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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on November 1, 2024, April 9, 2025, June 16, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1 and 10 are objected to because of the following informalities:
Claim 1, “the first image and a second image having different distortions” should read “the first image and the second image having different distortions”
Claim 10, “the first image such a distortion” should read “the first image such that a distortion”
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.
Claim(s) 1-2, 4-8, 11-15 and 17-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kim, Boah, et al. "CycleMorph: cycle consistent unsupervised deformable image registration." Medical image analysis 71 (2021): 102036. (hereinafter Kim).
Regarding independent claim 1, Kim discloses A processor-implemented method with data processing using a neural network (page 12, left column, “CycleMorph on the CPU;” page 2, left column, “More specifically, we train two convolutional neural networks (CNN), G X and G Y , that generate forward and reverse directional deformation vector fields, respectively.”), the method comprising:
determining a first translated image by translating a first image based on a second image, the first image and a second image having different distortions, such that a distortion of the first translated image corresponds to a distortion of the second image (page 3, left column, “The overall learning framework of the proposed CycleMorph is illustrated in Fig. 2 . Specifically, for the moving source and fixed target images, X and Y, which may come from different sub- jects (i.e. different anatomical shapes) regardless of the contrast of each image (i.e. both inter/intramodal registration), we define two registration networks as G X : (X, Y ) → φXY and G Y : (Y, X) → φY X , where φXY (resp. φY X ) denotes the deformation fields from X to Y (resp. Y to X).”);
determining a first retranslated image by translating the first translated image such that a distortion of the first retranslated image corresponds to a distortion of the first image (page 4, left column, “. Specifically, an image X is first deformed to an image ˆ Y , after which the deformed image is registered again by another network to generate image ˜ X in the proposed framework. Then, the cycle consistency is applied between the re-deformed image ˜ X and its original image X to impose X ≃ ˜ X . Similarly, an image Y should be successively deformed by the two networks to generate image ˜ Y , and the cycle consistency allows to impose Y ≃ ˜ Y .”); and
training a first deformation field generator configured to determine a first relative deformation field that represents a relative deformation from the first image to the second image (page 2, left column, “More specifically, we train two convolutional neural networks (CNN), G X and G Y , that generate forward and reverse directional deformation vector fields, respectively. When a moving source im- age is deformed to the other fixed image by the deformation fields from G X , then the deformed image can be reversed to the origi- nal image using the deformation fields from G Y , by applying the cycle consistency to the reversed image and the original image.”), based on a loss between the first retranslated image and the first image (page 4, left column, “Therefore, the cycle loss is computed by: L cycle (X, Y, G X , G Y ) = ∥T ( ˆ Y , ˆ φY X ) − X ∥ 1 + ∥T ( ˆ X , ˆ φXY ) −Y ∥ 1 ,where || ·|| 1 denotes the l 1 -norm.”).
Regarding dependent claim 2, the rejection of claim 1 is incorporated herein. Additionally, Kim further discloses further comprising:
determining a second translated image by translating the second image such that the distortion of the second image corresponds to the distortion of the first image (page 4, left column, “Similarly, an image Y should be successively deformed by the two networks to generate image ˜ Y , and the cycle consistency allows to impose Y ≃ ˜ Y .”);
determining a second retranslated image by translating the second translated image such that a distortion of the second translated image corresponds to the distortion of the second image (page 4, left column, “Similarly, an image Y should be successively deformed by the two networks to generate image ˜ Y , and the cycle consistency allows to impose Y ≃ ˜ Y .” … “Therefore, the cycle loss is computed by: L cycle (X, Y, G X , G Y ) = ∥T ( ˆ Y , ˆ φY X ) − X ∥ 1 + ∥T ( ˆ X , ˆ φXY ) −Y ∥ 1 ,where || ·|| 1 denotes the l 1 -norm.”); and
training the first deformation field generator based on a loss between the second retranslated image and the second image (page 4, left column, “Therefore, the cycle loss is computed by: L cycle (X, Y, G X , G Y ) = ∥T ( ˆ Y , ˆ φY X ) − X ∥ 1 + ∥T ( ˆ X , ˆ φXY ) −Y ∥ 1 ,where || ·|| 1 denotes the l 1 -norm.”).
