CTFR 18/192,937 CTFR 90667 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Priority No foreign priority is claimed. Claims have priority date of provisional filing 04/29/2022. Election/Restrictions 08-06 AIA Claim s 13-21 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected Invention , there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 11/17/2025 . Response to Amendment Office action in response to remarks entered 2/27/2026. No substantive amendments are made to the claims. Response to Arguments 07-37 AIA Applicant's arguments filed 2/27/2026 have been fully considered but they are not persuasive. Claims 1-12 remain rejected as unpatentable over Qiao in view of Parker . Applicant first submits that the office action improperly “equates a radio frequency fingerprint (RFFP) of a reference signal (e.g., RS-P) with ECID, OTDOA, uplink reference signal-based positioning, uplink AoA, multi-RTT, or TDOA.” Applicant reasons that “Qiao's reference signal uses geometric/time/angle-based positioning techniques, whereas RFFP is a device-specific physical-layer identity feature derived from hardware impairments (physical-layer signature extracted from a transmitted signal).” Examiner respectfully disagrees. In response to applicants remarks, examiner points to ¶202 of the instant specification, as was provided in the non-final rejection and also reproduced below in Claim Interpretation section. Specification clearly notes that such “features” that applicant argues are different from the type of measurements that Qiao teaches, are in fact not different at all. “ Examples of positioning measurement features comprise a time-of-arrival (e.g., TOA, TDOA, OTDOA, etc.), reference signal time-difference, angle of departure (AoD), channel impulse response (CIR) (e.g., a timing and magnitude of a pre-defined number of peaks in the channel estimate, etc.), channel frequency response (CFR), etc. Positioning measurement features can be used for ML-based feature extraction in various contexts (e.g., RFFP, etc.)” ¶202 Instant specification. Applicant second questions the prior art date of Parker. In response to applicants remarks regarding Parker and filing dates, Examiner notes that at least ¶¶124-150 of provisional application 63/316,880, filed on Mar. 4, 2022 and prior to the instant application’s best priority date of 04/29/2022, provide support for the teaching of Parker as cited in the rejection herein. The rejection is thus maintained. All remaining claims are maintained for the same reasons . 07-30-03-h AIA Claim Interpretation Examiner notes pertinent specification definition of terms that appear in the claim below for reference. [0202] As used herein, a positioning measurement “feature” is a processed (e.g., compressed) representation of raw positioning measurement data, or alternatively of one or more other positioning measurement features (e.g., a feature of a feature, etc.). In some designs, processing (e.g., or refining or compressing) of raw positioning measurement data into respective positioning measurement feature(s) may be implemented for various reasons, such as reducing the amount of positioning measurement data to be transported over a physical channel between the UE and the gNB. Examples of positioning measurement features comprise a time-of-arrival (e.g., TOA, TDOA, OTDOA, etc.), reference signal time-difference, angle of departure (AoD), channel impulse response (CIR) (e.g., a timing and magnitude of a pre-defined number of peaks in the channel estimate, etc.), channel frequency response (CFR), etc. Positioning measurement features can be used for ML-based feature extraction in various contexts (e.g., RFFP, etc.) although references to features in aspects described below are generally directed to RFFP-based features. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over US 12185189 B2 Qiao; Weihua et al. in view of US 20230284178 A1 Parker; Valerie et al . Consider independent Claim 1 Qiao teaches A method of operating a user equipment (UE) (Fig. 23, 26, 27, 28) , comprising: transmitting a reference signal for positioning (RS-P) (Col. 63 “The (R)AN 2308 may perform a positioning measurement (e.g., step 2356, step 2708) based on the information elements/parameters received from the wireless device 2304 (e.g., via message 2352). . . .The (R)AN 2308 may perform uplink ECID positioning measurement for the positioning of the wireless device 2304 (e.g., based on the ECID positioning method). The (R)AN 2308 may perform uplink OTDOA positioning measurement for the positioning of the wireless device 2304 (e.g., based on the OTDOA positioning method). The (R)AN 2308 may perform multiple RTT positioning measurement for the positioning of the wireless device 2304 (e.g., based on the multiple-RTT positioning method).” Where measurements on uplink signals teach or suggest that UE 2304 transmits reference signals for positioning) ; obtaining one or more features associated with the RS-P (Col. 