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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) was filed on August 19, 2024 (8/19/2024). The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement was considered by the examiner.
Claim Rejections - 35 USC § 102
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.
Claims 1-3, 6, 8-9, 11-13, 16-17, 20, 22-26, and 29-30 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Karampatsis et al. ( WO 2023/110161)
Regarding Claim 1, Karampatsis teaches a method of wireless communication performed by a first network node, comprising: receiving one or more request location information messages from a network entity, (Fig. 7, See 724, and Par. [0107] “At 724, the LMF 704 instructs the UE 708 to provide location measurement reports with the procedure described in 3GPP TS 37.355. The LMF 704 additionally includes an indication to provide measurement reports using AI/ML based positioning based on the determination at step 722. The LMF 704 may include in the request a minimum accuracy for the measurement/ confidence level”),
wherein the one or more request location information messages configure the first network node to use machine learning to derive one or more features of a wireless channel between the first network node and a second network node; (Par. [0105] “the LMF 704 may specify to the UE 708 that the UE 708 is to provide measurement reports using AI/ML based positioning. The LMF 704 may include, in the request to the UE 708, a minimum measurement accuracy/ confidence level for applying AI/ML based positioning” and Par. [0118] “The LMF 704 is further arranged to, for the one or more UEs 708 and/ or for a target area in which the one or more UEs 708 are located, acquire statistics or a prediction (i.e., analytics) that location measurements are based LOS or NLOS communication with a RAN node in the wireless communication network”),
and transmitting one or more provide location information messages to the network entity, (Par. [0111] “At 728, the UE reports the measurements or measurement reports to the LMF 704”),
wherein the one or more provide location information messages include the one or more features of the wireless channel, and wherein the one or more features of the wireless channel are derived based on a machine learning model (Par. [0111] The UE 708 may include, in the response to the LMF 704, the accuracy/ confidence of the measurement or measurements taking into account the AI/ML model and the AI/ML model used for the measurement”).
Regarding Claim 2, Karampatsis teaches the invention of Claim 1, further teaching wherein the one or more request location information messages include: an identifier of the machine learning model, (Par. [0105] “If it is determined that an AI/ML model is to be applied, the LMF 704 may select an applicable AI/ML model from the list of AI/ML models provided by the UE at 712”),
and an identifier of a parameter set of the machine learning model (Par. [0105] “The LMF 704 may include, in the request to the UE 708, a minimum measurement accuracy/ confidence level for applying AI/ML based positioning”).
Regarding Claim 3, Karampatsis teaches the invention of Claim 1, further teaching obtaining the machine learning model from the network entity or a machine learning model repository server (Par. [0114] “Based on the information provided by the UE 708, when the LMF 704 determines that an AI/ML model is to be used by the UE 708, as in step 722, the LMF 704 sends a request to the UE 708, as in step 724. The request may include the AI/ML model to use and may also provide the trained AI/ML mode (if not stored at the UE 708)”).
Regarding Claim 6, Karampatsis teaches the invention of Claim 1, further teaching wherein the one or more provide location information messages include: an identifier of the machine learning model, and an identifier of a parameter set of the machine learning model.
(Par. [0111] “At 728, the UE reports the measurements or measurement reports to the LMF 704. The UE 708 may include, in the response to the LMF 704, the accuracy/confidence of the measurement or measurements taking into account the AI/ML model and the AI/ML model used for the measurement”).
Regarding Claim 8, Karampatsis teaches the invention of Claim 1, further teaching wherein the one or more request location information messages and the one or more provide location information messages are for a machine learning-based positioning procedure (Par. [0107] “At 724, the LMF 704 instructs the UE 708 to provide location measurement reports with the procedure described in 3GPP TS 37.355. The LMF 704 additionally includes an indication to provide measurement reports using AI/ML based positioning based on the determination at step 722” and Par. [0110] “At 726, the UE 708 uses the AI/ML model to provide location reports. The UE 708 provides the measurement reports by applying an AI/ML model” and Par. [0111] “At 728, the UE reports the measurements or measurement reports to the LMF 704. The UE 708 may include, in the response to the LMF 704, the accuracy/ confidence of the measurement or measurements taking into account the AI/ML model and the AI/ML model used for the measurement”).