Regarding dependent claim 4, the rejection of claim 1 is incorporated herein. Additionally, Kim further discloses wherein the first relative deformation field and a second relative deformation field have an inverse deformation relationship (page 3, left column, “ Thus, the estimated deformation F from X to Y is not equal to the inverse of the estimated deformation R from Y to X. In consistent image registration approaches ( Christensen and Johnson, 20 01; Ashburner, 20 07; Leow et al., 2005 ), this problem is allevi- ated by imposing additional inverse consistency: R ≃ F −1 . In particular, the forward and inverse mappings F and R are only defined through the corresponding deformation fields φXY and φY X , so the corresponding inverse-consistency is usually enforced as a regularization term to the deformation vector fields.”), and
wherein the second relative deformation field represents a relative deformation from the second image to the first image (page 3, left column, “the forward and inverse mappings F and R are only defined through the corresponding deformation fields φXY and φY X , so the corresponding inverse-consistency is usually enforced as a regularization term to the deformation vector fields.””).
Regarding dependent claim 5, the rejection of claim 1 is incorporated herein. Additionally, Kim further discloses wherein the determining of the first translated image comprises:
determining the first translated image having the distortion of the second image by applying the first relative deformation field to the first image (page 3, left column, “We use a spatial transformation layer T in the networks to warp the moving image by the estimated deformation fields, so that the registration networks can be trained by mini- mizing the loss function on the deformed image and fixed image. Accordingly, when a pair of images are given to the registration networks, the moving image is deformed to align with the fixed image.”).
Regarding dependent claim 6, the rejection of claim 1 is incorporated herein. Additionally, Kim further discloses wherein the determining of the first retranslated image comprises:
determining the first retranslated image having the distortion of the first image by applying a second relative deformation field to the first translated image (see equations 7 and 8 on page 4, left column),
wherein the second relative deformation field represents a relative deformation from the second image to the first image (see equations 7-9 on page 4, left column; page 4, left column, “the cycle consistency is applied between the re-deformed image ˜ X and its original image X to impose X ≃ ˜ X . Similarly, an image Y should be successively deformed by the two networks to generate image ˜ Y , and the cycle consistency allows to impose Y ≃ ˜ Y .”).
Regarding dependent claim 7, the rejection fo claim 1 is incorporated herein. Additionally, Kim further discloses wherein the first image and the second image are unpaired images comprising either one or both of different contents and different scenes (page 3, left column, “The overall learning framework of the proposed CycleMorph is illustrated in Fig. 2 . Specifically, for the moving source and fixed target images, X and Y, which may come from different sub- jects (i.e. different anatomical shapes) regardless of the contrast of each image (i.e. both inter/intramodal registration)
Regarding dependent claim 8, the rejection of claim 1 is incorporated herein. Additionally, Kim further discloses wherein the training of the first deformation field generator comprises:
training the first deformation field generator through unsupervised learning without information associated with the distortions of the first image and the second image (page 3, right column, “As shown in Fig. 3 , our method is trained in an unsupervised manner without ground-truth deformation fields.”).
Regarding dependent claim 11, the rejection of claim 1 is incorporated herein. Additionally, Kim discloses wherein the second image has no corresponding label image (page 3, right column, “in Fig. 3 , our method is trained in an unsupervised man- ner”)
Regarding dependent claim 12, the rejection of claim 1 is incorporated herein. Additionally, Kim further discloses A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, configure the one or more processors to perform the method of claim 1 (page 6, left column, “The proposed deformable registration method was imple- mented in Python using PyTorch library. The specific implemen- tation details for face and medical image registration tasks are as follows. The code is available at https://github.com/jongcye/ MEDIA _ CycleMorph .”).