63 “..The wireless device 2304 may receive (e.g., step 2612) the message 2360 indicating the positioning measurement . . . RRC message sent by the (R)AN 2308 to the wireless device 2304 may comprise a positioning measurement report . The positioning measurement report may comprise at least one of: an ECID positioning measurement report , an OTDOA positioning measurement report , a relative time of arrival report, an uplink reference signal received power measurements report , an uplink AoA measurements report , a multiple RTT positioning measurement report , a base station RX-TX time difference measurements report , and/or any other positioning measurement report .”) , the one or more features extracted from one or more radio frequency fingerprint (RFFPs) of the RS-P by one or more entities ( Col. 63 ECID, OTDOA, uplink reference signal, uplink AOA, multi-RTT, TDOA examples of features) via one or more machine learning (ML) feature extraction models (structure of receiving features at UE taught suitable for use with any type of model ML or not used at network entity; ML model is considered intended use does not change structure) ; and determining a position estimate for the UE ( Col. 63 “The wireless device 2304 may determine (e.g., step 2368) the position of the wireless device 2304. . .”) based at least in part on an output of a UE-based ML feature fusion model and the one or more features ( Col. 63 “. . .based on the positioning measurement report(s ) received from the at least one base station and/or the positioning measurement performed by the wireless device 2304.”; Col. 63 “The wireless device 2304 may determine the position of the wireless device 2304, for example, based on the ECID positioning measurement report (e.g. uplink ECID measurement) from the (R)AN 2308 and/or the ECID positioning measurement (e.g. downlink ECID measurement ) performed by the wireless device 2308. The wireless device 2304 may determine the position of the wireless device 2304, for example, based on the OTDOA positioning measurement report (e.g. uplink OTDOA measurement ) from the (R)AN 2308, the OTDOA positioning measurement 140 report (e.g. downlink OTDOA measurement ) performed by the wireless device 2304, and/or Bluetooth positioning measurement performed by the wireless device 2304.”). Qiao does not explicitly teach the use of machine learning (ML) feature extraction models [at the network] and ML feature fusion model [at the UE] . Parker teaches the use of machine learning (ML) feature extraction models [at the network] (¶129 “the location engine circuitry 140 generates machine-learning models as neural network models. . .other types of machine learning models could additionally or alternatively be used such as supervised learning ANN models, clustering models, classification models, etc., and/or any combination(s) thereof. . .”; ¶142 “ one or more of the base stations 204, 206, 208, 210, 212 can be implemented by the location engine circuitry 140 of FIG. 1. In some examples, one or more of the base stations 204, 206, 208, 210, 212 can be separate and/or otherwise different from the location engine circuitry 140 of FIG. 1. . .”) and ML feature fusion model [at the UE] (Fig. 32 and ¶360 “ the location engine 3206 can be configured and/or switched into a mode of operation to use a UE- based location determination technique (e.g., a technique based on UE(s)).”; ¶862 “Example 195 is user equipment circuitry to perform the method of any of Examples 88-116, Examples 117-136, or Examples 139-168.”; ¶114 “. . .he first and second instances of the location engine circuitry 140 can exchange, share, and/or otherwise provide each other with multi-spectrum, multi-modal data that they have respectively obtained and/or processed. In some examples, the first and second instances of the location engine circuitry 140 can combine, fuse, and/or otherwise merge data from the different spatial relational spaces, domains, etc. . .” and ¶147 “. . . the device 302 can transmit cellular data to multiple ones of the base stations 304, 306, 308. In example operation, the multiple ones of the base stations 304, 306, 308 can determine a respective TOA measurement based on the received cellular data (e.g., cellular input data, cellular data inputs, etc.) from the device 302. The base stations 304, 306, 308 can provide the TOA measurements to the location engine circuitry 140 of FIG. 1. The location engine circuitry 140 can determine a TDOA measurement based on the TOA measurements. . .”). Parker also teaches transmitting a reference signal for positioning (RS-P) (¶142 “the device 202 can transmit cellular data (e.g., 5G SRS data) to example base stations 204 , 206 , 208 , 210 , 212 (e.g., cellular base stations, 5G cellular base stations, etc.)