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 .
Response to Amendment/Arguments
Applicant’s arguments with respect to the rejection of claims 1-20 under 35 U.S.C. § 103 have been considered but are moot in view of the new grounds of rejection presented below.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) is acknowledged. Due to the claim amendment, one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) is not met for the current claim set as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application (S/N 63/408,807), fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. The provisional application does not provide support for the subject matter of independent claims 1, 8 and 15, namely, inter alia, transmitting/receiving “ground-truth CSI.” Accordingly, claims 1-20 are not afforded the priority date of the '807 provisional application.
Claim Objections
Claims 8-14 are objected to because of the following informalities:
Claim 8 recites the limitation "second information related to a selection of a ML model for CSI reporting" in line 6, and also "second information related to configuring a first ML model" in line 8. Thus, there are two instances of “second information”: one that is received and one that is transmitted. However, it is noted that the crossed out language: “ appropriate correction is required.
Claim Rejections - 35 USC § 112
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 11 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 inventors regard as the invention.
Claim 11 recites “the second information” in lines 1-2. However, as noted int eh claim objection above, there are two instances of “second information” previously recited in claim 8, and thus it is unclear which instance is being referenced in claim 11.
Appropriate correction is required.
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.
Claims 1-4, 7-11 and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Mu et al. U.S. Pat. App. Pub. No. 2025/0202556 (hereinafter “Mu”) in view of Hao et al. U.S. Pat. App. Pub. No. 2025/0173612 (hereinafter “Hao”), Fei et al. U.S. Pat. App. Pub. No. 2025/0287244 (hereinafter “Fei”) and Bai et al. U.S. Pat. App. Pub. No. 2021/0091838* (hereinafter “Bai”) (*previously cited by Applicant).
Regarding claims 1 and 15, Mu discloses a user equipment (UE) and method to report channel state information (CSI), the UE (i.e. terminal 11 – Fig. 1; 800 – Fig. 17) comprising: a transceiver (i.e. 816 – Fig. 8) configured to: transmit first information related to an applicability of AI/ML-based CSI feedback to the UE/capability of the UE to support machine learning based CSI reporting (Fig. 14: S1420 – terminal information indicating AI capability, ¶ [0223]; also see ¶ [0064]: CSI computation may be a computation capability of the AI model/terminal); receive second information related to configuring a ML model for determining the CSI (Fig. 14: step S1450 – ¶ [0226]); and a processor 820 coupled to the transceiver, configured to process CSI
Mu does not expressly disclose that the second information related to configuring a first ML model for determining the CSI includes at least an identifier (ID) for pairing the first model.
Hao discloses that a base station may provide a ML model ID to a UE (¶ [0158]). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to provide a ML model ID to a UE, as suggested by Hao, in the method/UE of Mu, to aid in determining a performance report sent to the base station (see ¶ [0168]-[0169]).
Mu also does not expressly disclose that the third information related to configuring a ML model includes information including at least quantizing a ML model output.
Fei discloses that output information of the AI model is quantized and reported back to the network device, where parameters for quantization may be configured/indicated by the network device (¶ [0576]). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to provide information to the UE regarding AI model quantization, as suggested by Fei, in the method/UE of Mu, to allow for feedback reporting that may be recovered by the network device while reducing feedback overhead.
Mu also does not expressly show receiving fourth information related to reception of CSI-RSs on a cell, and the CSI-RSs based on the fourth information; determining, based on the second and third information and the reception of the CSI-RSs, a CSI report using the first ML model; and transmitting; the CSI report and ground-truth CSI.
Bai discloses a UE configured to: receive information related to reception of CSI reference signals (CSI-RSs) on a cell, as the UE receives configuration information from a base station using downlink control information or RRC signaling where such information indicates a frame structure (¶ [0044]), and the frame structure indicates positions of control information, which may include CSI-RSs (¶¶ [0045]-[0047]), and receive CSI-RSs associated with the configuration information (i.e. Reference signals 428), and a processor 359 configured to determine 432 a CSI report based on the determined predictive ML model and the CSI-RSs, and transmit the CSI report 436 (¶ [0077]). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to determine a CSI report based on provided information, and sending a report based on the CSI-RSs and the received information, as suggested by Bai, in the method/UE of the proposed combination, where information for configuring a ML model is provided as indicated by Mu and Hao, and information related to processing the ML model is provided including information for quantizing the ML model, as suggested by Mu and Fei, in order to provide accurate CSI reporting despite challenging channel conditions, as stated by Bai (¶ [0011]).