Regarding independent claim 13, the rejection of claim 1 applies directly. Additionally, Kim further discloses A processor-implemented method with data processing using a neural network (page 12, left column, “CycleMorph on the CPU;” page 2, left column, “More specifically, we train two convolutional neural networks (CNN), G X and G Y , that generate forward and reverse directional deformation vector fields, respectively.”), the method comprising:
determining, using a trained first deformation field generator, a relative deformation field that represents a relative deformation from a source image to a target image based on the source image and the target image that have different distortions (page 2, right column, “the learning-based registration algorithms are inductive in the sense that once a neural network is trained, it can instantaneously predict deformation vector fields for a new data.”); and
determining a translated source image having a distortion of the target image by applying the relative deformation field to the source image (Figure 2, “The spatial transform function deforms the moving image according to the vector fields to match a shape of the fixed image”),
wherein the first deformation field generator is trained based on a loss between a first retranslated image and a first image, and the first retranslated image is determined by translating a first translated image such that a distortion of the first retranslated image corresponds to a distortion of the first image (page 4, left column, “the cycle consistency is applied between the re-deformed image ˜ X and its original image X to impose X ≃ ˜ X . Similarly, an image Y should be successively deformed by the two networks to generate image ˜ Y , and the cycle consistency allows to impose Y ≃ ˜ Y .”).
Regarding independent claim 14, the rejection of claim 1 applies directly. Additionally, Kim further discloses A device with data processing (page 6, left column, “The proposed deformable registration method was imple- mented in Python using PyTorch library. The specific implemen- tation details for face and medical image registration tasks are as follows. The code is available at https://github.com/jongcye/ MEDIA _ CycleMorph .” a processor is read as implementing the software), comprising:
one or more processors (page 6, left column, “The proposed deformable registration method was imple- mented in Python using PyTorch library. The specific implemen- tation details for face and medical image registration tasks are as follows. The code is available at https://github.com/jongcye/ MEDIA _ CycleMorph .” a processor is read as implementing the software) configured to:
determine a first translated image by translating a first image based on a second image, the first image and a second image having different distortions, such that a distortion of the first translated image corresponds to a distortion of the second image (see claim 1 analysis);
determine a first retranslated image by translating the first translated image such that a distortion of the first retranslated image corresponds to a distortion of the first image (see claim 1 analysis); and
train a first deformation field generator configured to determine a first relative deformation field that represents a relative deformation from the first image to the second image (see claim 1 analysis) or a second deformation field generator configured to determine a second relative deformation field that represents a relative deformation from the second image to the first image (NOTE: based on “or” limitation is not required), based on a loss between the first retranslated image and the first image (see claim 1 analysis).
Regarding dependent claim 15, the rejection of claim 14 is incorporated herein. Additionally, Kim further discloses wherein the one or more processors are configured to:
determine a second translated image by translating the second image such that the distortion of the second image corresponds to the distortion of the first image (see claim 2 analysis);
determine a second retranslated image by translating the second translated image such that a distortion of the second translated image corresponds to the distortion of the second image (see claim 2 analysis); and
train the first deformation field generator or the second deformation field generator based on a loss between the second retranslated image and the second image (see claim 2 analysis).
Regarding dependent claim 17, the rejection of claim 14 is incorporated herein. Additionally, Kim further discloses wherein the first relative deformation field and the second relative deformation field have an inverse deformation relationship (see claim 4 analysis).
Regarding dependent claim 18, the rejection of claim 14 is incorporated herein. Additionally, Kim further discloses wherein, for the determining of the first translated image, the one or more processors are configured to:
determine the first translated image having the distortion of the second image by applying the first relative deformation field to the first image (see claim 5 analysis).
Regarding dependent claim 19, the rejection of claim 14 is incorporated herein. Additionally, Kim further discloses wherein the one or more processors are configured to:
determine the first retranslated image having the distortion of the first image by applying the second relative deformation field to the first translated image (see claim 6 analysis).
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.
Claim(s) 20 is rejected under 35 U.S.C. 103 as being unpatentable over Kim.
Regarding dependent claim 20, the rejection of claim 14 is incorporated herein. However, Kim fails to explicitly disclose wherein the device is at least one of a mobile phone, a smartphone, a personal digital assistant (PDA), a netbook, a tablet computer, a laptop, a mobile device, a smartwatch, a smart band, smart eyeglasses, a wearable device, a desktop, a server, a computing device, a television (TV), a smart TV, a refrigerator, a home appliance, a door lock, a security device, and a vehicle.
