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 .
This Office Action is in response to communication filed on 06/25/2026.
Claims 1-6 and 8-19 are pending and rejected. Claims 7 and 20 are cancelled.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/25/2026 has been entered.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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 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.
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 1-3, 8-16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al (US 20240313838 A1) (hereinafter “Wu’) in view of YouTube (CS480/680 Lecture 19: Attention and Transformer Networks, from the University of Waterloo, dated 07/16/2019, https://youtu.be/OyFJWRnt_AY?si=hNC8WMZKA6XoA71- ; Video transcript) (hereinafter “Waterloo”).
Regarding claim 1, Wu discloses a method (see para. [0004], [0008]- [0016] discloses method for CSI), comprising:
acquiring, by a processor of a user equipment (UE) that is in wireless communication with a base station node (see Fig. 10, para. [0005] discloses apparatus for wireless communication at a UE including processor and memory, configured to receive configuration associated with a first channel state information scheme), channel state information (CSI) at least associated with the wireless communication (see Fig. 10, para. [0005] discloses apparatus for wireless communication at a UE including processor and memory, configured to receive configuration associated with a first channel state information scheme); and
compressing, by the processor, the CSI into CSI feedback for the base station node via an artificial intelligence (Al) or machine-learning (ML)-based encoder (see Fig. 3, para. [0109]-[0110] discloses join training implemented in Machine Learning in the UE; the auto encoder may involve one or more neural networks of the auto encoder in the UE; the auto encoder may provide an output which may be referred to as a feedback vector).
Wu fails to disclose but Waterloo teaches that implements multi-head re-attention (MHRA) (see transcript 41:32 Multihead Attention),
wherein the MHRA defines new attention based on a linear combination of an attention score for query-key pairs to generate new attention maps with features for use by the Al or ML-based encoder that processes the CSI (see transcript 11:29 query q based on a key k for attention mechanism; transcript 33:32 encoder-decoder based attention).
Wu and Waterloo are considered analogous to the claimed invention because both are in the technical fields that allow evaluation and prediction of parameters such as CSI using AI, artificial intelligence or ML, Machine Learning.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Wu to include the Multi Head Re-attention as described by Waterloo.
The motivation to combine both references would come from improving CSI feedback operation.
Regarding claim 2, Wu discloses a method (see para. [0004], [0008]- [0016] discloses method for CSI), wherein the CSI acquired by the UE is raw CSI (see para. [0038] discloses a base station transmitting a reference signal (CSI-RS) that may be monitored or received by a UE. A receiving UE may perform calculations based on measured or predicted characteristics of the reference signal to support various techniques of estimation), further comprising, prior to compressing the CSI into the CSI feedback, pre- processing, by the processor, the CSI into pre-processed CSI using a pre-processing function of the UE (see Fig. 6, para [0134] discloses preprocessing performed according to a sequence of operation on the input values, such as format compatible with the machine learning algorithm).
Regarding claim 3, Wu discloses a method (see para. [0004], [0008]- [0016] discloses method for CSI).
Wu fails to disclose but Waterloo teaches wherein implementing the convolutional projection includes:
applying a square-shaped kernel that moves around a layer of CSI elements to capture correlations between the CSI elements for each of Key, Query, and Value parameters (see transcript 22:24 Kernel; transcript 27:09 attention value) and
applying a flattening function to flatten the correlations in the CSI elements as captured for each of the Key, Query, and Value parameters into a corresponding word for each of the Key, Query, and Value parameters (see transcript 30:11 machine translation).
Wu and Waterloo are considered analogous to the claimed invention because both are in the technical fields that allow evaluation and prediction of parameters such as CSI using AI, artificial intelligence or ML, Machine Learning.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Wu to include the Key, Query and Value parameter as described by Waterloo.
The motivation to combine both references would come from improving CSI feedback operation.
Regarding claim 8, Wu discloses a method (see para. [0004], [0008]- [0016] discloses method for CSI), wherein the Al or ML-based encoder includes at least one of a convolutional transformer (CVT) block (This part is optional), a convolutional transformer with re-attention (CVT-RA) block (This part is optional), or expandable kernels to process the CSI (see para. [0039]; [0096]; [0133]-[0134] machine learning techniques for channel compression including Convolutional Neural Networks, describe machine learning algorithm with input layers).
Regarding claim 9, Wu discloses a method (see para. [0004], [0008]- [0016] discloses method for CSI), wherein the Al or ML-based encoder includes expandable kernels and at least of a convolution neural network (CNN) (see para. [0039];[0096];[0133]-[0134] machine learning techniques for channel compression including Convolutional Neural Networks, describe machine learning algorithm with input layers), a deep neural network (DNN) (This part is optional), or a transformer to process the CSI (This part is optional).
