CTNF 18/811,049 CTNF 90895 DETAILED ACTION 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. Claims 1-9 are pending. Priority 02-25 Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Japan on 1/29/2024. It is noted, however, that applicant has not filed a certified copy of the application JP2024-011331 as required by 37 CFR 1.55. Claim Interpretation - 35 USC § 112(f) 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as "configured to" or "so that"; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. 07-30-06 This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or preAIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: Claim 1: “a model generation unit that…”. Claim 7: “a data generation unit that…”. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or preAIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 103 07-20-fti The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action: (a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made. 07-21-aia AIA Claim (s) 1 and 5-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Terjek (US20200372297A1) in view of Khoreva et al (US20220262106A1) . Regarding claims 1 , 8 and 9 , Terjek teaches an information processing device comprising a model generation unit that generates a generative adversarial network including a discriminator and a generator, wherein the model generation unit ( Terjek , "method for training a generative adversarial network, in particular a Wasserstein generative adversarial network. The generative adversarial network includes a generator and a discriminator", [Abstract]; Terjek teaches a unit or computer program that generates and trains a generative adversarial network comprising both a discriminator and a generator) separates the discriminator into a feature extraction network that generates, from data input to the discriminator, feature vectors of the data and a last layer in which the feature vectors distributed in a feature vector space are applied to a one-dimensional space, and Terjek does not expressly disclose but Khoreva teaches: ( Khoreva , "the discriminator is configured to determine the intermediate representation of the provided image by forwarding the provided image through a first plurality of layers which then determines the intermediate representation.", [0034]; "The output of the global pooling layer is hence a vector, wherein each component of the vector is the globally pooled value of a feature map of the intermediate representation (i).", [0057]; "The last layer (S2) is preferably a fully connected layer configured to determine the content value (yc) based on the output of the last residual block (R2) of the second neural network.", [0059]; separating a discriminator into a first plurality of layers that generate intermediate feature representations which are pooled into vectors, and a last layer that applies this to a 1-dimensional content value) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the teachings of Khoreva into the system or method of Terjek in order to extract robust intermediate representations and improve the discriminator's evaluation accuracy at different scales. The combination of Terjek and Khoreva also teaches other enhanced capabilities. The combination of Terjek and Khoreva further teaches: trains each of the feature extraction network and the last layer, and thereby generates a metrizable discriminator that is a discriminator capable of evaluating a distance between a probability distribution of generated feature vectors that are feature vectors of generated data generated by the generator and a probability distribution of real feature vectors that are feature vectors of real data included in a data set for training. ( Terjek , "the discriminator approximates or learns the Wasserstein distance between a real probability distribution (represented by the predefined dataset) and the generated probability distribution", [0028]; Khoreva , "an intermediate loss value is determined for each residual block (R1,R2,R3) and for the last layer (S2) of the second neural network", [0064]; Terjek teaches training the discriminator to act as a metrizable discriminator capable of evaluating the Wasserstein distance between real and generated probability distributions. Khoreva teaches training each intermediate feature extraction layer and the last layer. Applying Terjek 's distance metric to the probability distributions evaluated at the feature vector representations learned by the architecture of Khoreva would optimize the discriminator's layer-wise evaluation capabilities) Regarding claim 5 , the combination of Terjek and Khoreva teaches its/their respective base claim(s). The combination further teaches the information processing device according to claim 1, wherein the model generation unit generates the metrizable discriminator by separating a loss function of the discriminator into a loss function of the feature extraction network and a loss function of the last layer to learn a value of a parameter of the feature extraction network and a value of a parameter of the last layer. ( Khoreva , "an intermediate loss value is determined for each residual block (R1,R2,R3) and for the last layer (S2) of the second neural network", [0064]; splitting the loss evaluation such that intermediate loss values are calculated for each feature extraction block and the last layer independently) Regarding claim 6 , the combination of Terjek and Khoreva teaches its/their respective base claim(s). The combination further teaches the information processing device according to claim 1, wherein the model generation unit uses the metrizable discriminator to generate the generator trained to reduce a distance between the probability distribution of the generated feature vectors and the probability distribution of the real feature vectors. ( Terjek , "generator g is trained to minimize this loss function.", [0044]; "the approximated Wasserstein distance between probability distribution P r of the training dataset and probability distribution P g of generator 110 is minimized.", [0047]; training the generator to minimize the approximated Wasserstein distance between the respective probability distributions of the real data and generated data) Regarding claim 7 , the combination of Terjek and Khoreva teaches its/their respective base claim(s). The combination further teaches the information processing device according to claim 1, further comprising a data generation unit that uses the generator generated by the model generation unit to generate the generated data from random vectors. ( Terjek , "The input that the generator typically receives is random noise.", [0004]; a generator receives random noise/vectors as input to generate the resulting data) 07-21-aia AIA Claim (s) 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Terjek (US20200372297A1) in view of Khoreva et al (US20220262106A1) and further in view of Chen et al (CN111445007A) . Regarding