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 2024-10-21 has been considered by the examiner and made of record in the application file.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2023/0036092 to Bao et al. (“Bao”) in view of U.S. Patent No. 10,908,299 to Tadayon et al. (“Tadayon”).
As to claim 1, Bao discloses a processor of a user equipment (UE) configured to perform operations (Bao, Figs. 1, 2A-B, 3A, and 10, UE 204, 302; ¶0230) comprising: receiving a configuration from a location management function (LMF) for a … UE positioning method (Bao, Fig. 10, stage 1030b; ¶0232, ¶0237); receiving a configuration for downlink (DL) reference signal (RS) reception on one or more positioning cells (Bao, Fig. 10, stage 1030b; ¶0232, ¶0237, "the LMF 270 may provide LPP assistance data in the form of downlink positioning reference signal (DL-PRS) configuration information to the NG-RAN node 1002 and the UE 204 for the selected positioning method(s)"; please also see ¶0227); estimating a channel response for the received DL RS from the one or more positioning cells (Bao, Fig. 10, step 1030b; ¶0238); transmitting the channel response to the LMF (Fig. 10, step 1030b; ¶0238, "Once configured with the DL-PRS and/or UL-PRS configurations, the NG-RAN node 1002 and the UE 204 transmit and receive/measure the respective PRS at the scheduled times. The NG-RAN node 1002 and the UE 204 then send their respective measurements to the LMF 270"), wherein the LMF estimates the UE position (Bao, Fig. 10, step 1030b; ¶0239); and receiving the UE position estimation from the LMF (Bao, Fig. 10, steps 1040 and 1050c; ¶0239).
Bao does not disclose: the UE positioning method is an artificial intelligence (AI) based UE positioning method using a neural network, wherein the LMF identifies a first training data set for training the NN and trains the neural network with the first training data set; and that the channel response to the LMF is used as the NN inference input.
However, Tadayon discloses: the UE positioning method is an artificial intelligence (AI) based UE positioning method using a neural network, wherein the LMF identifies a first training data set for training the NN and trains the neural network with the first training data set (Tadayon, Fig. 7 and Col. 14: lines 43-53. Please also see Fig. 4 and Col. 9: lines 17-31); and that the channel response to the LMF is used as the NN inference input (Tadayon, Fig. 7 and Col. 15: lines 9-19, "…inferencing is performed at the network side, by the LMF in the example in FIG. 7. During the operations phase for embodiment 1, the UE transmits reference signaling such as SRS signaling to the gNB, which determines and signals ranges/angles and channel tensors or covariance tensors to the LMF, as shown at 712, 714, 716. The LMF then performs inferencing and positioning determination, and signals positioning information back to the UE").
Bao and Tadayon are considered to be similar to the claimed invention because they are in one or more of the same fields of: positioning of user equipment (UE) in wireless communication networks. As such, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bao to incorporate the teachings of Tadayon to include: the UE positioning method is an artificial intelligence (AI) based UE positioning method using a neural network, wherein the LMF identifies a first training data set for training the NN and trains the neural network with the first training data set; and that the channel response to the LMF is used as the NN inference input. Doing so would provide a "simpler UE positioning determination approach that leverages existing infrastructure and supports UE positioning determination" (Tadayon, Col. 1: lines 60-62). Additionally, it would improve security, scalability, accuracy, and overall user experience (Tadayon, Col. 28: lines 20-51). Finally, it would be obvious to combine the teachings of Tadayon and Bao because doing so merely combines prior art elements according to known methods (i.e., using the NN-based positioning method of Tadayon's LMF with Bao's LMF UE positioning method) to yield predictable results with a reasonable expectation of success.
As to claim 2, Bao in view of Tadayon discloses the processor of claim 1, wherein the operations further comprise: identifying a calibration location to calibrate the trained NN; and transmitting the calibration to the LMF (Tadayon, Figs. 4 and 7 and Col. 9: lines 32-51. The UE, when being "used by an LMF as a calibrating terminal", transmits its location information to the LMF which then uses the information for calibration of the NN. Please also see Col. 14: line 57 through Col. 15: line 8), wherein the LMF estimates the UE location based on NN inference input for calibration, determines whether the error between the calibration location and the UE location based on the NN inference input for calibration is greater than a threshold, and, when the error is greater than the threshold, re-trains the neural network with second training data (Tadayon, Fig. 7 and Col. 14: line 57 through Col. 15: line 8).
As to claim 3, Bao in view of Tadayon discloses the processor of claim 2, wherein the calibration location is identified based on a radio access technology (RAT) independent positioning technique or a RAT dependent positioning technique (Tadayon, Figs. 4 and 7 and Col. 9: lines 32-51. The UE, when being "used by an LMF as a calibrating terminal", may provide location data derived from SRSs (RAT dependent positioning) or IMU/GPS measurements (RAT independent positioning)).
