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 2/27/24 and 6/13/25. 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) 1 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Calabro et al. (US Publication 2019/0319659).
Regarding claims 1 and 20 Calabro teaches an apparatus and a method implemented by a first wireless communication device for communicating, using a hybrid wireless communications processing chain, with a second wireless communication device, the method comprising: (i.e. fig. 6 shows a wireless device comprising a processor, transceiver and memory for executing programmed instructions; see paragraphs 59 - 61)
selecting, using the first wireless communication device, a modulation machine-learning (ML) configuration for forming a modulation deep neural network (DNN) that generates a modulated signal using encoded bits, received from an encoding module, as an input; (i.e. fig. 1 shows a transmitter chain (102) that comprises a modulating stage that generates a modulated signal based on an input of encoded bits or symbols wherein the transmitter chain (102) may include deep neural network(s) (DNN) (106) as part of the chain, the DNN may replace a classic modulation component; see paragraphs 25 - 27
forming, based on the modulation ML configuration, the modulation DNN as part of a hybrid transmitter processing chain that includes the modulation DNN and at least one static algorithm module; (i.e. fig. 1 shows the transmission chain including the modulation component may comprise a mixture of neural network and classical communication modules, defining a hybrid transmission chain; see paragraph 25, 26) (See Also; fig. 3a shows a hybrid transmission/receiver chain comprising both DNN and classical components or modules; see paragraphs 35, 36) and
transmitting wireless communications associated with the second wireless
communication device using the hybrid transmitter processing chain. (i.e. fig. 1 shows wireless communications are performed with a receiving (second) device (104) utilizing the DNN configured transmission chain; see paragraph 25, 26)
Claim(s) 1 – 14, 16 - 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al. (US Publication 2021/0158151).
Regarding claims 1 and 20 Wang teaches an apparatus and a method implemented by a first wireless communication device for communicating, using a hybrid wireless communications processing chain, with a second wireless communication device, the method comprising: (i.e. fig. 2 shows a wireless apparatus comprising a processor, transceiver and memory for executing programmed instructions; see paragraphs 35 - 37)
selecting, using the first wireless communication device, a modulation machine-learning (ML) configuration for forming a modulation deep neural network (DNN) that generates a modulated signal using encoded bits, received from an encoding module, as an input; (i.e. fig. 5 shows a transmitter chain (504) that comprises a modulating stage that generates a modulated signal based on an output of an encoding stage which converts binary data into symbols; see paragraphs 81 - 83) (See Also; figure 5 which shows the transmitter chain (504) may include one or more deep neural network(s) (DNN) (510) as part of the chain, the DNN may correspond to the modulating stage; see paragraphs 88, 89)
forming, based on the modulation ML configuration, the modulation DNN as part of a hybrid transmitter processing chain that includes the modulation DNN and at least one static algorithm module; (i.e. fig. 5 shows the DNNs can perform any high level and/or low level operation in the transmitter chain; see paragraph 89) (See Also; different implementations of the transmitter chain includes one or more DNN(s) in the transmitter processing chain, the prior indicates any combination of neural network based and classical communications blocks is supported; see paragraphs 88, 89) (i.e. Further, DNNs in the transmitter/receiver chains can be reconfigured to replace high level operations and/or low level operations, indicating ability to change dynamically based on operating environment. This permits flexibility in transmission chains allowing DNNs and classical components to be reconfigured accordingly; see paragraph 90; wherein each DNN has a specific purpose (i.e. encoding, modulation etc.); see paragraph 91) and
transmitting wireless communications associated with the second wireless
communication device using the hybrid transmitter processing chain. (i.e. fig. 5 shows wireless communications are performed with a receiving (second) device (506) utilizing the DNN configured transmission chain; see paragraph 81)
Regarding claims 2 Wang teaches the method as recited in claim 1, wherein
selecting the modulation ML configuration further comprises: selecting a modulation ML configuration that forms a DNN that performs multiple-input, multiple-output (MIMO) antenna processing. (i.e. the DNN configuration / reconfiguration of transmission chain components can be one the supports MU-MIMO; see paragraphs 51, 103)