However, Kim does disclose at page 6, left column, “The proposed deformable registration method was implemented in Python using PyTorch library. The specific implementation details for face and medical image registration tasks are as follows.” Based on this excerpt, Kim does disclose the execution of a system that can operate Python. Further, one of ordinary skill in the art before the effective filing date of the claimed invention would easily be aware laptops can operate Python, and further are more convenient for processing on the go as opposed to being tied down to a desk. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Kim in order to allow the system to be executed on a laptop for convenient processing in a mobile manner.
Double Patenting
Non-statutory
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,169,917 (hereinafter US ‘917). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are more broad in scope than those of the US ‘917 patent, as indicated below.
Claim 1: Regarding claim 1, claim 1 compares to claim 1 of the US ‘917 patent as indicated below. As seen in the chart below, claim 1 of the instant application is more broad in scope than claim 1 of the US ‘917 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 1 of the US ‘917 patent.
Instant Application - Claim 1
US '917 - Claim 1
Notes
A processor-implemented method with data processing using a neural network, the method comprising:
A processor-implemented method with data processing using a neural network, the method comprising:
Verbatim
determining a first translated image by translating a first image based on a second image, the first image and a second image having different distortions, such that a distortion of the first translated image corresponds to a distortion of the second image;
determining a first translated image by translating a first image based on a second image, the first image and a second image having different distortions, such that a distortion of the first translated image corresponds to a distortion of the second image;
Verbatim
determining a first retranslated image by translating the first translated image such that a distortion of the first retranslated image corresponds to a distortion of the first image; and
determining a first retranslated image by translating the first translated image such that a distortion of the first retranslated image corresponds to a distortion of the first image; and
Verbatim
training a first deformation field generator configured to determine a first relative deformation field that represents a relative deformation from the first image to the second image, based on a loss between the first retranslated image and the first image.
training a first deformation field generator configured to determine a first relative deformation field that represents a relative deformation from the first image to the second image and a second deformation field generator configured to determine a second relative deformation field that represents a relative deformation from the second image to the first image, based on a loss between the first retranslated image and the first image.
Instant application more broad
Claim 2: Regarding claim 2, claim 2 compares to claim 2 of the US ‘917 patent as indicated below. As seen in the chart below, claim 2 of the instant application is more broad in scope (when also incorporating claim dependency) than claim 2 of the US ‘917 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 2 of the US ‘917 patent.
Instant Application - Claim 2
US '917 - Claim 2
Notes
The method of claim 1, further comprising:
The method of claim 1, further comprising:
Verbatim
determining a second translated image by translating the second image such that the distortion of the second image corresponds to the distortion of the first image;
determining a second translated image by translating the second image such that the distortion of the second image corresponds to the distortion of the first image;
Verbatim
determining a second retranslated image by translating the second translated image such that a distortion of the second translated image corresponds to the distortion of the second image; and
determining a second retranslated image by translating the second translated image such that a distortion of the second translated image corresponds to the distortion of the second image; and
Verbatim
training the first deformation field generator based on a loss between the second retranslated image and the second image.
training the first deformation field generator and the second deformation field generator based on a loss between the second retranslated image and the second image.
Instant application more broad
Claim 3: Regarding claim 3, claim 3 compares to claim 3 of the US ‘917 patent as indicated below. As seen in the chart below, claim 3 of the instant application is more broad in scope (when also incorporating claim dependency) than claim 3 of the US ‘917 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 3 of the US ‘917 patent.
Instant Application - Claim 3
US '917 - Claim 3
Notes
The method of claim 1, wherein an initial parameter of the first deformation field generator is determined through training based on a loss between the first translated image and a third translated image determined from the first image based on a fisheye simulation.
The method of claim 1, wherein an initial parameter of the first deformation field generator is determined through training based on a loss between the first translated image and a third translated image determined from the first image based on a fisheye simulation.
Instant application more broad
Claim 4: Regarding claim 4, claim 4 compares to claim 4 of the US ‘917 patent as indicated below. As seen in the chart below, claim 4 of the instant application is more broad in scope (when also incorporating claim dependency) than claim 4 of the US ‘917 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 4 of the US ‘917 patent.
Instant Application - Claim 4
US '917 - Claim 1/4
Notes
wherein the second relative deformation field represents a relative deformation from the second image to the first image.
