CTNF 18/696,159 CTNF 81206 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. 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) 1-3,18, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yong (“Deep Learning Based Channel Estimation Algorithm for Fast Time-Varying MIMO-OFDM Systems – as cited in the IDS dated 4/23/2024) in view of O’Shea (US 20200343985) . Re claim 1: Yong discloses a method of channel estimation for a receiver side antenna array, the method comprising: receiving a first signal associated with a pilot tone transmitted by a transmitter side antenna array ( Figure 1 Pilot pattern and Section II System Model – Let us consider MIMO-OFDM system with Nt transmit anntennas and Nt receive antennas ) ; obtaining a first group of neural network models trained for channel estimation based on the pilot tone ( Fig.2A and Section III B 1 st Paragraph ) ; inputting a representation of the received first signal into each neural network model of the first group and generating a channel estimate for the received first signal ( Figure 2B 2D CNN followed by 2D CNN and Section III B-1) Input Data ) ; based on the channel estimate for the received first signal, performing one-dimensional interpolation for second signals associated with data tones in at least one of time domain and frequency domain, thereby generating interpolated channel estimates for the second signals, which include interpolation errors ( Section III-B 2) Interpolation of Frequency-Domain and 3) Time-Domain Channel Prediction ) ; obtaining a second group of neural network models trained for channel estimation in presence of interpolation errors based on the data tones ( Section III-C. Model Training ) ; and for each one-dimensional interpolation, inputting an interpolated channel estimate of the generated interpolated channel estimates into each neural network model of the second group and generating a corrected interpolated channel estimate for a second signal ( Formula 5 and 6 and Figure 2b Predicted CSI ) . As shown above, Yong discloses a system of a first group of neural networks and a second group of neural networks that receives signals from the first group of neural networks. Yong also discloses the models improve accuracy ( Section IV Simulation Results paragraph 5 starting “The superior performance” ) . Yong does not explicitly state an interpolation error; however, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that the first group of neural networks would contain interpolation errors, which are corrected by the second group of neural networks. Thus, the error propagation is reduced ( Yong Section IV Simulation Results paragraph 5 starting “The superior performance” ) . Yong does not explicitly disclose an antenna array. O’Shea discloses an antenna array ( Para.[0059] For example, when larger antenna arrays, such as a 64-element antenna array, is in use for transmission or reception within a communications system (e.g., in massive MIMO cellular networks) ) . Yong and O’Shea are analogous because they both pertain to data communications. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yong to explicitly show an antenna array as taught by O’Shea in order to improve performance ( O’Shea Para.[0064] ) . Re claim 2: Yong discloses obtaining at least one neural network model trained for channel estimation in presence of the interpolation errors based on the pilot tone ( Fig.2a and 2b ) ; and for each one-dimensional correction, inputting the corrected interpolated channel estimate into the at least one neural network model and generating a post-processed channel estimate for the second signal ( Fig.2a and 2b ) , wherein the at least one neural network model comprises at least one of a neural network model trained for channel estimation in presence of the interpolation errors based on the pilot tone in time domain and a neural network model trained for channel estimation in presence of the interpolation errors based on the pilot tone in frequency domain ( Section III-B 2) Interpolation of Frequency-Domain and 3) Time-Domain Channel Prediction ) . Re claim 3: As discussed above, Yong in view of O’Shea meets all the limitations of the parent claim. Yong further discloses the method of claim 1 wherein the neural network models of the first group comprise neural network models for frequency, spatial and time domains, and the neural network models of the second group comprise at least neural network models for spatial domain ( Section III-B 2) Interpolation of Frequency-Domain and 3) Time-Domain Channel Prediction ) . Yong does not explicitly disclose models for spatial domain. O’Shea discloses models for spatial domain ( Para.[0055] An estimation task, such as the channel estimation, may then consume multiple input values such as a three-dimensional (3D) array over time, frequency and space ) . Yong and O’Shea are analogous because they both pertain to data communications. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yong to include models for spatial domain as taught by O’Shea in order to improve performance ( O’Shea Para.[0064] ) . Re claim 18: Claim 18 is rejected on the same grounds of rejection set forth in claim 1. Re claim 19: Claim 19 is rejected on the same grounds of rejection set forth in claim 1. The processor, memory, and code are inherent components . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 4-17 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. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Chen (CN 110868368) shows interpolation for an initial channel estimate and then a further aligned interpolation model . Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD SAJID ADHAMI whose telephone number is (571)272-8615. The examiner can normally be reached 8:30-5:00 PM. 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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. /MOHAMMAD S ADHAMI/ Primary Examiner, Art Unit 2471 Application/Control Number: 18/696,159 Page 2 Art Unit: 2471 Application/Control Number: 18/696,159 Page 3 Art Unit: 2471 Application/Control Number: 18/696,159 Page 4 Art Unit: 2471 Application/Control Number: 18/696,159 Page 5 Art Unit: 2471 Application/Control Number: 18/696,159 Page 6 Art Unit: 2471
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