DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/25/2024 and 12/16/2024 were filed for consideration. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 1-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al (US 2022/0247468 A1), hereinafter, “Huang” in view of R1-2211478_OPPO et al (3GPP TSG-RAN WG1 Meeting #111; Toulouse, France, November 14th-181h, 2022; Source: OPPO; Title: Evaluation methodology and preliminary results on Al/ML for CSI feedback enhancement Agenda Item: 9.2.2.1; Document for: Discussion and Decision), hereinafter, “OPPO”.
Regarding claim 1, Huang discloses: A user equipment (UE) (Huang: fig 2, UE 115, para [0128]) comprising: a memory (Huang: fig 2, UE 115, para [0008], where, the UE includes memory coupled with the processor); and a baseband processor coupled to the memory, the baseband processor configured to (Huang: fig 8, baseband processor 840, para [0192]), when executing instructions stored in the memory, cause the UE to: receive a channel state information (CSI) report configuration that indicates a number of PRBs (Huang: fig 2 and fig 4, steps 410 and 425, where, the base station, on step 410 transmit CSI Reporting configuration to the UE 115 and receives Full Duplex CSI Report on step 425, para [0192], para [0134], where, the CSI information indicates number of PRBs or subbands); determine a number of subbands and a respective number of PRBs in each respective subband (Huang: fig, para [0032], where, “receiving the CSI reporting configuration that indicates a number of PRBs or subbands as the uplink bandwidth”); and Huang does not explicitly teach: generate a plurality of eigen-vectors, wherein one or more of the eigen-vectors summarizes a channel state matrix across a respective subband, further wherein a number eigen-vectors in the plurality corresponds to number of inputs of an artificial intelligence (AI) encoder used for CSI feedback compression.
OPPO teaches: generate a plurality of eigen-vectors (OPPO: page 2, Agreement: Method 2: where, the UE generate the eigenvectors using the formula stated above), wherein one or more of the eigen-vectors summarizes a channel state matrix across a respective subband (OPPO: SECTION 3. Fig 7 and 8; Discussion on CSI prediction; SECTION 3.1 Evaluation methodology: WHERE, “in RANI -#l JObi~-e, both r,1w ch<1nnd rnalrixes and eigenveclors can be u,..ed "" the AT/ML mode) input. For raw channel prt;diuion, the inpulioutpu! of AI/ML model '..VOuld be the channel matrix HE a:Nt"<NTxNs,:, where N,, N,,, Nsc ae the rmmbers of T:,,; port, Rx pon and subcarrier For eigenvector predictiou, the :inpu1/outpul of .Al/ML model would be the e1genvector W E Cvtx,v,v, where N,1; 1s the number of sub--barnL”), further wherein a number eigen-vectors in the plurality corresponds to number of inputs of an artificial intelligence (AI) encoder used for CSI feedback compression (OPPO: fig 1 (a) and (b) and fig 2, SECTION 2.4 – 2.6 AI/ML Model. Scalability, where, “In current stage, since the scalability issue has not been well studied, companies are encouraged provide the details of methodologies to achieve the scalability of AI/ML model, including the pre-processing on the input and post-processing on the output of the AI/ML model, and the advanced training method to obtain the AI/ML model with good scalability”).
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the invention to use “generate a plurality of eigen-vectors, wherein one or more of the eigen-vectors summarizes a channel state matrix across a respective subband, further wherein a number eigen-vectors in the plurality corresponds to number of inputs of an artificial intelligence (AI) encoder used for CSI feedback compression” as taught by OPPO into Huang in order to reduce the fine-tuning performance may improve with more samples in fine -tuning database for AI/ML model update (Oppo: Section 2.5).
Regarding claim 9, the claim includes features identical to the subject matter mentioned in the rejection to claim 1 above. The claims are mere reformulation of claim 1 in order to define the corresponding packet processing system for baseband processor, and the rejection to claim 1 is applied hereto.
Regarding claim 13, the claim includes features identical to the subject matter mentioned in the rejection to claim 1 above. The claims are mere reformulation of claim 1 in order to define the corresponding packet processing system for baseband processor, and the rejection to claim 1 is applied hereto. Additionally the claim requires transmitting PDCCH/PDSCH, where, Huang teaches PDSCH (Huang: para [0127]).
