CTNF 18/382,714 CTNF 97153 DETAILED ACTION Notice of 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. Priority Regarding Japanese Patent App. No. JP2022-174357 (filed 10/31/2022), receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Information Disclosure Statement The information disclosure statement submitted on 10/23/2023 has been considered. 07-30-03-h AIA Claim Interpretation 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. 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) 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): (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). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) 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). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) 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) 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) except as otherwise indicated in an Office action. 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) 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 limitations [and associated disclosure] are: Claim Limitation Applicable Claims Associated Disclosure “feature extraction unit” 1-10 Fig. 4, feature extraction unit 20, paras. 0020-0024 “motion data generation unit” 1-10 Fig. 4, motion generation unit 30, paras. 0020, 0025 “identification feature extraction unit” 3-6, 9 Fig. 4, identification feature extraction unit 40, paras. 0020, 0026 “motion identification feature extraction unit” 4-6 Fig. 4, motion identification feature extraction unit 42, paras. 0020, 0026 “individual identification feature extraction unit” 4-6 Fig. 4, individual identification feature extraction unit 41, paras. 0020, 0026 “learning unit” 8-10 Fig. 4, learning unit 52, para. 0039 Because these claim limitations are being interpreted under 35 U.S.C. 112(f), they 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 these limitations interpreted under 35 U.S.C. 112(f) applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f). Claim Rejections - 35 USC § 112 07-30-01 The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. Claims 4-6 and 8-10 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claims contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, had possession of the claimed invention. 07-31-02 Claims 4-6 and 8-10 are rejected under 35 U.S.C. 112(a) as failing to comply with the enablement requirement. The claims contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. As described in the instant disclosure, each of these claimed components may be implemented using software. (instant specification, paras. 0007, 0020) As to the written description requirement, MPEP 2161.01 I details determining whether there is adequate written description for a computer-implemented functional claim limitation, noting: Similarly, original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsection IV. (emphasis added.) Further as to the written description requirement, MPEP 2163.03 VI details written description circumstances arising from indefiniteness of a means-plus-function limitation, noting: A claim limitation expressed in means- (or step-) plus-function language "shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof." 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. If the specification fails to disclose sufficient corresponding structure, materials, or acts that perform the entire claimed function, then the claim limitation is indefinite because the applicant has in effect failed to particularly point out and distinctly claim the invention as required by 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. In re Donaldson Co., 16 F.3d 1189, 1195, 29 USPQ2d 1845, 1850 (Fed. Cir. 1994) (en banc). Such a limitation also lacks an adequate written description as required by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph , because an indefinite, unbounded functional limitation would cover all ways of performing a function and indicate that the inventor has not provided sufficient disclosure to show possession of the invention. See also MPEP § 2181. (emphasis added.) As to the enablement requirement, MPEP 2164.06(c) II details determining whether there is adequate enablement for block elements within a computer, noting: While no specific universally applicable rule exists for recognizing an insufficiently disclosed application involving computer programs, an examining guideline to generally follow is to challenge the sufficiency of disclosures that fail to include the programmed steps, algorithms or procedures that the computer performs necessary to produce the claimed function. These can be described in any way that would be understood by one of ordinary skill in the art, such as with a reasonably detailed flowchart which delineates the sequence of operations the program must perform. In programming applications where the software disclosure only includes a flowchart, as the complexity of functions and the generality of the individual components of the flowchart increase, the basis for challenging the sufficiency of such a flowchart becomes more reasonable because the likelihood of more than routine