Regarding Claim 9, Karampatsis teaches the invention of Claim 1, further teaching wherein the one or more request location information messages and the one or more provide location information messages are for a cellular- based positioning procedure (Par. [0107] “At 724, the LMF 704 instructs the UE 708 to provide location measurement reports with the procedure described in 3GPP TS 37.355. The LMF 704 additionally includes an indication to provide measurement reports using AI/ML based positioning based on the determination at step 722”).
Regarding Claim 11, Karampatsis teaches the invention of Claim 9, further teaching wherein the one or more request location information messages configure the first network node to use the machine learning model to derive the one or more features of the wireless channel based on the one or more request location information messages including a flag configuring the first network node to report the one or more features of the wireless channel as positioning measurements for the cellular-based positioning procedure (Par. [0107] “At 724, the LMF 704 instructs the UE 708 to provide location measurement reports with the procedure described in 3GPP TS 37.355. The LMF 704 additionally includes an indication to provide measurement reports using AI/ML based positioning based on the determination at step 722. The LMF 704 may include in the request a minimum accuracy for the measurement/ confidence level” and Par. [0110] “At 726, the UE 708 uses the AI/ML model to provide location reports. The UE 708 provides the measurement reports by applying an AI/ML model” and Par. [0111] “At 728, the UE reports the measurements or measurement reports to the LMF 704. The UE 708 may include, in the response to the LMF 704, the accuracy/ confidence of the measurement or measurements taking into account the AI/ML model and the AI/ML model used for the measurement”).
Regarding Claim 12, Karampatsis teaches the invention of Claim 1, further teaching wherein the machine learning model is specific to: the second network node, a type of the second network node, a vendor of the second network node, a type of the one or more features of the wireless channel, a type of the wireless channel, or any combination thereof. (Par. [0102] “At 716, the LMF 704 requests analytics from the NWDAF 702 on NLOS and/ or LOS measurement statistics/predictions. Thus, the LMF 704 requests from the NWDAF 702 data specifying statistics or a prediction that location measurements of the UE 708 are based on LOS or NLOS communication with a node of the RAN 706. At 718, the NWDAF 702 derives (or the NWDAF 702 has previously derived) the requested analytics, i.e. the statistics or the prediction” and Par. [0105] “At 722, the LMF 704 determines, based on the analytics received, if an AI/ML model for positioning is to be used by the UE 708 for location measurements. If it is determined that an AI/ML model is to be applied, the LMF 704 may select an applicable AI/ML model from the list of AI/ML models provided by the UE at 712”).
Regarding Claim 13, Karampatsis teaches the invention of Claim 1, further teaching the first network node is a user equipment (UE), and the second network node is a transmission-reception point (TRP) (Par. [0118] The LMF 704 is further arranged to, for the one or more UEs 708 and/ or for a target area in which the one or more UEs 708 are located, acquire statistics or a prediction (i.e., analytics) that location measurements are based LOS or NLOS communication with a RAN node in the wireless communication network).
