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 .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
1. Claims 1, 3-10 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by D1 (WO 2024/039400 found in IDS dated 11/7/2025).
Regarding claims 1 and 20. D1 teaches a method and a network node, comprising: one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors (UE comprising processor, memory, program and transceiver in figure 7, 0161-0169, Base Station comprising processor, memory, program and transceiver in figure 8, 0170-0178, LMF comprising machine learning, processor, memory, program and transceiver in figure 9, 0179-0188), either alone or in combination, configured to:
transmit, via the one or more transceivers, a digital twin model update request, the digital twin model update request including one or more parameters defining location information for one or more user equipment’s (UEs) to be returned by the one or more UEs (0090, 0095, 0152, 0156 - request for updating machine learning model, 0129-0130 – request training data to update/refine the machine learning model, 0062 – machine learning model uses emulation tools to generate (emulated) (e.g., digital twin) positioning measurements, 0082, 0136 – the emulated reference signal measurement information may be obtained, for example, by using emulation tools such as ray tracing, digital twin, etc.);
receive, via the one or more transceivers, the location information for the one or more UEs (0055, 0059 – positioning measurements, such as RSTD, RSRP, 0082 – reference signal measurement information may comprise CIR measurements, figure 3 at steps 310-313, 0108 – receive reference signal measurement information relating to DL PRS, UL SRS, and/or SL PRS wherein the reference signal measurement information may comprise at least cannel impulse response (CIR) measurements, figure 4 at steps 409 and 411, 0126 – the signal measurement information relating to DL PRS, UL SRS and/or SL PRS, emulated reference signal measurement information or simulated reference signal measurement information may comprise at least channel impulse response (CIR) measurements, figure 5 at step 503 – receive at least one of the following: a message indicative of an updated machine learning model, or a second training data, figure 6 at step 603 – transmit one of the following: a message indictive of an updated machine learning model, or a second training data, 0145 – UE obtains, for example, CIR measurements); and
update a digital twin model based on the location information for the one or more UEs (figure 3 at steps 310, 312, 314 – update and validate or modify, figure 4 at step 412 – update and validate, figure 5 at step 504 – transmit the updated machine learning model, figure 6 at 603 – transmit at least one of the following: a message indicative of an updated machine learning model, or a second training data), wherein the digital twin model is trained based on (1) a first dataset of channel measurements obtained by a set of UEs and (2) a second dataset of channel measurements modeled by a digital twin representation of an environment in which the set of UEs is located (0062 – the central ML unit uses the above positioning measurements (the reported measurements and/or the emulated measurements) to train the ML based localization framework, 0082, 0126 – the first training data may comprise at least one of the following: reference signal measurement information measured at one or more first network nodes from one or more received positioning reference signals (e.g,. DL PRS, UL SRS, and/or SL PRS), emulated reference signal measurement information, or simulated reference signal measurement information related to the one or more network nodes. For example, the reference signal measurement information may comprise at least channel impulse response (CIR) measurements, which may be simulated or measured at one or more network nodes from one or more positioning reference signals. The emulated reference signal measurement information may be obtained, for example, by using emulation tools such as ray tracing, digital twin, etc.).
Regarding claim 3. D1 teaches wherein the location information for the one or more UEs includes: locations of the one or more UEs, channel measurements at the locations of the one or more UEs, location uncertainty values associated with the locations of the one or more UEs, or any combination thereof (0082).
Regarding claim 4. D1 teaches wherein the location information for the one or more UEs is received: individually for each of the one or more UEs, or as a group for all of the one or more UEs (0054-0055, 0059, 0062, 0082).
Regarding claim 5. D1 teaches the digital twin model is trained based on an aggregated set of the first dataset of channel measurements and the second dataset of channel measurements, or the digital twin model is pretrained based on the first dataset of channel measurements and fine-tuned based on the second dataset of channel measurements (0062, 0082, 0108, 0126, 0129).
Regarding claim 6. D1 teaches wherein: the network node is a digital twin management function (DTMF), the digital twin model update request is transmitted to a location management function (LMF), and the location information for the one or more UEs is received from the LMF (figures 3,4 and 9, 0179 – LMF).
Regarding claim 7. D1 teaches wherein: the network node is a base station, the digital twin model update request is transmitted to an LMF, and the location information for the one or more UEs is received from the LMF (figure 6, 0170-0178).
Regarding claim 8. D1 teaches wherein: the network node is a base station, the digital twin model update request is transmitted to the one or more UEs, and
the location information for the one or more UEs is received from the one or more UEs (figure 6, 0170-0178).
