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 .
DETAILED ACTION
1. The following Office action is in response to communications filed on 7/2/2026. Claims 1-9, 11, and 16-17 are currently pending within this application.
Information Disclosure Statement
2. The information disclosure statement(s) (IDS) submitted on 5/15/2026 and 7/27/2026 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Response to Arguments
3. Applicant’s arguments regarding the previously pending claims and the rejections thereof in view of the applied prior art references have been fully considered but they are not persuasive.
The Applicant argues that the claims are not directed towards an abstract idea because the claim as a whole integrates the identified mathematical concepts into a practical application under Step 2A, Prong Two of the Subject Matter Eligibility Analysis. Specifically, the claims are expressly limited to processing CT images according to the ordered steps and not a mathematical relationship untethered to a practical technological use. When analyzing the claimed subject matter in view of Prong Two of Step 2A, the Examiner must analyze whether there are any additional elements recited in the claim beyond the identified judicial exception (such as the claimed pre-processing a CT image by applying preserving denoising to the CT image and then applying an adaptive spike algorithm to the pre-processed image to obtain a processed CT image) and evaluate the additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. In terms of the claimed additional elements, the independent claims mention “a memory” and “a processor” at high levels of generality rendering the operations performed in conjunction with the memory and processor as mere instructions to apply the exception using a generic computer. In terms of evaluating the claimed elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application, it is maintained that the claim limitations are high level instructions to implement the abstract idea on a computer, or use the computer as a tool to perform the abstract idea of the mathematical algorithms embodied by the edge-preserving denoising and spike suppression algorithms (see MPEP 2106.05(f)), while stating within the claims that the algorithms are being performed on CT images merely generally links the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), and in view of the applied prior, art as explained within the previous Office action as well as further below, the claimed order of the use of the algorithms and the algorithms as claimed are well-understood, routine, and conventional within the industry (see MPEP 2106.05(d) and 2106.07(a)) and therefore also ineligible under Step 2B of the analysis. Neither the claimed limitations themselves nor the order they are performed introduce improvements to the functioning technology field, tie the operations to a particular machine, effect a transformation or reduction of a particular article to a different state, or apply the judicial exception in a meaningful way beyond generally linking the use of the exception to a particular technological environment (in this case CT imaging) as required by Prong 2 of Step 2A, nor do they add specific limitation(s) other than what is well-understood, routing, and conventional activities in the field as required by Step 2B. Therefore, based on the above analysis, the rejection of the claims under 35 USC 101 are maintained.
Also, the Applicant argues that the previously applied prior art reference Avinash does not anticipate the claimed limitations of “performing one or more pre-processing steps on a CT image so as to obtain a pre-processed CT image, wherein the one or more pre-processing steps comprise applying an edge-preserving denoising algorithm” because the smoothing and sharpening operations shown in Figure 4 just identify and process structural features of the input image and non-structural features or regions and do not reduce noise while preserving edges. The Examiner disagrees. Throughout the specification, it is mentioned that both spike noise and pattern noise are corrected within the same image where the pattern noise includes elements of image blur (Paragraphs 0001-0002, 0004-0006), and that the pattern noise reduction takes place within the filter shown in Figure 4 where structures within the input image have both anisotropic smoothing and sharpening operations applied thereto in that order (Paragraphs 0023-0027). As is known in the art, anisotropic smoothing operations are operations that reduce image noise without removing significant image components such as edges and lines, and because the identified structures of the input image are having both anisotropic smoothing and sharpening operations performed thereto, this allows the structural components of the image (such as 62 in Figure 2) to be emphasized while de-emphasizing of non-structure elements are performed through isotropic smoothing operations (Paragraphs 0022, 0026, 0030). Therefore, because the filter component within Avinash is implemented within the imaging system in order to perform patterned noise reduction operations that includes performing separate anisotropic smoothing and sharpening operations on specific image segments, it is maintained that the prior art fully anticipates “performing one or more pre-processing steps on a CT image so as to obtain a pre-processed CT image, wherein the one or more pre-processing steps comprise applying an edge-preserving denoising algorithm” as claimed.
Therefore, in view of the above citations/explanations of the prior art references and the Examiner’s interpretation(s) thereof, the rejections of the claims under 35 USC 101, 35 USC 102, and 35 USC 103 are maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
4. Claims 1-7, 11, and 16-17 are rejected under rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Independent claims 1, 16, and 17 recite a method, a device, and a non-transitory CRM which are recognized statutory categories of invention.
