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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/7/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to the claim(s) 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.
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) 1-8 and 10-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sundararajan et al. (US 2022/0046577) hereinafter “Sundararajan” in view of Gopalakrishnan et al. (WO 2022/082151) hereinafter “Gopalakrishnan” and Liu et al. (2025/0261159) hereinafter “Liu”.
As to claim 1, Sundararajan discloses A method of wireless communication performed by a user equipment (UE) (UE 302), comprising:
receiving (step 910), from a location server (306), a configuration (see [0213], [0189]);
obtaining (step 920) a first set of measurements of the one or more PRS resources in each of the plurality of PFLs (see [0214],[0191],[0194]); and
applying a machine learning model (step 930) to the first set of measurements to obtain positioning information associated with the first set of measurements (see [0215]-[0216]).
Sundararajan fails to explicitly recite the received configuration of a plurality of positioning frequency layers (PFLs) and one or more positioning reference signal (PRS) resources in each of the plurality of PFLs. In an analogous art, Gopalakrishnan discloses receiving, a configuration of a plurality of positioning frequency layers (PFLs) and one or more positioning reference signal (PRS) resources in each of the plurality of PFLs (see [0114] & [0105], [0107]. Before the effective filing date of the instant application it would have been obvious to one or ordinary skill in the art to modify Sundararajan by having it receive a configuration of a plurality of positioning frequency layers (PFLs) and one or more positioning reference signal (PRS) resources in each of the plurality of PFLs as taught by Gopalakrishnan for the purpose of enabling the UE to configure the radio accordingly and maximize the NR abilities.
Sundararajan fails to explicitly recite, yet Liu in an analogous art discloses applying a machine learning model to the first set of measurements of the one or more PRS resources in each of the plurality of PFLs to obtain positioning information associated with the first set of measurements, (see [0113]-[0114]: “…target prediction model can be applied to a reference signal(s) received on S1 in resource set 0 in each of PFL0 and PFL1”.”
wherein the machine learning model is configured to process measurements of PRS resources from different PFLs (PFL0 & PFL1). See also, [0128]-[0130], etc. Before the effective filing date of the instant application it would have been obvious to one or ordinary skill in the art to modify Sundararajan by applying a machine learning model to the first set of measurements of the one or more PRS resources in each of the plurality of PFLs to obtain positioning information associated with the first set of measurements, wherein the machine learning model is configured to process measurements of PRS resources from different PFLs, as taught by Liu for the purpose of applying the latest technology in providing location estimation.
As to claim 2, Sundararajan discloses The method of claim 1, further comprising: transmitting the positioning information to the location server. See [0216].
As to claim 3, Sundararajan discloses The method of claim 1, further comprising: receiving, from the location server, the machine learning model or an identifier of the machine learning model. See [0213].
As to claim 4, Sundararajan discloses The method of claim 1, wherein the first set of measurements comprises:
channel state measurements of the one or more PRS resources in each of the plurality of PFLs, signal strength measurements of the one or more PRS resources in each of the plurality of PFLs, positioning measurements of the one or more PRS resources in each of the plurality of PFLs, or any combination thereof. (see [0205],[0214],[0191]])
As to claim 5, Sundararajan discloses The method of claim 1, wherein the positioning information comprises: latent representations of the first set of measurements, a second set of measurements, a position estimate of the UE, or a combination thereof. See [0215]
As to claim 6, Sundararajan discloses The method of claim 5, wherein the second set of measurements comprises: intermediate positioning quantities (“coarse location estimate”), latent representations of
measurements at different PFLs of the plurality of PFLs, or a combination thereof. See [0215], [0220]. Also note that the BRI of the claim reads on the ‘position estimate of the UE’ from claim 5.
As to claim 7, Sundararajan discloses The method of claim 1, wherein: the machine learning model comprises a plurality of layers, each layer of the plurality of layers is trained for a specific PFL (see [0215]“Different neural network functions may be defined for each respective set of positional measurement features…”), and each PFL of the plurality of PFLs is assigned to a different layer of the plurality of layers. It is considered that each beam configuration would be measured as a set. See also [0205], [02014], [0189] & [0158]-[0166].
As to claim 8, Sundararajan discloses The method of claim 7, wherein the configuration further indicates to which layer (nn function) of the plurality of layers to assign a PFL of the plurality of PFLs (see [0210]).
As to claim 10, Sundararajan discloses The method of claim 1, wherein: the machine learning model comprises a first (first neural network function) portion and a second (second neural network function) portion, the first portion is specific to measurements of multiple PFLs, and the second portion is a general positioning machine learning model (different granularities). See [0220], [0222], [0022]-[0023].
As to claim 11, Sundararajan discloses The method of claim 10, wherein: the first portion of the machine learning model is applied to the first set of measurements at the UE and the second portion of the machine learning model is applied to the positioning information at the UE, or the first portion of the machine learning model is applied to the first set of measurements at the UE and the second portion of the machine learning model is applied to the positioning information at the location server. See [0220], [0222], [0022]-[0023].
