DETAILED ACTION
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 (IDSs) submitted on 3/25/2026 has been entered and considered by the examiner.
Claim Objections
Claims 12, 15, and 18 are objected to because of the following informalities: The acronym "MIMO” is utilized without first defining it. Appropriate correction is required.
35 USC 112 CLAIM REJECTIONS
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 6 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding claim 6, it recites “a transmitter transformer encoder that takes, as input, the set of embedding vectors”. Therefore, it is not clear to the Examiner if this is a different set of embedding vectors or if it was supposed to be “the set of linear embedding vectors”. Since claim 2 refers to “a set of linear embedding vectors” and the other variations of sets of embedding vectors are listed until claims 3 and 4, Examiner interprets the claims to read “a transmitter transformer encoder that takes, as input, the set of linear embedding vectors”.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-30 of US Patent 11871261 in view of U.S. Patent Publication 2026/0040120. Please see the direct claim comparison below.
Instant Application Claim 1
11871261 Claim 1
An apparatus for wireless communication at a user equipment (UE), comprising:
A user equipment (UE) for wireless communication, comprising:
one or more memories; and
one or more processors, coupled to the one or more memories, which, individually or in any combination, are operable to cause the apparatus to:
one or more memories; and
one or more processors, coupled to the one or more memories, configured to:
receive a transformer configuration that includes a transmitter neural network configured to be used to generate at least one latent vector corresponding to one or more channel state information (CSI) feedback tasks of a plurality of CSI feedback tasks associated with a transformer-based cross-node machine learning system; and
receive a transformer configuration that includes a transmitter neural network configured to be used to generate at least one latent vector corresponding to one or more computation tasks of a plurality of computation tasks associated with a transformer-based cross-node machine learning system, wherein the transformer-based cross-node machine learning system comprises the transmitter neural network instantiated by the UE; and
transmit the at least one latent vector based at least in part on instantiating the transmitter neural network.
transmit the at least one latent vector based at least in part on instantiating the transmitter neural network.
However, instant application does not specifically disclose at least one latent vector corresponding to one or more computation tasks of a plurality of computation tasks, although one of ordinary skill in the art would understand CSI feedback would be a computation task.
Hindy teaches a use equipment (UE) configured with multiple artificial intelligence (AI) models each of which has been configured (e.g., trained) based on training data sets corresponding to one or more of different conditions (Abstract). He further teaches at least one latent vector corresponding to one or more computation tasks of a plurality of computation tasks (Para. 0092-0093 and 0174; An aperiodic trigger state in turn is defined as a list of up to 16 aperiodic CSI Report Settings, identified by a CSI Report Setting ID for which the UE calculates simultaneously CSI and transmits it on the scheduled PUSCH transmission).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to utilize the teachings as in Hindy with the teachings as in Pat. No. 11871261. The motivation for doing so would have been to allow the AI models to more accurately generate measurement reports (e.g., channel state information (CSI) measurement reports) based on the current conditions of the UE at the time the measurement report is generated (Hindy in Para. 0005).
Therefore, this is clearly an obvious type non-statutory double patenting issue.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-30 of US PG PUB 2025/0062810 in view of U.S. Patent Publication 2020/0394559. Please see the direct claim comparison below.
Instant Application Claim 1
2025/0062810 Claim 1
An apparatus for wireless communication at a user equipment (UE), comprising:
An apparatus for wireless communication at a user equipment (UE)
one or more memories; and
one or more processors, coupled to the one or more memories, which, individually or in any combination, are operable to cause the apparatus to:
one or more memories; and
one or more processors, coupled to the one or more memories, which, individually or in any combination, are operable to cause the apparatus to:
receive a transformer configuration that includes a transmitter neural network configured to be used to generate at least one latent vector corresponding to one or more channel state information (CSI) feedback tasks of a plurality of CSI feedback tasks associated with a transformer-based cross-node machine learning system; and
receive, from a network node, decoder configuration information associated with a transmitter neural network configured to be used to generate at least one latent vector corresponding to one or more computation tasks of a plurality of computation tasks associated with a query-based cross-node machine learning system; receive, from the network node, query configuration information associated with a query-based decoder; and
transmit the at least one latent vector based at least in part on instantiating the transmitter neural network.
transmit, to the network node and based at least in part on instantiation of the transmitter neural network by the UE, the at least one latent vector.