experimentation being required to generate a working program from such a flowchart also increases. As stated earlier, once USPTO personnel have advanced a reasonable basis or presented evidence to question the adequacy of a computer system or computer programming disclosure, the applicant must show that the specification would enable one of ordinary skill in the art to make and use the claimed invention without resorting to undue experimentation. In most cases, efforts to meet this burden involve submitting affidavits, referencing prior art patents or technical publications, presenting arguments of counsel, or combinations of these approaches. (emphasis added.) The claimed “individual identification feature extraction unit” is illustrated by the software process performed by element 41 in Fig. 4 and described further in paragraph 0026, noting that the “ individual identification feature extraction unit ” is a “model E” neural network. However, the specification does not provide details about how such model is structured and further does not provide details regarding the claimed elements in the form of an algorithm, software, or other functional code of the computer block disclosure. The claimed “motion identification feature extraction unit” is illustrated by the software process performed by element 42 in Fig. 4 and described further in paragraph 0026, noting that the “ motion identification feature extraction unit ” is a “model D” neural network. However, the specification does not provide details about how such model is structured and further does not provide details regarding the claimed elements in the form of an algorithm, software, or other functional code of the computer block disclosure. The claimed “learning unit” is illustrated by the software process performed by element 52 in Fig. 4 and described further in paragraph 0020, However, the specification does not provide details about how such learning unit is structured and further does not provide details regarding the claimed elements in the form of an algorithm, software, or other functional code of the computer block disclosure. Since the claimed “individual identification feature extraction unit” and “motion identification feature extraction unit” and “ learning unit ” are disclosed as a computer block element, the specification is required to disclose the software, an algorithm, or a flow chart of the claimed elements in sufficient detail. However, the specification only discloses the operations of the claimed limitations at a high level and does not provide the requisite level of detail (e.g., code/algorithm) necessary that would indicate to one of ordinary skill in the art: That the inventor(s) at the time the application was effectively filed, had possession of the claimed inventor; or how to make or use the invention without undue experimentation In sum, claims 4-6 and 8-10 fail to meet the written description requirement of 35 U.S.C. 112a. The lack of disclosure of the code/algorithms to implement the claimed “individual identification feature extraction unit” and “motion identification feature extraction unit” and “ learning unit ” (as detailed above) in a manner understandable to a person of ordinary skill in the art results in a failure to reasonably convey that the inventor(s) at the time the application was effectively filed, had possession of the claimed invention. Further, claims 4-6 and 8-10 fail to meet the enablement requirement of 35 U.S.C. 112a. The lack of disclosure of the code/algorithms to implement the claimed “individual identification feature extraction unit” and “motion identification feature extraction unit” and “ learning unit ” (as detailed above) in a manner understandable to a person of ordinary skill in the art results in claimed subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. Claims 4-6 are rejected under 35 U.S.C. 112(bas being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims 4-6 recite “individual identification feature extraction unit” and “motion identification feature extraction unit” limitations interpreted under 35 U.S.C. 112(f) as noted above. Claims 8-10 recite the “ learning unit ” limitation interpreted under 35 U.S.C. 112(f) as noted above. As described in the instant disclosure, each of these claimed components may be implemented using software. (instant specification, paras. 0007, 0020) As noted above in the rejection under 35 U.S.C. 112a, the specification fails to adequately disclose an algorithm for performing the claimed specific computer function for the claimed “individual identification feature extraction unit” and “motion identification feature extraction unit” and “ learning unit ” limitations. The specification also fails to detail any other structure to perform the claimed “individual identification feature extraction unit” and “motion identification feature extraction unit” and “ learning unit ” limitations. As such, the claimed “individual identification feature extraction unit” and “motion identification feature extraction unit” and “ learning unit ” limitations are indefinite under 35 U.S.C. 112b due to the specification’s failure to adequately disclose the algorithm or structural details. Claim Rejections - 35 USC § 103 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. 