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:
Typographical error at paragraph [0098] reading “…random deformations into an actual distorted images”, should read “…random deformations into actual distorted images”.
Appropriate correction is required.
Claim Objections
Claims 3 and 18 are objected to because of the following informalities:
Typographical errors reading “…generating a distortion map in dependence the distorted image”, should read “…generating a distortion map in dependence on the distorted image”.
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.
Claim(s) 1-3, 7, 10, 13, 14, and 16-18 is/are rejected under 35 U.S.C. 102(a)(1)/(2) as being anticipated by Nasrabadi et al. of US 2020/0265211 A1 (hereinafter referred to as Nasrabadi).
Regarding claim 1, Nasrabadi discloses a non-transitory computer readable medium that has stored therein a computer program, wherein the computer program comprises code that, when executed by a computer system, instructs the computer system to perform a method for generating synthetic distorted images, (see Nasrabadi par. [0060]) the method comprising: obtaining an input set that comprises a plurality of distorted images (see Nasrabadi par. [0033]: “The Tsinghua distorted fingerprint database was used to statistically model geometric distortion”); determining, using a model, distortion modes of the distorted images in the input set (see Nasrabadi par. [0034] “Using distortion samples of the Tsinghua database and computing the estimated distortion fields 106, it is possible to statistically model distortion by its principal components using principle component analysis (PCA)”); generating a plurality of different combinations of the distortion modes (see Nasrabadi par. [0035] “Each normal fingerprint was transformed to 400 distorted images by sampling each of the two principal distortion components extracted from the Tsinghua database”); generating, for each one of the plurality of combinations of the distortion modes, a synthetic distorted image in dependence on the combination; and including each of the synthetic distorted images in an output set (see Nasrabadi par. [0035] “The generated dataset has 1033x401=414,233 samples, in which each ID has one normal ample and 400 distorted samples”).
Claims 13 and 16 are rejected under the same analysis as claim 1 above.
Regarding claim 2, Nasrabadi discloses the computer readable medium according to claim 1, wherein generating each synthetic distorted image in the output set comprises: modelling, in dependence on one of the plurality of combinations of the distortion modes, a distortion map; and applying the modelled distortion map to a distorted image in the input set (see Nasrabadi par. [0033]”Since minutiae are anomalies in the fingerprint ridge map and have random positions, a similar grid of points can be defined to have a reference of distortion to be compared among different fingers.”).
Claim 17 is rejected under the same analysis as claim 2 above.
Regarding claim 3, Nasrabadi discloses the computer readable medium according to claim 1, further comprising, for each one of the distorted images in the input set, generating a distortion map in dependence the distorted image; wherein the model determines the distortion modes in dependence on the distortion maps of the distorted images in the input set (see Nasrabadi par. [0034] “Using sampling grid pairs from the original and distorted fingerprints, it is possible to represent distortion as a displacement of corresponding points on the original grid and the distorted grid” The sampling grid pairs are used to create the distortion fields from the Tsinghua database and applied to the input set of images.).
Claim 18 is rejected under the same analysis as claim 3 above.
Regarding claim 7, Nasrabadi discloses the computer readable medium according to claim 1, wherein each of the plurality of different combinations of the distortion modes is a weighted combination of distortion modes (see Nasrabadi par. [0035] “Each sample was generated by randomly sampling distortion bases c1, c2.” and Nasrabadi Figure 2).
Regarding claim 10, Nasrabadi discloses the computer readable medium according to claim 1, wherein the model is a statistical deformation model (see Nasrabadi par. [0033] “The Tsinghua distorted fingerprint database was used to statistically model geometric distortion”).