However, instant application does not specifically disclose computation tasks associated with a query-based cross-node machine learning system; receive, from the network node, query configuration information associated with a query-based decoder.
Zhang teaches a method of training a model comprising a generative network mapping a latent vector to a feature vector, wherein weights in the generative network are modelled as probabilistic distributions (Abstract). He further teaches computation tasks associated with a query-based cross-node machine learning system; receive, from the network node, query configuration information associated with a query-based decoder (Para. 0121-0123; We also analyse the feature selection pattern of the Icebreaker. We gather all the rows that have been queried with at least one feature during training acquisition and count how many features are queried for each. We repeat this for the first 5 acquisitions).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to utilize the teachings as in Zhang with the teachings as in U.S. Patent Publication 2020/0394559. The motivation for doing so would have been to improve the efficiency of an initial training period, and/or to allow the model to be deployed with relatively little training data and then obtain further observations “in-the-field”, during ongoing use (Zhang in Para. 0011).
Therefore, this is clearly an provisional obvious type non-statutory double patenting issue.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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, 7, 8, 10, 13, 17, and 19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kim et al (US2025/0055537 A1) support provided by provision 63/531057.
Regarding claims 1 and 19, Kim teaches an apparatus/method for wireless communication at a user equipment (UE) (Abstract), comprising:
one or more memories; and one or more processors, coupled to the one or more memories, which, individually or in any combination, are operable to cause the apparatus to (Para. 0125):
receive a transformer configuration that includes a transmitter neural network configured to be used to generate at least one latent vector corresponding to one or more channel state information (CSI) feedback tasks of a plurality of CSI feedback tasks associated with a transformer-based cross-node machine learning system (Figs. 6-7 and 10; Paras. 0022, 0047; 0084, 0097, and 0118; The gNB requests UE to measure downlink channel and report ground-truth CSI. Specific format for ground-truth CSI can be configured; FIG. 10 shows empirical distributions of (raw and quantized) latent vector elements with a TF-based encoder-decoder; FIG. 1 is a high level block diagram of AIML (autoencoder) supported CSI feedback scheme (V: estimated channel eigenvector 105, which is a “target CSI” in the depicted example). Target CSI can be considered as the version of CSI that the NW-side 111 should retrieve if the CSI feedback mechanism is lossless; Enhancing granularity of the input arguments (i.e., CSI feedback) to the training loss function being used for UE-side training by the 1st training entity (NW-side) providing raw (unquantized) CSI feedback (raw latent vector; ze) instead of its quantized version zq as for usual conventional cases); and
transmit the at least one latent vector based at least in part on instantiating the transmitter neural network (Figs. 6-7 and 10; Paras. 0022, 0047; 0084, 0097, and 0118; Enhancing granularity of the input arguments (i.e., CSI feedback) to the training loss function being used for UE-side training by the 1st training entity (NW-side) providing raw (unquantized) CSI feedback (raw latent vector; ze) instead of its quantized version zq as for usual conventional cases).
Regarding claim 7, Kim teaches the limitations of the previous claims. Kim further teaches wherein the transformer configuration indicates at least one of: a set of transmitter transformer encoder parameters, a position embedding matrix, or a linear projection matrix (Para. 0071; Training is to be done without NW-side decoder 614 nor its runtime image via API in place. Loss function for AI encoder 634 training is based on (raw/unquantized) CSI feedback 605, which is an output of AI encoder 604 at UE-side 602. The main subject of training is AI encoder 634 at UE-side 624 in this practice. AI encoder parameter update shall be done on ze domain, which has much richer and finer granularity than zq domain).
Regarding claim 8, Kim teaches the limitations of the previous claims. Kim further teaches wherein the transformer-based cross-node machine learning system comprises the transmitter neural network instantiated by the UE (Figs. 6-8 and 10; Paras. 0022, 0047, 0084, 0097, and 0118-0121; The method described herein may be related to Rel-18 and can be used both by the UE-side and the NW-side for the NW-first separate training case of AIML supported CSI enhancement feature).
Regarding claim 10, Kim teaches the limitations of the previous claims. Kim further teaches wherein the transmitter neural network includes an encoder, wherein channel information is used as input to the encoder, and wherein the encoder performs tasks associated with channel state information (CSI) compression (Para. 0081; The gNB, knowing UE's supported training scheme of over-the-air DS sharing for AIML-enabled CSI compression functionality in the context of NW-first separate training via UE's (capability) report, commands enabling of AIML-enabled CSI compression with over-the-air DS sharing feature support in use).