As to claim 12, the combination of Sundararajan and Gopalakrishnan discloses The method of claim 1, Gopalakrishnan further discloses wherein the one or more PRS resources in each of the plurality of PFLs are transmitted by a same base station (502) see [0124]. Before the effective filing date of the instant application it would have been obvious to one or ordinary skill in the art to further modify Sundararajan by having the one or
more PRS resources in each of the plurality of PFLs are transmitted by a same base station as taught by Gopalakrishnan for the purpose of configuring a network maximizing the NR abilities.
As to claim 13, Sundararajan discloses A method of communication performed by a network entity (304), comprising:
transmitting (step 1010), to a user equipment (UE), a configuration (see [0218], [0189]); and
receiving (step 1020), from the UE, positioning information associated with a first set of measurements of the one or more PRS resources in each
of the plurality of PFLs, the positioning information obtained by the UE by applying a machine learning model to the first set of measurements. See [0219].
Sundararajan fails to explicitly recite the transmitted configuration of a plurality of positioning frequency layers (PFLs) and one or more positioning reference signal (PRS) resources in each of the plurality of PFLs. In an analogous art, Gopalakrishnan discloses transmitting, a configuration of a plurality of positioning frequency layers (PFLs) and one or more positioning reference signal (PRS) resources in each of the plurality of PFLs (see [0114] & [0105], [0107]. Before the effective filing date of the instant application it would have been obvious to one or ordinary skill in the art to modify Sundararajan by having it transmit a configuration of a plurality of positioning frequency layers (PFLs) and one or more positioning reference signal (PRS) resources in each of the plurality of PFLs as taught by Gopalakrishnan for the purpose of enabling the UE to configure the radio accordingly and maximize the NR abilities.
Sundararajan fails to explicitly recite, yet Liu in an analogous art discloses the positioning information obtained by the UE by applying a machine learning model to the first set of measurements of the one or more PRS resources in each of the plurality of PFLs, wherein the machine learning model is configured to process measurements of PRS resources from different PFLs. (see [0113]-[0114]: “…target prediction model can be applied to a reference signal(s) received on S1 in resource set 0 in each of PFL0 and PFL1”.” See also, [0128]-[0130], etc. Before the effective filing date of the instant application it would have been obvious to one or ordinary skill in the art to modify Sundararajan wherein the positioning information obtained by the UE by applying a machine learning model to the first set of measurements of the one or more PRS resources in each of the plurality of PFLs, wherein the machine learning model is configured to process measurements of PRS resources from different PFLs, as taught by Liu for the purpose of applying the latest technology in providing location estimation.
As to claim 14, Sundararajan discloses The method of claim 13, further comprising: applying a positioning machine learning model to the positioning information to determine a position estimate of the UE. See [0215],[0220].
As to claim 15, Sundararajan discloses The method of claim 13, further comprising: transmitting, to the UE, the machine learning model or an identifier of the machine learning model. See [0218], [0213].
As to claim 16, Sundararajan discloses The method of claim 13, wherein the positioning information comprises: a second set of measurements, a position estimate of the UE, or a combination thereof. See [0215].
As to claim 17, Sundararajan discloses The method of claim 16, wherein the second set of measurements comprises: intermediate
positioning quantities (“coarse location estimate”), latent representations of measurements at different PFLs of the plurality of PFLs, or a combination thereof. See [0215],[0220].
As to claim 18, Sundararajan discloses The method of claim 13, wherein the first set of measurements comprises: channel state measurements of the one or more PRS resources in each of the plurality of PFLs, signal strength measurements of the one or more PRS resources in each of the plurality of PFLs, positioning measurements of the one or more PRS resources in each of the plurality of PFLs, or any combination thereof. (see [0205],[0214],[0191]]).
As to claim 19, Sundararajan discloses The method of claim 13, wherein: the machine learning model comprises a plurality of layers (nn function), each layer of the plurality of layers is trained for a specific PFL (see [0215]“Different neural network functions may be defined for each respective set of positional measurement features…”), and each PFL of the plurality of PFLs is assigned to a different layer of the plurality of layers. It is considered that each beam configuration would be measured as a set. See also [0205], [02014], [0189] & [0158]-[0166].
As to claim 20, Sundararajan discloses The method of claim 19, wherein the configuration further indicates to which layer (function) of the plurality of layers to assign a PFL of the plurality of PFLs (see [0210]).
As to claim 21, the combination of Sundararajan, Gopalakrishnan, and Liu discloses The method of claim 1, Gopalakrishnan further discloses wherein the one or more PRS resources in each of the plurality of PFLs are transmitted by a same base station (502) see [0124]. Before the effective filing date of the instant application it would have been obvious to one or ordinary skill in the art to further modify Sundararajan by having the one or more PRS resources in each of the plurality of PFLs are transmitted by a same base station as taught by Gopalakrishnan for the purpose of configuring a network maximizing the NR abilities.
