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 statements submitted on 3/09/26 has been considered by the examiner and made of record in the application file.
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 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.
Claims 15-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Thomas, WO 2024027939 A1 (hereinafter Thomas).
Regarding claim 15, Thomas teaches a method of wireless communication at a first network entity, comprising: transmitting, to a second network entity, a request for at least one of (1) an initiation of a set of UE positioning related measurements for artificial intelligence (AI) or machine learning (ML) (AI/ML) training at the first network entity, (Thomas; para. 93 & 113 - - teaches a network node in a wireless communication network, comprising: a transmitter arranged to send a positioning training dataset configuration to at least one data source of the wireless communication network, the positioning training dataset configuration comprising a request for a positioning training dataset for training a machine learning positioning model. Further in para. 113, Thomas teaches an activation request for activating the sending of the positioning training dataset configuration.
or (2) a ground truth label for the AI/ML training at the first network entity; (Thomas; para. 120 & 128 & fig. 12- - teaches the training dataset should comprise of ground truth measurements including at least location information associated to each of the measurements. Further, para. 128 teaches in a first step 1201 a request of training data (ground truth data collection) is made).
and receiving, based on the request, at least one of: (1) an indication to perform the set of UE positioning related measurements or (2) the ground truth label. (Thomas; para. 131 & fig. 12 - - teaches in a further step 1206, gNB nodes 1220 and 1230 respond with a set of structured training data comprising of measurements and timestamp/location of each measurement. This response may be signaled using NRPPa.)
Regarding claim 16, Thomas teaches an apparatus for wireless communication at a second network entity, comprising: at least one memory; and at least one processor coupled to the at least one memory, the at least one processor, individually or in any combination, is configured to: (Thomas; para. 50 & fig. 3 - - teaches the network node 300 includes a processor 305 and a memory 310).
receive a request for at least one of (1) an initiation of a set of UE positioning related measurements for artificial intelligence (AI) or machine learning (ML) (AI/ML) training at a first network entity, (Thomas; para. 113 - - teaches some embodiments further comprise receiving an activation request for activating the sending of the positioning training dataset configuration, the receiving of the positioning training dataset and/ or the training of the machine learning positioning model).
or (2) a ground truth label for the AI/ML training at the first network entity; (Thomas; para. 120 & 128 & fig. 12- - teaches the training dataset should comprise of ground truth measurements including at least location information associated to each of the measurements. Further, para. 128 teaches in a first step 1201 a request of training data (ground truth data collection) is made).
and transmit, to the first network entity based on the request, at least one of: (1) an indication to perform the set of UE positioning related measurements or (2) the ground truth label. (Thomas; para. 131 & fig. 12 - - teaches in a further step 1206, gNB nodes 1220 and 1230 respond with a set of structured training data comprising of measurements and timestamp/location of each measurement. This response may be signaled using NRPPa.)
Regarding claim 17, Thomas teaches the apparatus of claim 16, wherein the indication to perform the set of UE positioning related measurements corresponds to a configuration for a set of sounding reference signals (SRS) to be transmitted from at least one user equipment (UE). (Thomas; para. 139 - - teaches in a further step 1302 the NG-RAN node 1320 may request for the collection of positioning training data from the configured data sources (such as UEs 1330, 1340, 1350), which may include RAT-independent positioning measurements/location estimates as well as SRS configurations for the UE to transmit SRS for the purposes of measurement collection and training at the NG-RAN node 1320.)
Regarding claim 18, Thomas teaches the apparatus of claim 16, wherein the request for the initiation of the set of UE positioning related measurements further includes at least one of: a second indication to provide a feedback for at least one AI/ML model associated with the AI/ML training, a duration for the at least one AI/ML model to be used for the AI/ML training, a measurement type for the at least one AI/ML model to be used for the AI/ML training, or a set of sounding reference signals (SRSs) characteristic associated with the at least one AI/ML model. (Thomas; para. 85 - - teaches there is the potential for 3GPP specification impact for aspects of AI/ML approaches for positioning accuracy enhancement including […] AI/ML model inference input (report/ feedback of model input for inference e.g. UE feedback as an input for network side mode inference; model input acquisition and pre-processing; type/ definition of model input), AI/ML model inference output (report/ feedback of mode inference output; post-processing of model inference output)).