Regarding claim 10, Wu discloses a method (see para. [0004], [0008]- [0016] discloses method for CSI), comprising:
receiving, at a base station node, channel state information (CSI) feedback from a user equipment (UE) (see para. [0084] discloses the UE may report feedback that indicated precoding, and the feedback may correspond to a number of configured beams), the CSI feedback being generated from CSI acquired by the UE via an artificial intelligence (AI) (see para. [0096];[0107]-[0110] discloses CSI compression schemes that may include and encoder such as CSI report training a decoder, such techniques may include one or more neural networks, that may be implemented in one or both of a transmission device (e.g. UE) the neural networks include convolutional neural networks) or machine-learning (ML)-based encoder of the UE that implements multi-head re-attention (MHRA) to compress the CSI into the CSI feedback,
generating, by a processor of the base station node, reconstructed CSI by at least decompressing the CSI feedback via an Al or ML-based decoder of the base station node (see para. [0039];[0090];[0095];[0096] discloses CSI report or related channel information may be compressed or decompressed in, machine learning (include one or more Neural Networks that may be implemented at the UE or the base station) may be used to support compression schemes; machine learning techniques may be used for the wireless communication system to support CSI compression schemes).
Wu fails to disclose but Waterloo teaches wherein the MHRA defines new attention based on a linear combination of an attention score for query-key pairs to generate new attention maps with features for use by the Al or ML-based encoder that processes the CSI (see transcript 11:29 attention mechanism and query key; see transcript 1:16:45 prediction what the next word is).
Wu and Waterloo are considered analogous to the claimed invention because both are in the technical fields that allow evaluation and prediction of parameters such as CSI.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Wu to include Multi Head Re-attention as described by Waterloo.
The motivation to combine both references would come from improving CSI feedback operation.
Regarding claim 11, Wu discloses a method (see para. [0004], [0008]- [0016] discloses method for CSI), further comprising performing, by a processor of the base station node, one or more tasks based on the reconstructed CSI (see Fig. 10, para. [0033]; [0151] devices supporting CSI techniques, and processor).
Regarding claim 12, Wu discloses a method (see para. [0004], [0008]- [0016] discloses method for CSI), wherein the one or more tasks include scheduling beamforming for one or more antennas of the base station node (see para. [0079]-[0084] discloses base station equipped with multiple antennas employing MIMO or beamforming; the use of multiple antennas to conduct beamforming operations, some signals may be transmitted multiple times in different directions).
Regarding claim 13, Wu discloses a method (see para. [0004], [0008]- [0016] discloses method for CSI), wherein the base station node is a gNodeB of a wireless carrier network (see para. [0048];[0050] discloses next generation gNodeB or gNB; the UE able to communicate with the gNBs).
Regarding claim 14, Wu discloses a method (see para. [0004], [0008]- [0016] discloses method for CSI).
Wu fails to disclose but Waterloo teaches, wherein the Al or ML-based decoder includes at least one of a convolutional transformer (CVT) block or a convolutional transformer with re-attention (CVT-RA) block with an MHRA function to process the CSI feedback (see transcript 41:32 Multihead Attention; transcript 33:32 Transformer, Encoder decoder based attention).
Wu and Waterloo are considered analogous to the claimed invention because both are in the technical fields that allow evaluation and prediction of parameters such as CSI.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Wu to include the multi head re-attention and transformer as described by Waterloo.
The motivation to combine both references would come from improving CSI feedback operation.
Regarding claim 15, Wu discloses an apparatus implementable in a user equipment (UE) that is in wireless communication with a base station node (see Fig. 10, para. [0005] discloses apparatus for wireless communication at a UE including processor and memory, configured to receive configuration associated with a first channel state information scheme), comprising:
a transceiver configured to communicate wirelessly (see Fig. 8, para [0146];[0148] discloses device that supports CSI and channel compression techniques; transceiver module in the UE); and
a processor coupled to the transceiver (see Fig. 10 (processor 1040 and transceiver 1015 coupled)) and configured to perform operations comprising:
acquiring channel state information (CSI) at least associated with the wireless communication (see para [0149] discloses communication manager or various components configured to perform various operations such as receiving monitoring, transmitting, etc.); and
compressing the CSI into CSI feedback for the base station node via an artificial intelligence (AI) or machine-learning (ML)-based encoder (see Fig. 3, para. [0109]-[0110] discloses join training implemented in Machine Learning in the UE; the auto encoder may involve one or more neural networks of the auto encoder in the UE; the auto encoder may provide an output which may be referred to as a feedback vector).
