DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This is in response to the applicant response filed on 12/18/2025. In the applicant’s response, claims 1 and 8 were amended. Accordingly, claims 1-14 are pending and being examined. Claims 1 and 8 are independent form.
Claim Rejections - 35 USC § 112(a)
3. The claim rejections under 35 USC § 112(a) make in the previous office action have been withdrawn in view of applicant’s amendment.
Claim Rejections - 35 USC § 103
4. 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 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.
5. 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 of this title, 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.
6. Claims 1, 7-8, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Fang et al (CN109242797, hereinafter “Fang”) in view of Dangi et al (US 2021/0360179, hereinafter “Dangi”).
Regarding claim 1, Fang (see, document CN109242797-Eng which was provided by the examiner in the former office action) discloses an image processing system for performing image enhancement on a source image to generate an output image (the method and the system for image enhancement by removing noises in an input image and generating a denoised image; see Abstract), comprising:
a material image generating circuit, configured to generate a material image (see para.86: generating and inputting a noised Lena image (with the variance of 10 Gaussian noise) shown by fig.3(a) from the original Lena image shown by fig.2(a); see step one and para.45: inputting the noised Lena image into the system; see also the top left rectangle of fig.1), comprising: a random noise generating circuit, configured to generate the material image consisting of random noise (ibid. It should be noticed that the Gaussian noise, also known as white noise, is a type of random noise that is distribute according to a normal distribution. In other words, fig.3(a) is an input material image with randomly noise);
at least one texture generating circuit, coupled to the material image generating circuit, configured to adjust texture characteristics of the material image to generate at least one texture image (see step two and para.46: selecting different denoising algorithms to remove different types of noises for different texture characteristics in different regions of the inputted image, such as LPG-PCA algorithm for homogeneous regions, and BM3D algorithm for heterogeneous regions; see also para.76, and the middle 4 parallel rectangles in fig.1),
an output controller, coupled to the at least one texture generating circuit, configured to analyze regional characteristics of the source image to generate an analysis result, determine a region weight according to the analysis result (calculating the weight of each block of the inputted noised image by calculating the arithmetic mean A and the geometric mean G in the each block; see para.68-69, and para.15-para.20), and synthesize the source image with the at least one texture image according to the region weight, thereby to generate the output image (see step three, para.47, and para.82: using the weights calculated based on each block of the inputted noised image, performing effective fusion on the two output images obtained in the step (2) and associated with the respective calculated weights, and obtaining the denoising image; see also the denoised image shown by fig.3(d), and the bottom rectangle of fig.1).
Fang does not explicitly disclose “wherein the at least one texture generating circuits is configured to adjust directionality and density of the random noise in the material image to generate the at least one texture image” as recited in the claim. However, in the same field of endeavor, Dangi discloses a method for generating a modified images based on an original image using a tuning noise map each pixel of which indicates a noise strength and/or direction at which to apply the image processing function to a corresponding pixel of the original image. See the original image 302 in fig.3A and the tuning map 303 in fig.3B; see the corresponding paragraph 116. See the [original] input image 402, the tuning [noise] map 404, and the modified image 415 in fig.4; see the corresponding paragraphs 120—121. It should be noticed that: wherein the [original] input image 402 and the modified image 415 are corresponding to “the material image” and the “texture image” generated by adjusting directionality and density of the random noise in the material image, respectively. Therefore, Dangi teaches the features missed by Fang. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the teachings of Dangi into the teachings of Fang and generate a modified [texture] images based on an original image using a tuning noise map each pixel of which indicates a noise strength and/or direction at which to apply the image processing function to a corresponding pixel of the original image for image augmentation. Suggestion or motivation for doing so would have been to generate a modified image based on an image and a noise map via machine learning system as taught by Dangi, cf., Abstract. Therefore, the combination of Fang and Dangi suggests or teaches all the limitations recited in the claim, and the claim is unpatentable over Fang in view of Dangi.
