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 .
1. (Currently Amended) A method for providing an intermediate feature to a network resource, comprising: obtaining receiving, via at least one transceiver, assistance data including one or more machine learning models for processing a plurality of reference signals; generating the intermediate feature corresponding to at least a subset of the plurality of reference signals based at least in part on the one or more machine learning models; and providing transmitting, via the at least one transceiver, the intermediate feature to the network resource, wherein the intermediate feature comprises at least one eigenvector associated with a channel.
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.
Claim(s) 1-5, 7, 9-21, 23-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hasegawa et al. (hereinafter Hasegawa)(US 2024/0295625) in view of Echigo et al. (hereinafter Echigo)(WO 2024/004194).
Regarding Claim 1, Hasegawa teaches a method for providing an intermediate feature to a network resource, comprising: receiving, via at least one transceiver assistance data including one or more machine learning models for processing a plurality of reference signals(items 507 and 508 in Fig. 5); generating the intermediate feature corresponding to at least a subset of the plurality of reference signals based at least in part on the one or more machine learning models(item 511, WTRU measurement; P[0232], subsets of TRPs from which RS is transmitted); and transmitting, via the at least one transceiver, the intermediate feature to the network resource(P[0238], training status can be reported by the WTRU).
Hasegawa did not teach specifically wherein the intermediate feature comprises at least one eigenvector associated with a channel. However, Echigo teaches in an analogous art wherein the intermediate feature comprises at least one eigenvector associated with a channel.(abstract; terminal applies the input to the AI/ML model relating to CSI ; Fig. 15; UE determines a precoding matrix/eigenvector as an input for the AI/ML mode\l). Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to use the method wherein the intermediate feature comprises at least one eigenvector associated with a channel in order to have improved precision.
Regarding claim 2, Hasegawa teaches the method of claim 1 wherein the intermediate feature correspond to one or more radio frequency fingerprint features(P0009], multiple reference signals from TP and measure RSRP).
Regarding claim 3, Hasegawa teaches the method of claim 2 wherein the one or more radio frequency fingerprint features include a reference signal received power value, a channel impulse response, a channel frequency response, a multipath profile of a channel, or combinations thereof(P[0011], measure RSRP).
Regarding claim 4, Hasegawa teaches the method of claim 1 wherein the one or more machine learning models is a machine learning identification value corresponding to a previously stored machine learning model(P[0290], past IDs algorithm parameters and weights from previous training phases).
Regarding claim 5, Hasegawa teaches the method of claim 1 wherein the one or more machine learning models are configured to process one subset of the plurality of reference signals at a time(P[0281], types of RS supported for positioning measurements).
Regarding claim 7, Hasegawa teaches the method of claim 1 further comprising providing one or more capabilities messages to the network resource, wherein the assistance data is based at least in part on the one or more capabilities messages(items 505 and 507 in Fig. 5; capability transfer).
Regarding claim 9, Hasegawa teaches the method of claim 1 wherein the one or more machine learning models are associated with a bandwidth of at least one of the plurality of reference signals(P[0064], bandwidths supported by all STAs).
Regarding claim 10, Hasegawa teaches the method of claim 1 wherein the one or more machine learning models associated with a number of antenna ports on a station that is transmitting at least one of the plurality of reference signals(P[0376], WTRU may receive parameters antenna reference point).
Regarding claim 11, Hasegawa teaches a method for determining a location estimate for a mobile device, comprising: providing assistance data to the mobile device(Fig. 5, item 509; Fig. 6, WTRU receive positioning reference signal configuration), the assistance data including one or more machine learning models for processing a plurality of reference signals(P[0231], indication from the network to train the machine learning model; P[0298], WTRU may receive ML/AI configuration); receiving one or more intermediate features corresponding to at least a subset of the plurality of reference signals from the mobile device(P[0232], subsets of TRPs from which RS is transmitted); generating a combined intermediate latent representation based on the one or more intermediate features(P[0308], performance improve progressively; P[0338], fix the parameters for the worst case so that the training can be accelerated/positioning accuracy can be improved; use more signaling and data exchange to improve all the poor performers); and determining the location estimate and confidence value for the mobile device based at least in part on the combined intermediate latent representation(P[0225], WTRU may send the position estimate; report confidence values related to the positioning estimate).