Regarding claim 13, Kim teaches an apparatus/method for wireless communication at a network entity (Abstract), comprising:
one or more memories; and one or more processors, coupled to the one or more memories, which, individually or in any combination, are operable to cause the apparatus to (Para. 0126):
receive a latent vector from a user equipment (UE), the latent vector corresponding to one or more channel state information (CSI) feedback tasks of a plurality of CSI feedback tasks associated with a transformer-based cross-node machine learning system (Figs. 6-7 and 10; Paras. 0022, 0047; 0084, 0097, and 0118; The gNB requests UE to measure downlink channel and report ground-truth CSI. Specific format for ground-truth CSI can be configured; FIG. 10 shows empirical distributions of (raw and quantized) latent vector elements with a TF-based encoder-decoder; FIG. 1 is a high level block diagram of AIML (autoencoder) supported CSI feedback scheme (V: estimated channel eigenvector 105, which is a “target CSI” in the depicted example). Target CSI can be considered as the version of CSI that the NW-side 111 should retrieve if the CSI feedback mechanism is lossless; Enhancing granularity of the input arguments (i.e., CSI feedback) to the training loss function being used for UE-side training by the 1st training entity (NW-side) providing raw (unquantized) CSI feedback (raw latent vector; ze) instead of its quantized version zq as for usual conventional cases); and
process the received latent vector using a receiver neural network, wherein the receiver neural network includes a decoder layer that includes a self-attention layer followed by a cross-attention layer that takes a mapped CSI feedback vector as key and value (Figs. 6-7 and 10; Paras. 0022-0024, 0047; 0084, 0097, and 0118; For typical autoencoder, AI encoder 106 and AI decoder 114 are trained at the same training session by the same training entity to come up with the best possible parameters at the AI encoder 106 and AI decoder 114 ([RANI #110] “Type 1: Joint training with single training entity”). Note in this case the training entity can take input to AI encoder (e.g., AI encoder input (channel eigenvectors 105) at UE-side 101) and reconstructed CSI 115 (AI decoder output at NW-side 111) as arguments of the loss function during training as depicted in FIG. 1).
Regarding claim 17, Kim teaches the limitations of the previous claims. Kim further teaches wherein the transmitter neural network includes an encoder, wherein channel information is used as input to the encoder, and wherein the encoder performs tasks associated with channel state information (CSI) compression (Figs. 6-7 and 10; Paras. 0022-0024, 0047; 0084, 0097, and 0118; At NW side 111 (depicted is network node 70), which receives as input CSI feedback information 120 from the UE side 101, the reverse operations, i.e., dequantization (112), AI decoding (114), is done to reconstruct the original channel information, in this case the channel eigenvector(s). The reconstructed channel information 115 is further post-processed at 116 (e.g., re-orthogonalization procedure), if required, and its outcome is termed as “output CSI” 118 at 3GPP).
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.
Claims 11-12, 14-15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al (US2025/0055537 A1) support provided by provision 63/531057 in view of Thangaraj et al (US 2025/0016593 A1).
Regarding claim 11, Kim teaches the limitations of the previous claims.
However, while Kim teaches the AIML model architecture can contain different layers (Para. 0077), he does not specifically disclose wherein the transmitter neural network includes a linear layer, wherein an output task embedding vector is provided, as input to the linear layer, wherein the linear layer computes a lower dimensional latent vector that represents a summary of a set of precoding vectors.
Thangaraj teaches measuring Channel State Information (CSI) associated with at least one reference signal and determining a trained Artificial Intelligence (AI) model to generate at least a portion of a report that includes the CSI associated with the at least one reference signal (Abstract). He further teaches wherein the transmitter neural network includes a linear layer, wherein an output task embedding vector is provided, as input to the linear layer, wherein the linear layer computes a lower dimensional latent vector that represents a summary of a set of precoding vectors (Paras. 0072 and 0081-0082; DNNs may typically comprise (e.g., consist of) multiple layers where each layer may comprise (e.g., consist of) linear transformation and a given non-linear activation functions).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Thangaraj with the teachings as in Kim. The motivation for doing so would have been to improve the resolution CSI feedback and can increase performance at reduced CSI-RS overhead (Thangaraj at para. 0072).
