CTNF 18/780,133 CTNF 87974 DETAILED ACTION Response to Amendment In response to amendment filed on 7/22/2024, claims 1- 10 and 15 are amended, claim 11 is cancelled. Claims 1- 10 and 12- 15 are pending for examinations. Claim Rejections - 35 USC § 101 Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 15 refers to “a computer program”. As per broadest reasonable interpretation, “a computer program” is interpreted as software program per se. Software program per se does not fall under any statutory category. Thus, claim 15 is interpreted as software per se and does not fall within any statutory category under 35 USC §101. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15 AIA Claim (s) 1- 2, 10, 12- 15 are rejected under 35 U.S.C. 102 ( a)(1 ) as being anticipated by MIELCZAREK et al. (US Pub. No. 2009/0265601 A1), hereafter Bar . Regarding Claim 1 , Bar teaches an apparatus comprising at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus (see Fig. 2 wherein apparatus as #36 (as a base station) and user equipment can be device #38) at least to: receive data transmitted by a device of a wireless communication system over a channel of said wireless communication system, wherein said data encodes a first estimate of a channel state information of the wireless channel as determined at said device (see Fig. 7 #170 see [0118].. In step 170, the encoded channel state information indices are fed back to the transmitter….; further see [0114- 0117].. [0114] 2. In step 162, each receive r estimates its channel matrix H[t]. [0115] 3. In step 164, each receiver performs the vector quantization of the channel state information . [0116] 4. In step 166, the channel state information indices are encoded using error detecting code (such as CRC). [0117] 5. (Optional) In step 168, the channel state information indices with the error-detection redundancy are encoded using error correcting code (such as convolutional or turbo codes) ) ; decode said data to generate a second estimate of the channel state information of the wireless channel (see [0119] step 7 of Fig. 7 7. (Conditional on 5) In step 172, the received channel state information indices are decoded in a channel decoder that attempts to correct possible channel transmission errors.) ; and generate a channel state information output by modifying the second estimate of the channel state information (see steps 174, 176, 178, 182 of Fig. 7 and [0120-0122 and 0126]) based, at least in part, on a first error indication indicative of estimated errors in the first channel state information estimate as generated at the device (already described above see [0114- 0117]) . Regarding Claim 2 , Bar teaches as per claim 1, wherein the instructions, when executed by the at least one processor, further cause the apparatus to: generate the first error indication; see steps 180 -184 of Fig. 7. Regarding Claim 10 , Bar teaches as per claim 1, wherein the device is a user device or user equipment of the mobile communication system; see Fig. 2 wherein apparatus as #36 (as a base station) and user equipment can be device #38 . Regarding Claim 12 , Bar teaches a method comprising (see Fig. 2 user equipment can be device #38) : receiving data transmitted by a device of a wireless communication system over a channel of said wireless communication system, wherein said data encodes a first estimate of a channel state information of the wireless channel as determined at said device (see Fig. 7 #170 see [0118].. In step 170, the encoded channel state information indices are fed back to the transmitter….; further see [0114- 0117].. [0114] 2. In step 162, each receive r estimates its channel matrix H[t]. [0115] 3. In step 164, each receiver performs the vector quantization of the channel state information . [0116] 4. In step 166, the channel state information indices are encoded using error detecting code (such as CRC). [0117] 5. (Optional) In step 168, the channel state information indices with the error-detection redundancy are encoded using error correcting code (such as convolutional or turbo codes) ) ; decoding said data to generate a second estimate of the channel state information of the wireless channel (see [0119] step 7 of Fig. 7 7. (Conditional on 5) In step 172, the received channel state information indices are decoded in a channel decoder that attempts to correct possible channel transmission errors.) ; and generating a channel state information output by modifying the second estimate of the channel state information (see steps 174, 176, 178, 182 of Fig. 7 and [0120-0122 and 0126]) based, at least in part, on a first error indication indicative of estimated errors in the first channel state information estimate as generated at the device (already described above see [0114- 0117]) . Regarding Claim 13 , Bar teaches as per claim 12, wherein the instructions, when executed by the at least one processor, further cause the apparatus to: generate the first error indication; see steps 180 -184 of Fig. 7. Regarding claim 14 , Bar teaches as per claim 13, wherein generating said channel state information