Wu fails to disclose but Waterloo teaches that implements (see transcript 41:32 Multihead Attention),
wherein the MHRA defines new attention based on a linear combination of an attention score for query-key pairs to generate new attention maps with features for use by the Al or ML-based encoder that processes the CSI (see transcript 11:29 Attention mechanism, query key for attention generation; see transcript 1:16:45 Prediction).
Wu and Waterloo are considered analogous to the claimed invention because both are in the technical fields that allow evaluation and prediction of parameters such as CSI.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Wu to include the Multi Head Re-Attention n as described by Waterloo.
The motivation to combine both references would come from improving CSI feedback operation.
Regarding claim 16, Wu discloses an apparatus (see Fig. 10, para. [0005] discloses apparatus for wireless communication at a UE including processor and memory, configured to receive configuration associated with a first channel state information scheme).
Wu fails to disclose but Waterloo teaches, wherein implementing the convolutional projection includes:
applying a square-shaped kernel that moves around a layer of CSI elements to capture correlations between the CSI elements for each of Key, Query, and Value parameters (see transcript 22:24 Kernel; transcript 27:09 attention value); and
applying a flattening function to flatten the correlations in the CSI elements as captured for each of the Key, Query, and Value parameters into a corresponding word for each of the Key, Query, and Value parameters (see transcript 30:11 machine translation).
Wu and Waterloo are considered analogous to the claimed invention because both are in technical fields that allow evaluation and prediction of parameters such as CSI.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Wu to include the Key, Query and Value parameters as described by Waterloo.
The motivation to combine both references would come from improving CSI feedback operation.
Regarding claim 19, Wu discloses an apparatus (see Fig. 10, para. [0005] discloses apparatus for wireless communication at a UE including processor and memory, configured to receive configuration associated with a first channel state information scheme).
Wu fails to disclose but Waterloo teaches, wherein implementing the MHRA includes processing a layer of CSI elements via a convolutional transformer with re- attention (CVT-RA) block of the Al or ML-based encoder that comprises an MHRA function (see Waterloo 41:00 discloses Multi Head Attention; 52:30 discloses Transformer).
Wu and Waterloo are considered analogous to the claimed invention because both are in the technical fields that allow evaluation and prediction of parameters such as CSI.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Wu to include the convolutional transformers as described by Waterloo.
The motivation to combine both references would come from improving CSI feedback operation.
Vahdat is added here to further clarify the Multi Head attention.
Vahdat teaches a method wherein implementing the MHRA includes processing a layer of CSI elements via a convolutional transformer with re- attention (CVT-RA) block of the Al or ML-based encoder that comprises an MHRA function (see Fig. 3, Fig. 5, para. [0034];[0058]-[0061] discloses a multi head attention mechanism (MHA) ; neural network including a multi head attention encoder that may include layers that perform functions, the input is the CSI for multiple UEs).
Wu and Vahdat are considered analogous to the claimed invention because both are in the field of wireless communication methods and CSI compression.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Wu to include the Multi Head Re-Attention as described by Vahdat.
The motivation to combine both references would come from improving CSI feedback operation.
Claims 4-5 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al (US 20240313838 A1) (hereinafter “Wu’) in view of YouTube (CS480/680 Lecture 19: Attention and Transformer Networks, from the University of Waterloo, dated 07/16/2019, https://youtu.be/OyFJWRnt_AY?si=hNC8WMZKA6XoA71- ; Video transcript) (hereinafter “Waterloo”), as applied to claim 1 above and further in view of Chen et al (US 20250055531 A1) (hereinafter “Chen”).
Regarding claim 4, Wu discloses a method (see para. [0004], [0008]- [0016] discloses method for CSI).
Wu fails to disclose but Chen teaches, further comprising, prior to compressing the CSI into the CSI feedback (see para. [0004];[0008] discloses CSI compression feedback method), translating, by the processor, the CSI that is in an antenna- frequency domain to a beam-delay domain to reduce an entropy of the CSI (see para. [0057] discloses reduction of feedback overhead by transforming CSI information from frequency domain to angle delay domain).
Wu and Chen are considered analogous to the claimed invention because both are in the field of wireless communication methods and CSI compression.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Wu to include the beam-delay domain as described by Chen.
The motivation to combine both references would come from reducing overhead of CSI feedback operation.
Regarding claim 5, Wu discloses a method (see para. [0004], [0008]- [0016] discloses method for CSI).