Regarding claim 9. D1 teaches wherein: the network node is a target UE, the digital twin model update request is transmitted to an LMF, and the location information for the one or more UEs is received from the LMF (figure 7, 0161-0169).
Regarding claim 10. D1 teaches wherein: the network node is a target UE, the digital twin model update request is transmitted to the one or more UEs, and the location information for the one or more UEs is received from the one or more UEs (figure 7, 0161-0169).
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.
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.
2. Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over D1 in view of Nguyen et al (2025/0167904).
Regarding claim 2. D1 does not teach wherein the one or more parameters include: a maximum number of UEs whose location information is requested to be reported, a minimum location information quality threshold, one or more types of channel measurements that are compatible with the digital twin model,
a time window by which the location information for the one or more UEs should be received at the network node, or any combination thereof.
Nguyen teaches using minimum location information quality threshold(s) (0100,-0106), minimum number of measurements (0108), best score measurements (0109), acceptance threshold (0112-0114), and measurements are carried out on the selected RX positions (e.g., maximum number of UEs) (0122) thereby reducing human intervention (0010) and saving resources (0011) and minimizes the RF measurements (0020, 0022).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify D1 to select digital twin parameters as taught by Nguyen reducing the total number of RF measurements that are needed thus saving resources.
3. Claims 11-14 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over D1 (WO 2024/039400 found in IDS dated 11/7/2025) in view of Alkhateeb et al (2025/0234331).
Regarding claim 11. A network node, comprising: one or more memories;
one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors (UE comprising processor, memory, program and transceiver in figure 7, 0161-0169, Base Station comprising processor, memory, program and transceiver in figure 8, 0170-0178, LMF comprising machine learning, processor, memory, program and transceiver in figure 9, 0179-0188), either alone or in combination, configured to:
obtain one or more channel measurements of one or more reference signals transmitted to a user equipment (UE) (0055, 0059 – positioning measurements, such as RSTD, RSRP, 0082 – reference signal measurement information may comprise CIR measurements, figure 3 at steps 310-313, 0108 – receive reference signal measurement information relating to DL PRS, UL SRS, and/or SL PRS wherein the reference signal measurement information may comprise at least cannel impulse response (CIR) measurements, figure 4 at steps 409 and 411, 0126 – the signal measurement information relating to DL PRS, UL SRS and/or SL PRS, emulated reference signal measurement information or simulated reference signal measurement information may comprise at least channel impulse response (CIR) measurements, figure 5 at step 503 – receive at least one of the following: a message indicative of an updated machine learning model, or a second training data, figure 6 at step 603 – transmit one of the following: a message indictive of an updated machine learning model, or a second training data, 0145 – UE obtains, for example, CIR measurements); and
(0062 – the central ML unit uses the above positioning measurements (the reported measurements and/or the emulated measurements) to train the ML based localization framework, 0082, 0126 – the first training data may comprise at least one of the following: reference signal measurement information measured at one or more first network nodes from one or more received positioning reference signals (e.g,. DL PRS, UL SRS, and/or SL PRS), emulated reference signal measurement information, or simulated reference signal measurement information related to the one or more network nodes. For example, the reference signal measurement information may comprise at least channel impulse response (CIR) measurements, which may be simulated or measured at one or more network nodes from one or more positioning reference signals. The emulated reference signal measurement information may be obtained, for example, by using emulation tools such as ray tracing, digital twin, etc.).
D1 does not explicitly teach apply a digital twin model to the one or more channel measurements to determine a location of the UE.
However, D1 discloses positioning techniques may be applied to estimate a physical location of a device and it is desirable to improve the positioning accuracy in order to estimate the location of the device more accurately (0002).
Alkhateeb teaches obtaining one or more channel measurements of one or more reference signals transmitted to a UE and applying a digital twin model to the one or more channel measurements to determine a location of the UE (abstract – a UE position measurement in real-time and the digital twin map are used to compute the most probable location of the UE, 0008, 0010, 0012, 0015 – measure a wireless communication-related parameter at a position of the UE and computing a likelihood of the position of the UE based on the measured wireless communication-related parameter and a subset of the digital replica simulated parameter, 0023, 0030, 0032, 0038 – the UE can be localized based on wireless measurements (e.g., received signal strength (RSS) measurements) wherein the digital twin RF maps reduce human effort in fingerprinting localization, allowing deployment at scale and improve positioning accuracy (0006).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify D1 to obtain channel measurements at a UE location and applying digital twin RF map as taught by Alkhateeb wherein the digital twin RF maps reduce human effort in fingerprinting localization, allowing deployment at scale and improve positioning accuracy ( Alkhateeb at 0006)
Regarding claim 12. D1 teaches wherein the one or more channel measurements comprise: one or more received signal strength indication (RSSI) measurements of the one or more reference signals, one or more reference signal received power (RSRP) measurements of the one or more reference signals, one or more channel impulse response (CIR) measurements of the one or more reference signals, one or more channel frequency response (CFR) measurements of the one or more reference signals, one or more channel energy response (CER) measurements of the one or more reference signals, one or more power delay profile (PDP) measurements of the one or more reference signals, or any combination thereof (0055, 0059, 0082, 0100).