As to claim 4, Bao in view of Tadayon discloses the processor of claim 2, wherein the calibration location is identified from a physical positioning reference point (Tadayon, Figs. 4 and 7 and Col. 9: lines 32-51, "The positioning server 412 processes received information, continuously in some embodiments, to identify LoS landmarks, shown as points 430 in FIG. 4, from which UE positions are backtracked and NLoS bias errors on the trajectory between two LoS landmarks are determined").
As to claim 5, Bao in view of Tadayon discloses the processor of claim 1, wherein the configuration for the DL RS reception is received from the LMF using a positioning protocol (Bao, ¶0233).
As to claim 6, Bao in view of Tadayon discloses the processor of claim 1, wherein the configuration for the DL RS reception is received from each of the one or more positioning cells (Bao, Fig. 10, stage 1030b; ¶0232, ¶0237; please also see ¶0227).
As to claim 7, Bao in view of Tadayon discloses the processor of claim 1, wherein the configuration for the DL RS reception is received from a serving cell for each of the one or more positioning cells (Bao, Fig. 10, stage 1030b; ¶0232, ¶0237; please also see ¶0227).
As to claim 8, Bao discloses a location management function (LMF) of a cellular core network configured to perform operations (Bao, Figs. 1, 2B, 3C, and 10, LMF 270; ¶0230) comprising: receiving, from a UE, a request for … positioning (Bao, Fig. 10, state 1010c; ¶0231); sending, to the UE, an … input request comprising a configuration for downlink (DL) reference signal (RS) reception on one or more positioning cells (Bao, Fig. 10, stage 1030b; ¶0232, ¶0237, "the LMF 270 may provide LPP assistance data in the form of downlink positioning reference signal (DL-PRS) configuration information to the NG-RAN node 1002 and the UE 204 for the selected positioning method(s)"; please also see ¶0227); receiving, from the UE, a channel response for the received DL RS from the one or more positioning cells (Fig. 10, step 1030b; ¶0238, "Once configured with the DL-PRS and/or UL-PRS configurations, the NG-RAN node 1002 and the UE 204 transmit and receive/measure the respective PRS at the scheduled times. The NG-RAN node 1002 and the UE 204 then send their respective measurements to the LMF 270"), and estimating the UE position based on the input (Bao, Fig. 10, step 1030b; ¶0239).
Bao does not disclose: training a neural network (NN) using a first training data set for an artificial intelligence (Al) based user equipment (UE) positioning method; and the positioning is AI-based and the channel response input is NN inference input.
However, Tadayon discloses: training a neural network (NN) using a first training data set for an artificial intelligence (Al) based user equipment (UE) positioning method (Tadayon, Fig. 7 and Col. 14: lines 43-53. Please also see Fig. 4 and Col. 9: lines 17-31); and the positioning is AI-based and the channel response input is NN inference input (Tadayon, Fig. 7 and Col. 15: lines 9-19, "…inferencing is performed at the network side, by the LMF in the example in FIG. 7. During the operations phase for embodiment 1, the UE transmits reference signaling such as SRS signaling to the gNB, which determines and signals ranges/angles and channel tensors or covariance tensors to the LMF, as shown at 712, 714, 716. The LMF then performs inferencing and positioning determination, and signals positioning information back to the UE").
Bao and Tadayon are considered to be similar to the claimed invention because they are in one or more of the same fields of: positioning of user equipment (UE) in wireless communication networks. As such, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bao to incorporate the teachings of Tadayon to include: training a neural network (NN) using a first training data set for an artificial intelligence (Al) based user equipment (UE) positioning method; and the positioning is AI-based and the channel response input is NN inference input. Doing so would provide a "simpler UE positioning determination approach that leverages existing infrastructure and supports UE positioning determination" (Tadayon, Col. 1: lines 60-62). Additionally, it would improve security, scalability, accuracy, and overall user experience (Tadayon, Col. 28: lines 20-51). Finally, it would be obvious to combine the teachings of Tadayon and Bao because doing so merely combines prior art elements according to known methods (i.e., using the NN-based positioning method of Tadayon's LMF with Bao's LMF UE positioning method) to yield predictable results with a reasonable expectation of success.
As to claim 9, Bao in view of Tadayon discloses the LMF of claim 8, wherein the operations further comprise: sending the UE position estimation to the UE (Bao, Fig. 10, steps 1040 and 1050c; ¶0239).