In the proposed combination, Hao also discloses that ground truth may be sent from the UE to the base station to make measurements (¶¶ [0148]-[0150]). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to provide ground-truth CSI associated with a CSI report, as suggested by Hao, in the method/UE of the proposed combination, to aid in measuring system performance (see Hao, ¶ [0151]).
Regarding claims 2 and 16, in the proposed combination, Mu discloses that first information provided by the UE to the base station includes information related to a number of supported ML model training methods (¶¶ [0062]-[0063], [0070]-[0071]).
Regarding claims 3 and 17, in the proposed combination, Hao further discloses receiving information related to retraining the ML model including information regarding a paired ML model for training (¶¶ [0183]-[0184]).
Regarding claims 4 and 18, in the proposed combination, Hao discloses that the second information indicates an index from a first number of supported ML models as represented by a corresponding model ID (¶ [0158]).
Regarding claim 7, in the proposed combination, Hao further discloses the UE receiving fifth information related to transmitting a performance monitoring report, including a triggering condition (¶¶ [0158], [0192]-[0193]) and an uplink channel for the transmission of the performance monitoring report, as the base station provides scheduling on channels for the UE on the uplink (¶¶ [0075], [0103]); determining, based on the fifth information, the performance monitoring report for the first ML model (see Fig. 13: step 2 – monitoring/report generation, ¶ [0160]); and transmitting a channel with the performance monitoring report (¶ [0162]).
Regarding claim 8, Mu discloses a base station (i.e. Fig. 1: gNB 12 – ¶ [0036]), comprising: a transceiver (¶ [0006], also see Fig. 18); the transceiver configured to: receive first information related to an applicability of AI/ML-based CSI feedback to the UE (Fig. 14: S1420 – gNB receives terminal information indicating AI capability, ¶ [0223]; also see ¶ [0064]: CSI computation may be a computation capability of the AI model/terminal); receive second information related to a selection of a ML model for CSI reporting (¶ [0164]); transmit second information related to configuring a ML model for determining the CSI (Fig. 14: step S1450 – ¶ [0226]); and receive a CSI report based on the first ML model and CSI-RSs (¶ [0044]).
Mu does not expressly disclose that the second information related to configuring a first ML model for determining the CSI includes at least an identifier (ID) for pairing the first model.
Hao discloses that a base station may provide a ML model ID to a UE (¶ [0158]). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to provide a ML model ID to a UE, as suggested by Hao, in the base station of Mu, to aid in determining a performance report sent to the base station (see ¶ [0168]-[0169]).
Mu also does not expressly disclose that the third information related to configuring a ML model includes information including at least quantizing the ML model output.
Fei discloses that output information of the AI model is quantized and reported back to the network device, where parameters for quantization may be configured/indicated by the network device (¶ [0576]). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to provide information to the UE regarding AI model quantization, as suggested by Fei, in the base station, to allow for feedback reporting that may be recovered by the network device while reducing feedback overhead.
Mu also does not disclose transmitting fourth information related to reception of CSI-RSs on a cell, and the CSI-RSs based on the fourth information; and receiving ground-truth CSI.
Bai discloses a base station (310 – Fig. 3) configured to: transmit information related to configuring a first ML model for determining the CSI, as the base station and UE negotiate to determine the predictive ML model (¶ [0070]), transmit information related to reception of CSI reference signals (CSI-RSs) on a cell, as the base station transmits configuration information using downlink control information or RRC signaling where such information indicates a frame structure (¶ [0044]), and the frame structure indicates positions of control information including include CSI-RSs (¶¶ [0045]-[0047]), transmit CSI-RSs associated with the configuration information (i.e. Reference signals 428), and receive a CSI report 436 based on the ML model and the CSI-RSs (¶ [0077]). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to determine a CSI report based on provided information, and sending a report based on the CSI-RSs and the received information to a base station, as suggested by Bai, in the base station of the proposed combination, where information for configuring a ML model is provided as indicated by Mu and Hao, and information related to processing the ML model is provided including information for quantizing the ML model, as suggested by Mu and Fei, in order to provide accurate CSI reporting despite challenging channel conditions, as stated by Bai (¶ [0011]).
In the proposed combination, Hao also discloses that ground truth may be sent from the UE to the base station to make measurements (¶¶ [0148]-[0150]). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to provide ground-truth CSI associated with a CSI report, as suggested by Hao, to the base station of the proposed combination, to aid in measuring system performance (see Hao, ¶ [0151]).
Regarding claim 9, in the proposed combination, Mu discloses that first information provided by the UE to the base station includes information related to a number of supported ML model training methods (¶¶ [0062]-[0063], [0070]-[0071]).
Regarding claim 10, in the proposed combination, Hao further discloses transmitting information related to retraining the ML model including information regarding a paired ML model for training (¶¶ [0183]-[0184]).