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 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, 2, 4, 5, 8-9, & 13 are rejected under 35 U.S.C. 103 as being unpatentable over Thomas WO 2024027939 A1, in view of Alawieh, US 20250056488 A1 (hereinafter Alawieh).
Regarding claim 1, Thomas teaches at least one processor coupled to the at least one memory, the at least one processor, individually or in any combination, is configured to: (Thomas; para. 8 - - teaches the network node further comprising a processor arranged to train the machine learning positioning model using the positioning training dataset.)
transmit, to a second network entity, a request for at least one of (1) an initiation of a set of UE positioning related measurements for artificial intelligence (AI) or machine learning (ML) (AI/ML) training at the first network entity, (Thomas; para. 93 & 113 - - teaches a network node in a wireless communication network, comprising: a transmitter arranged to send a positioning training dataset configuration to at least one data source of the wireless communication network, the positioning training dataset configuration comprising a request for a positioning training dataset for training a machine learning positioning model. Further in para. 113, Thomas teaches an activation request for activating the sending of the positioning training dataset configuration.
or (2) a ground truth label for the AI/ML training at the first network entity; (Thomas; para. 120 & 128 & fig. 12- - teaches the training dataset should comprise of ground truth measurements including at least location information associated to each of the measurements. Further, para. 128 teaches in a first step 1201 a request of training data (ground truth data collection) is made).
and receive, based on the request, at least one of: (1) an indication to perform the set of UE positioning related measurements or (2) the ground truth label. (Thomas; para. 131 & fig. 12 - - teaches in a further step 1206, gNB nodes 1220 and 1230 respond with a set of structured training data comprising of measurements and timestamp/location of each measurement. This response may be signaled using NRPPa.)
Thomas fails to clearly specify an apparatus for wireless communication at a first network entity and at least one memory.
However, Alawieh teaches an apparatus for wireless communication at a first network entity, comprising at least one memory (Alawieh; para. 3 - - teaches the wireless communication system may also be accessed by mobile or stationary IoT (Internet of Things) devices which connect to a base station or to a user).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Alawieh into the invention of Thomas to include the feature of an apparatus for wireless communication at a first network entity and at least one memory, in order to provide a concrete hardware platform on which the AI/ML positioning training functions taught by Thomas are executed, improving the reliability of network -side AI/ML positioning training (see Alawieh, para. 3).
Regarding claim 2, the combination of Thomas and Alawieh teaches the apparatus of claim 1. The combination of Thomas and Alawieh further teach wherein reception of the indication to perform the set of UE positioning related measurements is based on transmission of the request for the initiation of the set of UE positioning related measurements for the AI/ML training at the first network entity. (Thomas; para. 92 & 93 - - the network nodes and methods described herein provide for selection and configuration of data sources depending on the type of training of positioning data as well as which network entity is performing the training of the AI/ML positioning model. [0093] Described herein is a network node in a wireless communication network, comprising: a transmitter arranged to send a positioning training dataset configuration to at least one data source of the wireless communication network, the positioning training dataset configuration comprising a request for a positioning training dataset for training a machine learning positioning model, and a required data format of the positioning training dataset based on a location of the network node.)
Regarding claim 4, the combination of Thomas and Alawieh teaches the apparatus of claim 1. The combination of Thomas and Alawieh further teach wherein the indication to perform the set of UE positioning related measurements corresponds to a configuration for a set of sounding reference signals (SRS) to be transmitted from at least one user equipment (UE). (Thomas; para. 139 - - teaches in a further step 1302 the NG-RAN node 1320 may request for the collection of positioning training data from the configured data sources (such as UEs 1330, 1340, 1350), which may include RAT-independent positioning measurements/location estimates as well as SRS configurations for the UE to transmit SRS for the purposes of measurement collection and training at the NG-RAN node 1320.)