Regarding claim 2, Huang modified by Oppo disclose: The UE of claim 1, wherein the baseband processor (Huang: fig 8, baseband processor 840, para [0192]), is configured to input the plurality of eigen-vectors to the Al encoder used for CSI feedback compression (OPPO: page 2, Agreement: Method 2: where, the UE generate the eigenvectors using the formula as stated above), and encode a CSI report based on an output of the Al encoder used for CSI feedback compression (Oppo: fig 1 and 2, section 2.6: AI/ML model).
Regarding claim 3, Huang modified by Oppo disclose: The UE of claim 1, wherein the baseband processor (Huang: fig 8, baseband processor 840, para [0192]), is configured to receive a configuration of the number of PRBs in each subband; determine the number of subbands based on the configured number of PRBs in each subband and the number of PRBs indicated by the CSI report configuration (Huang: fig, para [0032], where, “receiving the CSI reporting configuration that indicates a number of PRBs or subbands as the uplink bandwidth”); and generate an eigen-vector for each sub-band (OPPO: page 2, Agreement: Method 2: where, the UE generate the eigenvectors using the formula as stated above).
Regarding claim 4, Huang modified by Oppo disclose: The UE of claim 1, wherein the baseband processor (Huang: fig 8, baseband processor 840, para [0192]) is configured to cause transmission of a UE capability report indicating a maximum number of eigen-vectors (Huang: fig 8, baseband processor 840, para [0192]), whether multiple model input sizes are supported, support of a variable subband size (OPPO: SECTION 3. Fig 7 and 8; Discussion on CSI prediction; SECTION 3.1 Evaluation methodology: WHERE, “in RANI -#l JObi~-e, both r,1w ch<1nnd rnalrixes and eigenveclors can be u,..ed "" the AT/ML mode) input. For raw channel prt;diuion, the inpulioutpu! of AI/ML model '..VOuld be the channel matrix HE a:Nt"<NTxNs,:, where N,, N,,, Nsc ae the rmmbers of T:,,; port, Rx pon and subcarrier For eigenvector predictiou, the :inpu1/outpul of .Al/ML model would be the e1genvector W E Cvtx,v,v, where N,1; 1s the number of subbands”), or whether the UE supports generation of padding eigen-vectors or adaptation of subband eigen-vectors for use in generating the plurality of eigen-vectors when the number of subbands is less than the number of inputs of the Al encoder used for CSI feedback compression (Oppo: fig 1-fig 2, encoder and decoder module, section 2.6: AI/ML module, where, “in the initial stage of this SI, companies are encouraged to open their utilized dataset(s) and/or reference model(s), which would be very helpful for crosscheck between companies. Furthermore, common dataset(s) and/or reference model(s) would be more efficient for performance calibration and drawing final conclusions. The reference model in our simulations for Al/ML based CSI feedback enhancement can be find in”).
Regarding claim 5, Huang modified by Oppo disclose: The UE of claim 1, wherein the baseband processor (Huang: fig 8, baseband processor 840, para [0192]) is configured to determine the number of subbands to be equal to an integer multiple of the number of inputs to the Al encoder used for CSI feedback compression ”), wherein the integer is greater than or equal to one (OPPO: SECTION 3. Fig 7 and 8; Discussion on CSI prediction; SECTION 3.1 Evaluation methodology: WHERE, “in RANI -#l JObi~-e, both r,1w ch<1nnd rnalrixes and eigenveclors can be u,..ed "" the AT/ML mode) input. For raw channel prt;diuion, the inpulioutpu! of AI/ML model '..VOuld be the channel matrix HE a:Nt"<NTxNs,:, where N,, N,,, Nsc ae the rmmbers of T:,,; port, Rx pon and subcarrier For eigenvector predictiou, the :inpu1/outpul of .Al/ML model would be the e1genvector W E Cvtx,v,v, where N,1; 1s the number of subbands); determine a number of PRBs for each of the subbands such that all PRBs indicated by the CSI report configuration are assigned to a subband and no subband includes a PRB outside the PRBs indicated by the CSI report configuration; generate the integer number of respective sets eigen-vectors, wherein each set includes a number of eigen-vectors equal to the number of inputs to the Al encoder used for CSI feedback compression (OPPO: page 2, Agreement: Method 2: where, the UE generate the eigenvectors using the formula as stated above); and successively input the respective sets of eigen-vectors to the Al encoder used for CSI feedback compression (Oppo: fig 1-fig 2, encoder and decoder module, section 2.6: AI/ML module, where, “in the initial stage of this SI, companies are encouraged to open their utilized dataset(s) and/or reference model(s), which would be very helpful for crosscheck between companies. Furthermore, common dataset(s) and/or reference model(s) would be more efficient for performance calibration and drawing final conclusions. The reference model in our simulations for Al/ML based CSI feedback enhancement can be find in”).
