CTFR 18/418,197 CTFR 89224 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Response to Amendment In the Reply filed 03/26/2026, Applicant has amended claims 1 and 11 to include “responsive to predicting that the random one of the plurality of target training images or the translated target images belongs to the target domain or was translated from the source domain to the target domain, determining…an adversarial loss term”, and “wherein the adversarial loss term is applied in the target domain” and argues that this/those limitation(s) was/were not taught with the reference(s) cited in the previous action dated 03/26/2026. However, the Examiner respectfully disagrees for the reasons laid out below. Response to Arguments 07-37 AIA Applicant's arguments filed 03/26/2026 specifically regarding the amendments noted above have been fully considered but they are not persuasive. As noted above, Applicant has amended the independent claims to include (1) “the source training dataset and the target training dataset are unpaired”, (2) “responsive to predicting that the random one of the plurality of target training images or the translated target images belongs to the target domain or was translated from the source domain to the target domain, determining…an adversarial loss term”, and (3) “wherein the adversarial loss term is applied in the target domain” and argues that those limitations were not taught in previous reference Tripathy. Regarding (1), the previously cited portions of Tripathy, specifically regarding Figure 2, show data in source domain x and target domain y are unpaired for the phase-2 training. Regarding (2), Figure 2 shows a “y” sample and the output from generator G1 being randomly fed into discriminator D1 and section 3.2 states “Generator networks are trained using adversarial loss that is implemented by two discriminator networks D1 and D2. D1 aims to distinguish between transformation results G1(x) and given training samples y ∈ Y” which indicates that D1 is to predict if it received a true target sample y or a generated sample G1(xi), and based on this the adversarial loss is formed. Finally regarding (3), the adversarial loss equation of Tripathy shown on page 5 is equivalent to that shown in paragraph [0040] of the present published application which is described in said paragraph as being “applied in the target domain”. Therefore, these amendments appear to be taught by the previously cited reference. Applicant’s arguments with respect to claim(s) 1 and 11 specifically regarding the amendments related to the “data processing hardware of a backend component” and “client computer” have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-6, 8, 11-16, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tripathy et al, “Learning image-to-image translation using paired and unpaired training samples” (published at https://arxiv.org/pdf/1805.03189, May 2018) in view of Godard et al, U.S. Publication No. 2019/0356905 . Regarding claim 11 , Tripathy teaches a system configured to perform operations comprising: obtaining a source training dataset sampled from a source domain, the source training dataset comprising a plurality of source training images (see Tripathy Figure 2, “phase-2” for unpaired data, wherein “x” samples are from the source domain and section 2, referring to “Image-to-image translation using unpaired training data” ) ; obtaining a target training dataset sampled from a target domain, the target training dataset comprising a plurality of target training images (see Figure 2, wherein “y” samples are from the target domain) , wherein the source training data set and the target training data set are unpaired (see Figure 2, phase-2 training with unpaired data) ; for each respective source training image in the source training dataset, translating, using a forward generator neural network, the respective source training image to a respective translated target image in the target domain (see Figure 2, sample xi being fed to G1 and section 3.2, “The generator G1 provides a mapping from domain X to domain Y denoted as G1 : X → Y (e.g. semantic map to photograph)” ) ; receiving, as input to a target discriminator network, a random one of the plurality of target training images or the translated target images (see Figure 2, output G1(xi) or “y” being fed to D1) ; predicting, using the target discriminator network, that the random one of the plurality of target training images or the translated target images belongs to the target domain or was translated from the source domain to the target domain; responsive to predicting that the random one of the plurality of target training images or the translated target images belongs to the target domain or was translated from the source domain to the target domain, determining an adversarial loss term representing how accurately the forward generator neural network generates an output that can be considered as being from the target domain (see section 3.2, “Generator networks are trained using adversarial loss that is implemented by two discriminator networks D1 and D2. D1 aims to distinguish between transformation results G1(x) and given training samples y ∈ Y” and explanation given above) , wherein the adversarial loss term is applied in the target domain (see page 5, adversarial loss equation which is equivalent to that given in paragraph [0040] of the published application) ; and jointly training based on the adversarial loss term, the forward generator neural network and the target discriminator network (see section 2, first paragraph, “The generator network produces output samples which should ideally be indistinguishable from the training distribution. Since it is infeasible to engineer a proper loss function, the assessment is done by the discriminator network. Both networks are differentiable, which allows to train them jointly” and equation (1) which shows a joint loss function for G1 and D1) . Tripathy does not expressively teach data processing hardware of a backend component; and memory hardware in communication with the data processing hardware, the memory hardware storing instructions that when executed on the data processing hardware cause the data processing hardware to perform the method steps; the source and target training datasets are obtained from a client computer; and wherein the backend component is remote from the client computer and interacts with the client computer over a communication network. However, Godard in a similar invention in the same field of endeavor teaches a system configured to train a network with a training dataset (see Godard Figure 1, depth estimation training system 170 and paragraph [0044]) as taught in Tripathy comprising data processing hardware of a backend component (see Figure 1, game server 120) ; and memory hardware in communication with the data processing hardware, the memory hardware storing instructions that when executed on the data processing hardware cause the data processing hardware to perform method steps (see paragraph [0087]) ; the training data set is obtained from a client computer (see Figure 1, client device 110 and paragraph [0068]) ; and wherein the backend component is remote from the client computer and interacts with