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 .
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, 4, 9, 16, 21 and 25 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al. (US 2021/0049451).
Regarding claims 1 and 25, Wang et al. disclose an apparatus (Figure 7, base station 121; Figure 2, base station 120) for wireless communication and a method of wireless communication performed at wireless node (Figure 7, base station 121; Figure 2, base station 120), comprising:
one or more memories comprising instructions (Figure 2 and paragraph 47, computer-readable storage media; Paragraph 255, computer-readable storage media comprising instructions); and
one or more processors configured to execute the instructions (Figure 2 and paragraph 47, CRM 262 may include any suitable memory or storage device such as random-access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), non-volatile RAM (NVRAM), read-only memory (ROM), or Flash memory useable to store device data 264 of the base station 120. The device data 264 includes network scheduling data, radio resource management data, beamforming codebooks, applications, and/or an operating system of the base station 120, which are executable by processor(s) 260) and cause the apparatus to:
obtain an indication that a model, associated with at least one of encoding or decoding, is to be used in association with a control channel (Figure 7 and paragraph 103, at 705 the base station 121 determines [obtaining] a neural network formation configuration. In determining the neural network formation configuration, the base station analyzes any combination of information, such as a channel type being processed by the deep neural network (e.g., downlink, uplink, data, control, etc.), transmission medium properties (e.g., power measurements, signal-to-interference-plus-noise ratio (SINR) measurements, channel quality indicator (CQI) measurements), encoding schemes, UE capabilities, BS capabilities, and so forth; Paragraph 104, The base station 121, for instance, receives message(s) [obtaining] from the UE 110 (not shown) that indicates one or more capabilities of the UE; Paragraphs 104-105, base station 121 selects neural network configuration);
output, after obtaining the indication, one or more model parameters associated with a data distribution of the control channel (Figure 7 and paragraph 106, At 710, the base station 121 communicates the neural network formation configuration to the UE 110…the base station transmits a message that includes neural network parameter configurations (e.g., weight values, coefficient values, number of filters). In some cases, the base station 121 specifies a purpose and/or processing assignment in the message, where the processing assignment indicates what channels, and/or where in a processing chain, the configured neural network applies to, such as a downlink control channel processing, an uplink data channel processing, downlink decoding processing, uplink encoding processing, etc.);
encode data by using the one or more model parameters (Figure 7 and paragraph 109, the base station 121 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; Paragraph 50, the training module 270 trains DNN(s) for different purposes, such as processing communications transmitted over a wireless communication system (e.g., encoding downlink communications); and
output the data for transmission via the control channel (Figure 7 and paragraph 109, At 720, the base station 121 communicates information based on the neural network formation configuration; Paragraph 149, UE receives downlink control channel communications from the base station and processes the communications using the neural network).
Regarding claim 4, Wang et al. disclose wherein the one or more processors are further configured to cause the apparatus to: train the model using the data distribution (Paragraph 50, The training module 270 teaches and/or trains DNNs using known input data. For instance, the training module 270 trains DNN(s) for different purposes, such as processing communications transmitted over a wireless communication system (e.g., encoding downlink communications, modulating downlink communications, demodulating downlink communications, decoding downlink communications, encoding uplink communications, modulating uplink communications, demodulating uplink communications, decoding uplink communications); and obtain, via the model and after training the model, the one or more model parameters (Paragraph 51, the training module 270 extracts learned parameter configurations from the DNN to identify the NN formation configuration elements and/or NN formation configuration, and then adds and/or updates the NN formation configuration elements and/or NN formation configuration in the neural network table 272. The extracted parameter configurations include any combination of information that defines the behavior of a neural network, such as node connections, coefficients, active layers, weights, biases, pooling, etc.).
Regarding claim 9, Wang et al. disclose wherein the one or more processors are further configured to cause the apparatus to: reset the data distribution after outputting the one or more model parameters (Figure 10 and paragraph 144, At 1025, the network entity updates the deep neural network based on the feedback. For example, the network entity (e.g., base station 121, core network server 302) analyzes multiple neural network formation configurations included in a neural network table (e.g., neural network table 216, neural network table 272, neural network table 316), and selects a second neural network formation configuration that aligns with new channel conditions indicated by the feedback).
