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 .
DETAILED ACTION
Response to Amendment
Applicant’s amendment, filed 06/17/2026, has been entered and carefully considered. Claims 1-30 are currently pending.
Response to Arguments
Applicant's arguments filed 1-30 have been fully considered but they are not persuasive. Applicant argued that
Timo does not disclose at least a UE configured to:
transmit an indication of a first training dataset associated with a network- based auto-encoder, wherein the first training dataset comprises one or more channel metrics obtained by the UE, a precoding metric, or both; and receive, in response to transmitting the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, wherein the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder, as recited in independent claim 1.
Examiner respectfully disagrees.
Timo discloses in various paragraphs as recited below in rejection of independent claims.
Specifically, Fig. 6, paragraphs 0177-0189 disclose the first communications node 521 may transmit an AE-decoder capability to the second communications node 511, for example, using Radio Resource Control (RRC) signalling on joining the communications network 100. The first communications node 521 has access to one or more trained NN-based AE-encoder models 521-A, 521-B for encoding the CSI and the second communications node 511 has access to one or more trained NN-based AE-decoder models 511-A, 511-B for decoding the CSI provided by the first communications node 521 (reads on transmitting an indication….)
Then the first communications node 521 may receive, from the second communications node 511, a decoder configuration comprising an indication to use an AE CSI reporting mode for which the first communications node 521 is configured to use the trained NN-based AE-encoder model 521-A compatible with the indicated NN-based AE-decoder model 511-A.
[0181] The indication of the NN-based AE-decoder model 511-A may be received from the second communications node 511 with a CSI reporting configuration.
[0182] The CSI reporting configuration may indicate one or more CSI feedback parameters associated with NN-based AE.
In action 603 of FIG. 6, the first communications node 521 selects a trained NN-based AE-encoder model 521-A out of the one or more trained NN-based AE-encoder models 511-A, 521-B to use for the NN-based AE-encoder based on the received indication of the NN-based AE-decoder model 511-A such that the selected trained NN-based AE-encoder model 521-A is compatible with the indicated NN-based AE-decoder model 511-A.
[0188] The trained NN-based AE-encoder models 521-A, 521-B may be either software e.g., running in a docker container, or a specialized hardware that only runs those trained NNs.
[0189] In action 604 of FIG. 6, the first communications node 521 transmits, e.g., over the radio-based air interface 123-UL and using a standardised radio transmission protocol, the CSI to the second communications node 511 based on output from the selected trained NN-based AE-encoder model 521-A.
Applicant stated that Timo does not disclose transmission of "an indication of a first training dataset" as claimed. For example, a previously trained model is not the same as and does not disclose a "training dataset," as recited in independent claim 1. Moreover, Timo is silent as to an "indication" of any "first training dataset" being transmitted from the UE, as claimed.
Examiner respectfully disagrees.
Applicant’s claim language discloses first training dataset comprises one or more channel metrics, which reads on CSI reporting, data packet, CSI resource configuration may comprise one or more of: a configuration of a number of CSI-RS ports (e.g. 16 or 32), of the second communications node 111, a CSI-RS port layout (e.g., parameters N1, N2, O1, O2), and an indication about CSI And then Then the first communications node 521 may receive, from the second communications node 511, a decoder configuration (second data) comprising an indication to use an AE CSI reporting mode for which the first communications node 521 is configured to use the trained NN-based AE-encoder model 521-A compatible with the indicated NN-based AE-decoder model 511-A. [0199] An NN-based CSI decoder in the second communications node 511, of which NN implementation is shared, partly or fully, to the manufacturers of the first communications node 521. [0200] Signalling of a NN decoder model identifier, e.g., a decoder index, from the second communications node 511 to the first communications node 521 to be used by the first communications node 521 to determine the encoder model to use. For example, the first communications node 521 may determine the encoder model to be used based on the NN decoder model identifier by selecting an encoder model which is compatible with the identified decoder model. The terms first/second training dataset are broad and can be read on various type of information disclosed in messages, indication, reporting, packets etc.
Based on broadest reasonable interpretation of training data sets, Timo discloses the above argued limitations of claim 1.
Similar arguments are applied to claims 1, 12, 23 and 30.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 4-7, 11-13, 15-18, 23-24, 26 and 30 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Timo et al. (US 2025/0047346 A1).