As to claim 10, Bao in view of Tadayon discloses the LMF of claim 8, wherein the operations further comprise: identifying a calibration location to calibrate the trained NN; and transmitting the calibration to the LMF (Tadayon, Figs. 4 and 7 and Col. 9: lines 32-51. The UE, when being "used by an LMF as a calibrating terminal", transmits its location information to the LMF which then uses the information for calibration of the NN. Please also see Col. 14: line 57 through Col. 15: line 8), wherein the LMF estimates the UE location based on NN inference input for calibration, determines whether the error between the calibration location and the UE location based on the NN inference input for calibration is greater than a threshold, and, when the error is greater than the threshold, re-trains the neural network with second training data (Tadayon, Fig. 7 and Col. 14: line 57 through Col. 15: line 8).
As to claim 11, Bao in view of Tadayon discloses the LMF of claim 10, wherein the calibration location is identified based on a radio access technology (RAT) independent positioning technique or a RAT dependent positioning technique (Tadayon, Figs. 4 and 7 and Col. 9: lines 32-51. The UE, when being "used by an LMF as a calibrating terminal", may provide location data derived from SRSs (RAT dependent positioning) or IMU/GPS measurements (RAT independent positioning)).
As to claim 12, Bao in view of Tadayon discloses the LMF of claim 10, wherein the calibration location is identified from a physical positioning reference point (Tadayon, Figs. 4 and 7 and Col. 9: lines 32-51, "The positioning server 412 processes received information, continuously in some embodiments, to identify LoS landmarks, shown as points 430 in FIG. 4, from which UE positions are backtracked and NLoS bias errors on the trajectory between two LoS landmarks are determined").
As to claim 13, Bao in view of Tadayon discloses the LMF of claim 8, wherein the configuration for the DL RS reception is received from the LMF using a positioning protocol (Bao, ¶0233).
As to claim 14, Bao in view of Tadayon discloses the LMF of claim 8, wherein the configuration for the DL RS reception is received from each of the one or more positioning cells (Bao, Fig. 10, stage 1030b; ¶0232, ¶0237; please also see ¶0227).
As to claim 15, Bao in view of Tadayon discloses the LMF of claim 8, wherein the configuration for the DL RS reception is received from a serving cell for each of the one or more positioning cells (Bao, Fig. 10, stage 1030b; ¶0232, ¶0237; please also see ¶0227).
As to claim 16, Bao discloses a processor of a user equipment (UE) configured to perform operations (Bao, Figs. 1, 2A-B, 3A, and 10, UE 204, 302; ¶0230) comprising: receiving a configuration from a location management function (LMF) for a … UE positioning method (Bao, Fig. 10, stage 1030b; ¶0232, ¶0237); receiving a configuration for uplink (UL) reference signal (RS) transmission to one or more positioning cells (Bao, Fig. 10, stage 1030b; ¶0232, ¶0237, "Alternatively or additionally, the NG-RAN node 1002 may provide DL-PRS and/or uplink PRS (UL-PRS) configuration information to the UE 204 for the selected positioning method(s)"; please also see ¶0227); transmitting the UL RS to the one or more positioning cells (Bao, Fig. 10, stage 1030b; ¶0238); and receiving a UE position estimation from the LMF (Bao, Fig. 10, steps 1040 and 1050c; ¶0239).
Bao does not disclose: the UE positioning method is an artificial intelligence (AI) based UE positioning method using a neural network, wherein the LMF identifies a first training data set for training the NN and trains the neural network with the first training data set.
However, Tadayon discloses: the UE positioning method is an artificial intelligence (AI) based UE positioning method using a neural network, wherein the LMF identifies a first training data set for training the NN and trains the neural network with the first training data set (Tadayon, Fig. 7 and Col. 14: lines 43-53. Please also see Fig. 4 and Col. 9: lines 17-31 as well as Col. 15: lines 9-19).
Bao and Tadayon are considered to be similar to the claimed invention because they are in one or more of the same fields of: positioning of user equipment (UE) in wireless communication networks. As such, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bao to incorporate the teachings of Tadayon to include: the UE positioning method is an artificial intelligence (AI) based UE positioning method using a neural network, wherein the LMF identifies a first training data set for training the NN and trains the neural network with the first training data set. Doing so would provide a "simpler UE positioning determination approach that leverages existing infrastructure and supports UE positioning determination" (Tadayon, Col. 1: lines 60-62). Additionally, it would improve security, scalability, accuracy, and overall user experience (Tadayon, Col. 28: lines 20-51). Finally, it would be obvious to combine the teachings of Tadayon and Bao because doing so merely combines prior art elements according to known methods (i.e., using the NN-based positioning method of Tadayon's LMF with Bao's LMF UE positioning method) to yield predictable results with a reasonable expectation of success.