Regarding claim 16, Wang et al. disclose an apparatus (Figure 7, UE 110; Figure 2, UE 110) for wireless communication, comprising:
one or more memories comprising instructions (Figure 2 and paragraph 43, computer-readable storage media 212 (CRM 212)… to store device data 214 of the user equipment 110. The device data 214 includes user data, multimedia data, beamforming codebooks, applications, neural network tables, and/or an operating system of the user equipment 110, which are executable by processor(s) 210); and
one or more processors configured to execute the instructions (Figure 2 and paragraph 43, The device data 214 includes user data, multimedia data, beamforming codebooks, applications, neural network tables, and/or an operating system of the user equipment 110, which are executable by processor(s) 210) and cause the apparatus to:
output an indication that a model, associated with at least one of encoding or decoding, is to be used in association with a control channel (Figure 7 and paragraph 103, at 705 the base station 121 determines a neural network formation configuration. In determining the neural network formation configuration, the base station analyzes any combination of information, such as a channel type being processed by the deep neural network (e.g., downlink, uplink, data, control, etc.), transmission medium properties (e.g., power measurements, signal-to-interference-plus-noise ratio (SINR) measurements, channel quality indicator (CQI) measurements), encoding schemes, UE capabilities, BS capabilities, and so forth; Paragraph 104, The base station 121, for instance, receives message(s) [output] from the UE 110 (not shown) that indicates one or more capabilities of the UE; Paragraphs 104-105, base station 121 selects neural network configuration);
obtain, after outputting the indication that the model is to be used, one or more model parameters associated with a data distribution of the control channel (Figure 7 and paragraph 106, At 710, the base station 121 communicates the neural network formation configuration to the UE 110…the base station transmits a message that includes neural network parameter configurations (e.g., weight values, coefficient values, number of filters). In some cases, the base station 121 specifies a purpose and/or processing assignment in the message, where the processing assignment indicates what channels, and/or where in a processing chain, the configured neural network applies to, such as a downlink control channel processing, an uplink data channel processing, downlink decoding processing, uplink encoding processing, etc.);
obtain data associated with the control channel (Figure 7 and paragraph 109, At 720, the base station 121 communicates information based on the neural network formation configuration; Paragraph 149, UE receives downlink control channel communications from the base station and processes the communications using the neural network); and
decode the data by using the one or more model parameters (Figure 7 and paragraph 109, at 725, the UE 110 recovers the information using the first neural network).
Regarding claim 21, Wang et al. disclose wherein the one or more model parameters are obtained via a user equipment (UE) (Figure 7, step 710, neural network formation configuration received by UE 110).
Claim Rejections - 35 USC § 103
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.
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.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. as applied to claim 1 above, and further in view of Choukroun et al. (US 2024/0039559).
Regarding claim 2, Wang et al. disclose the claimed invention above but do not disclose the following limitations that are disclosed by Choukroun et al.: wherein the data distribution includes a relative frequency associated with one or more codewords over a time interval (Choukroun et al. , Abstract, systems and method for training neural network based decoders for decoding error correction codes, comprising obtaining a plurality of training samples comprising one or more codewords encoded using an error correction code and transmitted over a transmission channel where the training samples are subject to gradual interference over a plurality of time steps and associate the encoded codeword(s)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang et al. with the cited disclosure from Choukroun et al. in order to outperform existing decoders while significantly reducing computing resources and/or computing time (Choukroun et al., Paragraph 103).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. as applied to claim 1 above, and further in view of Saber et al. (US 2023/0131694).
Regarding claim 6, Wang et al. disclose the claimed invention above but do not disclose the following limitations that are disclosed by Saber et al.: wherein the one or more processors, to cause the apparatus to obtain the indication that the model is to be used, are configured to cause the apparatus to: obtain a first communication indicating that the model is enabled for use (Saber et al., Paragraph 5, processor may be configured to activate the machine learning model based on model identification information received using the receiver); or obtain a second communication indicating that the model is to be reconfigured for the control channel.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang et al. with the cited disclosure from Saber et al. in order to activate the model for use to handle different channel environments, different matrix dimensions, and/or the like (Saber et al., Paragraph 44).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. as applied to claim 1 above, and further in view of Nair et al. (US 2023/0262489).