Regarding claim 12, Hasegawa teaches the method of claim 11 wherein the one or more intermediate features correspond to one or more radio frequency fingerprint features(P0009], multiple reference signals from TP and measure RSRP).
Regarding claim 13, Hasegawa teaches the method of claim 12 wherein the one or more radio frequency fingerprint features include a reference signal received power value, a channel impulse response, a channel frequency response, a multipath profile of a channel, or combinations thereof(P[0011], measure RSRP).
Regarding claim 14, Hasegawa teaches the method of claim 11 further comprising receiving one or more capabilities messages from the mobile device, wherein the assistance data is based at least in part on the one or more capabilities messages(Fig. 5, item 505, capability transfer).
Regarding claim 15, Hasegawa teaches the method of claim 14 wherein the one or more capabilities messages includes an indication of a number of measurements the mobile device is configured to include in a single report(P[0280], number of positioning methods/configurations that may be supported concurrently; P[0038], multi-mode capabilities cellular, IEEE 802).
Regarding claim 16, Hasegawa teaches the method of claim 11 wherein the combined intermediate latent representation is based on at least a first intermediate feature and a second intermediate feature received subsequent to the first intermediate feature(P[0122], weight be associated with the position estimate; position estimate pos1 and positioning estimate pos2).
Regarding claim 17, Hasegawa teaches the method of claim 11 further comprising providing a drop measurements message in response to the confidence value meeting a threshold value(P[0122], weight be associated with the position estimate; level of confidence in the accuracy of the measurement or location estimate).
Regarding claim 18, Hasegawa teaches the method of claim 17 wherein the drop measurements message is provided via a downlink control information message, a medium access control message, a radio resource control message, or combinations thereof(P[0369], DCI, MAC-CE).
Regarding claim 19, Hasegawa teaches the e method of claim 11 wherein the assistance data includes measurement gap configuration information including a short measurement gap repetition period and a long measurement gap repetition period(P[0139], configured to send semi-static reporting based on threshold).
Claim 20-21 and 23-24 are rejected for the same reason as set forth in claims 1, 7, 9-10 respectively.
Claim 25-30 are rejected for the same reason as set forth in claims 14-17 and 19 respectively.
Claim(s) 8 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hasegawa et al. (hereinafter Hasegawa)(US 2024/0295625) in view of Echigo et al. (hereinafter Echigo)(WO 2024/004194) and Wang et al. (hereinafter Wang)(US 2025/0247189).
Regarding claim 8, Hasegawa in view of Echigo teaches all the particulars of the claim except the method of claim 7 wherein the one or more capabilities messages include an indication of a time required to generate the intermediate feature. However, Wang teaches in an analogous art wherein the one or more capabilities messages include an indication of a time required to generate the intermediate feature(P[0049], capability may be used to indicate the time delay of processing AI/ML model). Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to use the method wherein the one or more capabilities messages include an indication of a time required to generate the intermediate feature in order to have improved efficiency.
Claim 22 is rejected for the same reason as set forth in claim 8.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hasegawa et al. (hereinafter Hasegawa)(US 2024/0295625) in view of Echigo et al. (hereinafter Echigo)(WO 2024/004194) and Rahman et al. (hereinafter Rahman)(US 2022/026333).
Regarding claim 6, Hasegawa in view of Echigo teaches all the particulars of the claim except, wherein the one or more machine learning models are configured to process multiple subsets of the plurality of reference signals at a time. However, Rahman teaches in an analogous art, wherein the one or more machine learning models are configured to process multiple subsets of the plurality of reference signals at a time (P[0277], multiple subsets CSI-RS resource configuration; CSI reporting). Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to use the method wherein the one or more machine learning models are configured to process multiple subsets of the plurality of reference signals at a time in order to have improved efficiency.
Response to Arguments
Applicant’s arguments with respect to claim(s) 2/4/2026 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
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/MUTHUSWAMY G MANOHARAN/Primary Examiner, Art Unit 2647