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. Terminal Disclaimer The terminal disclaimer filed on 11/10/25 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of 18/532638 has been reviewed and is accepted. The terminal disclaimer has been recorded. 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. 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. 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 pre-AIA 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: control circuitry in claim s (11, 13, 16, 18-20) . 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 pre-AIA 35 U.S.C. 112, sixth paragraph. 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) (1- 20 ) are rejected under 35 U.S.C. 103 as being unpatentable over Dupont et al . (hereinafter Dupont)(US 2025/0168368 A1) Re claim 1, Dupont discloses a method comprising: accessing, via a content server, video data comprising a sequence of frames (See fig. 1: 102; ¶ 60, 71 where it teaches input data comprised a plurality of input data; the encoder device (such as a server).) ; generating a first frame based on averaging pixel attributes of the sequence of frames . (See fig. 1: 102; ¶ 71 where it teaches input data 102 comprises a plurality of input data values. For example, the input data may correspond to an image or video, or to part (e.g., a patch) of one image or video, wherein suggesting that the average was taken.) But the reference of Dupont fails to explicitly teach determining a sequence level representation based on the first frame; training a neural network model based on the sequence of frames to determine a cross- resolution representation corresponding to the sequence of frames, wherein the training comprises generating a plurality of model parameters for reconstructing the sequence of frames based on the sequence level representation and the cross-resolution representation, wherein the plurality of model parameters comprises neural radiance network parameters; and transmitting, via the content server, bitstreams of the plurality of model parameters, the sequence level representation, and the cross-resolution representation. However, the reference of Dupont does suggest determining a sequence level representation based on the first frame (See fig. 1: 112; ¶ 72, 75 where it teaches generating synthesis neural network parameters ) ; training a neural network model based on the sequence of frames to determine a cross- resolution representation corresponding to the sequence of frames, wherein the training comprises generating a plurality of model parameters for reconstructing the sequence of frames based on the sequence level representation and the cross-resolution representation, wherein the plurality of model parameters comprises neural radiance network parameters (See fig. 1: 1 06 , 116; ¶ 72, 75-79 where it teaches generating decoder neural network parameters based on the input data. ; reconstruction data is generated based on parameters 106 and 112.) ; and transmitting, via the content server, bitstreams of the plurality of model parameters, the sequence level representation, and the cross-resolution representation. (See fig. 1: 104; ¶ 87 where it teaches transmitting the encoded data (108, 112, 118) to one or more client devices for decoding.) Therefore, it would have been obvious to one of ordinary skills in the art to incorporate these features into the system of Dupont, in the manner as claimed, for the benefit of optimize the objective function. (See ¶ 74, 77) Re claim 2, Dupont discloses wherein the cross-resolution representation comprises latent features corresponding to each frame of the sequence of frames. (See ¶ 28 where it teaches features of the latent values.) Re claim 3, Dupont discloses generating a bitstream of the cross-resolution representation based on quantization of the latent features. (See ¶ 7 where it teaches quantizing the latent values.) Re claim 4, Dupont discloses reconstructing the sequence of frames by combining, via a channel transformer, the sequence level representation and the latent features. (See fig. 1: 114; ¶ 79) Re claim 5, Dupont discloses wherein the sequence level representation comprises first pixel attribute information corresponding to the first frame, wherein the cross- resolution representation comprises second pixel attribute information corresponding to the sequence of frames, and wherein the neural network model is trained to determine pixel attribute information for reconstructing the sequence of frames. (See fig. 1; ¶ 72-79) Re claim 6, Dupont discloses storing encodings of the plurality of model parameters, the sequence level representation, and the cross-resolution representation. (See ¶ 43) Re claim 7, Dupont discloses wherein the sequence of frames corresponds to a first resolution, and wherein generating the first frame further comprises downscaling the first frame based on the first resolution. (See ¶ 71 where it teaches a patch of an image, wherein suggesting the image was downscaled and/or ¶ 6 having different resolutions.) Re claim 8, Dupont discloses wherein the downscaling comprises using a convolutional network model comprising a plurality of residual spatial attention blocks to produce, from first feature channels, expanded feature channels. (See ¶ 28) Re claim 9, Dupont discloses wherein determining a cross-resolution representation corresponding to the sequence of frames comprises using a convolutional network model comprising a plurality of residual spatial attention blocks to produce, from first feature channels, expanded feature channels. (See ¶ 28) Re claim 10, Dupont discloses wherein determining the sequence level representation is executed concurrently with training the neural network model based on the sequence of frames. (See fig. 1; ¶ 71-79) Claims (11-20) is a system claim corresponding to method claims (1-10). Hence, the steps performed in method claims (1-10) would have necessitated the elements in system claims (11-20). Therefore, claims (11-20) have been analyzed and rejected w/r to claims (1-10) respectively. Contact Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT LEON FLORES whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-1201 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F 8am - 6pm . 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, FILLIN "SPE Name?" \* MERGEFORMAT HENOK SHIFERAW can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 571-272-4637 . 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