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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 11/14/24 and 7/14/25. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
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 –
(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.
Claim(s) 21 – 24 and 31 – 34 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by O’Shea et al. (US Publication 2020/0343985).
Regarding claims 21 and 31, O’Shea teaches an apparatus and a method, comprising: (i.e. fig, 7 shows a network apparatus comprising a processor, memory and transceiver for executing programmed instructions; see paragraphs 137, 138)
obtaining one or more to-be-modulated bits and modulation condition information (MCI); (i.e. fig. 6 shows a wireless communications network wherein a machine learning network may receive bits or symbols for transmission along with transmission configuration values such as modulation and coding (MCI) information; see paragraph 134)
obtaining a first modulated signal based on the one or more to-be-modulated bits, the MCI, and a first neural network, wherein the one or more to-be-modulated bits comprise N x M bits, M is a modulation order, N is a quantity of first modulation symbols comprised in the first modulated signal, and both N and M are positive integers; and outputting the first modulated signal. (i.e. fig. 6 shows encoding and modulation of a signal may be provided based on the bits or symbols to be transmitted, the MCI, and the machine learning network (605); see paragraphs 134, 135 ) (see Also, it is inherent in network communications that a modulation order (bits encoded per symbol) x a number (quantity) of symbols equals the total number of bits transmitted)
Regarding claims 22 and 32, O’Shea teaches the method according to claim 21, wherein the MCI indicates a mapping relationship between at least one bit and at least one modulation symbol. (i.e. the modulation and coding information indicated the encoding scheme is utilized to map data to symbols by definition (e.g. 1 bit per symbol (BPSK) thru (256QAM) ); see paragraph 134)
Regarding claims 23 and 33, O’Shea teaches the method according to claim 21, wherein the MCI is determined based on first information, the first information comprises environment information or requirement information, the environment information indicates a channel environment, and the requirement information indicates a requirement on communication performance. (i.e. it is inherent to wireless networks that MCI is chosen based upon channel conditions. Access points and client devices constantly use Link Adaptation to select the ideal MCS (Modulation and Coding Scheme) based on real-time radio frequency (RF) conditions.) Strong Signals (High SNR): When channel conditions are optimal a wireless system uses a higher MCS index This packs more bits per symbol and uses less error correction, maximizing data speed. Poor Signals (Low SNR): When conditions degrade, the system drops to a lower MCS index. This uses simpler modulation and more error correction, ensuring the packet survives the noisy environment at the cost of reduced throughput)
Regarding claims 24 and 34, O’Shea teaches the method according to claim 21, further comprising: sending second information, wherein the second information indicates the first neural network. (i.e. fig. 6 shows when encoding bits/symbols for network transmission machine learning (neural) network (605) is identified; see paragraphs 130 – 132, 134)
Claim(s) 26 – 29 and 36 - 39 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Melodia et al. (US Publication 2022/0217035).
Regarding claims 26 and 36, Melodia teaches an apparatus and a method, comprising:
obtaining a first modulated signal and modulation condition information (MCI), wherein the first modulated signal comprises N first modulation symbols, and N is a positive integer; and demodulating the first modulated signal based on the MCI and a third neural network. (i.e. fig. 1 shows a receiver may obtain a modulated signal (10) and modulation and coding parameters wherein the signal comprises a plurality of different modulated symbols, wherein the signal is demodulated based on the parameters and a deep learning neural network inference (12); see paragraphs 38 - 41)
Regarding claims 27 and 37, Melodia teaches the method according to claim 26, wherein the MCI indicates a mapping relationship between at least one bit and at least one modulation symbol. (i.e. the modulation and coding information indicated the encoding scheme is utilized to map data to symbols by definition (e.g. 1 bit per symbol (BPSK) thru (256QAM) ); see paragraph 38, 92)
Regarding claims 28 and 38, Melodia teaches the method according to claim 26, wherein the MCI is determined based on first information, the first information comprises environment information or requirement information, the environment information indicates a channel environment, and the requirement information indicates a requirement on communication performance. (i.e. it is inherent to wireless networks that MCI is chosen based upon channel conditions. Access points and client devices constantly use Link Adaptation to select the ideal MCS (Modulation and Coding Scheme) based on real-time radio frequency (RF) conditions.) Strong Signals (High SNR): When channel conditions are optimal a wireless system uses a higher MCS index This packs more bits per symbol and uses less error correction, maximizing data speed. Poor Signals (Low SNR): When conditions degrade, the system drops to a lower MCS index. This uses simpler modulation and more error correction, ensuring the packet survives the noisy environment at the cost of reduced throughput)
Regarding claims 29 and 39, O’Shea teaches the method according to claim 21, further comprising: sending second information, wherein the second information indicates the first neural network. (i.e. fig. 1 shows when demodulating bits/symbols for a neural network (12) is identified; see paragraphs 38, 42, 58)
Allowable Subject Matter
Claim 25 and 35 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.
Claim 30 and 40 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
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ROBERT J. LOPATA
Primary Examiner
Art Unit 2471
/ROBERT J LOPATA/
July 14, 2026Primary Examiner, Art Unit 2471
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