Claim 1: a second relative deformation field that represents a relative deformation from the second image to the first image
verbatim
The method of claim 1, wherein the first relative deformation field and a second relative deformation field have an inverse deformation relationship, and
Claim 4: The method of claim 1, wherein the first relative deformation field and the second relative deformation field have an inverse deformation relationship.
Instant application more broad
Claim 5: Regarding claim 5, claim 5 compares to claim 5 of the US ‘917 patent as indicated below. As seen in the chart below, claim 5 of the instant application is more broad in scope (when also incorporating claim dependency) than claim 5 of the US ‘917 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 5 of the US ‘917 patent.
Instant Application - Claim 5
US '917 - Claim 5
Notes
The method of claim 1, wherein the determining of the first translated image comprises:
The method of claim 1, wherein the determining of the first translated image comprises:
Verbatim
determining the first translated image having the distortion of the second image by applying the first relative deformation field to the first image.
determining the first translated image having the distortion of the second image by applying the first relative deformation field to the first image.
Verbatim
Claim 6: Regarding claim 6, claim 6 compares to claim 6 of the US ‘917 patent as indicated below. As seen in the chart below, claim 6 of the instant application is more broad in scope (when also incorporating claim dependency) than claim 6 of the US ‘917 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 6 of the US ‘917 patent.
Instant Application - Claim 6
US '917 - Claim 6
Notes
The method of claim 1, wherein the determining of the first retranslated image comprises:
The method of claim 1, wherein the determining of the first retranslated image comprises:
verbatim
determining the first retranslated image having the distortion of the first image by applying a second relative deformation field to the first translated image,
determining the first retranslated image having the distortion of the first image by applying the second relative deformation field to the first translated image.
verbatim
wherein the second relative deformation field represents a relative deformation from the second image to the first image.
Claim 1: a second relative deformation field that represents a relative deformation from the second image to the first image
verbatim
Claim 7: Regarding claim 7, claim 7 compares to claim 7 of the US ‘917 patent as indicated below. As seen in the chart below, claim 7 of the instant application is more broad in scope (when also incorporating claim dependency) than claim 7 of the US ‘917 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 7 of the US ‘917 patent.
Instant Application - Claim 7
US '917 - Claim 7
Notes
The method of claim 1, wherein the first image and the second image are unpaired images comprising either one or both of different contents and different scenes.
The method of claim 1, wherein the first image and the second image are unpaired images comprising either one or both of different contents and different scenes.
verbatim
Claim 8: Regarding claim 8, claim 8 compares to claim 8 of the US ‘917 patent as indicated below. As seen in the chart below, claim 8 of the instant application is more broad in scope (when also incorporating claim dependency) than claim 8 of the US ‘917 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 8 of the US ‘917 patent.
Instant Application - Claim 8
US '917 - Claim 8
Notes
The method of claim 1, wherein the training of the first deformation field generator comprises:
The method of claim 1, wherein the training of the first deformation field generator and the second deformation field generator comprises:
Instant application more broad
training the first deformation field generator through unsupervised learning without information associated with the distortions of the first image and the second image.
training the first deformation field generator and the second deformation field generator through unsupervised learning without information associated with the distortions of the first image and the second image.
Instant application more broad
Claim 9: Regarding claim 9, claim 9 compares to claim 9 of the US ‘917 patent as indicated below. As seen in the chart below, claim 9 of the instant application is more broad in scope (when also incorporating claim dependency) than claim 9 of the US ‘917 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 9 of the US ‘917 patent.
Instant Application - Claim 9
US '917 - Claim 9
Notes
The method of claim 1, further comprising: training an inference model for the second image based on the first translated image and a translated label image determined by translating a label image corresponding to the first image such that a distortion of the label image corresponds to the distortion of the second image.
The method of claim 1, further comprising: training an inference model for the second image based on the first translated image and a translated label image determined by translating a label image corresponding to the first image such that a distortion of the label image corresponds to the distortion of the second image.
verbatim
Claim 10: Regarding claim 10, claim 10 compares to claim 10 of the US ‘917 patent as indicated below. As seen in the chart below, claim 10 of the instant application is more broad in scope (when also incorporating claim dependency) than claim 10 of the US ‘917 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 10 of the US ‘917 patent.