Regarding claim 5, the combination of Thomas and Alawieh teaches the apparatus of claim 1. The combination of Thomas and Alawieh further teaches wherein the request for the initiation of the set of UE positioning related measurements further includes at least one of: a second indication to provide a feedback for at least one AI/ML model associated with the AI/ML training, a duration for the at least one AI/ML model to be used for the AI/ML training, a measurement type for the at least one AI/ML model to be used for the AI/ML training, or a set of sounding reference signals (SRSs) characteristic associated with the at least one AI/ML model. (Thomas; para. 85 - - teaches there is the potential for 3GPP specification impact for aspects of AI/ML approaches for positioning accuracy enhancement including […] AI/ML model inference input (report/ feedback of model input for inference e.g. UE feedback as an input for network side mode inference; model input acquisition and pre-processing; type/ definition of model input), AI/ML model inference output (report/ feedback of mode inference output; post-processing of model inference output)).
Regarding claim 8, the combination of Thomas and Alawieh teaches the apparatus of claim 1. The combination of Thomas and Alawieh further teaches wherein reception of the ground truth label is based on transmission of the request for the ground truth label for the AI/ML training at the first network entity. (Thomas; para. 128 - - teaches in a first step 1201 a request of training data (ground truth data collection) is made. In this step 1201 the location server node 1210 (e.g., LMF) requests training data set from the NG-RAN nodes 1220 and 1230).
Regarding claim 9, the combination of Thomas and Alawieh teach the apparatus of claim 8. The combination of Thomas and Alawieh further teach wherein the ground truth label is received via at last one of a New Radio Positioning Protocol A (NRPPa) message or a Next Generation Access Point (NGAP) message, including at least one of a routing identifier (ID), a correlation ID, or at least one ID of at least one user equipment (UE). (Thomas; para. 128 - - teaches in a first step 1201 a request of training data (ground truth data collection) is made. In this step 1201 the location server node 1210 (e.g., LMF) requests training data set from the NG-RAN nodes 1220 and 1230 including an indication, whether the requested training data (measurement data) is used for offline and/ or online training and location for each measurement. This request may include a request for RAT-independent and UL-based positioning measurements. This request may be signaled using the NRPPa interface).
Regarding claim 13, the combination of Thomas and Alawieh teach the apparatus of claim 1. The combination of Thomas and Alawieh further teach the apparatus of claim wherein the request further includes a second indication to provide feedback for at least one AI/ML model associated with the AI/ML training, the at least one processor, individually or in any combination, is further configured to: receive, from the second network entity, the feedback related to the at least one AI/ML model. (Thomas; para. 85 - - teaches there is the potential for 3GPP specification impact for aspects of AI/ML approaches for positioning accuracy enhancement including […] AI/ML model inference input (report/ feedback of model input for inference e.g. UE feedback as an input for network side mode inference; model input acquisition and pre-processing; type/ definition of model input), AI/ML model inference output (report/ feedback of mode inference output; post-processing of model inference output)).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Thomas WO 2024027939 A1, in view of Alawieh US 20250056488 A1 as applied to claim 2, further in view of Edge, US 20220046578 A1 (hereinafter Edge).
Regarding claim 3, the combination of Thomas and Alawieh teaches the apparatus of claim 2. The combination of Thomas and Alawieh fail to teach wherein the second network entity is an access and mobility management function (AMF) or a gateway mobile location center (GMLC).
However, Edge teaches wherein the second network entity is an access and mobility management function (AMF) or a gateway mobile location center (GMLC). (Edge; para. 257 - - teaches the first entity may be a Location Management Function (e.g. an LMF 124) and the second entity may be an Access and Mobility Management Function (e.g. an AMF 122)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Edge into the invention of Thomas and Alawieh to include the feature of the second network entity being an access and mobility management function (AMF) or a gateway mobile location center (GMLC), in order to route the request for the initiation of the set of UE positioning related measurements from the first network entity to the location management function through the AMF as the intermediary network entity, improving the proper delivery and correlation of the AI/ML positioning measurement requests across the network entities (see Edge; para. 257).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Thomas WO 2024027939 A1, in view of Alawieh US 20250056488 A1 as applied to claim 1, further in view of Li, US 20240397472 A1, further in view of Zhang, EP 4319340 A1, (hereinafter Zhang).