Regarding claim 12, the combination of Kim and Thangaraj teach the limitations of the previous claims. Thangaraj further teaches wherein an output of the linear layer is quantized to a latent vector using a vector quantization component, wherein the latent vector is reported to a network entity, and wherein the latent vector comprises CSI feedback for a particular MIMO stream (Para. 0033; the WTRU 102 may employ MIMO technology. Thus, in an embodiment, the WTRU 102 may include two or more transmit/receive elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals over the air interface).
Regarding claim 14, Kim teaches the limitations of the previous claims.
However, while Kim teaches the AIML model architecture can contain different layers (Para. 0077), he does not specifically disclose wherein the receiver neural network includes a linear layer, and wherein the linear layer of the receiver neural network maps a latent vector to a mapped embedding vector.
Thangaraj teaches measuring Channel State Information (CSI) associated with at least one reference signal and determining a trained Artificial Intelligence (AI) model to generate at least a portion of a report that includes the CSI associated with the at least one reference signal (Abstract). He further teaches wherein the receiver neural network includes a linear layer, and wherein the linear layer of the receiver neural network maps a latent vector to a mapped embedding vector (Paras. 0072 and 0081-0082; DNNs may typically comprise (e.g., consist of) multiple layers where each layer may comprise (e.g., consist of) linear transformation and a given non-linear activation functions).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Thangaraj with the teachings as in Kim. The motivation for doing so would have been to improve the resolution CSI feedback and can increase performance at reduced CSI-RS overhead (Thangaraj at para. 000472).
Regarding claim 15, the combination of Kim and Thangaraj teach the limitations of the previous claims. Thangaraj further teaches wherein the receiver neural network includes a receiver transformer decoder that takes, as input, the mapped embedding vector and a set of learned embedding vectors for a particular MIMO stream as precoding vector queries (Para. 0033; the WTRU 102 may employ MIMO technology. Thus, in an embodiment, the WTRU 102 may include two or more transmit/receive elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals over the air interface).
Regarding claim 18, Kim teaches the limitations of the previous claims.
However, while Kim teaches the AIML model architecture can contain different layers (Para. 0077), he does not specifically disclose wherein the receiver neural network is configured to handle multiple MIMO streams, with separate processing for each stream.
Thangaraj teaches measuring Channel State Information (CSI) associated with at least one reference signal and determining a trained Artificial Intelligence (AI) model to generate at least a portion of a report that includes the CSI associated with the at least one reference signal (Abstract). He further teaches wherein the receiver neural network is configured to handle multiple MIMO streams, with separate processing for each stream (Para. 0033; the WTRU 102 may employ MIMO technology. Thus, in an embodiment, the WTRU 102 may include two or more transmit/receive elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals over the air interface).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Thangaraj with the teachings as in Kim. The motivation for doing so would have been to improve the resolution CSI feedback and can increase performance at reduced CSI-RS overhead (Thangaraj at para. 000472).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Kim et al (US2025/0055537 A1) support provided by provision 63/531057 in view of Lee et al (US 2024/0039588 A1).
Regarding claim 11, Kim teaches the limitations of the previous claims.
However, while Kim teaches the AIML model architecture can contain different layers (Para. 0077), he does not specifically disclose wherein the decoder layer further includes: a multi-layer perceptron (MLP) that performs a post-processing task.
Lee teaches receiving a reference signal from a base station; generating a precoding vector based on the reference signal; generating a low-dimensional precoding vector by performing dimensionality reduction transformation on the precoding vector; quantizing the low-dimensional precoding vector; and transmitting the quantized low-dimensional precoding vector to the base station (Abstract). He further teaches wherein the decoder layer further includes: a multi-layer perceptron (MLP) that performs a post-processing task (Paras. 0024 and 0177; The artificial neural network may be one of a fully-connected neural network, a multi-layer perceptron, a convolutional neural network, or a transformer).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Lee with the teachings as in Kim. The motivation for doing so would have been to develop and train the receiver's encoder and the transmitter's decoder by one entity to perform expected operations (Lee at para. 0005).
Allowable Subject Matter
Claims 2-6, 9, and 20 are allowable except for the Double Patenting rejections and 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, along with filing an eTerminal Disclaimer.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENT KRUEGER whose telephone number is (303)297-4238. The examiner can normally be reached on M-F 8:00-5:00 MT.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Thier can be reached on (571) 272-2832. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KENT KRUEGER/Primary Examiner, Art Unit 2474
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