the claimed combinations.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed (i.e., a descriptive title that distinguishes the invention and is not a generic or general description). The new title should take into account any amendments to the claims to best indicate the claimed invention.
The title must be as short and specific as possible (see 37 CFR 1.72(a)). Applicant should distill a description of the claimed invention into as few words as possible to capture the essence of the claimed invention. Rather than reciting statutory categories (apparatus, method, product) and some generic descriptor (e.g., information processing), a title that is specific, but characterizes the essence or key aspect(s) of the claimed invention, should be submitted.
Information Disclosure Statement
The information disclosure statement(s) submitted on 05 August 2024 and 18 July 2025 were filed in compliance with the provisions of 37 CFR 1.97 and 1.98. Accordingly, the statement(s) (has / have) been considered by the examiner as indicated below.
Applicant has provided an explanation of relevance of cited document WO 2010/112426 A1 on pages 2-3 of the specification.
The relevance of the cited document(s), in addition to any applied above, can be found in the International Search Report and/or Written Opinion from the ISA dated 31 May 2022 for PCT/JP2022/006889 (of record).
The relevance of the cited document(s), in addition to any applied above, can be found in the Extended European Search Report and/or Written Opinion from the EPO dated 27 June 2025 for EP Application No. 22927186.1 (of record).
Cited Art
The prior art and other references made of record and not relied upon are considered pertinent to applicant's disclosure.
Ferretti et al (US 8587471 B2) disclose a process that includes acquiring radar images on the same area, calculating a vector of n amplitude or intensity values relating to a sample pixel in the n images available, identifying the vector as a sample vector, and defining an estimation window for the sample pixel for identifying a set of pixels around the sample pixel. Additionally, calculating vectors of N amplitude or intensity values for each pixel contained in the estimation window, comparing each vector with the sample vector, identifying pixels associated with the vectors that have passed a statistical test and identifying pixels associated with the vectors that have not passed the statistical test, eliminating the pixels contained in the estimation window that are not connected to the sample pixel, and identifying the set of pixels that are statistically homogeneous with the sample pixel.
Yang (US 10803554 B2) discloses an image processing method and an image processing device that involve acquiring an initial image, performing super-pixel segmentation on the initial image, and acquiring final image blocks; extracting a region of interest from the final image blocks in accordance with an image feature of a target image; and performing super-resolution reconstruction on the region of interest and acquiring an optimized image. Performing super-pixel segmentation on the initial image includes: (a) segmenting the initial image equally into the plurality of image blocks, setting an initial cluster center for each of the plurality of image blocks, and assigning a cluster tag to the each of the plurality of image blocks; (b) calculating a similarity between each pixel point in the initial image and a cluster center of each of the plurality of image blocks, selecting a cluster center corresponding to a highest similarity, and assigning, to the pixel point, a same cluster tag as a cluster tag assigned to one image block of the plurality of image blocks, wherein the cluster center corresponding to the highest similarity is located in the one image block of the plurality of image blocks; (c) determining new segmentation regions in accordance with the cluster tag assigned for each pixel point, and calculating a new cluster center for each of the new segmentation regions; (d) performing an iterative operation by repeating the (b) and the (c), until final cluster centers are acquired after the iterative operation converges; and (e) performing a cluster searching operation in accordance with the final cluster centers and cluster tags of segmentation regions in which the final cluster centers are located, and determining pixel points of each segmentation region of the segmentation regions in which each of the final cluster centers corresponding to the segmentation region is located, and acquiring the final image blocks corresponding to the segmentation regions.
Tanaka (US 20260127851 A1) discloses a signal processing system that includes an assignment unit that assigns each pixel to a cluster in such a way as to maximize a complex metric that indicates a degree of match between a complex vector obtained from corresponding pixels of a plurality of complex images and a probability parameter of a cluster, and a parameter calculation unit that calculates, for each cluster, the probability parameter in such a way as to maximize the complex metric for all pixels assigned to the cluster, wherein the assignment unit and the parameter calculation unit operate alternately.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Scott Rogers whose telephone number is 571-272-7467. The examiner can normally be reached 8 am to 7 pm flex.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abderrahim Merouan can be reached on 571-270-5254. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Scott A Rogers/
Primary Examiner, Art Unit 2683
05 September 20-26
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