Instant Application - Claim 10
US '917 - Claim 10
Notes
The method of claim 1, further comprising: training an inference model for the second image through an unsupervised domain adaptation using the second image, a translated label image determined by translating a label image corresponding to the first image such a distortion of that the label image corresponds to the distortion of the second image, and a fourth translated image determined by translating the first image such that the distortion of the first image corresponds to the distortion and a texture of the second image.
The method of claim 1, further comprising: training an inference model for the second image through an unsupervised domain adaptation using the second image, a translated label image determined by translating a label image corresponding to the first image such a distortion of that the label image corresponds to the distortion of the second image, and a fourth translated image determined by translating the first image such that the distortion of the first image corresponds to the distortion and a texture of the second image.
verbatim
Claim 11: Regarding claim 11, claim 11 compares to claim 11 of the US ‘917 patent as indicated below. As seen in the chart below, claim 11 of the instant application is more broad in scope (when also incorporating claim dependency) than claim 11 of the US ‘917 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 11 of the US ‘917 patent.
Instant Application - Claim 11
US '917 - Claim 11
Notes
The method of claim 1, wherein the second image has no corresponding label image.
The method of claim 1, wherein the second image has no corresponding label image.
verbatim
Claim 12: Regarding claim 12, claim 12 compares to claim 12 of the US ‘917 patent as indicated below. As seen in the chart below, claim 12 of the instant application is more broad in scope (when also incorporating claim dependency) than claim 12 of the US ‘917 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 12 of the US ‘917 patent.
Instant Application - Claim 12
US '917 - Claim 12
Notes
A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, configure the one or more processors to perform the method of claim 1.
A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, configure the one or more processors to perform the method of claim 1.
verbatim
Claim 13: Regarding claim 13, claim 13 compares to claim 13 of the US ‘917 patent as indicated below. As seen in the chart below, claim 13 of the instant application is more broad in scope than claim 13 of the US ‘917 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 13 of the US ‘917 patent.
Instant Application - Claim 13
US '917 - Claim 13
Notes
A processor-implemented method with data processing using a neural network, the method comprising:
A processor-implemented method with data processing using a neural network, the method comprising:
Verbatim
determining, using a trained first deformation field generator, a relative deformation field that represents a relative deformation from a source image to a target image based on the source image and the target image that have different distortions; and
determining, using a trained first deformation field generator, a relative deformation field that represents a relative deformation from a source image to a target image based on the source image and the target image that have different distortions; and
verbatim
determining a translated source image having a distortion of the target image by applying the relative deformation field to the source image,
determining a translated source image having a distortion of the target image by applying the relative deformation field to the source image,
Verbatim
wherein the first deformation field generator is trained based on a loss between a first retranslated image and a first image, and the first retranslated image is determined by translating a first translated image such that a distortion of the first retranslated image corresponds to a distortion of the first image.
wherein the first deformation field generator is trained based on a loss between a first retranslated image and a first image, and the first retranslated image is determined by translating a first translated image using a second deformation field generator such that a distortion of the first retranslated image corresponds to a distortion of the first image.
Instant application more broad
Claim 14: Regarding claim 14, claim 14 compares to claim 14 of the US ‘917 patent as indicated below. As seen in the chart below, claim 14 of the instant application is more broad in scope than claim 14 of the US ‘917 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 14 of the US ‘917 patent.
Instant Application - Claim 14
US '917 - Claim 14
Notes
A device with data processing, comprising:
A device with data processing, comprising:
Verbatim
one or more processors configured to:
one or more processors configured to:
Verbatim
determine a first translated image by translating a first image based on a second image, the first image and a second image having different distortions, such that a distortion of the first translated image corresponds to a distortion of the second image;
determine a first translated image by translating a first image based on a second image, the first image and a second image having different distortions, such that a distortion of the first translated image corresponds to a distortion of the second image;
verbatim
determine a first retranslated image by translating the first translated image such that a distortion of the first retranslated image corresponds to a distortion of the first image; and
determine a first retranslated image by translating the first translated image such that a distortion of the first retranslated image corresponds to a distortion of the first image; and
verbatim
train a first deformation field generator configured to determine a first relative deformation field that represents a relative deformation from the first image to the second image or a second deformation field generator configured to determine a second relative deformation field that represents a relative deformation from the second image to the first image, based on a loss between the first retranslated image and the first image.
train a first deformation field generator configured to determine a first relative deformation field that represents a relative deformation from the first image to the second image and a second deformation field generator configured to determine a second relative deformation field that represents a relative deformation from the second image to the first image, based on a loss between the first retranslated image and the first image.