Regarding claim 6, the combination of Thomas and Alawieh teach the apparatus of claim 1. the combination of Thomas and Alawieh fail to teach wherein the request for the initiation of the set of UE positioning related measurements further includes at least one identifier (ID) of at least one user equipment (UE) that can be used for identifying the at least one UE over a New Generation (NG) interface, the at least one processor, individually or in any combination, is further configured to: receive, from a location management function (LMF), one or more New Radio Positioning Protocol A (NRPPa) messages with a correlation ID, wherein the correlation ID is derived based on the at least one ID of the at least one UE.
However, Li teaches wherein the request for the initiation of the set of UE positioning related measurements further includes at least one identifier (ID) of at least one user equipment (UE) that can be used for identifying the at least one UE over a New Generation (NG) interface. (Li; paragraphs 36, 168, 169 - - In some other implementations, the UE may firstly send a request to gNB for certain positioning assistance data. The request may be sent with assistance request information which includes additional information concerning the UE's approximate location […] This assistance data request information may include the UE's last known location if available, the cell IDs of the UE serving NG-RAN node and possibly neighbour NG-RAN nodes, UE IDs, as well as NR E-CID measurements.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Li into the invention of Thomas and Alawieh to include the feature of the request for the initiation of the set of UE positioning related measurements further includes at least one identifier (ID) of at least one user equipment (UE) that can be used for identifying the at least one UE over a New Generation (NG) interface, in order to enable the network to identify and locate the particular UE to which the requested positioning measurements correspond, improving the accuracy and correlation of the positioning measurement initiation and reporting for the AI/ML training (see Li; para. 36).
The combination of Thomas, Alawieh, and Li fail to teach the at least one processor, individually or in any combination, is further configured to: receive, from a location management function (LMF), one or more New Radio Positioning Protocol A (NRPPa) messages with a correlation ID, wherein the correlation ID is derived based on the at least one ID of the at least one UE.
However, Zhang teaches the at least one processor, individually or in any combination, is further configured to: receive, from a location management function (LMF), one or more New Radio Positioning Protocol A (NRPPa) messages with a correlation ID, wherein the correlation ID is derived based on the at least one ID of the at least one UE. (Zhang; para. 87 - - teaches the LMF sends NRPPa, such as a POSITIONING INFORMATION REQUEST or other similar messages, to the base station to configure the PRS resource for per UE, and the message includes at least one of the following: an ID of BWP corresponding to an added DL-PRS resource, or a changed DL-PRS resource, or a deleted DL-PRS resource of per UE; an ID of a positioning frequency layer.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Zhang into the invention of Thomas, Alawieh, and Li to include the feature of receiving, from a location management function (LMF), one or more New Radio Positioning Protocol A (NRPPa) messages with a correlation ID, wherein the correlation ID is derived based on the at least one ID of the at least one UE in order to associate the NRPPa positioning messages with the particular UE identified by its ID across the LMF and the base station, improving the correlation of positioning measurement requests and responses to the correct UE during the AI/ML positioning procedure (see Zhang; para. 87).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Thomas WO 2024027939 A1, in view of Alawieh US 20250056488 A1 as applied to claim 6, further in view of Li, US 20240397472 A1, further in view of Chen, US 20250212160 A1, further in view of Manolakos, KR 20230128006 A (hereinafter Manolakos).
Regarding claim 7, the combination of Thomas, Alawieh, Li, and Chen teach the apparatus of claim 6. The combination of Thomas, Alawieh, Li, and Chen fail to teach wherein the first network entity is a base station and the second network entity is an access and mobility management function (AMF).