the client computer over a communication network (see Figure 1, network 105 and paragraph [0031]) . One of ordinary skill in the art before the effective filing date of the invention would have found it obvious to combine the teaching of training a network via a backend component with training data collected from a client computer as taught in Godard with the system taught in Tripathy, the motivation being to allow more powerful centralized training via the backend component with real world images collected via such client computers. Method claim 1 recites similar limitations as claim 11, and is rejected under similar rationale. Regarding claim 12 , Tripathy in view of Godard teaches all the limitations of claim 11, and further teaches wherein the instructions further cause the data processing hardware to, for each respective target training image in the target training dataset, translate, using a backward generator neural network, the respective target training image to a respective translated source image in the source domain (see Tripathy Figure 2, sample yj being fed into G2 in the “backward cycle” of phase-2 for unpaired data and section 3.2, “whereas the second generator learns the inverse of this mapping denoted as G2 : Y → X (e.g. photograph to semantic map)” ) . Method claim 2 recites similar limitations as claim 12, and is rejected under similar rationale. Regarding claim 13 , Tripathy in view of Godard teaches all the limitations of claim 12, and further teaches wherein the instructions further cause the data processing hardware to: receive, as input to a source discriminator network, a random one of the plurality of source training images or the translated source images (see Tripathy Figure 2, output G2(yi) or “x” being fed to D2 and caption which indicates D2 is a discriminator) ; and predict, using the source discriminator network, that the random one of the plurality of source training images or the translated source images belongs to the source domain or was translated from the target domain to the source domain (see Tripathy section 3.2, “D1 aims to distinguish between transformation results G1(x) and given training samples y ∈ Y. D2 works similarly for G2(y) and x ∈ X” ) . Method claim 3 recites similar limitations as claim 13, and is rejected under similar rationale. Regarding claim 14 , Tripathy in view Godard teaches all the limitations of claim 13, and further teaches wherein instructions further cause the data processing hardware to: responsive to predicting that the random one of the plurality of source training images or the translated source images belongs to the source domain or was translated from the target domain to the source domain, determine a second adversarial loss term, the second adversarial loss term representing how accurately the backward generator neural network generates an output that can be considered as being from the source domain (see Tripathy section 3.2, “Generator networks are trained using adversarial loss that is implemented by two discriminator networks D1 and D2” ) ; and jointly train the backward generator neural network and the source discriminator network based on the second adversarial loss term (see Tripathy section 2, first paragraph, “The generator network produces output samples which should ideally be indistinguishable from the training distribution. Since it is infeasible to engineer a proper loss function, the assessment is done by the discriminator network. Both networks are differentiable, which allows to train them jointly” and section 3.2, “The corresponding objective LGAN(G2(Y ),D2(X)) for G2 and D2 is obtained similarly” ) . Method claim 4 recites similar limitations as claim 14, and is rejected under similar rationale. Regarding claim 15 , Tripathy in view of Godard teaches all the limitations of claim 12, and further teaches determining a circularity loss component that ensures the forward generator neural network and the backward generator neural network are a pair of inverse translation operations (see Tripathy section 3.3 “Following [4], we circumvent the problem by adding a cycle consistency loss that encourages the cascaded mapping to reproduce the original input. Mathematically, G2(G1(x)) ≈ x and G1(G2(y)) ≈ y, where x ∈ X and y ∈ Y . The intuition behind the cycle consistency is that if we translate from one domain to another and then back again we should arrive where we started (e.g. translating a sentence from one language to another and then back again)” ) . Method claim 5 recites similar limitations as claim 15, and is rejected under similar rationale. Regarding claim 16 , Tripathy in view of Godard teaches all the limitations of claim 15, and further teaches wherein the circularity loss component further ensures: a source training image from the source training dataset can be translated to a circularly-translated source image that is the same as the source training image after the source training image is translated by the forward generator neural network and subsequently by the backward generator neural network; and a target training image from the target training dataset can be translated to a circularly-translated target image that is the same as the target training image after the target training image is translated by the backward generator neural network and subsequently the forward generator neural network (see Tripathy Figure 2, xr and yr output from G2 and G1 in phase-2 being fed along with xi and yi into D3 and D4, respectively, and section 3.3, second paragraph) . Method claim 6 recites similar limitations as claim 16, and is rejected under similar rationale. Regarding claim 18 , Tripathy in view of Godard teaches all the limitations of claim 11, and further teaches wherein: the plurality of source training images each comprise a map of a geographic region; and the plurality of target training images each comprise an aerial photo of a corresponding geographic region (see Tripathy Figure 4, second row with caption and section 4.2) . Method claim 8 recites similar limitations as claim 18, and is rejected under similar rationale. Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CASEY L KRETZER whose telephone number is (571)272-5639. The examiner can normally be reached M-F 10:00-7:00 PM Pacific Time. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Payne can be reached at (571)272-3024. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /CASEY L KRETZER/Primary Examiner, Art Unit 2635 Application/Control Number: 18/418,197 Page 2 Art Unit: 2635 Application/Control Number: 18/418,197 Page 3 Art Unit: 2635 Application/Control Number: 18/418,197 Page 4 Art Unit: 2635 Application/Control Number: 18/418,197 Page 5 Art Unit: 2635 Application/Control Number: 18/418,197 Page 6 Art Unit: 2635 Application/Control Number: 18/418,197 Page 7 Art Unit: 2635 Application/Control Number: 18/418,197 Page 8 Art Unit: 2635 Application/Control Number: 18/418,197 Page 9 Art Unit: 2635 Application/Control Number: 18/418,197 Page 10 Art Unit: 2635 Application/Control Number: 18/418,197 Page 11 Art Unit: 2635 Application/Control Number: 18/418,197 Page 12 Art Unit: 2635 Application/Control Number: 18/418,197 Page 13 Art Unit: 2635
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