Regarding claims 1 and 23, Timo discloses a user equipment (UE), comprising: one or more memories storing processor-executable code; and one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the UE to (Fig. 8 discloses processor 804, memory 802, receiving unit 810 and transmitting unit 830, paragraph 0246):
transmit an indication of a first training dataset associated with a network-based auto-encoder (Fig. 5b, paragraph 0172-0175 discloses UE estimates the downlink channel using configured downlink reference signal(s), e.g., CSI-RS. For example, the UE estimates the downlink channel. The UE uses a trained AE (auto encoder) encoder to compress the estimated channel and the first communications node 521 has access to one or more trained NN-based AE-encoder models 521-A, 521-B for encoding the CSI. The binary codework is reported/transmitted to the network over an uplink control channel and/or data channel),
wherein the first training dataset comprises one or more channel metrics obtained by the UE, a precoding metric, or both (Fig. 5b, paragraph 0172-0175, Fig. 4b, paragraphs 0048-0054 disclose the UE reports CSI (e.g., channel quality index (CQI), precoding matrix indicator (PMI), rank indicator (RI)) to the wireless communication network over an uplink control channel and/or over a data channel);
and receive, in response to transmitting the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder (Fig. 5b, paragraph 0172-0175 discloses the second communications node 511 may comprise an AE-decoder 511-1. The second communications node 511 has access to one or more trained NN-based AE-decoder models 511-A, 511-B for decoding the CSI provided by the first communications node 521. Fig. 4b, paragraphs 0048-0054 disclose the network uses a trained AE decoder to reconstruct the estimated channel. The decompressed output of the AE decoder is used by the network in, for example, MIMO precoding, scheduling, and link adaption. The architecture of an AE, e.g., structure, number of layers, nodes per layer, activation function etc., may need to be tailored for each particular use case, e.g., for CSI reporting),
wherein the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder (paragraphs 0174-0175 also 0177-0178, FIG. 6, the first communications node 521 may receive, from the second communications node 511, a decoder configuration comprising an indication to use an AE CSI reporting mode for which the first communications node 521 is configured to use the trained NN-based AE-encoder model 521-A compatible with the indicated NN-based AE-decoder model 511-A. In action 602 of FIG. 6, the first communications node 521 receives, from the second communications node 511, an indication of an NN-based AE-decoder model 511-A out of the one or more trained NN-based AE-decoder models 511-A, 511-B).
Regarding claims 12 and 30, Timo discloses a network entity, comprising: one or more memories storing processor-executable code; and one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the network entity to (Fig. 9 discloses processor 904, memory 902, receiving unit 920 and transmitting unit 910, paragraph 0247):
obtain, from a user equipment (UE), an indication of a first training dataset associated with a network-based auto-encoder (Fig. 5b, paragraph 0172-0175 disclose UE estimates the downlink channel (or important features thereof) using configured downlink reference signal(s), e.g., CSI-RS. For example, the UE estimates the downlink channel. The UE uses a trained AE (auto encoder) encoder to compress the estimated channel and the first communications node 521 has access to one or more trained NN-based AE-encoder models 521-A, 521-B for encoding the CSI. The binary codework is reported/transmitted to the network over an uplink control channel and/or data channel), wherein the first training dataset comprises one or more channel metrics obtained by the UE, a precoding metric, or both (Fig. 5b, paragraph 0172-0175, Fig. 4b, paragraphs 0048-0054 disclose the UE reports CSI (e.g., channel quality index (CQI), precoding matrix indicator (PMI), rank indicator (RI)) to the wireless communication network over an uplink control channel and/or over a data channel);
and provide for output, in response to obtaining the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder (Fig. 5b, paragraph 0172-0175 discloses the second communications node 511 may comprise an AE-decoder 511-1. The second communications node 511 has access to one or more trained NN-based AE-decoder models 511-A, 511-B for decoding the CSI provided by the first communications node 521. Fig. 4b, paragraphs 0048-0054 disclose the network uses a trained AE decoder to reconstruct the estimated channel. The decompressed output of the AE decoder is used by the network in, for example, MIMO precoding, scheduling, and link adaption. The architecture of an AE, e.g., structure, number of layers, nodes per layer, activation function etc., may need to be tailored for each particular use case, e.g., for CSI reporting),
wherein the second training dataset is based at least in part on training the network-based auto-encoder using first training inputs obtained from the first training dataset (Paragraph 0113 discloses the network constructs a training dataset for each UE AE encoder by logging the UE's CSI report received over the air interface, e.g., the AE encoder output, together with the network's SRS-based estimate of the UL channel. The resulting dataset may then be used to train the network's AE decoder without having to know the UE's AE encoder since the network knows, from the dataset, both the input and the output of the encoder)
and wherein the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder (paragraphs 0174-0175 also 0177-0178, FIG. 6, the first communications node 521 may receive, from the second communications node 511, a decoder configuration comprising an indication to use an AE CSI reporting mode for which the first communications node 521 is configured to use the trained NN-based AE-encoder model 521-A compatible with the indicated NN-based AE-decoder model 511-A. In action 602 of FIG. 6, the first communications node 521 receives, from the second communications node 511, an indication of an NN-based AE-decoder model 511-A out of the one or more trained NN-based AE-decoder models 511-A, 511-B).