However, Manolakos teaches wherein the first network entity is a base station and the second network entity is an access and mobility management function (AMF). (Manolakos; para. 223 - - teaches the first network entity is a first base station, a first AMF, or a first location management function (LMF), and the second network entity is a second base station, a second AMF, or a second LMF.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Manolakos into the invention of Thomas, Alawieh, Li, and Chen to include the feature of wherein the first network entity is a base station and the second network entity is an access and mobility management function (AMF), in order to route the request for the initiation of the set of UE positioning related measurements from the base station to the location management function through the AMF as the binding entity between the base station and the LMF, improving the proper correlation and delivery of the AI/ML positioning measurement requests across the network entities (see Manolakos; para. 223).
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Thomas WO 2024027939 A1, in view of Alawieh US 20250056488 A1 as applied to claims 1 and 8, further in view of Challita, US 12386017 B2 (hereinafter Challita).
Regarding claim 10, the combination of Thomas and Alawieh teach the apparatus of claim 8. The combination of Thomas and Alawieh fail to teach wherein the at least one processor, individually or in any combination, is further configured to: perform the AI/ML training or AI/ML monitoring based on the ground truth label.
However, Challita teaches wherein the at least one processor, individually or in any combination, is further configured to: perform the AI/ML training or AI/ML monitoring based on the ground truth label. (Challita; col. 14, lines 10-15- - teaches the method 300 may comprise obtaining ground truth measurements of the radio conditions at different drone locations (e.g. obtained from drones or UEs). The ground truth measurements may be used to train a model using a machine learning process).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Challita into the invention of Thomas and Alawieh to include the feature of performing the AI/ML training or AI/ML monitoring based on the ground truth label in order to train the AI/ML positioning model using the labeled ground truth dara so the model learns the relationship between the positioning measurements and the actual UE locations, improving the accuracy and reliability of the AI/ML based positioning (see Challita; col. 14, lines 10-15).
Regarding claim 11, the combination of Thomas and Alawieh teach the apparatus of claim 1. The combination of Thomas and Alawieh fail to teach wherein the ground truth label corresponds to at least one of a location of a user equipment (UE) derived by the second network entity or a true UE position obtained based on a set of sounding reference signals (SRSs) measurements.
However, Challita teaches wherein the ground truth label corresponds to at least one of a location of a user equipment (UE) derived by the second network entity or a true UE position obtained based on a set of sounding reference signals (SRSs) measurements. (Challita; col. 8, lines 1-6 & col. 7, lines 27-30 - - teaches the method 300 may further comprise obtaining ground truth location measurements and corresponding ground truth measurements of radio conditions at the locations. Such measurements may be obtained from drones (e.g. airborne UEs) that report trusted location information. Col. 7 specifies the measurements of the radio conditions may also comprise of the sounding reference signal (SRS).)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Challita into the invention of Thomas and Alawieh to include the feature of the ground truth label corresponding to at least one of a location of a user equipment (UE) derived by the second network entity or a true UE position obtained based on a set of sounding reference signals (SRSs) measurements, in order to provide the actual UE location as the ground truth associated with the positioning measurements for training the AI/ML positioning model, improving the accuracy of the AI/ML based positioning by training the model against true UE location data (see Challita; col. 8, lines 1-6 & col. 7, lines 27-30).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Thomas WO 2024027939 A1, in view of Alawieh US 20250056488 A1 as applied to claim 1, further in view of Kovisto, US 20100248708 A1 (hereinafter Kovisto), further in view of Priyanto, CN 121941936 A (hereinafter Priyanto).
Regarding claim 12, the combination of Thomas and Alawieh teach the apparatus of claim 1. The combination of Thomas and Alawieh fail to teach wherein the at least one processor, individually or in any combination, is further configured to: transmit, to the second network entity, a measurement report associated with the set of UE positioning related measurements, wherein the measurement report indicates whether the set of UE positioning related measurements is based on an actual measurement or an AI/ML derived measurement; and receive, from the second network entity, a feedback related to the measurement report.