Regarding Claim 16, Karampatsis teaches a method of communication performed by a network entity, comprising: transmitting one or more request location information messages to a first network node, (Fig. 7, See 724, and Par. [0107] “At 724, the LMF 704 instructs the UE 708 to provide location measurement reports with the procedure described in 3GPP TS 37.355. The LMF 704 additionally includes an indication to provide measurement reports using AI/ML based positioning based on the determination at step 722. The LMF 704 may include in the request a minimum accuracy for the measurement/ confidence level”),
wherein the one or more request location information messages configure the first network node to use machine learning to derive one or more features of a wireless channel between the first network node and a second network node; (Par. [0105] “the LMF 704 may specify to the UE 708 that the UE 708 is to provide measurement reports using AI/ML based positioning. The LMF 704 may include, in the request to the UE 708, a minimum measurement accuracy/ confidence level for applying AI/ML based positioning” and Par. [0118] “The LMF 704 is further arranged to, for the one or more UEs 708 and/ or for a target area in which the one or more UEs 708 are located, acquire statistics or a prediction (i.e., analytics) that location measurements are based LOS or NLOS communication with a RAN node in the wireless communication network”),
and receiving one or more provide location information messages from the first network node, (Par. [0111] “At 728, the UE reports the measurements or measurement reports to the LMF 704”),
wherein the one or more provide location information messages include the one or more features of the wireless channel, and wherein the one or more features of the wireless channel are derived based on a machine learning model (Par. [0111] The UE 708 may include, in the response to the LMF 704, the accuracy/ confidence of the measurement or measurements taking into account the AI/ML model and the AI/ML model used for the measurement”).
Regarding Claim 17, Karampatsis teaches the invention of Claim 16, further teaching wherein the one or more request location information messages include: an identifier of the machine learning model, (Par. [0105] “If it is determined that an AI/ML model is to be applied, the LMF 704 may select an applicable AI/ML model from the list of AI/ML models provided by the UE at 712”),
and an identifier of a parameter set of the machine learning model (Par. [0105] “The LMF 704 may include, in the request to the UE 708, a minimum measurement accuracy/ confidence level for applying AI/ML based positioning”).
Regarding Claim 20, Karampatsis teaches the invention of Claim 16, further teaching wherein the one or more provide location information messages include: an identifier of the machine learning model, and an identifier of a parameter set of the machine learning model (Par. [0111] “At 728, the UE reports the measurements or measurement reports to the LMF 704. The UE 708 may include, in the response to the LMF 704, the accuracy/confidence of the measurement or measurements taking into account the AI/ML model and the AI/ML model used for the measurement”).
Regarding Claim 22, Karampatsis teaches the invention of Claim 16, further teaching wherein the one or more request location information messages and the one or more provide location information messages are for a machine learning-based positioning procedure (Par. [0107] “At 724, the LMF 704 instructs the UE 708 to provide location measurement reports with the procedure described in 3GPP TS 37.355. The LMF 704 additionally includes an indication to provide measurement reports using AI/ML based positioning based on the determination at step 722” and Par. [0110] “At 726, the UE 708 uses the AI/ML model to provide location reports. The UE 708 provides the measurement reports by applying an AI/ML model” and Par. [0111] “At 728, the UE reports the measurements or measurement reports to the LMF 704. The UE 708 may include, in the response to the LMF 704, the accuracy/ confidence of the measurement or measurements taking into account the AI/ML model and the AI/ML model used for the measurement”).
Regarding Claim 23, Karampatsis teaches the invention of Claim 16, further teaching wherein the one or more request location information messages and the one or more provide location information messages are for cellular- based positioning procedure (Par. [0107] “At 724, the LMF 704 instructs the UE 708 to provide location measurement reports with the procedure described in 3GPP TS 37.355. The LMF 704 additionally includes an indication to provide measurement reports using AI/ML based positioning based on the determination at step 722. The LMF 704 may include in the request a minimum accuracy for the measurement/ confidence level” and Par. [0110] “At 726, the UE 708 uses the AI/ML model to provide location reports. The UE 708 provides the measurement reports by applying an AI/ML model” and Par. [0111] “At 728, the UE reports the measurements or measurement reports to the LMF 704. The UE 708 may include, in the response to the LMF 704, the accuracy/ confidence of the measurement or measurements taking into account the AI/ML model and the AI/ML model used for the measurement”).