..” It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the invention of Qiao to include the noted teachings of Parker in order to estimate a location of the target device based on the at least one set of the TOA measurements or the TDOA measurements (Parker abstract). Consider independent Claim 8 The combination teaches A method of operating an entity; obtaining one or more radio frequency fingerprints (RFFPs) associated with a reference signal for positioning (RS-P) from a user equipment (UE) (Qiao Col. 63 “The (R)AN 2308 may perform a positioning measurement (e.g., step 2356, step 2708) based on the information elements/parameters received from the wireless device 2304 (e.g., via message 2352). . .”) ; extracting one or more features associated with the one or more RFFPs via one or more machine learning (ML) feature extraction models (Qiao Col. 63 ECID, OTDOA, uplink reference signal, uplink AOA, multi-RTT, TDOA examples of features) ; and transmitting the one or more extracted features to one or more target devices (Qiao Col. 63 “..The wireless device 2304 may receive (e.g., step 2612) the message 2360 indicating the positioning measurement . . . RRC message sent by the (R)AN 2308 to the wireless device 2304 may comprise a positioning measurement report . . ) Qiao does not explicitly teach the use of machine learning (ML) feature extraction models [at the network]. Parker teaches the use of machine learning (ML) feature extraction models [at the network] (¶129 “the location engine circuitry 140 generates machine-learning models as neural network models. . .other types of machine learning models could additionally or alternatively be used such as supervised learning ANN models, clustering models, classification models, etc., and/or any combination(s) thereof. . .”; ¶142 “ one or more of the base stations 204, 206, 208, 210, 212 can be implemented by the location engine circuitry 140 of FIG. 1. In some examples, one or more of the base stations 204, 206, 208, 210, 212 can be separate and/or otherwise different from the location engine circuitry 140 of FIG. 1. . .”). Parker also teaches transmitting a reference signal for positioning (RS-P) (¶142 “the device 202 can transmit cellular data (e.g., 5G SRS data) to example base stations 204 , 206 , 208 , 210 , 212 (e.g., cellular base stations, 5G cellular base stations, etc.)..”). Parker is in the same field and considers the same technical problems (¶125 “. . . the location engine circuitry 140 can determine a location of a device/object with a network-assisted technique or a UE-assisted based technique. For example, in network and UE-assisted techniques, the network and the UE can coordinate the generation of data, calculation of measurements, and location determination between each other . . .”) It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the invention of Qiao to include the noted teachings of Parker in order to estimate a location of the target device based on the at least one set of the TOA measurements or the TDOA measurements (Parker abstract). Consider Claim 2 The combination teaches The method of claim 1, wherein the one or more features are extracted by the one or more transmission reception points (TRPs) (Qiao Col. 57 L38-40 “a base station and/or a (R)AN (e.g., (R)AN 2308, a base station corresponding to the (R)AN 2308)”) . Consider Claim 3 The combination teaches The method of claim 2, wherein the one or more ML feature extraction models comprise one or more entity-specific ML feature extraction models or a common ML feature extraction model, in the same manner as for claim 1 (Parker ¶129 “the location engine circuitry 140 generates machine-learning models as neural network models. . .other types of machine learning models could additionally or alternatively be used such as supervised learning ANN models, clustering models, classification models, etc., and/or any combination(s) thereof. . .”). Examiner Note: Upon review of specification examiner could not find any definition or distinguishing characteristic of an “entity-specific ML feature extraction model” or a “common ML feature extraction model.” It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the invention of Qiao to include the noted teachings of Parker in order to estimate a location of the target device based on the at least one set of the TOA measurements or the TDOA measurements (Parker abstract). Consider Claim 4 and 10 The combination teaches The method of claim 1 and claim 8, wherein the one or more features are extracted by a network position estimation entity (Qiao Col. 57 L38-40 “a base station and/or a (R)AN (e.g., (R)AN 2308, a base station corresponding to the (R)AN 2308)”) . Consider Claim 5 The combination teaches The method of The method of wherein the one or more features comprise a first set of features extracted by one or more transmission reception points (TRPs) via a first set of ML feature extraction models, and wherein the one or more features comprise a second set of features extracted by a network position estimation entity via a second set of ML feature extraction models. (Qiao Col. 63 “. . . The positioning measurement report