Regarding claim 11, in the proposed combination, Hao discloses that the second information indicates an index from a first number of supported ML models as represented by a corresponding model ID (¶ [0158]).
Regarding claim 14, in the proposed combination, Hao further discloses the base station transmitting fifth information related to transmitting a performance monitoring report, including a triggering condition (¶¶ [0158], [0192]-[0193]) and an uplink channel for the transmission of the performance monitoring report, as the base station provides scheduling on channels for the UE on the uplink (¶¶ [0075], [0103]), wherein the performance monitoring report for the first ML model is determined based on the fifth information (see Fig. 13: step 2 – monitoring/report generation, ¶[0160]); and receiving a channel with the performance monitoring report (¶ [0162]).
Claims 5, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Mu et al. in view of Hao et al., Fei et al. and Bai et al., as applied to claims 1, 8 and 15, respectively, and further in view of Google 3GPP TSG RAN WG1 #110 document R1-2206196 “On Enhancement of AI/ML based CSI”* (hereinafter “Google”).
Regarding claims 5 and 19, Mu in combination with Hao, Fei and Bai disclose a UE for reporting CSI, as described above, but do not expressly disclose that information related to processing a ML model output indicates at least one of: parameters related to a CSI payload size including a size of an output vector of the first ML model, and parameters related to quantizing the output vector of the first ML model including a quantization method and granularity.
Fei discloses that information including AI model parameters are provided by the network node to the UE (step 305 – Fig. 3), and Google further discloses that a selected AI/ML is based on the model with the lowest compression ratio that can fit for the payload size restriction with regard to CSI omission (section 2.6). Accordingly, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to base AI/ML model selection based on CSI payload size of an output vector of a ML model, as suggested by Google, in the UE/method of Mu in combination with Hao, Fei and Bai, in order to effect selection of a model that complies with desired criteria associated with CSI compression.
Regarding claim 12, Mu in combination with Hao, Fei and Bai disclose a base station receiving a CSI report, as described above, but do not expressly disclose information related to processing a ML model output indicates at least one of: parameters related to a CSI payload size including a size of an output vector of the first ML model, and parameters related to quantizing the output vector of the first ML model including a quantization method and granularity.
Fei discloses that information including AI model parameters are provided by the network node to the UE (step 305 – Fig. 3), and Google further discloses that a selected AI/ML is based on the model with the lowest compression ratio that can fit for the payload size restriction with regard to CSI omission (section 2.6). Accordingly, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to base AI/ML model selection based on CSI payload size of an output vector of a ML model, as suggested by Google, in the base station of Mu in combination with Hao, Fei and Bai, in order to effect selection of a model that complies with desired criteria associated with CSI compression.
Claims 6, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mu et al. in view of Hao et al., Fei et al. and Bai et al., as applied to claims 1, 8 and 15, respectively, and further in view of Gu et al. U.S. Pat. App. Pub. No. 2025/0379796 (hereinafter “Gu”).
Regarding claims 6 and 20, Mu in combination with Hao, Fei and Bai disclose a UE for reporting CSI, where Hao discloses the transmission of ground-truth CSI, as described above, but the references do not expressly disclose that the ground-truth CSI is a channel matrix or a precoding matrix, and provided using a Type-II codebook or via a quantization of a raw channel.
Gu discloses the ground-truth CSI is a channel matrix that may be provided as feedback, where the ground truth is the raw CSI (channel matrix after channel estimation – ¶ [0066]), and the raw CSI is compressed and reported to the gNB (¶ [0030]). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to provide ground-truth raw CSI compressed as feedback, as taught by Gu, in the method/UE of Mu in combination with Hao, Fei and Bai, in order to reduce feedback on the channel.
Regarding claim 13, Mu in combination with Hao, Fei and Bai disclose a base station receiving a CSI report, where Hao discloses transmission of ground-truth CSI, as described above, but the references do not disclose that the ground-truth CSI is a channel matrix or a precoding matrix, and provided using a Type-II codebook or via a quantization of a raw channel.
Gu discloses the ground-truth CSI is a channel matrix that may be provided as feedback, where the ground truth is the raw CSI (channel matrix after channel estimation – ¶ [0066]), and the raw CSI is compressed and reported to the gNB (¶ [0030]). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to provide ground-truth raw CSI compressed as feedback, as taught by Gu, to the base station of Mu in combination with Hao, Fei and Bai, in order to reduce feedback on the channel.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to David B. Lugo whose telephone number is 571-272-3043. The examiner can normally be reached M-F, 9-6.
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/DAVID B LUGO/Primary Examiner, Art Unit 2631
2/25/2026