output includes at least partially correcting errors introduced in the data over the wireless connection between the device and the network node; Bar see Fig. 7 steps 176, 180, 182, 184, 186. Regarding Claim 15 , Bar teaches a computer program comprising instructions which, when executed by an apparatus, cause the apparatus to (see Fig. 2 wherein apparatus as #36 (as a base station) and user equipment can be device #38) at least to: receive data transmitted by a device of a wireless communication system over a channel of said wireless communication system, wherein said data encodes a first estimate of a channel state information of the wireless channel as determined at said device (see Fig. 7 #170 see [0118].. In step 170, the encoded channel state information indices are fed back to the transmitter….; further see [0114- 0117].. [0114] 2. In step 162, each receive r estimates its channel matrix H[t]. [0115] 3. In step 164, each receiver performs the vector quantization of the channel state information . [0116] 4. In step 166, the channel state information indices are encoded using error detecting code (such as CRC). [0117] 5. (Optional) In step 168, the channel state information indices with the error-detection redundancy are encoded using error correcting code (such as convolutional or turbo codes) ) ; decode said data to generate a second estimate of the channel state information of the wireless channel (see [0119] step 7 of Fig. 7 7. (Conditional on 5) In step 172, the received channel state information indices are decoded in a channel decoder that attempts to correct possible channel transmission errors.) ; and generate a channel state information output by modifying the second estimate of the channel state information (see steps 174, 176, 178, 182 of Fig. 7 and [0120-0122 and 0126]) based, at least in part, on a first error indication indicative of estimated errors in the first channel state information estimate as generated at the device (already described above see [0114- 0117]) . Claim Rejections - 35 USC § 103 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 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) 3- 7 are rejected under 35 U.S.C. 103 as being unpatentable over MIELCZAREK et al. (US Pub. No. 2009/0265601 A1), hereafter Bar in view of Yoo et al. (US Pub. No. 11936452 B2) . Regarding claim 3 , Bar teaches as per claim 2, but fails to state about wherein the instructions, when executed by the at least one processor, cause the apparatus to: generate said first error indication comprises a first machine learning module; however Yoo in context with claim 18 teaches in claim 19 about obtain a second CSI instance for the channel; train a neural network model based at least in part on encoding the second CSI instance into second encoded CSI, decoding the second encoded CSI into second decoded CSI, and comparing the second CSI instance and the second decoded CSI (i.e. error generation is performers by comparing) ; and update the one or more decoder weights based at least in part on training the neural network model . It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention was made to consider the teachings of Yoo with the teachings of Bar to make system more effective. Having a mechanism wherein the instructions, when executed by the at least one processor, cause the apparatus to: generate said first error indication comprises a first machine learning module; greater way more reliable communication can be carried out in the communication system. Regarding claim 4 , Bar in view of Yoo teaches as per claim 3, wherein the instructions, when executed by the at least one processor, further cause the apparatus to: train said first machine learning module by minimising a loss function including labelled code data; Yoo in context with claims 14, pls refer to claim 16 regarding to train the neural network model based at least in part on one or more of a target size of the second encoded CSI or a target distance measure between the second CSI instance and the second decoded CSI (i.e. target distance measure is a loss function specifically the error between origin and decoded CSI and training a neural network by minimizing such a distance measure is inherent to all neural network training). Regarding claim 5 , Bar in view of Yoo teaches as per claim 2, wherein the first error indication is a binary scalar; Yoo see lines 1- 5 of col. 20 and claims 4, 12 and 25 binary sequence (i.e. ordered succession of binary scalars). Regarding claim 6 , Bar teaches as per claim 1, but fails to state about, wherein the instructions, when executed by the at least one processor, cause the apparatus to: generate said channel state information output comprising a second machine learning module; however Yoo states in claim 18… wherein the first encoded CSI is a first CSI instance for the channel that is encoded by the UE, based at least in part on one or more encoder weights that correspond to a neural network model associated with a CSI encoder and a CSI decoder ...