Regarding claim 13. D1 teaches the digital twin model is trained based on an aggregated set of the first dataset of channel measurements and the second dataset of channel measurements, or the digital twin model is pretrained based on the first dataset of channel measurements and fine-tuned based on the second dataset of channel measurements (0062, 0082, 0108, 0126, 0129).
Regarding claim 14. D1 teaches wherein: the network node is a digital twin management function (DTMF), and the one or more channel measurements are received in a measurement report from a location management function (LMF) or the UE (figures 3,4 and 9, 0179 – LMF).
Regarding claim 17. D1 teaches wherein: the network node is a base station,
the one or more reference signals are one or more positioning reference signals (PRS), and the one or more channel measurements are received in a measurement report from an LMF or the UE (figure 8, 0170-0178, 0082 and 0108 – DL PRS).
4. Claims 15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over D1 in view of Alkhateeb further in view of Yuan et al (2024/0160466).
Regarding claim 15. D1 in view of Alkhateeb do not teach receive, via the one or more transceivers, from the UE, one or more capabilities of the UE to report measurements compatible with the digital twin model.
Yuan teaches receiving UE capabilities relating to digital twin model (0009, 0055) which provides for more accurate information to be used for UE localization.
It would have been obvious for one or ordinary skill in the art before the effective filing date to modify D1 in view of Alkhateeb to consider UEs with digital twin capability as taught by Yuan in order to provide for more accurate location information.
Regarding claim 19. D1 in view of Alkhateeb do not teach transmit, via the one or more transceivers, to an LMF, one or more capabilities of the UE to report measurements compatible with the digital twin model.
Yuan teaches receiving UE capabilities relating to digital twin model (0009, 0055) which provides for more accurate information to be used for UE localization.
It would have been obvious for one or ordinary skill in the art before the effective filing date to modify D1 in view of Alkhateeb to consider UEs with digital twin capability as taught by Yuan in order to provide for more accurate location information.
5. Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over D1 in view of Alkhateeb in view of Yuan further in view of Mandelli et al (2025/0168684).
Regarding claim 16. D1 in view of Alkhateeb and Yuan do not teach transmit, via the one or more transceivers, to the UE, a request capabilities message requesting the one or more capabilities of the UE.
Mandelli teaches digital twin (0075) wherein request for UE capability is used to determine the sensing capability of the UEs (0045-0048, 0058-0063) which enables the network to aggregate UE measurements having the same sensing capabilities thus providing for more accurate location and reduces overhead (0004-0008).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify D1 in view of Alkhateeb and Yuan to request UE sensing capabilities as taught by Mandelli which enables the network to aggregate UE measurements having the same sensing capabilities thus providing for more accurate location and reduces overhead (0004-0008).
6. Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over D1 in view of Alkhateeb further in view of Shrestha et al (2023/0296722).
Regarding claim 18. D1 in view of Alkhateeb do not teach receive, via the one or more transceivers, a PRS configuration from the LMF; and transmit, via the one or more transceivers, the one or more PRS to the UE based on the PRS configuration.
Shrestha teaches using digital twin model (0076, 0089) for UE localization. The network provides PRS configuration to the BS to provide to the UE for channel measurement (0096) which enables the network to better configure PRS that is tailored to the UE environment and UE location.
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify D1 in view of Alkhateeb to provide PRS configuration to the BS as taught by Shrestha which enables the network to better configure PRS that is tailored to the UE environment and UE location (Shrestha at 0096).
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
---(2022/0014946) Merwaday et al teaches LMF communicating with base stations and UEs in conjunction with Digital Twin (DT) circuitry (see figure 8) wherein the DT creates a virtual environment and obtains wireless network environment information, such as channel conditions which is used to predict UE future location(s) (0117-0118).
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BARRY W TAYLOR whose telephone number is (571)272-7509. The examiner can normally be reached Monday-Thursday: 7-5.
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/BARRY W TAYLOR/Primary Examiner, Art Unit 2646
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