Regarding claim 14, Nasrabadi discloses a system for aligning a synthetic distorted image, the system comprising: a computer readable medium according to claim 1 for generating synthetic distorted images; a non-transitory computer readable medium that has stored therein a computer program, wherein the computer program comprises code that, when executed by a computer system, instructs the computer system to perform a method for: training a machine learning model in dependence on the generated synthetic distorted images (see Nasrabadi par. [0031] “In the training phase, the DCNN 103 learns” (trained) “to estimate the distortion parameters of the estimated distortion field 106 of the input training images (e.g., synthetic distorted samples) 109 by minimizing the difference between the estimated parameters of the estimated distortion field 106 and the actual values of the corresponding training targets 112” and par. [0041] “Network training was performed using a synthetic distorted dataset”, as well as Figure 1); and determining a transformation, by use of the machine learning model, for aligning a distorted image; and aligning the distorted image based on the determined transformation (see Nasrabadi par. [0031] “Using the estimated distortion template 106 and the input fingerprint 115, it is possible to rectify the distorted fingerprint 115 by inverse TPS 118 or other appropriate geometric transformation of the distortion, thereby producing a rectified fingerprint 121.”).
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.
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.
Claim(s) 4, 5, 8, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nasrabadi in view of Uzunova, H., Wilms, M., Handels, H., Ehrhardt, J. (2017). Training CNNs for Image Registration from Few Samples with Model-based Data Augmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D., Duchesne, S. (eds) Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science(), vol 10433. Springer, Cham, Pages 223-231. https://doi.org/10.1007/978-3-319-66182-7_26 (hereinafter referred to as Uzunova).
Regarding claim 4, Nasrabadi fails to disclose wherein the model determines the distortion modes in dependence on one or more locality processes.
However, Uzunova discloses wherein the model determines the distortion modes in dependence on one or more locality processes (see Uzunova section 2.2 “This locality-based approach assumes that local shape variations have limited effects in distant areas”).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the image analysis system of Nasrabadi with the locality processes of Uzunova because it is predictable that doing so would increase the randomness of the distortions and allow specific areas of the distorted image to become distorted, which would represent the real-world distortions better and allow the training set to be more realistic.
Regarding claim 5, Nasrabadi fails to disclose wherein the one or more locality processes effectively isolate deformations that occur in different regions of each distortion map from each other.
However, Uzunova discloses wherein the one or more locality processes effectively isolate deformations that occur in different regions of each distortion map from each other (see Uzunova section 2.2 “For small thresholds tau, each eigenvector tends to reflect only local shape variations present in the training set”).
Claim 5 has the same analysis of obviousness as claim 4.
Claims 19 and 20 are rejected under the same analysis as claims 4 and 5 above.
Regarding claim 8, Nasrabadi fails to disclose wherein coefficients of the weighted combinations are sampled from a normal distribution.
However, Uzunova discloses wherein coefficients of the weighted combinations are sampled from a normal distribution (see Uzunova section 2.3 “Assuming Gaussian distributions for the shape and appearance parameters”).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the image analysis system of Nasrabadi with the normal distribution set of Uzunova because it is predictable that doing so would allow more tunability in the training set and allow the parameters of the system to be taken from a normal distribution set, therefore increasing randomness while also restraining the coefficients to more realistic distributions.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nasrabadi in view of San Andres, Luis. "(Undamped) Modal Analysis of MDOF Systems." Rotordynamics Laboratory, Texas A&M University, 2008, https://rotorlab.tamu.edu/me617/HD%207%20Modal%20Analysis%20Undamped%20MDOF.pdf. Accessed March 6, 2026. (hereinafter referred to as San Andres).
Regarding claim 6, Nasrabadi fails to disclose wherein the distortion modes are all orthogonal to each other.
However, San Andres discloses wherein the distortion modes are all orthogonal to each other (see San Andres pg. 6 “The natural modes (or eigenvectors) satisfy important orthogonality properties.” Another example of orthogonality properties regarding natural modes is cited in relevant art.).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the image analysis system of Nasrabadi with the orthogonal distortion modes of San Andres because it is predictable that doing so would implement the basic orthogonality principles that natural (or distortion) modes have, therefore stating an inherent quality of distortion modes.