However, Koivisto teaches wherein the at least one processor, individually or in any combination, is further configured to: transmit, to the second network entity, a measurement report associated with the set of UE positioning related measurements, (Kovisto; para 54, fig. 4 - - teaches the UE 70 may then communicate a measurement report (e.g., including a service ID) to the originating eNB 72 at operation 112).
and receive, from the second network entity, a feedback related to the measurement report. (Kovisto; para 54, fig. 4 - - teaches the originating eNB 72 may then communicate a handover command to the UE 70 at operation 124).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Kovisto into the invention of Thomas and Alawieh to include the feature of wherein the at least one processor, individually or in any combination, is further configured to: transmit, to the second network entity, a measurement report associated with the set of UE positioning related measurements; and receive, from the second network entity, a feedback related to the measurement report, in order to enable the LMF to distinguish between actual and AI/ML derived measurements and provide feedback to the base station for improving AI/ML performance, improving the accuracy and reliability of the AI/ML based positioning by ensuring the LMF can properly evaluate and respond to the reported measurement results (see Kovisto; para 54, fig. 4).
The combination of Thomas, Alawieh, and Kovisto fail to clearly specify wherein the measurement report indicates whether the set of UE positioning related measurements is based on an actual measurement or an AI/ML derived measurement;
However, Priyanto teaches wherein the measurement report indicates whether the set of UE positioning related measurements is based on an actual measurement or an AI/ML derived measurement; (Priyanto; para. 9 - - teaches the association of the tag report with the channel response report (interpreted as the measurement report) also enables training of the model in the assisted AI-based location, wherein the channel response report is provided by a wireless network node that acts as a location measurement node that does not know the actual location of the WD).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Priyanto into the invention of Thomas, Alawieh, and Kovisto to include the feature of the measurement report indicating whether the set of UE positioning related measurements is based on an actual measurement or an AI/ML derived measurement in order to enable the second network entity to distinguish between actual measurements and AI/ML derived measurements in the measurement report so that it can properly train and refine the AI/ML positioning model, improving the accuracy and reliability of the AI/ML-based positioning by ensuring the network entity can evaluate the reported measurements against actual ground truth data (see Priyanto; para. 9).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Thomas WO 2024027939 A1, in view of Alawieh US 20250056488 A1 as applied to claim 1, further in view of Li, US 20240397472 A1 (hereinafter Li).
Regarding claim 14, the combination of Thomas and Alawieh teach the apparatus of claim 1. The combination of Thomas and Alawieh fail to teach further comprising: receiving, from a location management function (LMF), a measurement initiation message that includes at least one of: (1) transmission reception point (TRP) information, (2) a set of sounding reference signal (SRS) configurations, (3) a set of TRP measurement quantities, (4) a duration for measurement, (5) a set of measurement types, or (6) a ground truth label.
However, Li teaches receiving, from a location management function (LMF), a measurement initiation message that includes at least one of: (1) transmission reception point (TRP) information, (2) a set of sounding reference signal (SRS) configurations, (3) a set of TRP measurement quantities, (4) a duration for measurement, (5) a set of measurement types, or (6) a ground truth label (Li; para. 169 - - teaches in some implementation, the LMF can trigger the UE to report the measurements including UE ID/UE pair ID, SL-PRS measurement results (SRS reference signal received power, SRS-RSRP), SL-PRS resource index, measurement results per SRS resource, SRS resource(s) indexes, measurement results per CLI-RSSI resource, CLI-RSSI resource(s) indexes).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Li into the invention of Thomas and Alawieh to include the feature of the first network entity receiving, from an LMF, a measurement initiation message that includes a set of measurement related information enabling the recipient to perform and report the UE positioning measurements, in order to enable the LMF to trigger and configure the reporting of positioning measurement results for the AI/ML training, improving the coordination and accuracy of network-side AI/ML positioning measurement collection (see Li; para. 169).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Thomas WO 2024027939 A1 as applied to claim 16 and further in view of Zhang, EP 4319340 A1.
Regarding claim 19, Thomas teaches the apparatus of claim 16. Thomas fails to teach wherein the request for the initiation of the set of UE positioning related measurements further includes a correlation ID, the at least one processor, individually or in any combination, is further configured to: transmit, to the first network entity, one or more New Radio Positioning Protocol A (NRPPa) messages with the correlation ID.