Step 2A, Prong One: The above mentioned independent claims recites an abstract idea corresponding to a mathematical concept which includes mathematical relationships, formulas, equations, and calculations. The claimed functions of “performing one or more pre-processing steps on a CT image so as to obtain a pre-processed CT image, wherein the one or more pre-processing steps comprise applying an edge-preserving denoising algorithm; and performing an adaptive spike suppression algorithm on the pre-processed CT image to obtain a processed CT image” are merely mathematical operations/manipulations being performed on various forms of image data. See MPEP 2106.04 and the 2019 PEG.
Step 2A, Prong Two: The abstract idea, as claimed, is not integrated into a practical application. The above mentioned independent claim recite additional elements of “a memory” and “a processor” recited at a high level of generality such that they amount to no more than mere instructions to implement the abstract idea on a conventional computer. The claims do not point to a specific improvement to computers in their communication role or provide a specific improvement in the way imaging systems operate, instead generally link the judicial exception of a mathematical concepts to the field of use of medical imaging. See MPEP 2106.04(d).
Step 2B: As explained in Step 2A, Prong Two, do not point to a specific improvement to computers in their communication role or provide a specific improvement in the way imaging systems operate, instead generally link the judicial exception of a mathematical concepts to the field of use of self-driving vehicles. The claims as a whole, looking at the additional elements individually and in combination, do not integrate the abstract idea into a practical application. These limitations generally link the use of the abstract idea to a particular technological environment. Moreover, the additional elements do not reflect an improvement to a technology or technical field, or include the use of a particular machine or particular transformation and are generally well-understood, routine, conventional activities in the imaging field specified at a high level of generality. The additional elements, taken individually and in combination, do not result in the claim, as a whole, amounting to significantly more than the abstract idea (See MPEP 2106.04(d) and MPEP 2106.05).
Therefore, based on the above analysis in conjunction with the 2019 Revised Patent Subject Matter Eligibility Guidance, it is determined that the independent claims are directed towards ineligible subject matter of an abstract idea without significantly more.
Dependent claims 2-7 and 11 are also rejected for being directed towards additional elements that do not add significantly more to implementing the above identified and discussed abstract idea.
Claim Rejections – 35 USC § 102
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.
5. Claims 1, 11, and 16-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Avinash (US PGPub 2005/0111751) [hereafter Avinash].
6. As to claim 1, Avinash discloses a computer-implemented method (as shown in Figures 3-5) for processing Computed Tomography (CT) images (input images 70), the method comprising: performing one or more pre-processing steps (image filter 72 shown in Figures 3 and 4) on a CT image so as to obtain a pre-processed CT image, wherein the one or more pre-processing steps comprise applying an edge-preserving denoising algorithm (smoothing and sharpening operations 92 and 94); and performing an adaptive spike suppression algorithm (blending operation 74 under the influence of characterization of spike noise 76 and determining blending regime 78 as shown in Figures 3 and 5) on the pre-processed CT image to obtain a processed CT image (output image 80) (Paragraphs 0017-0020, 0023-0034, 0037, 0039-0042, an imaging system shown in Figure 1, under the control of an image processing circuit 24 that executes instructions stored within memory circuitry 26, obtains CT images and includes an image filtering component that reduced patterned noise within the CT image by identifying structural elements within the image and performing anisotropic smoothing and sharpening operations on the image to preserve and enhance the edged and textures depicted within the image in order to generate a filtered image which is blended according to spike noise dependent blending information that creates a mask used to isolate artifacts as spike noise and adjust blending parameters for a blending operation performed on the filtered image to generate an output image that is free from patterned and spike noise artifacts).
7. As to claim 11, Avinash discloses capturing the CT image by means of a photon counting CT imaging system comprising a low-dose CT scan; and/or storing the processed CT image on a storage device and/or outputting the processed CT image on a display device; and/or using the processed CT image for image-guided navigation and/or for computerized image recognition methods (Paragraphs 0017-0018, 0024, the processed CT image is output for display on an output device 32 and stored within a memory 26).