Claim(s) 9 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nasrabadi in view of Corcoran et al. of US 10984284 B1 (hereinafter referred to as Corcoran).
Regarding claim 9, Nasrabadi fails to disclose wherein the plurality of different combinations of the distortion modes are generated in dependence on random, or pseudo- random, combinations of the distortion modes
However, Corcoran discloses wherein the plurality of different combinations of the distortion modes are generated in dependence on random, or pseudo- random, combinations of the distortion modes (see Corcoran column 4 lines 48-60, especially lines 48-51: “In another embodiment, the augmentation modules 104 may be combined randomly in a sequence such that 1-n augmentation modules 104 are applied in a random order on the document image 102.”).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the image analysis system of Nasrabadi with the distorted image processing module of Corcoran because it is predictable that doing so would allow the system of Nasrabadi to be able to generate distortion modes randomly which would enhance the variability in the training set, strengthen the machine learning model, and increase its ability to generate a variety of synthetic distorted images.
Regarding claim 11, Nasrabadi fails to disclose further comprising performing two or more cycles of processes for generating an output set of distorted images in dependence on an input set of distorted images; wherein, for each cycle apart from the last cycle, the output set of distorted images of the cycle is used as the input set of distorted images to the subsequent cycle.
However, Corcoran discloses further comprising performing two or more cycles of processes for generating an output set of distorted images in dependence on an input set of distorted images; wherein, for each cycle apart from the last cycle, the output set of distorted images of the cycle is used as the input set of distorted images to the subsequent cycle (see Corcoran column 4 lines 22-55, especially lines 22-25 “The system 100 includes a plurality of augmentation modules 104, where each augmentation module 104 operates to augment an original document image 102, or a previously augmented image, in a specific manner”).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the image analysis system of Nasrabadi with the distorted image processing module of Corcoran because it is predictable that doing so would allow the system of Nasrabadi to be able to generate distortion modes in cycles to add more distortion to the images, which would enhance the variability in the training set, strengthen the machine learning model, and increase its ability to generate realistic synthetic distorted images.
Claim(s) 12 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nasrabadi in view of Sam et al. of KR 20030050320 A (hereinafter referred to as Sam).
Regarding claim 12, Nasrabadi fails to disclose wherein each distorted image is a scanning electron microscope image.
However, Sam discloses wherein each distorted image is a scanning electron microscope image (see Sam pg. 5 “The detected secondary electrons are transmitted to the image data conversion unit, and the image data conversion unit forms image data of the specific region from the detected secondary electrons”).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the image analysis system of Nasrabadi with the scanning electron microscope and imaging system of Sam because it is predictable that doing so would equip the system of Nasrabadi with the capability to capture its own scanning electron microscope images regarding the distortion images that the system operates on, therefore broadening functionality and allowing the system to take its own images.
Regarding claim 15, Nasrabadi fails to disclose an inspection tool comprising: an imaging system configured to image a portion of a semiconductor substrate.
However, Sam discloses an inspection tool comprising: an imaging system configured to image a portion of a semiconductor substrate (see Sam pg. 2 “a method for inspecting a semiconductor substrate using the scanning electron microscope, … subsequently, the detected secondary electrons are converted into image data and displayed on a monitor”).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the image analysis system of Nasrabadi with the scanning electron microscope and imaging system of Sam because it is predictable that doing so would equip the system of Nasrabadi with the capability to capture its own scanning electron microscope images regarding the distortion images that the system operates on, therefore broadening functionality and allowing the system to take its own images.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bower, Allan. "Analytical techniques and solutions for linear elastic solids." Applied Mechanics of Solids, , 2008, https://solidmechanics.org/text/Chapter5_10/Chapter5_10.htm.
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/MARIO ANTHONY RODIN/ Examiner, Art Unit 2675
/ANDREW M MOYER/ Supervisory Patent Examiner, Art Unit 2675