Wu fails to disclose but Chen teaches, wherein implementing the expandable kernels includes adjusting sizes of kernels as kernel striding occurs over an input layer of CSI elements in the beam-delay domain based on magnitudes of delays indicated in the beam-delay domain (see para. [0057]; [0161]-[0162] discloses reduction of feedback overhead by transforming CSI information from frequency domain to angle delay domain; restored convolutional neural network including seven convolutional layers, with increase kernel convolution).
Wu and Chen are considered analogous to the claimed invention because both are in the field of wireless communication methods and CSI compression.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Wu to include kernels as described by Chen.
The motivation to combine both references would come from reducing overhead of CSI feedback operation.
Regarding claim 17, Wu discloses an apparatus (see Fig. 10, para. [0005] discloses apparatus for wireless communication at a UE including processor and memory, configured to receive configuration associated with a first channel state information scheme).
Wu fails to disclose but Chen teaches, wherein the operations further comprise, prior to compressing the CSI into the CSI feedback (see para. [0004];[0008] discloses CSI compression feedback method), translating the CSI that is in an antenna-frequency domain to a beam-delay domain to reduce an entropy of the CSI (see para. [0057] discloses reduction of feedback overhead by transforming CSI information from frequency domain to angle delay domain).
Wu and Chen are considered analogous to the claimed invention because both are in the field of wireless communication methods and CSI compression.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Wu to include the beam-delay domain as described by Chen.
The motivation to combine both references would come from reducing overhead of CSI feedback operation.
Regarding claim 18, Wu discloses an apparatus (see Fig. 10, para. [0005] discloses apparatus for wireless communication at a UE including processor and memory, configured to receive configuration associated with a first channel state information scheme).
Wu fails to disclose but Chen teaches, wherein implementing the expandable kernels includes adjusting sizes of kernels as kernel striding occurs over an input layer of CSI elements in the beam-delay domain based on magnitudes of delays indicated in the beam-delay domain (see para. [0161]-[0162] discloses restored convolutional neural network including seven convolutional layers, with increase kernel convolution).
Wu and Chen are considered analogous to the claimed invention because both are in the field of wireless communication methods and CSI compression.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Wu to include the size of kernels as described by Chen.
The motivation to combine both references would come from improving CSI feedback operation.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Wu et al (US 20240313838 A1) (hereinafter “Wu’) in view of YouTube (CS480/680 Lecture 19: Attention and Transformer Networks, from the University of Waterloo, dated 07/16/2019, https://youtu.be/OyFJWRnt_AY?si=hNC8WMZKA6XoA71- ; Video transcript) (hereinafter “Waterloo”) as applied to claim 1 above and further in view of Vahdat et al (US 20210144779 A) (hereinafter “Vahdat”).
Regarding claim 6, Wu discloses a method (see para. [0004], [0008]- [0016] discloses method for CSI).
Wu fails to disclose but Waterloo teaches, wherein implementing the MHRA includes processing a layer of CSI elements via a convolutional transformer with re-attention (CVT- RA) block of the Al or ML-based encoder that comprises an MHRA function (see transcript 41:32 Multihead Attention; transcript 33:32 Transformer, Encoder decoder based attention).
Wu and Waterloo are considered analogous to the claimed invention because both are in the technical fields that allow evaluation and prediction of parameters such as CSI.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Wu to include the convolutional transformers as described by Waterloo.
The motivation to combine both references would come from improving CSI feedback operation.
Vahdat is added here to further clarify the Multi Head attention.
Vahdat teaches a method wherein implementing the MHRA includes processing a layer of CSI elements via a convolutional transformer with re- attention (CVT-RA) block of the Al or ML-based encoder that comprises an MHRA function (see Fig. 3, Fig. 5, para. [0034];[0058]-[0061] discloses a multi head attention mechanism (MHA); neural network including a multi head attention encoder that may include layers that perform functions, the input is the CSI for multiple UEs).
Wu and Vahdat are considered analogous to the claimed invention because both are in the field of wireless communication methods and CSI compression.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Wu to include the processing as described by Vahdat.
The motivation to combine both references would come from improving CSI feedback operation.
Response to Arguments
Applicant’s arguments, see pages 8-10, filed 06/25/2026, with respect to the rejection(s) of claim 1, 10 and 15 under35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Waterloo.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUIS GUILLERMO LEMA LEMOS whose telephone number is (571)-272-5710. The examiner can normally be reached M-F 8-5 EST.
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/LUIS GUILLERMO LEMA LEMOS/Examiner, Art Unit 2419
/Nishant Divecha/Supervisory Patent Examiner, Art Unit 2419
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