Regarding claim 12, Wang et al. disclose the claimed invention above but do not disclose the following limitations that are disclosed by Nair et al.: wherein the one or more processors, to cause the apparatus to output the one or more model parameters, are configured to cause the apparatus to: output, for transmission to a server, the one or more model parameters (Nair et al., Paragraph 16, sending a current set of model parameters to a server, the current set of model parameters being parameters of a trained machine learning model).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang et al. with the cited disclosure from Nair et al. in order to update machine learning model parameters in the server (Nair et al., Paragraph 74).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. as applied to claim 16 above, and further in view of Luo et al. (US 2020/0210816).
Regarding claim 17, Wang et al. disclose the claimed invention above but do not disclose the following limitations that are disclosed by Luo et al.: wherein the one or more processors are further configured to cause the apparatus to: obtain, by using the one or more model parameters, a prior distribution, wherein the decoding further uses the prior distribution (Luo et al., Paragraph 20, training may occur using known pairs of anticipated input and desired output data. For example, training may utilize known encoded data and decoded data pairs to train a neural network to decode subsequent encoded data into decoded data. In some examples, training may utilize known noisy encoded data and decoded data pairs to train a neural network to decode subsequent noisy encoded data into decoded data. In some examples, training may utilize known noisy encoded data and encoded data pairs to train a neural network to provide encoded data having reduced noise than input noisy encoded data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang et al. with the cited disclosure from Luo et al. in order to decode encoded data (Luo et al., Paragraph 20).
Allowable Subject Matter
Claims 3, 5, 7, 8, 10, 11, 13-15, 18-20 and 22-24 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.
The following is an examiner’s statement of reasons for allowance:
regarding claim 3, the prior art does not disclose or adequately suggest that the control channel encoding/decoding model includes an autoregressive model or a transformer, wherein the autoregressive model and the transformer are configured to predict a subsequent data value of the control channel;
regarding claims 5 and 18, the prior art does not disclose or adequately suggest encoding using a codebook that is a per-codeword power shaping codebook including power shaping parameters for codewords, the power shaping parameters being associated with model parameters;
regarding claim 7, the prior art does not disclose or adequately suggest obtaining, via the model and data distribution, the model parameters after a second communication indicating model reconfiguration for the control channel;
regarding claim 8, the prior art does not disclose or adequately suggest the data distribution being associated with a time period between obtaining a second communication indicating model reconfiguration for the control channel, and obtaining the first communication or obtaining a previous communication indicating reconfiguration;
regarding claim 10 (with further dependent claim 11), the prior art does not disclose or adequately suggest outputting model parameters to a network node after having obtained the model associated with control channel encoding/decoding;
regarding claim 13, the prior art does not include or adequately suggest that after outputting model parameters for transmission to the server, obtaining a codebook and power shaping parameters that are used by the encoding operation;
regarding claim 14, the prior art does not disclose or adequately suggest obtaining an indication that the model is not to be used in association with the control channel and outputting other data that was encoded independent of the model after that indication;
regarding claim 15, the prior art does not disclose or adequately suggest that the apparatus is a UE comprises a transceiver that obtains model indication, outputs model parameters, encodes data using the model parameters, and outputs control channel data for transmission;
regarding claim 19 (with further dependent claim 20), the prior discloses outputting indication that the mode is enabled for use but does not disclose or adequately suggest a second communication indicating that the mode is to be reconfigured for the control channel;
regarding claim 22, the prior art does not disclose or adequately suggest transmission to a UE an indication that the mode parameters were successfully obtained;
regarding claim 23, the prior art does not disclose or adequately suggest obtaining a codebook and power shaping parameters via a server and decoding using the codebook and the power shaping parameters;
regarding claim 24, the prior art does not disclose that the apparatus is a network node comprising a transceiver to output indication of model use with a control channel, obtain model parameters and obtain control channel data.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OTIS L THOMPSON, JR whose telephone number is (571)270-1953. The examiner can normally be reached Monday - Friday, 6:30am - 7:00pm.
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/OTIS L THOMPSON, JR/Primary Examiner, Art Unit 2477
July 23, 2026
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