However, Zhang teaches wherein the request for the initiation of the set of UE positioning related measurements further includes a correlation ID, the at least one processor, individually or in any combination, is further configured to: transmit, to the first network entity, one or more New Radio Positioning Protocol A (NRPPa) messages with the correlation ID. (Zhang; para. 87 - - teaches the LMF sends NRPPa, such as a POSITIONING INFORMATION REQUEST or other similar messages, to the base station to configure the PRS resource for per UE, and the message includes at least one of the following: an ID of BWP corresponding to an added DL-PRS resource, or a changed DL-PRS resource, or a deleted DL-PRS resource of per UE; an ID of a positioning frequency layer).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Zhang into the invention of Thomas to include the feature of transmitting, to the first network entity, one or more New Radio Positioning Protocol A (NRPPa) messages with the correlation ID, in order to associate the NRPPa positioning messages and the configured measurement resources with the corresponding per-UE positioning session, improving the correlation of the positioning measurement requests and responses to the correct UE during the AI/ML positioning procedure (see Zhang; para. 87).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Thomas WO 2024027939 A1 and as applied to claim 16, in view of Kovisto, US 20100248708 A1, further in view of Priyanto, CN 121941936 A.
Regarding claim 20, Thomas teaches the apparatus of claim 16. Thomas fails to teach wherein the at least one processor, individually or in any combination, is further configured to: receive, from the first network entity, a measurement report associated with the set of UE positioning related measurements, wherein the measurement report indicates whether the set of UE positioning related measurements is based on an actual measurement or an AI/ML derived measurement; and transmit, to the first network entity, a feedback related to the measurement report.
However, Kovisto teaches wherein the at least one processor, individually or in any combination, is further configured to: receive, from the first network entity, a measurement report associated with the set of UE positioning related measurements (Kovisto; para 54, fig. 4 - - teaches the UE 70 may then communicate a measurement report (e.g., including a service ID) to the originating eNB 72 at operation 112).
and transmit, to the first network entity, a feedback related to the measurement report. (Kovisto; para 54, fig. 4 - - teaches the originating eNB 72 may then communicate a handover command to the UE 70 at operation 124).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Kovisto into the invention of Thomas to include the feature of wherein the at least one processor, individually or in any combination, is further configured to: transmit, to the second network entity, a measurement report associated with the set of UE positioning related measurements; and receive, from the second network entity, a feedback related to the measurement report, in order to enable the LMF to distinguish between actual and AI/ML derived measurements and provide feedback to the base station for improving AI/ML performance, improving the accuracy and reliability of the AI/ML based positioning by ensuring the LMF can properly evaluate and respond to the reported measurement results (see Kovisto; para 54, fig. 4).
The combination of Thomas and Kovisto fail to clearly specify wherein the measurement report indicates whether the set of UE positioning related measurements is based on an actual measurement or an AI/ML derived measurement.
However, Priyanto teaches wherein the measurement report indicates whether the set of UE positioning related measurements is based on an actual measurement or an AI/ML derived measurement; (Priyanto; para. 9 - - teaches the association of the tag report with the channel response report (interpreted as the measurement report) also enables training of the model in the assisted AI-based location, wherein the channel response report is provided by a wireless network node that acts as a location measurement node that does not know the actual location of the WD).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Priyanto into the invention of Thomas and Kovisto to include the feature of the measurement report indicating whether the set of UE positioning related measurements is based on an actual measurement or an AI/ML derived measurement in order to enable the second network entity to distinguish between actual measurements and AI/ML derived measurements in the measurement report so that it can properly train and refine the AI/ML positioning model, improving the accuracy and reliability of the AI/ML-based positioning by ensuring the network entity can evaluate the reported measurements against actual ground truth data (see Priyanto; para. 9).
Conclusion
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/ANDRES RAFAEL SANCHEZ/Examiner, Art Unit 2645
/ANTHONY S ADDY/Supervisory Patent Examiner, Art Unit 2645
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