Regarding Claim 24, Karampatsis teaches the invention of Claim 23, further teaching wherein the one or more request location information messages configure the first network node to use the machine learning model to derive the one or more features of the wireless channel based on the one or more request location information messages including a flag configuring the first network node to report the one or more features of the wireless channel as positioning measurements for the cellular-based positioning procedure (Par. [0107] “At 724, the LMF 704 instructs the UE 708 to provide location measurement reports with the procedure described in 3GPP TS 37.355. The LMF 704 additionally includes an indication to provide measurement reports using AI/ML based positioning based on the determination at step 722. The LMF 704 may include in the request a minimum accuracy for the measurement/ confidence level” and Par. [0110] “At 726, the UE 708 uses the AI/ML model to provide location reports. The UE 708 provides the measurement reports by applying an AI/ML model” and Par. [0111] “At 728, the UE reports the measurements or measurement reports to the LMF 704. The UE 708 may include, in the response to the LMF 704, the accuracy/ confidence of the measurement or measurements taking into account the AI/ML model and the AI/ML model used for the measurement”).
Regarding Claim 25, Karampatsis teaches the invention of Claim 16, further teaching wherein the machine learning model is specific to: the second network node, a type of the second network node, a vendor of the second network node, a type of the one or more features of the wireless channel, a type of the wireless channel, or any combination thereof. (Par. [0102] “At 716, the LMF 704 requests analytics from the NWDAF 702 on NLOS and/ or LOS measurement statistics/predictions. Thus, the LMF 704 requests from the NWDAF 702 data specifying statistics or a prediction that location measurements of the UE 708 are based on LOS or NLOS communication with a node of the RAN 706. At 718, the NWDAF 702 derives (or the NWDAF 702 has previously derived) the requested analytics, i.e. the statistics or the prediction” and Par. [0105] “At 722, the LMF 704 determines, based on the analytics received, if an AI/ML model for positioning is to be used by the UE 708 for location measurements. If it is determined that an AI/ML model is to be applied, the LMF 704 may select an applicable AI/ML model from the list of AI/ML models provided by the UE at 712”).
Regarding Claim 26, Karampatsis teaches the invention of Claim 16, further teaching the first network node is a user equipment (UE), and the second network node is a transmission-reception point (TRP) (Par. [0118] The LMF 704 is further arranged to, for the one or more UEs 708 and/ or for a target area in which the one or more UEs 708 are located, acquire statistics or a prediction (i.e., analytics) that location measurements are based LOS or NLOS communication with a RAN node in the wireless communication network).
Regarding Claim 29, Karampatsis teaches a first network node, comprising: a memory; (Fig. 2, See 210, Par. [0043] “The user equipment apparatus 200 includes… a memory 210”),
at least one transceiver; (Fig. 2, See 225, Par. [0043] “The user equipment apparatus 200 includes… a transceiver 225”),
and at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor configured to: (Fig. 2, See 205, Par. [0043] “The user equipment apparatus 200 includes a processor 205”),
receive, via the at least one transceiver, one or more request location information messages from a network entity, (Fig. 7, See 724, and Par. [0107] “At 724, the LMF 704 instructs the UE 708 to provide location measurement reports with the procedure described in 3GPP TS 37.355. The LMF 704 additionally includes an indication to provide measurement reports using AI/ML based positioning based on the determination at step 722. The LMF 704 may include in the request a minimum accuracy for the measurement/ confidence level”),
wherein the one or more request location information messages configure the first network node to use machine learning to derive one or more features of a wireless channel between the first network node and a second network node; (Par. [0105] “the LMF 704 may specify to the UE 708 that the UE 708 is to provide measurement reports using AI/ML based positioning. The LMF 704 may include, in the request to the UE 708, a minimum measurement accuracy/ confidence level for applying AI/ML based positioning” and Par. [0118] “The LMF 704 is further arranged to, for the one or more UEs 708 and/ or for a target area in which the one or more UEs 708 are located, acquire statistics or a prediction (i.e., analytics) that location measurements are based LOS or NLOS communication with a RAN node in the wireless communication network”),
and transmit, via the at least one transceiver, one or more provide location information messages to the network entity, (Par. [0111] “At 728, the UE reports the measurements or measurement reports to the LMF 704”),
wherein the one or more provide location information messages include the one or more features of the wireless channel, and wherein the one or more features of the wireless channel are derived based on a machine learning model (Par. [0111] The UE 708 may include, in the response to the LMF 704, the accuracy/ confidence of the measurement or measurements taking into account the AI/ML model and the AI/ML model used for the measurement”).