Regarding claims 2 and 24, Timo discloses wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to: train the UE-based encoder using the second training dataset (Paragraph 0187, in action 603 of FIG. 6, the first communications node 521 selects a trained NN-based AE-encoder model 521-A out of the one or more trained NN-based AE-encoder models 511-A, 521-B to use for the NN-based AE-encoder based on the received indication of the NN-based AE-decoder model 511-A such that the selected trained NN-based AE-encoder model 521-A is compatible with the indicated NN-based AE-decoder model 511-A).
Regarding claims 4 and 26, Timo discloses wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to: receive information associated with the network-based auto-encoder, wherein transmitting the indication of the first training dataset is based at least in part on the information (Paragraphs 0178-0179 disclose the first communications node 521 may receive, from the second communications node 511, a decoder configuration comprising an indication to use an AE CSI reporting mode for which the first communications node 521 is configured to use the trained NN-based AE-encoder model 521-A compatible with the indicated NN-based AE-decoder model 511-A. In action 602 of FIG. 6, the first communications node 521 receives, from the second communications node 511, an indication of an NN-based AE-decoder model 511-A out of the one or more trained NN-based AE-decoder models 511-A, 511-B).
Regarding claims 5 and 16, Timo discloses wherein the information associated with the network-based auto-encoder comprises an identifier associated with the network-based auto-encoder (Paragraphs 0200-0201 disclose the first communications node 521 may determine the encoder model to be used based on the NN decoder model identifier by selecting an encoder model which is compatible with the identified decoder model. In some embodiments the first communications node 521 may select a most optimal compatible encoder model out of a set of compatible encoder models. Further step 0602, paragraph 0179 discloses receive indication of NN-based AE-decoder model identifier from second communication node).
Regarding claims 6 and 17, Timo discloses wherein the information associated with the network-based auto-encoder indicates a set of communication parameters associated with the one or more channel metrics obtained by the UE (Paragraphs 0181-0183 disclose the indication of the NN-based AE-decoder model 511-A may be received from the second communications node 511 with a CSI reporting configuration. The CSI reporting configuration may indicate one or more CSI feedback parameters associated with NN-based AE. The CSI configuration may indicate whether the CSI report shall use periodic PUCCH or aperiodic PUSCH to convey the CSI report to the second communications node 511).
Regarding claims 7 and 18, Timo discloses wherein the information associated with the network-based auto-encoder indicates at least one of a time period or a location in which the network-based auto-encoder is active (Paragraphs 0181-0183, 0274-0275 disclose the communication system 3300 further includes the UE 3330 already referred to. It’s hardware 3335 may include a radio interface 3337 configured to set up and maintain a wireless connection 3370 with a base station serving a coverage area in which the UE 3330 is currently located).
Regarding claim 11, Timo discloses receiving an indication that the second training dataset is for training the UE-based encoder of the UE (Paragraph 0113 discloses the network constructs a training dataset for each UE AE encoder by logging the UE's CSI report received over the air interface, e.g., the AE encoder output, together with the network's SRS-based estimate of the UL channel. The resulting dataset may then be used to train the network's AE decoder without having to know the UE's AE encoder since the network knows, from the dataset, both the input and the output of the encoder).
Regarding claim 13, Timo discloses wherein the one or more processors are individually or collectively further operable to execute the code to cause the network entity to: input the first training dataset into a network-based encoder of the network-based auto-encoder, wherein an output of the network-based encoder provides the second training dataset (Fig. 5b, paragraph 0172-0175 discloses the second communications node 511 may comprise an AE-decoder 511-1. The second communications node 511 has access to one or more trained NN-based AE-decoder models 511-A, 511-B for decoding the CSI provided by the first communications node 521. Fig. 4b, paragraphs 0048-0054 disclose the network uses a trained AE decoder to reconstruct the estimated channel. The decompressed output of the AE decoder is used by the network in, for example, MIMO precoding, scheduling, and link adaption. The architecture of an AE, e.g., structure, number of layers, nodes per layer, activation function etc., may need to be tailored for each particular use case, e.g., for CSI reporting).