(i.e. second machine learning). It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention was made to consider the teachings of Yoon with the teachings of Bar to make system more standardized. Having a mechanism wherein the instructions, when executed by the at least one processor, cause the apparatus to: generate said channel state information output comprising a second machine learning module; greater way more standardized approach can be carried out in the communication system. Regarding claim 7 , Bar in view of Yoo teaches as per claim 6, wherein the instructions, when executed by the at least one processor, further cause the apparatus to: train said second machine learning module based on comparisons of generated channel state information with labelled channel state information data; Yoon in context with claim 18; pls refer to claim 19.. train a neural network model based at least in part on encoding the second CSI instance into second encoded CSI, decoding the second encoded CSI into second decoded CSI, and comparing the second CSI instance and the second decoded CSI … Regarding claim 8 , Bar in view of Yoo teaches as per claim 6, wherein the instructions, when executed by the at least one processor, further cause the apparatus to: generate said channel state information output is configured to at least partially correct errors introduced in the data transmitted over the wireless connection between the device and the network node; Bar see Fig. 7 steps 176, 180, 182, 184, 186. Regarding claim 9 , Bar in view of Yoo teaches as per claim 8, wherein the instructions, when executed by the at least one processor, further cause the apparatus to: detect the errors introduced in the data transmitted over the wireless connection between the device and the network node Bar see Fig. 7 steps 174, 176, 180, 182, 184, 186 . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see PTO-892 form for considered prior arts for record . Reference Zeng et al. (US Pat. No. 11387880 B2) teaches in abstract about f acilitating feedback of robust channel state information (CSI) , such as to provide full CSI feedback or otherwise providing CSI feedback, are described. CSI encoders and/or decoders used by network nodes may implement channel compression/reconstruction based upon neural-network (NN) training of collected channels . A structured payload having an interpretable payload portion and an uninterpretable payload portion may utilized with respect to CSI feedback. The channel compression provided according to some aspects of the disclosure supports feedback of robust CSI, in some instances including full CSI, as determined by a particular network node. Other aspects and features are also claimed and described. Reference Yoo et al. (US Pub. No. 2021/0266763 A1), hereafter Yoo1 teaches in abstract about … method also includes training the CSI decoder and CSI encoder based on observed channel and interference conditions to obtain updated decoder coefficients and updated encoder coefficients . The method further includes receiving an indication of resources for transmission of the updated encoder coefficients and updated decoder coefficients. The method includes transmitting the updated decoder coefficients and updated encoder coefficients to the base station in accordance with the indication of resources. Further, the method includes receiving an updated CSI decoder and updated CSI encoder from the base station for further training ; further see [0080].. large amount of CSI feedback can be compressed with neural network processing, for example, with an auto-encoder at the UE. The UE can encode the channel state feedback and transmit the encoded feedback over the air to the base station. The channel state feedback can be sent from the UE in accordance with timelines configured by radio resource control (RRC) signaling. Upon receiving the information, the base station feeds the received compressed channel state feedback values into the decoder to approximate the channel state feedback . Any inquiry concerning this communication or earlier communications from the examiner should be directed to PARTH PATEL whose telephone number is (571)270-1970. The examiner can normally be reached 7 a.m. -7 p.m. PST. 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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. PARTH PATEL Primary Examiner Art Unit 2479 /PARTH PATEL/ Primary Examiner, Art Unit 2479 Application/Control Number: 18/780,133 Page 2 Art Unit: 2479 Application/Control Number: 18/780,133 Page 3 Art Unit: 2479 Application/Control Number: 18/780,133 Page 4 Art Unit: 2479 Application/Control Number: 18/780,133 Page 5 Art Unit: 2479 Application/Control Number: 18/780,133 Page 6 Art Unit: 2479 Application/Control Number: 18/780,133 Page 7 Art Unit: 2479 Application/Control Number: 18/780,133 Page 8 Art Unit: 2479 Application/Control Number: 18/780,133 Page 9 Art Unit: 2479 Application/Control Number: 18/780,133 Page 10 Art Unit: 2479 Application/Control Number: 18/780,133 Page 11 Art Unit: 2479 Application/Control Number: 18/780,133 Page 12 Art Unit: 2479
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