Regarding Claim 30, Karampatsis teaches a network entity, comprising: a memory; (Par. [0057] “The network node 300 may be one implementation of an entity in the wireless communications network. The network node 300 includes…a memory 310,”),
at least one transceiver; (Par. [0057] “The network node 300 may be one implementation of an entity in the wireless communications network. The network node 300 includes… a transceiver 325”),
and at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor configured to: (Par. [0057] “The network node 300 may be one implementation of an entity in the wireless communications network. The network node 300 includes a controller 305” and Par. [0060] “The controller 305 may include any known controller capable of executing computer-readable instructions and/ or capable of performing logical operations. For example, the controller 305 may be a microcontroller, a microprocessor, a CPU, a GPU, an auxiliary processing unit, a FPGA, or similar programmable controller. The controller 305 may execute instructions stored in the memory 310 to perform the methods and routines described herein”),
transmit, via the at least one transceiver, one or more request location information messages to a first network node, (Fig. 7, See 724, and Par. [0107] “At 724, the LMF 704 instructs the UE 708 to provide location measurement reports with the procedure described in 3GPP TS 37.355. The LMF 704 additionally includes an indication to provide measurement reports using AI/ML based positioning based on the determination at step 722. The LMF 704 may include in the request a minimum accuracy for the measurement/ confidence level”),
wherein the one or more request location information messages configure the first network node to use machine learning to derive one or more features of a wireless channel between the first network node and a second network node; (Par. [0105] “the LMF 704 may specify to the UE 708 that the UE 708 is to provide measurement reports using AI/ML based positioning. The LMF 704 may include, in the request to the UE 708, a minimum measurement accuracy/ confidence level for applying AI/ML based positioning” and Par. [0118] “The LMF 704 is further arranged to, for the one or more UEs 708 and/ or for a target area in which the one or more UEs 708 are located, acquire statistics or a prediction (i.e., analytics) that location measurements are based LOS or NLOS communication with a RAN node in the wireless communication network”),
and receive, via the at least one transceiver, one or more provide location information messages from the first network node, (Par. [0111] “At 728, the UE reports the measurements or measurement reports to the LMF 704”),
wherein the one or more provide location information messages include the one or more features of the wireless channel, and wherein the one or more features of the wireless channel are derived based on a machine learning model (Par. [0111] The UE 708 may include, in the response to the LMF 704, the accuracy/ confidence of the measurement or measurements taking into account the AI/ML model and the AI/ML model used for the measurement”).
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.
Claims 4-5, 7, 10, 14, 15, 18-19, 21, 27 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Karampatsis et al. (WO 2023/110161) in view of Thomas et al. (US 2025/0142291).
Regarding Claim 4, Karampatsis teaches the invention of Claim 1, but does not teach
wherein: the one or more request location information messages include a plurality of identifiers of a plurality of machine learning models, and the machine learning model is selected to derive the one or more features of the wireless channel based on one or more rules configured to the first network node.
In the same field of endeavor, Thomas teaches wherein: the one or more request location information messages include a plurality of identifiers of a plurality of machine learning models, (Par. [0128] “At 1110 of FIG. 11a, the location server 1108 (e.g., the LMF 120) transmits a request to one or more UEs 104 to each transfer and deploy a UE trained AI inference model at the location server 1108 including information related to the model training criteria and indication whether to provide labelled or unlabelled data” and Par. [0133] “At least one supervised or unsupervised model may be trained but there may be instances where the UE may train multiple models, based on the model training criteria”),
and the machine learning model is selected to derive the one or more features of the wireless channel based on one or more rules configured to the first network node (Par. [0133] “At 1118, each target UE 104 trains its own AI inference model based on the UE-based measurements. At least one supervised or unsupervised model may be trained but there may be instances where the UE may train multiple models, based on the model training criteria including, a defined area in which the measurements were made (e.g., specific TRP/cell list, geographic area comprising either latitude/longitude coordinates, etc.), a defined time duration in which the measurements were performed, and so forth”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thomas’s multiple AI models with Karampatsis’s location measurement method to improve position measurement reporting.
