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. Claim Rejections - 35 USC § 103 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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 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 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hinton et al ( US 20180343073 A1 ) hereinafter as Hinton in view of Honkala et al ( US 20230403182 A1 ) hereinafter as Honkala . Regarding claim(s) 1,9,17, Hinton discloses a processing system ( See Fig(s) . 1) comprising: one or more memories comprising processor-executable instructions; and one or more processors configured to execute the processor-executable instructions ( See Fig(s) . 1, 5, See ¶ 45) and cause the processing system to: generate a set of simulated channel information for a wireless signal propagating in a simulated physical space ( See Fig(s) . 1, 5, See ¶ abstract, 4,13-15, 25-26, calculate channel parameters for a radio channel simulator from postulated physical information about simulated motion of radio hardware (such as a radio transmitter and/or a receiver); signal attenuation (e.g. from walls, fog, free space path loss (FSPL), etc.); multipath (e.g. reflections from buildings, water or other large surfaces, etc.); and/or other environmental conditions in a postulated physical environment. ). Hinton fails to disclose generate a set of latent tensors based on the set of simulated channel information using a transformation machine learning model; generate a channel estimate based on the set of latent tensors using a decoder machine learning model; and take one or more actions based on the channel estimate. Honkala discloses generate a set of latent tensors based on the set of simulated channel information using a transformation machine learning model ( See Fig(s) . 1, See ¶ abstract, 4-6,79-80, , implement a machine learning model comprising at least one neural network and a transformation , wherein the transformation comprises at least one multiplicative layer or equalization; and input data into the machine learning model ) ; generate a channel estimate based on the set of latent tensors using a decoder machine learning model ( See ¶ 84, a crude estimate of the channel (raw channel estimate) may be needed. To this end, the radio receiver 100 may calculate the raw channel estimate and interpolate it over the whole time-frequency ) ; and take one or more actions based on the channel estimate ( See Fig(s) . 2, See ¶ 95, 192, based on the channel estimates a scaling of channel gain can be made) . Machine learning modeling with appropriate transformations can improve channel estimates with appropriate scaling of channel gains. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to incorporate the teachings of Honkala within Hinton , so as to improve overall channel transmission quality. Regarding claim(s) 2, 10, Honkala discloses wherein the transformation machine learning model comprises at least one of: ( i ) a transformer model, or (ii) a diffusion model ( See Fig(s) . 5 transformer model) . Reasons for combining same as claim 1. Regarding claim(s) 3, 11,18, Honkala discloses wherein, to generate the channel estimate, the one or more processors are configured to execute the processor-executable instructions and cause the processing system to: generate a vector representation based on processing the set of latent tensors using a vector quantization operation; and process the vector representation using the decoder machine learning model ( See Fig(s) . 2-3, See ¶ 84, 107, a crude estimate of the channel (raw channel estimate) may be needed. To this end, the radio receiver 100 may calculate the raw channel estimate and interpolate it over the whole time-frequency ) . Reasons for combining same as claim 1. Regarding claim(s) 4, 12, Honkala discloses wherein the vector quantization operation comprises a learned codebook ( See Fig(s) . 2-3, See ¶ 54-56) . Reasons for combining same as claim 1. Regarding claim(s) 5, 13,19, Hinton discloses wherein: the simulated physical space corresponds to a real physical space, and the transformation machine learning model is site-specific to the real physical space ( See ¶ abstract, 22) . Regarding claim(s) 6, 14,20, Hinton discloses wherein, to take the one or more actions, the one or more processors are configured to execute the processor-executable instructions and cause the processing system to: ( i ) adjust one or more transmission parameters for wireless signals transmitted in the real physical space, or (ii) perform positioning for one or more objects in the real physical space ( See ¶ 25, The channel simulator 106 produces, as output (using radio signals from the signal source 112 as input, as well as channel parameters from the channel parameter calculator 110 to modify the input signals) . Regarding claim(s) 7, 15, Honkala discloses wherein the channel estimate comprises at least one of: ( i ) a channel frequency response, or (ii) a channel impulse response ( See ¶ 84, the radio receiver 100 may calculate the raw channel estimate and interpolate it over the whole time- frequenc y grid using, for example, a simple nearest neighbor interpolation. ) . Reasons for combining same as claim 1. Regarding claim(s) 8,16, Hinton discloses wherein the simulated channel information comprises simulated multipath components ( See Fig(s) . 4, See ¶ 11) . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Raj Jain whose telephone number is (571) 272-3145. The examiner can normally be reached on M-Th ~8 ~6. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Derrick Ferris can be reached on 571-272-3123. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). 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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form. /RAJ JAIN/ Primary Examiner, Art Unit 2411