Regarding claim 7, 14, the combination of Fang and Dangi discloses, wherein the output controller comprises: a region analysis circuit, configured to divide the source image into NxM regions, and respectively determine a plurality of regional characteristics of the NxM region, thereby to obtain the analysis result (Fang, see para.9: “the sliding step length setting window, setting the original image according to the size of a window is divided into several sub-blocks, calculating the weight coefficient of each sub block.” See, e.g., fig.2(b) and para.85, where 3x3 blocks are applied to regional characteristics of the image); and a weight generating circuit, coupled to the region analysis circuit, configured to determine a plurality of weight coefficients respectively corresponding to the NxM regions according to the analysis result, wherein the region weight is composed of the plurality of weight coefficients (Fang, see para.9: “the sliding step length setting window, setting the original image according to the size of a window is divided into several sub-blocks, calculating the weight coefficient of each sub block.”); wherein the regional characteristics include regional brightness (see para,13: “the homogeneous area is as follows: grey value of all pixel points in the region are in a set range”).
Regarding claim 8, claim 8 is an inherent variation of claim 1, thus it is interpreted and rejected for the reasons set forth above in the rejection of claim 1.
7. Claims 2 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Fang in view of Dangi and further in view of Ivanovic et al (US 20160117794, hereinafter “Ivanovic”).
Regarding claim 2, 9, the combination of Fang and Dangi does not disclose, the random noise generating circuit includes a linear feedback shift register (LFSR), as recited in the claims. However, this feature is well known and widely used in the field of applying noise to images. As evidence, in the same field of endeavor, Ivanovic teaches “The applied noise [to an image] may in other cases be quasi-random noise generated by a device such as a linear feedback shift register (LFSR).” See para.36. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the teachings of Ivanovic into the teachings of the combination of Fang and Dangi and generate a noised image taught by Fang by using a linear feedback shift register taught by Ivanovic. Suggestion or motivation for doing so would have been to “generate a weighted value associated with a current region of the image frame based on the current region of the image frame” as taught by Ivanovic, cf., Abstract. Therefore, the combination of Fang, Dangi, and Ivanovic suggests or teaches all the limitations recited in the claim, and the claim is unpatentable.
8. Claim 3-6 and 10-13 are rejected under 35 U.S.C. 103 as being unpatentable over Fang in view of Dangi and further in view of Western et al (JP 2018511443, hereinafter “443’”).
Regarding claim 4, 11, the combination of Fang and Dangi does not explicitly disclose, a directional filter, configured to perform directional filtering on the material image to generate a directional-filtered image; and a low-pass filter, coupled to the directional filter, configured to perform low-pass filtering on the directional-filtered image to generate the at least one texture image. However, in the same field of endeavor, that is, in the field of image denoising, 443’ teaches, to extract the edge map of an image, a directional low pass filter which is applied to the image to build a directional ban-pass filter bank. See [0060], on pg.17 of JP2018511443-Eng. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the teachings of 443’ into the teachings of the combination of Fang and Dangi and generate a denoised image taught by Fang by using a directional low pass filter taught by 443’. Suggestion or motivation for doing so would have been to enhance objects in an image and denoise image as taught by 443’, cf., para.22. Therefore, the claim is unpatentable over Fang in view of Dangi and further in view of 443’.
Regarding claim 5, 12, the combination of Fang, Dangi, and 443’ discloses, a filter parameter bank, configured to provide one or more sets of specific filter parameters for at least one of the directional filter and the low-pass filter for performing filtering based on the analysis result (443’, see par.60).
Regarding claim 6, 13, the combination of Fang, Dangi, and 443’ discloses, wherein the at least one texture generating circuit comprises: a convolutional neural network is configured to process the material image to generate the at least one texture image (443’, see para.53: “The edge map B1 may be calculated using a simple edge filter, or may be calculated using a convolutional neural network trained from rib annotated images using a supervised learning process”).
Regarding claim 3, 10, the combination of Fang, Dangi, and 443’ discloses, wherein the material image generating circuit comprises: a pattern extracting circuit, configured to extract a pattern with a specific frequency from the source image to generate the material image, wherein the pattern extracting circuit includes a Sobel filter or a discrete cosine transform unit (this feature is an obvious variation of 443’ because 443’ teaches “[t]he edge map B1 may be calculated using a simple edge filter”, see para.53, wherein a Sobel filter is a well-known, widely used simple edge filter for one of ordinary skill in the art.).
Response to Arguments
9. Applicant's arguments filed on 12/18/2025 have been considered but are moot in view of the new ground(s) of rejection.
Conclusion
10. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RUIPING LI whose telephone number is (571)270-3376. The examiner can normally be reached 8:30am--5:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, HENOK SHIFERAW can be reached on (571)272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RUIPING LI/Primary Examiner, Ph.D., Art Unit 2676