As to claim 17, Bao in view of Tadayon discloses the processor of claim 16, wherein the operations further comprise: identifying a calibration location to calibrate the trained NN; and transmitting the calibration to the LMF (Tadayon, Figs. 4 and 7 and Col. 9: lines 32-51. The UE, when being "used by an LMF as a calibrating terminal", transmits its location information to the LMF which then uses the information for calibration of the NN. Please also see Col. 14: line 57 through Col. 15: line 8), wherein the LMF estimates the UE location based on NN inference input for calibration, determines whether the error between the calibration location and the UE location based on the NN inference input for calibration is greater than a threshold, and, when the error is greater than the threshold, re-trains the neural network with second training data (Tadayon, Fig. 7 and Col. 14: line 57 through Col. 15: line 8).
As to claim 18, Bao in view of Tadayon discloses the processor of claim 17, wherein the calibration location is identified based on a radio access technology (RAT) independent positioning technique or a RAT dependent positioning technique (Tadayon, Figs. 4 and 7 and Col. 9: lines 32-51. The UE, when being "used by an LMF as a calibrating terminal", may provide location data derived from SRSs (RAT dependent positioning) or IMU/GPS measurements (RAT independent positioning)).
As to claim 19, Bao in view of Tadayon discloses the processor of claim 17, wherein the calibration location is identified from a physical positioning reference point (Tadayon, Figs. 4 and 7 and Col. 9: lines 32-51, "The positioning server 412 processes received information, continuously in some embodiments, to identify LoS landmarks, shown as points 430 in FIG. 4, from which UE positions are backtracked and NLoS bias errors on the trajectory between two LoS landmarks are determined").
As to claim 20, Bao in view of Tadayon discloses the processor of claim 16, wherein the configuration for the UL RS reception is received from one of (i) each of the one or more positioning cells or (ii) a serving cell for each of the one or more positioning cells (Bao, Fig. 10, stage 1030b; ¶0232, ¶0237; please also see ¶0227).
References Cited
Bao, Jingchao et al. (2023). Request for on-demand positioning reference signal positioning session at a future time (US 2023/0036092 A1). Filed 2022-08-17.
Tadayon, Navid et al. (2021). User equipment positioning apparatus and methods (US 10,908,299 B1). Filed 2019-08-30.
Other Pertinent References
The following prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Bao, Jingchao et al. (2022). Positioning reference signal adjustment based on repetitive signal performance (US 20220053411 A1). Filed 2021-08-11.
Cha, Hyunsu et al. (2022). Positioning method in wireless communication system, and device for supporting same (US 20220150865 A1). Filed 2020-02-14.
Dai, Yucheng et al. (2023). Scheduled positioning of target devices using mobile anchor devices (US 20230345204 A1). Filed 2022-04-26.
Fischer, Sven et al. (2024). Signaling and procedures for supporting reference location devices (US 20240073853 A1). Filed 2022-02-02.
Fischer, Sven et al. (2022). On-demand positioning reference signal configuration (US 20220373636 A1). Filed 2022-07-18.
Ghimire, Birendra et al. (2024). Network verified user device position in a wireless communication network (US 20240284396 A1). Filed 2023-12-28.
Hill, Johan et al. (2025). Methods for enabling estimation of a position of a wireless terminal, a first wireless node and a positioning node (US 20250020755 A1). Filed 2022-11-22.
Hasegawa et al. (2023). Positioning in wireless systems (US 20230388959 A1). Filed 2021-10-13.
Khoryaev, Alexey et al. (2022). Systems and methods of providing new radio positioning (US 20220110085 A1). Filed 2020-01-10.
Manolakos, Alexandros et al. (2024). Storing positioning-related capabilities in the network (US 20240015501 A1). Filed 2021-12-28.
Manolakos, Alexandros et al. (2024). Signalling for requesting preferred on-demand positioning reference signal (prs) configurations or parameters from a transmitting device (US 20240137901 A1). Filed 2022-03-16.
Tadayon, NAVID et al. (2021). Connectivity-based positioning determination in wireless communication networks (US 20210136527 A1). Filed 2019-11-06.
Thomas, Robin Rajan et al. (2025). Measurement and reporting for artificial intelligence based positioning (US 20250142291 A1). Filed 2023-02-03.
Vogedes, Jerome et al. (2024). New radio positioning reference signal enhancements (US 20240171340 A1). Filed 2022-03-25.
Conclusion
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/SAMUEL H. LEONARD/Examiner, Art Unit 2649 /YUWEN PAN/Supervisory Patent Examiner, Art Unit 2649
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