Regarding Claim 5, Karampatsis teaches the invention of Claim 1, but does not teach wherein: the one or more request location information messages include an identifier of a network-side machine learning model to be used to decode the one or more features of the wireless channel, (Par. [0128] “At 1110 of FIG. 11a, the location server 1108 (e.g., the LMF 120) transmits a request to one or more UEs 104 to each transfer and deploy a UE trained AI inference model at the location server 1108 including information related to the model training criteria and indication whether to provide labelled or unlabelled data”)
and the machine learning model is selected to derive the one or more features of the wireless channel based on the machine learning model being compatible with the network-side machine learning model (Par. [0127] “In the example procedure 1100, the signalling overhead and functionality achieved by the location server 1108 of procedure 1000 of FIG. 10 is distributed to the base stations and the UEs for prediction analytics of the positioning QoS. The UEs and base stations may each establish their respective AI-based training of AI inference models and then have the capability to deploy their respective trained AI inference models at the location server 1108” and Par. [0128] “At 1110 of FIG. 11a, the location server 1108 (e.g., the LMF 120) transmits a request to one or more UEs 104 to each transfer and deploy a UE trained AI inference model at the location server 1108 including information related to the model training criteria and indication whether to provide labelled or unlabelled data”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thomas’s AI model deployed at the location server with Karampatsis’s location measurement method to improve position measurement reporting.
Regarding Claim 7, Karampatsis teaches the invention of Claim 1, but does not teach wherein the one or more provide location information messages include: an identifier of a network-side machine learning model to be used to decode the one or more features of the wireless channel, and an identifier of a parameter set of the network-side machine learning model.
In the same field of endeavor, Thomas teaches wherein the one or more provide location information messages include: an identifier of a network-side machine learning model to be used to decode the one or more features of the wireless channel, and an identifier of a parameter set of the network-side machine learning model (Par. [0139] “At 1130 of FIG. 11b, additional UE measurements may be reported by the one or more target UEs 104 to the location server 1108 as input into the AI inference model (e.g., the UE trained AI inference model) for the desired prediction” and Par. [0145] “Each of the AI inference models derived at a UE 1104 and/or a base station 1102, 1104, or 1106 may be associated with an ID, which may be transferred in the contained message at 1130 and/or 1132, respectively”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thomas’s AI model deployed at the location server with Karampatsis’s location measurement method to improve position measurement reporting.
Regarding Claim 10, Karampatsis teaches the invention of Claim 9, but does not teach wherein the cellular-based positioning procedure comprises a downlink time difference of arrival (DL-TDOA) positioning procedure, a round-trip-time (RTT) positioning procedure, an enhanced cell identifier (ECID) positioning procedure, a downlink angle of arrival (DL-AOD) positioning procedure or any combination thereof.
In the same field of endeavor, Thomas teaches wherein the cellular-based positioning procedure comprises a downlink time difference of arrival (DL-TDOA) positioning procedure,
a round-trip-time (RTT) positioning procedure, an enhanced cell identifier (ECID) positioning procedure, a downlink angle of arrival (DL-AOD) positioning procedure or any combination thereof (Par. [0038] “Various RAT-dependent positioning techniques (also referred to as positioning methods, or positioning procedures) are supported for a UE, for UE-assisted, LMF-based, and/or for NG-RAN node assisted. The RAT-dependent positioning techniques that are supported include downlink-time difference of arrival (DL-TDOA), downlink-angle of departure (DL-AoD), multi-round trip time (multi-RTT), new radio enhanced cell-ID (NR E-CID); uplink-time difference of arrival (UL-TDOA); and uplink-angle of arrival (UL-AoA)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thomas’s RAT-dependent positioning techniques with Karampatsis’s location measurement method to improve position measurement reporting.