Instant application more broad
Claim 15: Regarding claim 15, claim 15 compares to claim 15 of the US ‘917 patent as indicated below. As seen in the chart below, claim 15 of the instant application is more broad in scope (when also incorporating claim dependency) than claim 15 of the US ‘917 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 15 of the US ‘917 patent.
Instant Application - Claim 15
US '917 - Claim 15
Notes
The device of claim 14, wherein the one or more processors are configured to:
The device of claim 14, wherein the one or more processors are configured to:
verbatim
determine a second translated image by translating the second image such that the distortion of the second image corresponds to the distortion of the first image;
determine a second translated image by translating the second image such that the distortion of the second image corresponds to the distortion of the first image;
verbatim
determine a second retranslated image by translating the second translated image such that a distortion of the second translated image corresponds to the distortion of the second image; and
determine a second retranslated image by translating the second translated image such that a distortion of the second translated image corresponds to the distortion of the second image; and
verbatim
train the first deformation field generator or the second deformation field generator based on a loss between the second retranslated image and the second image.
train the first deformation field generator and the second deformation field generator based on a loss between the second retranslated image and the second image.
Instant application more broad
Claim 16: Regarding claim 16, claim 16 compares to claim 16 of the US ‘917 patent as indicated below. As seen in the chart below, claim 16 of the instant application is more broad in scope (when also incorporating claim dependency) than claim 16 of the US ‘917 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 16 of the US ‘917 patent.
Instant Application - Claim 16
US '917 - Claim 16
Notes
The device of claim 15, wherein an initial parameter of the first deformation field generator is determined through training based on a loss between the first translated image and a third translated image determined from the first image based on a fisheye simulation.
The device of claim 15, wherein an initial parameter of the first deformation field generator is determined through training based on a loss between the first translated image and a third translated image determined from the first image based on a fisheye simulation.
verbatim
Claim 17: Regarding claim 17, claim 17 compares to claim 17 of the US ‘917 patent as indicated below. As seen in the chart below, claim 17 of the instant application is more broad in scope (when also incorporating claim dependency) than claim 17 of the US ‘917 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 17 of the US ‘917 patent.
Instant Application - Claim 17
US '917 - Claim 17
Notes
The device of claim 14, wherein the first relative deformation field and the second relative deformation field have an inverse deformation relationship.
The device of claim 14, wherein the first relative deformation field and the second relative deformation field have an inverse deformation relationship.
Verbatim
Claim 18: Regarding claim 18, claim 18 compares to claim 18 of the US ‘917 patent as indicated below. As seen in the chart below, claim 18 of the instant application is more broad in scope (when also incorporating claim dependency) than claim 18 of the US ‘917 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 18 of the US ‘917 patent.
Instant Application - Claim 18
US '917 - Claim 18
Notes
The device of claim 14, wherein, for the determining of the first translated image, the one or more processors are configured to:
The device of claim 14, wherein, for the determining of the first translated image, the one or more processors are configured to:
Verbatim
determine the first translated image having the distortion of the second image by applying the first relative deformation field to the first image.
determine the first translated image having the distortion of the second image by applying the first relative deformation field to the first image.
verbatim
Claim 19: Regarding claim 19, claim 19 compares to claim 19 of the US ‘917 patent as indicated below. As seen in the chart below, claim 19 of the instant application is more broad in scope (when also incorporating claim dependency) than claim 19 of the US ‘917 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 19 of the US ‘917 patent.
Instant Application - Claim 19
US '917 - Claim 19
Notes
The device of claim 14, wherein the one or more processors are configured to:
The device of claim 14, wherein the one or more processors are configured to:
verbatim
determine the first retranslated image having the distortion of the first image by applying the second relative deformation field to the first translated image.
determine the first retranslated image having the distortion of the first image by applying the second relative deformation field to the first translated image.
verbatim
Claim 20: Regarding claim 20, claim 20 compares to claim 20 of the US ‘917 patent as indicated below. As seen in the chart below, claim 20 of the instant application is more broad in scope (when also incorporating claim dependency) than claim 20 of the US ‘917 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 20 of the US ‘917 patent.