07-23-aia AIA 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. 07-21-aia AIA Claim s 1-3 and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over US 20230196712 A1, hereinafter referenced as ASSOULINE , in view of Dong, Yuzhu, et al. "Adult2child: Motion style transfer using cyclegans." Proceedings of the 13th ACM SIGGRAPH Conference on Motion, Interaction and Games . 2020, hereinafter referenced as DONG . Regarding Claim 1 ASSOULINE teaches: An information processing device comprising: (ASSOULINE, para. 0017: “The disclosed techniques improve the efficiency of using the electronic device by using a combination of machine learning techniques (e.g., neural networks) to extract appearance and motion features simultaneously of a person depicted in one image and to render a new image that depicts the person with the same appearance but different motion features corresponding to a person depicted in a different image.”) at least one memory configured to store instructions; and at least one processor configured to execute instructions to: (ASSOULINE, para. 0123: “The machine 900 may include processors 902, memory 904 , and input/output (I/O) components 938, which may be configured to communicate with each other via a bus 940. In an example, the processors 902 (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) Processor, a Complex instruction Set Computing (CISC) Processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor 906 and a processor 910 that execute the instructions 908 .”) by a feature extraction unit, extract, from input data that is motion data representing a motion of a person, basic feature data representing a feature of the motion data ... , motion feature data representing a feature of the motion data ... , and person feature data representing a feature of the motion data corresponding to the person; (ASSOULINE, para. 0075: “ A transformation system can capture an image or video stream on a client device (e.g., the client device 102) and perform complex image manipulations locally on the client device 102 while maintaining a suitable user experience, computation time, and power consumption. The complex image manipulations may include size and shape changes, emotion transfers (e.g., changing a face from a frown to a smile), state transfers (e.g., aging a subject, reducing apparent age, changing gender), style transfers , graphical element application, 3D human pose estimation, 3D body mesh reconstruction , and any other suitable image or video manipulation implemented by a convolutional neural network that has been configured to execute efficiently on the client device 102.”; ASSOULINE, para. 0099: “The motion and appearance transfer system 224 applies an appearance extraction module 512 to extract appearance features of the first user from the first image or video that depicts the first user . In some cases, the appearance extraction module 512 extracts the appearance features and motion features of the first user from the first image or video that depicts the first user simultaneously but only retains the appearance features. The motion and appearance transfer system 224 also extracts the motion features of the second user from the second image or video that depicts the second user by applying the motion extraction module 514 to the second image or video. The motion extraction module 514 can extract the appearance features and motion features of the second user from the second image or video that depicts the second user simultaneously but only retains the motion features. The motion and appearance features can be extracted by applying the first and second images or videos to a feature extraction network (e.g., a first neural network or first machine learning technique). The first and second images or videos can be applied to the first neural network in parallel or sequentially to extract the appearance and/or motion features from the first and second images or videos. Namely, the appearance extraction module 512 and the motion extraction module 514 can be implemented by the first neural network or machine learning technique.”; Examiner’s Note: appearance extraction module 512 and motion extraction module 514 collectively correspond to the recited “feature extraction unit”, which extract appearance and motion features about a user from image or video (corresponding to recited “input data that is motion data representing a motion of a person”), where the appearance extraction module 512 extracts the recited “person feature data” and the motion extraction module 514 extracts both the recited “basic feature data” and “basic motion data”) by a motion data generation unit, ... generate second motion data on a basis of the basic feature data and the person feature data; and (ASSOULINE, para. 0100: “The motion and appearance transfer system 224 can then render a new image or video that depicts the first user in the appearance of the first user but performing motion of the second user. In one example, the motion and appearance