Regarding claim 15, Timo discloses wherein the one or more processors are individually or collectively further operable to execute the code to cause the network entity to: provide for output information associated with the network-based auto-encoder, wherein obtaining the indication of the first training dataset is based at least in part on the information (Paragraphs 0178-0179 disclose the first communications node 521 may receive, from the second communications node 511, a decoder configuration comprising an indication to use an AE CSI reporting mode for which the first communications node 521 is configured to use the trained NN-based AE-encoder model 521-A compatible with the indicated NN-based AE-decoder model 511-A. In action 602 of FIG. 6, the first communications node 521 receives, from the second communications node 511, an indication of an NN-based AE-decoder model 511-A out of the one or more trained NN-based AE-decoder models 511-A, 511-B).
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 of this title, 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.
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.
Claims 3, 8-10, 14, 19-22, 25 and 27-29 are rejected under 35 U.S.C. 103 as being unpatentable over Timo et al. (US 2025/0047346 A1) in view of Kyung (US 2024/0380443 A1).
Regarding claims 3 and 25, Timo does not explicitly disclose transmit the second training dataset to a UE server associated with the UE; and receive an updated encoder model for the UE-based encoder from the UE server, wherein the updated encoder model is based at least in part on the second training dataset.
In an analogous art, Kyung discloses transmit the second training dataset to a UE server associated with the UE; and receive an updated encoder model for the UE-based encoder from the UE server, wherein the updated encoder model is based at least in part on the second training dataset (Figs. 3C-3E paragraphs 0081-0104 disclose the cloud server 305 can perform the online training by training the entire encoder-decoder model pair based on the collected channel data to obtain an updated model for the encoder model 303 and an updated model for the decoder model 304. For example, through the online training, the cloud server 305 can obtain updated values for at least partial weights of the encoder-decoder model pair. [0085] At step S334, after the entire encoder-decoder model pair has been trained and updated, the cloud server 305 can send updated models for the encoder 303 and the decoder 304 to the BS 302).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the technique of Kyung to the system of Timo to provide the updated model for the decoder model is sent from the UE to the BS. The encoder model is updated at the UE based on the updated model for the encoder model (Paragraph 0005).
Regarding claims 8 and 27, Timo discloses Timo does not explicitly disclose cause the UE to transmit the first training dataset as a non-compressed dataset.
In an analogous art, Kyung discloses cause the UE to transmit the first training dataset as a non-compressed dataset (Paragraphs 0056-0061 discloses provide methods and embodiments to feedback a compressed version of raw CSI to a transmitter. Based on the compressed CSI, the transmitter is able to optimally compute a precoder for precoding a transmitting signal, and also optimally decide on other transmission parameters such as RI, MCS, and the like. Further, a compression ratio used in compressing the raw CSI can be decided dynamically after the raw CSI has been estimated). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the technique of Kyung to the system of Timo to provide the updated model for the decoder model is sent from the UE to the BS. The encoder model is updated at the UE based on the updated model for the encoder model (Paragraph 0005).
Regarding claim 19, Timo does not explicitly disclose the first training dataset as a non-compressed dataset.
In an analogous art, Kyung discloses the first training dataset as a non-compressed dataset (Paragraphs 0056-0061, 0062-0066 discloses provide methods and embodiments to feedback a compressed version of raw CSI to a transmitter. Based on the compressed CSI, the transmitter is able to optimally compute a precoder for precoding a transmitting signal, and also optimally decide on other transmission parameters such as RI, MCS, and the like. Further, a compression ratio used in compressing the raw CSI can be decided dynamically after the raw CSI has been estimated). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the technique of Kyung to the system of Timo to provide the updated model for the decoder model is sent from the UE to the BS. The encoder model is updated at the UE based on the updated model for the encoder model (Paragraph 0005).
Regarding claims 9 and 28, Timo does not explicitly disclose apply a compression algorithm to the first training dataset prior to transmission.