Instant Application - Claim 20
US '917 - Claim 20
Notes
The device of claim 14, wherein the device is at least one of a mobile phone, a smartphone, a personal digital assistant (PDA), a netbook, a tablet computer, a laptop, a mobile device, a smartwatch, a smart band, smart eyeglasses, a wearable device, a desktop, a server, a computing device, a television (TV), a smart TV, a refrigerator, a home appliance, a door lock, a security device, and a vehicle.
The device of claim 14, wherein the device is at least one of a mobile phone, a smartphone, a personal digital assistant (PDA), a netbook, a tablet computer, a laptop, a mobile device, a smartwatch, a smart band, smart eyeglasses, a wearable device, a desktop, a server, a computing device, a television (TV), a smart TV, a refrigerator, a home appliance, a door lock, a security device, and a vehicle.
verbatim
Allowable Subject Matter
Claims 3, 9-10 and 16 would be allowable if rewritten to overcome the non-statutory double patenting rejection(s) set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Claims 3 and 16:
The following is a statement of reasons for the indication of allowable subject matter: the closest prior arts of record teach methods of utilizing deformation field generators to determine deformation between two images.
However, none of them alone or in any combination teaches determining an initial parameter of a first deformation field generator based on training using a loss between the first translated image and a third translated image determined from the first image based on a fisheye simulation.
The closest prior art being Kim discloses at page 2, left column, “More specifically, we train two convolutional neural networks (CNN), G X and G Y , that generate forward and reverse directional deformation vector fields, respectively.” Further, Kim discloses utilizing a loss function at page 3, left column, “the registration networks can be trained by mini-mizing the loss function on the deformed image and fixed image.”
However, Kim fails to disclose determining an initial parameter of a first deformation field generator based on training using a loss between the first translated image and a third translated image determined from the first image based on a fisheye simulation.
Claim 9:
The following is a statement of reasons for the indication of allowable subject matter: the closest prior arts of record teach methods of utilizing neural networks to analyze deformation fields between images.
However, none of them alone or in any combination teaches training an inference model for the second image using the translated image and the translated label image which is determined by translating a label image corresponding to the first image such that a distortion of the label image corresponds to the distortion of the second image.
The closest prior art Kim discloses page 2, left column, “More specifically, we train two convolutional neural networks (CNN), G X and G Y , that generate forward and reverse directional deformation vector fields, respectively.”
However, Kim fails to disclose training an inference model for the second image using the translated image and the translated label image which is determined by translating a label image corresponding to the first image such that a distortion of the label image corresponds to the distortion of the second image.
Claim 10:
The following is a statement of reasons for the indication of allowable subject matter: the closest prior arts of record teach methods of utilizing neural networks to analyze deformation fields between images.
However, none of them alone or in any combination teaches training a model for the second image using unsupervised domain adaptation of the second image, a translated label image determined by translating a label image corresponding to the first image such a distortion of that the label image corresponds to the distortion of the second image, and a fourth translated image determined by translating the first image such that the distortion of the first image corresponds to the distortion and a texture of the second image.
The closest prior art Kim discloses page 2, left column, “More specifically, we train two convolutional neural networks (CNN), G X and G Y , that generate forward and reverse directional deformation vector fields, respectively.”
However, Kim fails to disclose training a model for the second image using unsupervised domain adaptation of the second image, a translated label image determined by translating a label image corresponding to the first image such a distortion of that the label image corresponds to the distortion of the second image, and a fourth translated image determined by translating the first image such that the distortion of the first image corresponds to the distortion and a texture of the second image.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Publication No. 2020/0020082 to Zahneisen et al. discloses, “ feed-forward estimating by a convolutional neural network (CNN) a phase distortion map from the acquired images; where the CNN is trained to minimize a similarity metric between un-warped up/down image pairs; and performing geometric distortion correction of the acquired images using the phase distortion map to unwarp the acquired images (abstract).”
Contact
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/COURTNEY JOAN NELSON/Primary Examiner, Art Unit 2661
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