transfer system 224 applies to an image generation system (e.g., a second neural network implemented by the image rendering module 518) the appearance features of the first user extracted from the first image or video together with the motion features of the second user extracted from the second image or video.”; Examiner’s Note: ASSOULINE teaches a neural network that generates a new image or video using appearance features of a first user together with the motion features of a second user based on images or video) However, ASSOULINE fails to explicitly teach: corresponding to a basic motion set with respect to the motion corresponding to a motion style set with respect to the motion generate first motion data on a basis of the basic feature data and the motion feature data, and learn the feature extraction unit and the motion data generation unit on a basis of the first motion data and the second motion data. However, in a related field of endeavor (synthesizing child motions from adult motion data, see p. 1, section 1), DONG teaches and makes obvious: basic feature data representing a feature of the motion data corresponding to a basic motion set with respect to the motion (DONG, p. 3, section 3: “We captured the following discrete action examples: “Throw a ball with left arm”, “Throw a ball with right arm”, “Punch”, “Kick”, “Jump with one leg”, “Jump with the other leg”, “Idle”, “Broad Jump Forward”, “Jump as high as you can in place” “Jump”, “5 Jumping jacks”; cyclic locomotion examples: “Walk”, “Walk as fast as you can”, “Hop Scotch”, “Sneaky Walk”, “Happy Walk”, “Jog”, “Run as fast as you can”, “Skip”; and dynamic combination examples: “Run and Jump”, “Walk, step over obstacle”.; Examiner’s Note: the ASSOULINE-DONG combination now modifies the motion extraction module 514 of ASSOULINE to capture the discrete motion action types of DONG) motion feature data representing a feature of the motion data corresponding to a motion style set with respect to the motion (DONG, p. 4, section 4.2: “Our network architecture consists of two GANs: one for adult2child translation and the other one for child2adult translation (see Figure 1). According to Zhu et al. [2017], having the two GANs forming a cycle enables training without paired data and prevents modes collapse.” DONG, p. 8, section 5.6: “In this work, we demonstrate that our adult2child framework, based on the well-known CycleGAN architecture, can be used to extract the style component and transfer it from one motion to another : the output motions appear more childlike compared to the original adult motions.” Examiner’s Note: the ASSOULINE-DONG combination now modifies the motion extraction module 514 of ASSOULINE to capture the discrete motion style types (adult and child) of DONG) generate first motion data on a basis of the basic feature data and the motion feature data, and (DONG, p. 6, section 5.2: “It can be observed that both the timing and the poses for our output childlike motions are more similar to the movements of a child than adults. In particular, the spine of the fake childlike motion generated using our method is more exaggerated, while the movement of the hands rise-up appears to be carefree and playful, compared to the tight and structured movement of the adult” DONG, p. 8, section 5.6: “In this work, we demonstrate that our adult2child framework, based on the well-known CycleGAN architecture, can be used to extract the style component and transfer it from one motion to another : the output motions appear more childlike compared to the original adult motions. ... Moreover, our network can translate style from one motion to a different motion type ” Examiner’s Note: Figs. 5-6 depict the output motion data that for particular motion types (“jump-as-high-as-you-can” and “walk-as-fast-as-you-can”) and transfer the adult style to the child style; the ASSOULINE-DONG combination now also generates motion data that takes a basic motion type and transfers a child style onto it) learn the feature extraction unit and the motion data generation unit on a basis of the first motion data and the second motion data. (DONG, p. 4, section 4.2: “We have two generators:Ga2c maps adult motions to fake child motions while Gc2a maps the child motions back to adult motions. We also have two discriminators: Da that distinguish original adult motions a from the fake adult motions Gc2a(c), and Dc that differentiates the original child motions c from the fake child motions Ga2c(a). ”; DONG, p. 4, section 4.3.1: “In our adversarial framework, the generator aims to create motion words that will be recognized as being a child’s motion, while the discriminator aims to catch generated instances as being translated motion rather than real motion capture. The discriminator Dc learns to assign 1 to motions that were captured from child actors and 0 to motions to style translated motion. The discriminator Da correspondingly learns to assign 1 to real adult motions and 0 to style translated motion .”; Examiner’s Note: DONG teaches an adversarial learning process, where discriminators are taught to distinguish real motion from the style-translated