Regarding Claim 14, Karampatsis teaches the invention of Claim 1, further teaching the first network node is a TRP, (Par. [0116] “In some embodiments, positioning measurements may be carried out at the RAN 706. Thus, the system and procedure described in more detail earlier above with reference to Figure 7 may be implemented with the UE 708 replaced by a RAN node”)
Karampatsis, however, does not teach the second network node is a UE.
In the same field of endeavor, Thomas teaches the second network node is a UE (Fig. 11a-11b, see 104, 1102, 1104, and 1006, Par. [0134] “At 1120, one or more of base stations 1102, 1104, and 1106 each trains its own model based on the base station based measurements. At least one supervised or unsupervised model may be trained but there may be instances where the UE may train multiple models, based on the model training criteria including, a defined area in which the measurements were made (e.g., specific TRPs/cell list, geographic area comprising either latitude/longitude coordinates, etc.), a defined time duration in which the measurements were performed, based on threshold number of served target-UEs, and so forth”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thomas’s UE and base stations with Karampatsis’s location measurement method to improve position measurement reporting.
Regarding Claim 15, Karampatsis teaches the invention of Claim 1, but does not teach wherein the network entity is a location server.
In the same field of endeavor, Thomas teaches wherein the network entity is a location server (Par. [0128] “At 1110 of FIG. 11a, the location server 1108 (e.g., the LMF 120) transmits a request to one or more UEs 104” and Par. [0129] “At 1112, the location server 1108 (e.g., the LMF 120) transmits a request to one or more base stations 1102, 1104, and 1106”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thomas’s location server with Karampatsis’s location measurement method to improve position measurement reporting.
Regarding Claim 18, Karampatsis teaches the invention of Claim 16, but does not teach wherein the one or more request location information messages include a plurality of identifiers of a plurality of machine learning models.
In the same field of endeavor, Thomas teaches wherein the one or more request location information messages include a plurality of identifiers of a plurality of machine learning models (Par. [0128] “At 1110 of FIG. 11a, the location server 1108 (e.g., the LMF 120) transmits a request to one or more UEs 104 to each transfer and deploy a UE trained AI inference model at the location server 1108 including information related to the model training criteria and indication whether to provide labelled or unlabelled data” and Par. [0133] “At least one supervised or unsupervised model may be trained but there may be instances where the UE may train multiple models, based on the model training criteria”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thomas’s multiple AI models with Karampatsis’s location measurement method to improve position measurement reporting.
Regarding Claim 19, Karampatsis teaches the invention of Claim 16, but does not teach wherein the one or more request location information messages include an identifier of a network-side machine learning model to be used to decode the one or more features of the wireless channel.
In the same field of endeavor, Thomas teaches wherein the one or more request location information messages include an identifier of a network-side machine learning model to be used to decode the one or more features of the wireless channel (Par. [0128] “At 1110 of FIG. 11a, the location server 1108 (e.g., the LMF 120) transmits a request to one or more UEs 104 to each transfer and deploy a UE trained AI inference model at the location server 1108 including information related to the model training criteria and indication whether to provide labelled or unlabelled data”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thomas’s AI model deployed at the location server with Karampatsis’s location measurement method to improve position measurement reporting.
Regarding Claim 21, Karampatsis teaches the invention of Claim 16, but does not teach wherein the one or more provide location information messages include: an identifier of a network-side machine learning model to be used to decode the one or more features of the wireless channel, and an identifier of a parameter set of the network-side machine learning model.