Regarding claim 7, Huang modified by Oppo disclose: The UE of claim 1, wherein the baseband processor (Huang: fig 8, baseband processor 840, para [0192]) is configured to, when the number of PRBs indicated by the CSI report configuration is X PRBs less than the number of inputs to the Al encoder used for CSI feedback compression assign each PRBs indicated by the CSI report configuration to a different subband (Oppo: fig 7 and 8, Section 3.1, where, “Ve can selec1 different length of observation window, and prediction window, therefore obtain different CSI-RS overhead reduction proportions using Al/ML based CSI prediction in time domain”); and generate X padding eigen-vectors or repetition eigen-vectors (Oppo: fig 1, Section 2.4, where, “Figure I: Zero-padding pre-processing on the input CSI of the encoder: ( a) zero-padding on antenna port dimension; (b) zero-padding on sub-band dimension; the zero-padding pre-processing on the input CSI of the encoder at the UE side can be utilized. For example, as shown in Figure 1 (a). the AI/ML model trained on dataset from Configuration#A with 32 port can be inferenced/tested on dataset from Configuration#B with 16 port and zero-padding on the antenna port domain. Similarly, as shown in Figure 2 (a). the AI/ML model trained on dataset from Configuration#A with 13 sub-band can be inferenced/tested on dataset from Configuration#B with 8 sub-band and zero-padding on the sub-band domain”).
Regarding claim 8, Huang modified by Oppo disclose: The UE of claim 1, wherein the baseband processor (Huang: fig 8, baseband processor 840, para [0192]) is configured to generate a channel quality indicator (CQI) report based on a configured integer multiple of the determined respective numbers of PRBs in each respective subband (Huang: para [0142], where, “a smaller SINR value may lead to a smaller channel quality indicator (CQI) and/or a smaller rank indicator (RI)”), wherein the integer is greater than or equal to one or based on subbands configured according to a legacy configuration (Huang: para [0142], where, “the full duplex SINR value (γ.sub.FD) may be equal to the non-full duplex SINR value (γ.sub.NFD) subtracting the interference boost value (i.e. γ.sub.FD=γ.sub.NFD−(P.sub.c-IpN−P.sub.IpN))”).
Regarding claim 10, Huang modified by Oppo disclose: The baseband processor (Huang: fig 8, baseband processor 840, para [0192]) of claim 9, configured to, when the number of subbands is less than the number of inputs of the AI encoder used for CSI feedback compression, generate padding eigen-vectors for input to the AI encoder used for CSI feedback compression (Oppo: fig 7 and 8, Section 3.1, where, “Ve can selec1 different length of observation window, and prediction window, therefore obtain different CSI-RS overhead reduction proportions using Al/ML based CSI prediction in time domain”).
Regarding claim 11, Huang modified by Oppo disclose: The baseband processor (Huang: fig 8, baseband processor 840, para [0192]) of claim 9, configured to, when the number of subbands is less than the number of inputs of the AI encoder used for CSI feedback compression, generate the plurality of eigen-vectors by adapting the eigen-vectors generated for the subbands (Oppo: fig 7 and 8, Section 3.1, where, “Ve can selec1 different length of observation window, and prediction window, therefore obtain different CSI-RS overhead reduction proportions using Al/ML based CSI prediction in time domain”).