motion; the ASSOULINE-DONG combination now utilizes adversarial learning with discriminators as in DONG to utilize the output first motion data and the output second motion data to improve the extraction modules 512 and 514 and appearance transfer system 224 of ASSOULINE) Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of ASSOULINE with DONG as explained above. As disclosed by DONG, one of ordinary skill would have been motivated to do so in order to “transfer both pose style and timing in a single network, in contrast to other recent works which do not handle temporal differences in style.” (p. 8, section 5.6). As further disclosed by DONG, one of ordinary skill would have been motivated to do so because DONG teaches techniques for GANs, which “work well with a small dataset compared to other deep learning architectures”, that overcome difficulties “in modeling the temporal dynamics of movement.” (p. 3, section 2). Regarding Claim 2 ASSOULINE and DONG teach the device of claim 1 as explained above. However, ASSOULINE fails to explicitly teach: in the feature extraction unit and the motion data generation unit, learn the feature extraction unit and the motion data generation unit so as to generate the first motion data and the second motion data from the input data and generate the input data from each of the first motion data and the second motion data. However, in a related field of endeavor (synthesizing child motions from adult motion data, see p. 1, section 1), DONG teaches and makes obvious: in the feature extraction unit and the motion data generation unit, learn the feature extraction unit and the motion data generation unit so as to generate the first motion data and the second motion data from the input data and generate the input data from each of the first motion data and the second motion data. (DONG, p. 4, section 4.2: “We have two generators:Ga2c maps adult motions to fake child motions while Gc2a maps the child motions back to adult motions . We also have two discriminators: Da that distinguish original adult motions a from the fake adult motions Gc2a(c), and Dc that differentiates the original child motions c from the fake child motions Ga2c(a).”; Examiner’s Note: DONG teaches having a reverse generator to re-generate the original adult motions; the ASSOULINE-DONG combination now modifies appearance transfer system 224 of ASSOULINE to also have reverse-generators for both the basic motion + appearance motion output and the basic motion + motion style outout) Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of ASSOULINE with DONG as explained above. As disclosed by DONG, one of ordinary skill would have been motivated to do so in order to “transfer both pose style and timing in a single network, in contrast to other recent works which do not handle temporal differences in style.” (p. 8, section 5.6). As further disclosed by DONG, one of ordinary skill would have been motivated to do so because DONG teaches techniques for GANs, which “work well with a small dataset compared to other deep learning architectures”, that overcome difficulties “in modeling the temporal dynamics of movement.” (p. 3, section 2). Regarding Claim 3 ASSOULINE and DONG teach the device of claim 1 as explained above. However, ASSOULINE fails to explicitly teach: by an identification feature extraction unit, generate an identification feature value that is a feature value for identifying whether each of the first motion data and the second motion data is data generated by the motion data generation unit or the input data learn the feature extraction unit, the motion data generation unit, and the identification feature extraction unit by using the identification feature value. However, in a related field of endeavor (synthesizing child motions from adult motion data, see p. 1, section 1), DONG teaches and makes obvious: by an identification feature extraction unit, generate an identification feature value that is a feature value for identifying whether each of the first motion data and the second motion data is data generated by the motion data generation unit or the input data; ( DONG, p. 4, section 4.3.1: “In our adversarial framework, the generator aims to create motion words that will be recognized as being a child’s motion, while the discriminator aims to catch generated instances as being translated motion rather than real motion capture. The discriminator Dc learns to assign 1 to motions that were captured from child actors and 0 to motions to style translated motion. The discriminator Da correspondingly learns to assign 1 to real adult motions and 0 to style translated motion .”; Examiner’s Note: DONG teaches discriminators that are trained to determine a value (0 or 1) for determining whether captured motion data is the original data, or style transferred data; the ASSOULINE-DONG combination now modifies the teachings of ASSOULINE to utilize discriminators for adversarial training as in DONG) learn the feature extraction unit, the motion data generation unit, and the identification feature extraction unit by using the identification feature value. (DONG, p. 4, section 4.2: “We