Regarding claims 3 Wang teaches the method as recited in claim 1, wherein the at least one static algorithm module is the encoding module, and the method
further comprises: generating the encoded bits using the encoding module. (i.e. DNNs in the transmitter/receiver chains can be reconfigured to replace high level operations and/or low level operations, indicating ability to change dynamically based on operating environment. This permits flexibility in transmission chains allowing DNNs and classical components to be reconfigured accordingly, the corresponds to a configuration where the DNN is modulation stage and a classic module comprises the encoding stage; see figure 5 and paragraph 90; wherein each DNN has a specific purpose (i.e. encoding, modulation etc.); see paragraph 91)
Regarding claims 4 Wang teaches the method as recited in claim 3, wherein
generating the encoded bits further comprises: using, by the encoding module, one or more of: a low-density parity-check (LPDC)encoding algorithm; a polar encoding algorithm; a turbo encoding algorithm; or a Viterbi encoding algorithm. (i.e. fig. 5 shows the encoding module supports forward error correction, modern Forward Error Correction (FEC) algorithms and systems inherently include one or more of Low-Density Parity-Check (LDPC), Polar, Turbo, or Viterbi encoding algorithms. These specific algorithms are the foundational pillars of channel coding used to minimize data errors in digital communications, such as 5G, WiFi, satellite, and storage devices; see paragraph 82)
Regarding claims 5 Wang teaches the method as recited in claim 1, wherein selecting the modulation ML configuration comprises selecting: a convolutional neural network architecture; a recurrent neural network architecture; a fully connected neural network architecture; or a partially connected neural network architecture. (i.e. selecting the modulation ML comprises a fully-connected layer neural network architecture, a convolutional layer neural network architecture, a recurrent neural network layer, a number of connected hidden neural network layers; see paragraphs 38, 68)
Regarding claims 6 Wang teaches the method as recited in claim 1, further comprising: indicating the modulation ML configuration to the second wireless
communication device. (i.e. fig, 7 a base station (first device) can notify the DNN configuration to a UE (second device); see paragraphs 103, 104)
Regarding claims 7 Wang teaches the method as recited in claim 1, wherein the first wireless communication device is a base station, wherein the second wireless communication device is a user equipment and wherein selecting the modulation ML configuration further comprises: selecting a base station-side (BS-side) modulation ML configuration for forming, as the modulation DNN, a BS-side modulation DNN that generates a modulated downlink signal using the encoded bits, received from the encoding module, as the input, and wherein forming the modulation DNN further comprises: forming the BS-side modulation DNN. (i.e. figure 7 shows wherein the first device may comprise a base station (120) and wherein the second device is a UE (110) and selecting the DNN configuration for the base station side (710) the modulates the signal according to the transmitter chain of figure 5 including an encoder and modulation module; see paragraphs 101 – 103)
Regarding claims 8 Wang teaches the method as recited in claim 7, further comprising: indicating the BS-side modulation ML configuration to the UE. (i.e. fig. 7 shows the base station may indicate the base station DNN configuration to the UE (715); see paragraphs 103 - 105)
Regarding claims 9 Wang teaches The method as recited in claim 8, wherein indicating the BS-side modulation ML configuration to the UE further comprises: indicating the BS-side modulation ML configuration using a field in downlink control information (DCI); or transmitting a reference signal mapped to the BS-side modulation ML configuration. (i.e. the base station may indicate the DNN configuration to the UE via downlink control information processing; see paragraph 104)
Regarding claims 10 Wang teaches the method as recited in claim 7, further comprising: receiving hybrid automatic repeat request (HARQ) feedback from the UE; and training the BS-side modulation DNN using the HARQ feedback. (i.e. training of the DNN may be performed via HARQ; see paragraph 46)
Regarding claims 11 Wang teaches the method as recited in claim 7, further comprising: selecting a user equipment-side (UE-side) modulation ML configuration that forms a UE-side modulation DNN for generating a modulated uplink signal; and indicating the UE-side modulation ML configuration to the UE. (i.e. the base station and/or the UE may also process uplink communications using the DNN configuration/reconfiguration process; see paragraphs 95 - 97)