As to claim 22, Sundararajan discloses A user equipment (UE), 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, either alone or in combination, (see [0026]) configured to (perform the steps of):
receiving (step 910), from a location server (306), a configuration (see [0213], [0189]);
obtaining (step 920) a first set of measurements of the one or more PRS resources in each of the plurality of PFLs (see [0214],[0191],[0194]); and
applying a machine learning model (step 930) to the first set of measurements to obtain positioning information associated with the first set of measurements (see [0215]-[0216]).
Sundararajan fails to explicitly recite wherein the received configuration of a plurality of positioning frequency layers (PFLs) and one or more positioning reference signal (PRS) resources in each of the plurality of PFLs. In an analogous art, Gopalakrishnan discloses receiving, a configuration of a plurality of positioning frequency layers (PFLs) and one or more positioning reference signal (PRS) resources in each of the plurality of PFLs (see [0114] & [0105], [0107]. Before the effective filing date of the instant application it would have been obvious to one or ordinary skill in the art to modify Sundararajan by having it receive a configuration of a plurality of positioning frequency layers (PFLs) and one or more positioning reference signal (PRS) resources in each of the plurality of PFLs as taught by Gopalakrishnan for the purpose of enabling the UE to configure the radio accordingly and maximize the NR abilities.
Sundararajan Fails to explicitly recite yet in an analogous art Liu discloses apply a machine learning model to the first set of measurements of the one or more PRS resources in each of the plurality of PFLs to obtain positioning information associated with the first set of measurements, wherein the machine learning model is configured to process measurements of PRS resources from different PFLs. (see [0113]-[0114]: “…target prediction model can be applied to a reference signal(s) received on S1 in resource set 0 in each of PFL0 and PFL1”.” See also, [0128]-[0130], etc. Before the effective filing date of the instant application it would have been obvious to one or ordinary skill in the art to modify Sundararajan wherein the positioning information obtained by the UE by applying a machine learning model to the first set of measurements of the one or more PRS resources in each of the plurality of PFLs to obtain positioning information associated with the first set of measurements, wherein the machine learning model is configured to process measurements of PRS resources from different PFLs as taught by Liu for the purpose of applying the latest technology in providing location estimation.
As to claims 23-29, the claims correspond to claims 2-5, 19, 7, & 12, respectively and are treated the same.
As to claim 30, A network entity, 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, either alone or in combination, configured to:
transmitting (step 1010), to a user equipment (UE), a configuration (see [0218], [0189]); and
receiving (step 1020), from the UE, positioning information associated with a first set of measurements of the one or more PRS resources in each of the plurality of PFLs, the positioning information obtained by the UE by applying a machine learning model to the first set of measurements. See [0219].
Sundararajan fails to explicitly recite the transmitted configuration of a plurality of positioning frequency layers (PFLs) and one or more positioning reference signal (PRS) resources in each of the plurality of PFLs. In an analogous art, Gopalakrishnan discloses transmitting, a configuration of a plurality of positioning frequency layers (PFLs) and one or more positioning
reference signal (PRS) resources in each of the plurality of PFLs (see [0114] & [0105], [0107]. Before the effective filing date of the instant application it would have been obvious to one or ordinary skill in the art to modify Sundararajan by having it transmit a configuration of a plurality of positioning frequency layers (PFLs) and one or more positioning reference signal (PRS) resources in each of the plurality of PFLs as taught by Gopalakrishnan for the purpose of enabling the UE to configure the radio accordingly and maximize the NR abilities.
Sundararajan fail to explicitly recite, yet in an analogous art Liu discloses the positioning information obtained by the UE by applying a machine learning model to the first set of measurements of the one or more PRS resources in each of the plurality of PFLs, wherein the machine learning model is configured to process measurements of PRS resources from different PFLs. (see [0113]-[0114]: “…target prediction model can be applied to a reference signal(s) received on S1 in resource set 0 in each of PFL0 and PFL1”.” See also, [0128]-[0130], etc. Before the effective filing date of the instant application it would have been obvious to one or ordinary skill in the art to modify Sundararajan wherein the positioning information obtained by the UE by by applying a machine learning model to the first set of measurements of the one or more PRS resources in each of the plurality of PFLs, wherein the machine learning model is configured to process measurements of PRS resources from different PFLs, as taught by Liu for the purpose of applying the latest technology in providing location estimation.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sundararajan, Gopalakrishnan, and Liu as applied to claim 7 above, and further in view of Zhou et al. (US 2023/0386486) hereinafter “Zhou”.
As to claim 9, Sundararajan, Gopalakrishnan, and Liu fail to explicitly recite but in an analogous art, Zhou discloses activating a layer of the
plurality of layers corresponding to a PFL of the plurality of PFLs using one-hot encoding to aid in training. See [0049] Before the effective filing date of the instant application it would have been obvious to one or ordinary skill in the art to further modify Sundararajan by activating a layer of the plurality of layers corresponding to a PFL of the plurality of PFLs using one-hot encoding to aid in training.
Conclusion
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LESTER G. KINCAID
Primary Patent Examiner
Art Unit 2649
/LESTER G KINCAID/Primary Examiner, Art Unit 2649