adopted the CycleGAN architecture to learn the mapping between adult motions to childlike motions. The network was trained on one motion word pair at a time. Usually CycleGAN networks are trained using unpaired data from two specific domains, e.g., horse and zebra images. Similarly, in our case, we train the network with motion words of the same motion type (e.g. adult jump with child jump, adult kick with child kick, etc.)”; Examiner’s Note: As generally taught by DONG in section 4.2, the entire CycleGAN architecture (see Fig. 1) is trained together, meaning that all the generators and discriminators are trained together by using the discriminators to improve the generators and vice versa; the ASSOULINE-DONG combination now modifies the teachings of ASSOULINE to utilize generators and discriminators for adversarial training as in DONG to improve the feature extractors, motion generators, and discriminators together) Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of ASSOULINE with DONG as explained above. As disclosed by DONG, one of ordinary skill would have been motivated to do so in order to “transfer both pose style and timing in a single network, in contrast to other recent works which do not handle temporal differences in style.” (p. 8, section 5.6). As further disclosed by DONG, one of ordinary skill would have been motivated to do so because DONG teaches techniques for GANs, which “work well with a small dataset compared to other deep learning architectures”, that overcome difficulties “in modeling the temporal dynamics of movement.” (p. 3, section 2). Regarding Claim 8 ASSOULINE teaches: An information processing method comprising: (ASSOULINE, para. 0017: “The disclosed techniques improve the efficiency of using the electronic device by using a combination of machine learning techniques (e.g., neural networks) to extract appearance and motion features simultaneously of a person depicted in one image and to render a new image that depicts the person with the same appearance but different motion features corresponding to a person depicted in a different image.”) The remaining limitations correspond to the device of claim 1, and therefore claim 8 is rejected for the same reasons explained above with respect to claim 1. Claim 9 depends from claim 8 and claims a method that corresponds to the device of claim 3, and is therefore rejected for the same reasons explained above with respect to claims 3 and 8. Regarding Claim 10 ASSOULINE teaches: A non-transitory computer-readable medium storing thereon a program comprising instructions for causing a computer to execute processing to: (ASSOULINE, para. 0123: “The machine 900 may include processors 902, memory 904 , and input/output (I/O) components 938, which may be configured to communicate with each other via a bus 940. In an example, the processors 902 (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) Processor, a Complex instruction Set Computing (CISC) Processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor 906 and a processor 910 that execute the instructions 908 .”) The remaining limitations correspond to the device of claim 1, and therefore claim 10 is rejected for the same reasons explained above with respect to claim 1 . 07-21-aia AIA Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over DONG in view of ASSOULINE and further in view of US 20200380260 A1, hereinafter referenced as RYMKOWSKI . Regarding Claim 7 ASSOULINE and DONG teach the device of claim 1 as explained above. However, ASSOULINE and DONG fail to explicitly teach: to learn the feature extraction unit and the motion data generation unit in such a manner that the basic feature data, the motion feature data, and the person feature data are classified into predetermined labels respectively. However, in a related field of endeavor (extracting information from video or still images, see para. 0003), RYMKOWSKI teaches and makes obvious: to learn the feature extraction unit and the motion data generation unit in such a manner that the basic feature data, the motion feature data, and the person feature data are classified into predetermined labels respectively. (RYMKOWSKI, para. 0012: “In an aspect, feature(s) extracted may be derived from object detection analysis, and so, for example, the classifying of the captured video may be based on detection of a predetermined object type from the captured video. In another aspect, the feature(s) extracted may be derived from scene recognition analysis, and so, for example, the classifying of the captured video may be based on recognition of a predetermined scene type from the captured video. In yet another aspect, the feature(s) extracted may be derived from motion recognition analysis, and so, for example, the classifying of the captured video may be based on recognition of a predetermined motion type from the captured video .”; Examiner’s Note: RYMKOWSKI teaches the concept of extracting features and classifying said features according to predetermined types; the ASSOULINE-DONG-RYMKOWSKI combination now modifies the appearance feature extractor of ASSOULINE to classify features according to predetermined types, and further modifies the motion feature extraction of ASSOULINE to classify motion data according to predetermined motion data types and predetermined motion style types) Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of ASSOULINE with DONG and RYMKOWSKI as explained above. As disclosed by RYMKOWSKI, one of ordinary skill would have been motivated to do so in order to save computational and storage resources to only identify the features that matter most to a user. (para. 0003) . Allowable Subject Matter Claims 4-6 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, provided that the rejections under 35 U.S.C. 112(a) and 112(b) are overcome. 