claim 2 , the combination of Terjek and Khoreva teaches its/their respective base claim(s). The combination does not expressly disclose but Chen teaches the information processing device according to claim 1, wherein a parameter of the last layer is a parameter related to a direction in which the probability distribution of the generated feature vectors and ( Chen , "taking the boundary vector M as output parameters of an output layer of the initial discrimination neural network", p11; using a boundary vector M as an output parameter of the last layer which relates to the multidimensional mapping of the outputs) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the teachings of Chen into the modified system or method of Terjek and Khoreva in order to parameterize the distribution mappings along a specific multidimensional boundary. The combination of Terjek, Khoreva and Chen also teaches other enhanced capabilities. The combination of Terjek, Khoreva and Chen further teaches: the probability distribution of the real feature vectors distributed in the feature vector space are separated, and ( Chen , "the boundary vector M is a multidimensional vector and is used for mapping the output of the initial discrimination neural network from a single dimension to a multidimensional corresponding to the boundary vector M", p11; mapping the outputs into multidimensional vectors corresponding to a boundary, thereby defining the spatial separation of the real and generated features in the vector space) the model generation unit generates the metrizable discriminator by training the last layer to take a value of a parameter corresponding to the direction in which a distance between the probability distribution of the generated feature vectors and ( Chen , "LD＝E[max(0,M-D(x))]+E[max(0,M+D(G(z)))]", p3; "updating the parameters of the initial confrontation generation neural network according to the discrimination loss value", p3; training the network based on a loss function that maximizes the margin (distance) defined by the boundary vector M between the real and generated data) the probability distribution of the real feature vectors is increased. ( Chen , "the output of the confrontation-generated neural network is mapped into the high-dimensional output through the multidimensional boundary vector, so that the confrontation-generated neural network can be trained in different dimensions and boundaries, a larger convergence gradient is obtained", p9; expanding the distance gradient between the probability distributions using the boundary vector to improve training stability) Regarding claim 3 , the combination of Terjek and Khoreva teaches its/their respective base claim(s). The combination of Terjek, Khoreva and Chen further teaches the information processing device according to claim 1, wherein the model generation unit generates the metrizable discriminator by training the feature extraction network ( Terjek and Khoreva , see comments on claim 1) so that if the probability distribution of the generated feature vectors is moved in a specific direction, the probability distribution of the generated feature vectors and the probability distribution of the real feature vectors overlap each other. ( Chen , "mapping a first output corresponding to the real sample set into a first multi-dimensional vector corresponding to the boundary vector", P12; "mapping a second output corresponding to the false sample set into a second multi-dimensional vector corresponding to the boundary vector", p17; mapping both real and fake sample distributions with respect to the specific multidimensional boundary vectors, meaning shifting the samples along these specific boundary vectors bridges the gap separating them, causing them to overlap) 07-21-aia AIA Claim (s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Terjek (US20200372297A1) in view of Khoreva et al (US20220262106A1) and further in view of Kim et al (US20230125839A1) . Regarding claim 4 , the combination of Terjek and Khoreva teaches its/their respective base claim(s). The combination does not expressly disclose but Kim teaches the information processing device according to claim 1, wherein the model generation unit generates the metrizable discriminator by training the feature extraction network such that the data and the feature vectors have a one-to-one correspondence. ( Kim , "The invertible neural network may include a first artificial neural network to generate an original data latent vector from the original data embedding vector, and a second artificial neural network to generate an estimated data embedding vector from the original data latent vector, wherein the first artificial neural network and the second artificial neural network are in an inverse function relation.", [0007]; "The invertible neural network may be a neural network that is capable of inferring an input-output relationship bidirectionally. For example, in the case that the generator is expressed as x=G(z) that receives a latent vector z as an input and generates data record x, the calculation of z=G−1(x), which is performed in inverse direction, is available.", [0037]; extracting features using an invertible neural network that guarantees a bidirectional, inverse mathematical relationship between the data records and the latent vectors, which explicitly requires a bijection (one-to-one mapping) between the data and the feature vectors) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the teachings of Kim into the modified system or method of Terjek and Khoreva in order to guarantee exact feature reconstruction through one-to-one correspondence in the extraction network. The combination of Terjek, Khoreva and Kim also teaches other enhanced capabilities. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIANXUN YANG whose telephone number is (571)272-9874. The examiner can normally be reached on MON-FRI: 8AM-5PM 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, Amandeep Saini can be reached on (571)272-3382. 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. /JIANXUN YANG/ Primary Examiner, Art Unit 2662 4/28/2026 Application/Control Number: 18/811,049 Page 2 Art Unit: 2662 Application/Control Number: 18/811,049 Page 3 Art Unit: 2662 Application/Control Number: 18/811,049 Page 4 Art Unit: 2662 Application/Control Number: 18/811,049 Page 5 Art Unit: 2662 Application/Control Number: 18/811,049 Page 6 Art Unit: 2662 Application/Control Number: 18/811,049 Page 7 Art Unit: 2662 Application/Control Number: 18/811,049 Page 8 Art Unit: 2662 Application/Control Number: 18/811,049 Page 9 Art Unit: 2662 Application/Control Number: 18/811,049 Page 10 Art Unit: 2662 Application/Control Number: 18/811,049 Page 11 Art Unit: 2662 Application/Control Number: 18/811,049 Page 12 Art Unit: 2662 Application/Control Number: 18/811,049 Page 13 Art Unit: 2662 Application/Control Number: 18/811,049 Page 14 Art Unit: 2662
Prosecution Insights