Regarding claim 12, Huang modified by Oppo disclose: The baseband processor (Huang: fig 8, baseband processor 840, para [0192]) of claim 9, configured to receive a configuration indicating either padding eigen-vectors or adaptation of subband eigen-vectors for use in generating the plurality of eigen-vectors when the number of subbands is less than the number of inputs of the AI encoder used for CSI feedback compression (Oppo: fig 7 and 8, Section 3.1, where, “can be selected different length of observation window, and prediction window, therefore obtain different CSI-RS overhead reduction proportions using Al/ML based CSI prediction in time domain”).
Regarding claim 14, Huang modified by Oppo disclose: The method of claim 13, further comprising: transmitting a configuration of the number of PRBs in each subband (Huang: para [0144], where, “this indication of the uplink bandwidth may be added into CSI report configuration 210 (e.g., in the form of the number of PRBs or subbands, where the size of one subband is preconfigured)”); and determining the number of subbands based on the configured number of PRBs and the number of PRBs indicated by the CSI report configuration (Huang: para [0144], where, “the CSI reporting configuration may indicate a number of PRBs or subbands as the uplink bandwidth”).
Regarding claims 15 and 16, Huang modified by Oppo disclose: The method of claim 13, further comprising removing padding eigen-vectors from the M estimated input eigen-vectors to generate the set of eigen-vectors (Oppo: fig 1, Section 2.4, where, “Figure I: Zero-padding pre-processing on the input CSI of the encoder: ( a) zero-padding on antenna port dimension; (b) zero-padding on sub-band dimension; the zero-padding pre-processing on the input CSI of the encoder at the UE side can be utilized. For example, as shown in Figure 1 (a). the AI/ML model trained on dataset from Configuration#A with 32 port can be inferenced/tested on dataset from Configuration#B with 16 port and zero-padding on the antenna port domain. Similarly, as shown in Figure 2 (a). the AI/ML model trained on dataset from Configuration#A with 13 sub-band can be inferenced/tested on dataset from Configuration#B with 8 sub-band and zero-padding on the sub-band domain”).
Regarding claim 17, Huang modified by Oppo disclose: The method of claim 13, further comprising transmitting a configuration to the UE that indicates whether padding eigen-vectors or adaptation of subband eigen-vectors are used for AI- based encoder input alignment (Oppo: Table 4, section 2.7.4 Multi-encoder evaluation: where, “Using common decoder with UE-specific encoder achieves higher SGCS than using common decoder with common encoder”).
Regarding claim 18, Huang modified by Oppo disclose: The method of claim 17, further comprising receiving a UE capability report indicating a maximum number of eigen-vectors (Oppo: fig 7, “CSI prediction model”, Section 3.1 “Evaluation methodology” where, “For eigenvector predictiou, the :input/output of AI/ML model would be the eigenvector”), whether multiple model input sizes are supported, or whether the UE supports generation of padding eigen-vectors or adaptation of subband eigen-vectors (Oppo: fig 7 and 8, Section 3.1, where, “can be selected different length of observation window, and prediction window, therefore obtain different CSI-RS overhead reduction proportions using Al/ML based CSI prediction in time domain”).
Regarding claim 19, Huang modified by Oppo disclose: The method of claim 13, further comprising transmitting a configuration indicating fixed or variable subband size to the UE (Huang: para [0144], where, “base station 105-a may configure UE 115-a to report CSI under self-full duplex mode with the indication of the uplink bandwidth to use. For example, this indication of the uplink bandwidth may be added into CSI report configuration 210 (e.g., in the form of the number of PRBs or subbands, where the size of one subband is preconfigured)”).
Regarding claim 20, Huang modified by Oppo disclose: The method of claim 13, further comprising receiving successive compressed CSI feedback associated with different respective portions of a same channel (Oppo: section 2.3: Generalization: where, “the input/output CSI dimension keeps the same with different configuration(s)/scenario(s) for both training and inference stages”); decoding each successive compressed CSI feedback to generate successive sets of eigen-vectors and including the successive sets of eigen-vectors in the set (OPPO: page 2, Agreement: Method 2: where, the UE generate the eigenvectors using the formula as stated above).
Allowable Subject Matter
Claims 6 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/NIZAM U AHMED/Primary Examiner, Art Unit 2461