13-03-01 AIA The following is a statement of reasons for the indication of allowable subject matter: Dependent claim 4 would be considered allowable because none of the references of record either alone or in combination fairly disclose or suggest the combination of limitations specified in claim 4, including at least: wherein the identification feature extraction unit includes a motion identification feature extraction unit that generates the identification feature value corresponding to each of the input data and the first motion data, and an individual identification feature extraction unit that generates the identification feature value corresponding to each of the input data and the second motion data, and the at least one processor is configured to execute the instructions to learn the feature extraction unit, the motion data generation unit, and the identification feature extraction unit by performing adversarial learning with use of the identification feature value generated by the motion identification feature extraction unit and the identification feature value generated by the individual identification feature extraction unit. The closest prior art of record discloses: US 20230196712 A1, hereinafter referenced as ASSOULINE , teaches a system for extracting motion and appearance features and generating new motion videos by transferring appearances to different motions. (paras. 0075, 0099-0100). Dong, Yuzhu, et al. "Adult2child: Motion style transfer using cyclegans." Proceedings of the 13th ACM SIGGRAPH Conference on Motion, Interaction and Games . 2020, hereinafter referenced as DONG , teaches a generative adversarial network, including a discriminator, that can transfer motion styles (adult/child) to different types of basic motions. (p. 4, section 4.2 and p. 8, section 5.6). US 20220012859 A1, hereinafter referenced as ZHOU , teaches that a discriminator of a GAN can have sub-components. (para. 0127). However, the examiner has found that the distinct feature of the Applicant's claimed invention over the prior art is the explicit claiming of the aforementioned limitations in combination with all the other limitations as specified in claim 4. Therefore, because claim 4 is not anticipated nor made obvious by the prior art of record, claim 4 would be allowable over the prior art if rewritten in independent form including all of the limitations of the base claim and any intervening claims, provided that the rejections under 35 U.S.C. 112(a) and 112(b) are overcome. Claims 5-6 depend from claim 4, and would be allowable for the same reasons explained with respect to claim 4, if rewritten in independent form including all of the limitations of the base claim and any intervening claims, provided that the rejections under 35 U.S.C. 112(a) and 112(b) are overcome . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Xia, Shihong, et al. "Realtime style transfer for unlabeled heterogeneous human motion." ACM Transactions on Graphics (TOG) 34.4 (2015): 1-10. “In this paper, we have developed a real-time data-driven animation system for stylistic motion synthesis and control. The main contribution of our work is to introduce a time-varying mixture of autoregressive models for representing the complex relationship between the input and output styles. In addition, we have developed an efficient online local regression model to predict the timing of synthesized poses.” (p. 9, section 8). Mason, Ian, et al. "Real-time style modelling of human locomotion via feature-wise transformations and local motion phases." Proceedings of the ACM on Computer Graphics and Interactive Techniques 5.1 (May 2022): 1-18. “In this paper, we propose a novel, real-time approach for style modelling. We do not require paired or aligned data, instead motions are stylised on a per-frame basis by conditioning on learned style representations.” (p. 2, section 1). Wang, Huaijun, et al. "A cyclic consistency motion style transfer method combined with kinematic constraints." Journal of Sensors 2021.1 (2021): 5548614. See Fig. 2, which shows the inputs/outputs for a GAN. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL C LEE whose telephone number is (571)272-4933. The examiner can normally be reached M-F 12:00 pm - 8:00 pm ET. 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, Omar Fernandez Rivas can be reached at 571-272-2589. 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. /MICHAEL C. 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