8. As to claim 16, Avinash discloses a device (imaging system shown in Figure 1) for processing Computed Tomography (CT) images, the device comprising: a memory (26) that stores a plurality of instructions; and a processor (24) coupled to the memory and configured to execute the plurality of instructions to: perform one or more pre-processing steps (image filter 72 shown in Figures 3 and 4) on a CT image so as to obtain a pre-processed CT image, wherein the one or more pre-processing steps comprise applying an edge-preserving denoising algorithm (smoothing and sharpening operations 92 and 94); and perform an adaptive spike suppression algorithm (blending operation 74 under the influence of characterization of spike noise 76 and determining blending regime 78 as shown in Figures 3 and 5) on the pre-processed CT image to obtain a processed CT image (output image 80) (Paragraphs 0017-0020, 0023-0034, 0037, 0039-0042, an imaging system shown in Figure 1, under the control of an image processing circuit 24 that executes instructions stored within memory circuitry 26, obtains CT images and includes an image filtering component that reduced patterned noise within the CT image by identifying structural elements within the image and performing anisotropic smoothing and sharpening operations on the image to preserve and enhance the edged and textures depicted within the image in order to generate a filtered image which is blended according to spike noise dependent blending information that creates a mask used to isolate artifacts as spike noise and adjust blending parameters for a blending operation performed on the filtered image to generate an output image that is free from patterned and spike noise artifacts).
9. As to claim 17, Avinash discloses a non-transitory computer-readable medium (26) comprising executable instructions which, when executed by at least one processor (24), cause the at least one processor to perform a method for processing Computed Tomography (CT) images, the method comprising: performing one or more pre-processing steps (image filter 72 shown in Figures 3 and 4) on a CT image so as to obtain a pre-processed CT image, wherein the one or more pre-processing steps comprise applying an edge-preserving denoising algorithm (smoothing and sharpening operations 92 and 94); and performing an adaptive spike suppression algorithm (blending operation 74 under the influence of characterization of spike noise 76 and determining blending regime 78 as shown in Figures 3 and 5) on the pre-processed CT image to obtain a processed CT image (output image 80) (Paragraphs 0017-0020, 0023-0034, 0037, 0039-0042, an imaging system shown in Figure 1, under the control of an image processing circuit 24 that executes instructions stored within memory circuitry 26, obtains CT images and includes an image filtering component that reduced patterned noise within the CT image by identifying structural elements within the image and performing anisotropic smoothing and sharpening operations on the image to preserve and enhance the edged and textures depicted within the image in order to generate a filtered image which is blended according to spike noise dependent blending information that creates a mask used to isolate artifacts as spike noise and adjust blending parameters for a blending operation performed on the filtered image to generate an output image that is free from patterned and spike noise artifacts).
16Claim 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 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.
10. Claims 2-7 are rejected under 35 U.S.C 103 as being unpatentable over Avinash (US PGPub 2005/0111751) [hereafter Avinash] in view of Qi (US PGPub 2017/0098317) [hereafter Qi].
11. As to claim 2, it is noted that Avinash fails to particularly disclose performing the adaptive spike suppression algorithm comprises fitting a model comprising a linear model for a three-dimensional neighborhood of a voxel of the pre-processed image.
On the other hand, Qi discloses performing an adaptive spike suppression algorithm comprises fitting a model comprising a linear model (NLM filtering model) for a three-dimensional neighborhood of a voxel of a pre-processed image (Paragraphs 0026, 0029-0030, 0033-0034, 0036-0037, 0039-0044, 0046-0047, 0060-0069, 0104-0112, an NLM filtering model is implemented on a three-dimensional model of a CT image including volume-pixels, or voxels, where similarity weights are first calculated for a denoised image and then refined based on pixel/voxel correspondences between anatomical image pixels/voxels and/or anatomical similarity weights which are used in the weighted sum operations of the NLM filter).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to include performing the adaptive spike suppression algorithm comprises fitting a model comprising a linear model for a three-dimensional neighborhood of a voxel of the pre-processed image as taught by Qi with the computer-implemented method of Avinash because the cited prior art are directed towards reducing noise within CT images through blending/summation operations and because each of the claimed limitations are fully disclosed within the cited prior art references and would yield results of implementing an image filtering operation with increased robustness that advantageously suppresses noise while simultaneously preserving organ boundaries by basing similarity measurements used to control image blending operations on the comparison of image patches according to a Kullback-Leibler distance measurement.