Regarding claims 12 Wang teaches the method as recited in claim 11, wherein indicating the UE-side modulation ML configuration to the UE further comprises: indicating the UE-side modulation ML configuration to the UE using downlink control information (DCI). (i.e. the base station may indicate the DNN configuration to the UE via downlink control information processing; see paragraph 104)
Regarding claims 13 Wang teaches the method as recited in claim 7, wherein the BS-side modulation ML configuration is a first BS-side ML configuration, the method further comprising: receiving, from the UE, an indication of a user equipment-selected (UE-selected) UE-side demodulation ML configuration; and updating the BS-side modulation DNN using a second BS-side modulation ML configuration that is complementary to the UE-selected, UE-side demodulation ML configuration. (i.e. the base station and/or the UE may also process uplink communications using the DNN configuration/reconfiguration process; see paragraphs 95 - 97) (See also; base station processes downlink communications using a second neural network configured with complementary functionality to the first neural network. In other words, the second neural network uses a second neural network formation configuration that is complementary to the neural network formation configuration; see paragraphs 53, 96, 107, 161)
Regarding claims 14 Wang teaches the method as recited in claim 13, wherein receiving the indication of the UE-selected, UE-side demodulation ML configuration further comprises: receiving the indication of the UE-selected, UE-side demodulation ML configuration in channel state information (CSI). (i.e. the base station / UE may exchange DNN configuration information via CSI; see paragraphs 55, 168, 208)
Regarding claims 16 Wang teaches the method as recited in claim 1, wherein the at least one static algorithm module is an encoding module, and wherein transmitting the wireless communications further comprises: receiving, as input, the encoded bits from the encoding module; and generating, using a UE-side modulation DNN in the hybrid transmitter processing chain and based on the encoded bits, a modulated uplink signal. (i.e. fig. 5 shows a transmission processing chain wherein a modulator may receive encoded data from and encoder, and generate a modulated signal for wireless transmission; see paragraphs 80 - 83) (See Also; fig. 6 shows the DNN elements in the transmission chain can be for either downlink or uplink transmissions, meaning the transmission chain can either be on the UE-side or the BS-side) (See Also; DNNs in the transmitter/receiver chains can be reconfigured to replace high level operations and/or low level operations, indicating ability to change dynamically based on operating environment. This permits flexibility in transmission chains allowing DNNs and classical components to be reconfigured accordingly, the corresponds to a configuration where the DNN is modulation stage and a classic module comprises the encoding stage; see figure 5 and paragraph 90; wherein each DNN has a specific purpose (i.e. encoding, modulation etc.); see paragraph 91)
Regarding claims 17 Wang teaches the method as recited in claim 16, wherein selecting the modulation ML configuration further comprises: receiving, from a base station, an indication of a UE-side modulation ML configuration; and selecting the modulation ML configuration using the indication. (i.e. the selection of the DNN configuration/reconfiguration can occur during UE-side transmissions or uplink wherein the active transmitter chain is in the UE for DNN selection; see paragraphs 150- 153)
Regarding claims 18 Wang teaches the method as recited in claim 17, wherein receiving the indication further comprises: receiving the indication in a field of downlink control information(DCI) for a physical uplink shared channel (PUSCH). (i.e. the base station/ UU may indicate the DNN configuration to the UE via downlink control information processing; see paragraph 104)
Regarding claims 19 Wang teaches the method as recited in claim 11, wherein selecting the UE-side modulation ML configuration further comprises: selecting the UE-side modulation ML configuration from a predefined set of modulation ML configurations. (i.e. the DNN configurations may be selected for a set of training data; see paragraph 113)
Allowable Subject Matter
Claim 15 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT J LOPATA whose telephone number is (571)270-5158. The examiner can normally be reached Mon-Fri 10-7 EST.
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ROBERT J. LOPATA
Primary Examiner
Art Unit 2471
/ROBERT J LOPATA/
April 9, 2026Primary Examiner, Art Unit 2471