In an analogous art, Kyung discloses apply a compression algorithm to the first training dataset prior to transmission (Paragraphs 0056-0061, 0062-0066 discloses provide methods and embodiments to feedback a compressed version of raw CSI to a transmitter. Based on the compressed CSI, the transmitter is able to optimally compute a precoder for precoding a transmitting signal, and also optimally decide on other transmission parameters such as RI, MCS, and the like. Further, a compression ratio used in compressing the raw CSI can be decided dynamically after the raw CSI has been estimated). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the technique of Kyung to the system of Timo to provide the updated model for the decoder model is sent from the UE to the BS. The encoder model is updated at the UE based on the updated model for the encoder model (Paragraph 0005).
Regarding claim 20, Timo does not explicitly disclose wherein the first training dataset comprises a compressed dataset based at least in part on a compression algorithm.
In an analogous art, Kyung discloses wherein the first training dataset comprises a compressed dataset based at least in part on a compression algorithm (Paragraphs 0056-0061, 0062-0066 discloses provide methods and embodiments to feedback a compressed version of raw CSI to a transmitter. Based on the compressed CSI, the transmitter is able to optimally compute a precoder for precoding a transmitting signal, and also optimally decide on other transmission parameters such as RI, MCS, and the like. Further, a compression ratio used in compressing the raw CSI can be decided dynamically after the raw CSI has been estimated).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the technique of Kyung to the system of Timo to provide the updated model for the decoder model is sent from the UE to the BS. The encoder model is updated at the UE based on the updated model for the encoder model (Paragraph 0005).
Regarding claims 10, 21 and 29, Timo does not explicitly disclose wherein the compression algorithm comprises at least one of a machine-learning-based algorithm or a non-machine-learning-based algorithm.
In an analogous art, Kyung discloses wherein the compression algorithm comprises at least one of a machine-learning-based algorithm or a non-machine-learning-based algorithm (Paragraphs 0062-0066 discloses There are various CSI compression algorithms, for example compressive sensing-based CSI compression and deep learning (or machine learning) based CSI compression. Compared with the compressive sensing-based CSI compression, the deep learning-based solution can provide a better reconstruction performance, for example, in terms of mean squared error, at a base station. In an embodiment, an encoder can use a deep neural network at a UE to compress original CSI and a decoder can use a deep neural network at a base station to decompress the compressed CSI and reconstruct the CSI).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the technique of Kyung to the system of Timo to provide the updated model for the decoder model is sent from the UE to the BS. The encoder model is updated at the UE based on the updated model for the encoder model (Paragraph 0005).
Regarding claim 14, Timo does not explicitly disclose wherein the one or more processors are individually or collectively further operable to execute the code to cause the network entity to: provide the first training dataset for output to a network server associated with the network entity; and obtain the second training dataset from the network server in response to providing the first training dataset for output
In an analogous art, Kyung discloses provide the first training dataset for output to a network server associated with the network entity; and obtain the second training dataset from the network server in response to providing the first training dataset for output (Figs. 3C-3E paragraphs 0081-0104 disclose the cloud server 305 can perform the online training by training the entire encoder-decoder model pair based on the collected channel data to obtain an updated model for the encoder model 303 and an updated model for the decoder model 304. For example, through the online training, the cloud server 305 can obtain updated values for at least partial weights of the encoder-decoder model pair. [0085] At step S334, after the entire encoder-decoder model pair has been trained and updated, the cloud server 305 can send updated models for the encoder 303 and the decoder 304 to the BS 302).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the technique of Kyung to the system of Timo to provide the updated model for the decoder model is sent from the UE to the BS. The encoder model is updated at the UE based on the updated model for the encoder model (Paragraph 0005).
Regarding claim 22, Timo discloses provide for output an indication that the second training dataset is for training the UE-based encoder of the UE (paragraphs 0174-0175 also 0177-0178, FIG. 6, the first communications node 521 may receive, from the second communications node 511, a decoder configuration comprising an indication to use an AE CSI reporting mode for which the first communications node 521 is configured to use the trained NN-based AE-encoder model 521-A compatible with the indicated NN-based AE-decoder model 511-A. In action 602 of FIG. 6, the first communications node 521 receives, from the second communications node 511, an indication of an NN-based AE-decoder model 511-A out of the one or more trained NN-based AE-decoder models 511-A, 511-B).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ahmed et al. (US 2024/0364393 A1) discloses method may include a UE transmitting CSI-RS based CSI feedback generated according to a configured codebook, generating DMRS based precoding matrix indicator feedback according to the configured codebook, and transmitting the DMRS based precoding matrix indicator feedback to a network entity
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/ROMANI OHRI/Primary Examiner, Art Unit 2413
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