12. As to claim 3, it is noted that Avinash fails to particularly disclose qualifying the model, wherein qualifying the model comprises checking whether the model meets one or more predetermined criteria; and in response to determining that the model does not meet the one or more predetermined criteria, adapting one or more model parameters of the model to obtain a modified model.
On the other hand, Qi discloses qualifying the model (NLM filter model), wherein qualifying the model comprises checking whether the model meets one or more predetermined criteria (threshold criteria and/or anatomical similarity weights and/or pixel correspondences between anatomical image pixels); and in response to determining that the model does not meet the one or more predetermined criteria, adapting one or more model parameters (transfer-domain coefficients and/or similarity weights) of the model to obtain a modified model (Paragraphs 0029, 0036-0037, 0039, 0044-0047, 0050, 0061-0062, 0064-0069, 0075, 0104-0112, the parameters within the NLM filter model are adjusted by comparing transformed CT image data to specific threshold criteria that alter various transfer-domain coefficients and/or similarity weights used in generating the final blended image free from noise/unwanted artifacts).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to include qualifying the model, wherein qualifying the model comprises checking whether the model meets one or more predetermined criteria; and in response to determining that the model does not meet the one or more predetermined criteria, adapting one or more model parameters of the model to obtain a modified model as taught by Qi with the computer-implemented method of Avinash because the cited prior art are directed towards reducing noise within CT images through blending/summation operations and because each of the claimed limitations are fully disclosed within the cited prior art references and would yield results of improving the effectiveness of the calculated blending weights through data refinement operations.
13. As to claim 4, Qi discloses repeating the steps of qualifying the modified model and adapting one or model parameters until the modified model meets the predetermined criteria (Paragraphs 0029, 0036-0037, 0039, 0044-0047, 0050, 0061-0062, 0064-0069, 0075, 0104-0112, the parameters within the NLM filter model are repeatedly adjusted for each image patch until each image patch satisfies the threshold criteria).
14. As to claim 5, Qi discloses in response to determining that the model or the modified model meets the predetermined criteria, applying the model or the modified model for the voxel (Paragraphs 0029-0030, 0036, based on the similarity/comparison of the captured image data within the patches of the CT image and the threshold conditions set, the refined similarity weights are output and used to modify the voxels of the image through the blending operation).
15. As to claim 6, Qi discloses fitting the model comprises, for each voxel, determining, for each of a plurality of closest-neighbor voxels, a weight factor incorporating at least one of 1) a weight based on spatial distance between the voxel and the closest-neighbor voxel, and 2) a weight based on value-distance between the voxel and the closest-neighbor voxel; and/or wherein performing the adaptive spike suppression algorithm comprise qualifying the model, wherein qualifying the model comprises checking whether the model meets one or more predetermined criteria, and wherein checking whether the model meets one or more predetermined criteria comprises checking whether a/the weight factor of one or more voxels is within a predetermined range; and/or wherein applying the model comprises, for each voxel, performing weighted averaging using the weight factor or weight factors determined for each of a plurality of closest-neighbor voxels (Paragraphs 0026, 0029, 0052-0053, 094-0095, the similarity weights and refined weights to be used for each pixel/voxel are based on spatial distances between patches of data within the CT image and used within the weighted averaging operations of the NLM filter).
16. As to claim 7, Qi discloses fitting the model for a three-dimensional neighborhood of a voxel of the pre-processed image is performed using a filter (filtering performed within operation 230 that calculates similarity weights shown in Figures 2-3) and wherein adapting one or more model parameters comprises adapting filter parameters (refining the calculated similarity weights 240 shown in Figures 2 and 6) (Paragraphs 0044-0046, 0060-0069, 0075, 0104-0112, the similarity weights are calculated based on filtering the CT image data and are further refined before being used within the image blending operations).
Claim Objections
17. Claims 8-9 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.
Conclusion
18. THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL S OSINSKI whose telephone number is (571) 270-3949. The examiner can normally be reached on Monday - Friday, 10:00am - 6:00pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Oneal Mistry can be reached on (313) 446-4912. The fax phone number for the organization where this application or proceeding is assigned is (571)-273-8300.
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MO
/MICHAEL S OSINSKI/Primary Examiner, Art Unit 2674
9/10/2026
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