In the same field of endeavor, Thomas teaches wherein the one or more provide location information messages include: an identifier of a network-side machine learning model to be used to decode the one or more features of the wireless channel, and an identifier of a parameter set of the network-side machine learning model (Par. [0139] “At 1130 of FIG. 11b, additional UE measurements may be reported by the one or more target UEs 104 to the location server 1108 as input into the AI inference model (e.g., the UE trained AI inference model) for the desired prediction” and Par. [0145] “Each of the AI inference models derived at a UE 1104 and/or a base station 1102, 1104, or 1106 may be associated with an ID, which may be transferred in the contained message at 1130 and/or 1132, respectively”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thomas’s AI model deployed at the location server with Karampatsis’s location measurement method to improve position measurement reporting.
Regarding Claim 27, Karampatsis teaches the invention of Claim 16, further teaching the first network node is a TRP, (Par. [0116] “In some embodiments, positioning measurements may be carried out at the RAN 706. Thus, the system and procedure described in more detail earlier above with reference to Figure 7 may be implemented with the UE 708 replaced by a RAN node”)
Karampatsis, however, does not teach the second network node is a UE.
In the same field of endeavor, Thomas teaches the second network node is a UE (Fig. 11a-11b, see 104, 1102, 1104, and 1006, Par. [0134] “At 1120, one or more of base stations 1102, 1104, and 1106 each trains its own model based on the base station based measurements. At least one supervised or unsupervised model may be trained but there may be instances where the UE may train multiple models, based on the model training criteria including, a defined area in which the measurements were made (e.g., specific TRPs/cell list, geographic area comprising either latitude/longitude coordinates, etc.), a defined time duration in which the measurements were performed, based on threshold number of served target-UEs, and so forth”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thomas’s UE and base stations with Karampatsis’s location measurement method to improve position measurement reporting.
Regarding Claim 28 Karampatsis teaches the invention of Claim 16, but does not teach wherein the network entity is a location server.
In the same field of endeavor, Thomas teaches wherein the network entity is a location server (Par. [0128] “At 1110 of FIG. 11a, the location server 1108 (e.g., the LMF 120) transmits a request to one or more UEs 104” and Par. [0129] “At 1112, the location server 1108 (e.g., the LMF 120) transmits a request to one or more base stations 1102, 1104, and 1106”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thomas’s location server with Karampatsis’s location measurement method to improve position measurement reporting.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wu (US 2024/0306119) discloses a method for a channel feature extraction model and a positioning information obtaining model that are used in a matched manner are used to implement or assist in implementing positioning of a UE, to obtain location information of the UE. In the method, the channel feature extraction model is deployed on a RAN side, for example, deployed in a base station or the UE, and is used to map channel information such as a channel response to a channel feature” (Par. [0163]).
3GPP TS 38.305, ("3GPP, TSG RAN, Stage 2 Functional Specification of UE Positioning in NG-RAN - Release 17", Mobile Competence Centre, 650, Route Des Lucioles, F-06921 Sophia-Antipolis Cedex, France, Vol. RAN WG2, No. V17.0.0, 14 April 2022, XP052134867, XP052145927) (disclosed in IDS) discloses “The LMF sends a LPP Request Location Information message to the UE for invocation of A-GNSS positioning” (Pg. 65, Par. 8.1.3.3.1 LMF initiated Location Information Transfer Procedure).
Manolakos et al. (US 2021/0160812) discloses “At stages 1115, 1116, each of the UEs 302-1, 302-2 report one or more feature vectors of measurements and/or one or more position-determination models and/or one or more position-determination-model parameter updates to the LMF 306 via the gNB 304” (Par. [0115]).
Barbu et al. (US 2025/0089001) discloses “At block 207 the method comprises providing the input to the mapping model to enable the mapping model to create a list of a plurality of configurations for signals that enable positioning. The configurations can comprise configurations that could be undertaken by the network 100. The mapping model can generate the list of configurations based on the generated input” (Par. [0124]).
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/J.N.D./Examiner, Art Unit 2641
/CHARLES N APPIAH/Supervisory Patent Examiner, Art Unit 2641
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