may comprise at least one of: an ECID positioning measurement report, an OTDOA positioning measurement report, a relative time of arrival report, an uplink reference signal received power measurements report, an uplink AoA measurements report, a multiple RTT positioning measurement report, a base station RX-TX time difference measurements report, and/or any other positioning measurement report.” Thus teaching multiple sets of features) , where Parker teaches first and second set of ML feature extraction models in the same manner as for claims 1 and 3. It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the invention of Qiao to include the noted teachings of Parker in order to estimate a location of the target device based on the at least one set of the TOA measurements or the TDOA measurements (Parker abstract). Consider Claim 6 and 11 The combination teaches The method of claim 1, wherein the RS-P corresponds to an uplink sounding reference signal (SRS) or a sidelink SRS (as cited in claim 1 Parker ¶122 and ¶125) . It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the invention of Qiao to include the noted teachings of Parker in order to estimate a location of the target device based on the at least one set of the TOA measurements or the TDOA measurements (Parker abstract). Consider Claims 7 and 12 The combination teaches The method of claim 1 and claim 8, wherein the one or more features comprise a multipath delay and angle feature, a latent device-specific feature trained jointly with a UE-based ML feature fusion model at a network-side training component, a latent device-specific feature trained independently from the UE-based ML feature fusion model at the network-side training component, a multipath feature that relates to an association between a multipath and a virtual anchor or reflector, or any combination thereof (Qiao Col. 63 ECID, OTDOA, uplink reference signal, uplink AOA, multi-RTT, TDOA examples of features) . Examiner Note: claimed features are broadly recited and no distinguishing characteristics are explained in the claim or specification based on examiner review of specification Consider Claim 9 The combination teaches The method of The method of wherein the entity corresponds to a respective transmission reception point (TRP) or another UE that measures the RS-P to obtain a respective RFFP (Qiao Col. 63, Figs 23-26, base station or RAN 2308) ), and wherein the one or more target devices comprise the UE, a network position estimation entity, or a combination thereof (Qiao Col. 63, Figs 23-26, wireless device 2304) . Pertinent Prior Art(s) The prior art made of record though not relied upon in the current rejection is considered pertinent to applicant's disclosure: QUALCOMM INCORPORATED: "Other Aspects on AI-ML for Positioning Accuracy Enhancement", 3GPP TSG RAN WG1 #109-e, R1- 2205029, 3RD Generation Partnership Project (3GPP), Mobile Competence Centre, 650, Route Des Lucioles, F-06921, Sophia-Antipolis Cedex, France, Vol. RAN WG1, No. e-Meeting, 20220509 - 20220520, 29 April 2022 PNG media_image1.png 428 848 media_image1.png Greyscale US 11310816 B2 Manolakos; Alexandros et al. (55) By contrast, U-TDOA works on the uplink channel, and the network (e.g., a group of eNBs) performs the TDOA measurements. In an example, U-TDOA positioning schemes may be network-assisted , whereby the uplink transmissions from the UE are received and measured by highly sensitive receivers at multiple eNBs which will determine the TDOAs that can be used to derive the position estimate of the UE, after which the TDOA measurements are reported back to the UE which then derives the position estimate for the UE. Conclusion 07-39 AIA THIS ACTION IS MADE FINAL. 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 UMAIR AHSAN whose telephone number is (571)272-1323. The examiner can normally be reached Monday - Friday 10-5 PM EST or by emailing UMAIR.AHSAN@USPTO.GOV. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. 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UMAIR AHSAN Primary Examiner Art Unit 2647 /UMAIR AHSAN/Primary Examiner, Art Unit 2647 Application/Control Number: 18/192,937 Page 2 Art Unit: 2647 Application/Control Number: 18/192,937 Page 3 Art Unit: 2647 Application/Control Number: 18/192,937 Page 4 Art Unit: 2647 Application/Control Number: 18/192,937 Page 5 Art Unit: 2647 Application/Control Number: 18/192,937 Page 6 Art Unit: 2647 Application/Control Number: 18/192,937 Page 7 Art Unit: 2647 Application/Control Number: 18/192,937 Page 8 Art Unit: 2647 Application/Control Number: 18/192,937 Page 9 Art Unit: 2647 Application/Control Number: 18/192,937 Page 10 Art Unit: 2647 Application/Control Number: 18/192,937 Page 11 Art Unit: 2647 Application/Control Number: 18/192,937 Page 12 Art Unit: 2647 Application/Control Number: 18/192,937 Page 13 Art Unit: 2647 Application/Control Number: 18/192,937 Page 14 Art Unit: 2647
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