CTNF 18/798,270 CTNF 101682 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 8/8/24 & 2/11/26 have been considered by the examiner and made of record in the application file. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-20-02-aia AIA 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. 07-21-aia AIA Claim s 1-4, 7, 11-14, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hirzallah, US 20240337721 A1 (hereinafter Hirzallah), and in view of Li, WO 2025081500 A1 (hereinafter Li) . Regarding claim 1, Hirzallah teaches a network device comprising: one or more processors configured to: receive a request for a positioning artificial intelligence or machine learning (AI/ML) model, (Hirzallah; paragraph 95- - teaches in some aspects, a network node, such as the wireless device 502 and/or the wireless device 506, may transmit a request to the network entity 508 to assist in collecting training data). wherein the request comprises an indication of environmental characteristics associated with the positioning AI/ML model; (Hirzallah; paragraph 99 - - Hirzallah teaches the network entity 508 making itself available to receive a request from the set of network devices. In other words, the network device 508 offers to provide its capability to be involved in a training data collection session. Wherein device 508 capabilities include receiving request messages that may include an indication of types of measurement that the wireless device 502 may support (e.g., CIR, CFR, PDP. ToA, RSTD, RSRP, RSRPP, and/or AoD associated with positioning signals transmitted by the wireless device 504). Further, Hirzallah teaches the network entity 508 may select the network nodes based on the capability information of the network nodes, for example all network nodes that support CIR of received SRSs). determine a target area to collect positioning training data based on the request for the positioning AI/ML model, wherein the target area is based on (i) the environmental characteristics associated with the positioning AI/ML model or (ii) availability of one or more network nodes that are capable of providing the positioning training data; (Hirzallah; paragraph 99 - - Hirzallah teaches the network entity 508 collects training data from the at least one set of network devices, such as the wireless device 502. The request message may include an indication of types of measurement that the wireless device 502 may support (e.g., CIR, CFR, PDP. ToA, RSTD, RSRP, RSRPP, and/or AoD associated with positioning signals transmitted by the wireless device 504). Further, Hirzallah teaches the network entity 508 may select the network nodes based on the capability information of the network nodes, for example all network nodes that support CIR of received SRSs. determine a list of network nodes that are capable of providing the positioning training data; (Hirzallah; paragraph 99 - - Hirzallah teaches the wireless device 502 may indicate its capability to collect training data based on an UL reference signal received by the wireless device 502. In addition, Hirzallah teaches the network entity 508 may select all, or a subset of, the network nodes that indicate that they are configured to provide such training assistance information. The network entity 508 may select the network nodes based on the capability information of the network nodes). determine a positioning measurement type associated with the positioning AI/ML model ; (Hirzallah; paragraph 99 - - teaches the network entity 508 may provide the wireless device 502 with one or more configurations, such as an UL reference signal configuration, types of measurements to be reported by the wireless device 502. Further, paragraph 99 also teaches the request message may include an indication of types of measurement that the wireless device 502 may support (e.g., CIR, CFR, PDP. ToA, RSTD, RSRP, RSRPP, and/or AoD associated with positioning signals transmitted by the wireless device 504). and receive a positioning training data report, wherein the positioning training data report comprises the positioning training data. (Hirzallah; paragraph 118 - - teaches the set of positioning neighbor wireless devices 704 may transmit the positioning feedback 732 at the positioning network entity 706. The positioning network entity 706 may receive the positioning feedback 732 (interpreted as the positioning training data report) from the set of positioning neighbor wireless devices 704 […] The positioning feedback 732 may include measurements and/or training associated information associated with measuring the set of positioning signals 724 received from the positioning target wireless device 702. The positioning feedback 732 may include training associated information. The positioning feedback 732 may include measurements taken at 728). Hirzallah fails to clearly specify sending a collection request for the positioning training data, wherein the collection request comprises an indication of the target area, the list of the network nodes, and the positioning measurement type. However, Li teaches sending a collection request for the positioning training data, wherein the collection request comprises an indication of the target area, the list of the network nodes, and the positioning measurement type (Li; paragraphs 262, 259, 266 - - teaches the request includes indication of the target area, read as “For example, in one embodiment of this disclosure, the cell information for which location data collection is requested may be the Physical Cell Identifier (PCI)” as seen in paragraph 262. Further, Li teaches the request includes the list of network nodes, read as ”the information used to request location data collection includes cell information for which location data collection is requested. The cell information is used to indicate the cells where location data collection is required. This community information is, for example, a list of communities.”, as seen in paragraph 259. Lastly, Li teaches the request “includes positioning measurement type, read as the information for requesting the collection of positioning data includes measurement information type information for requesting the collection of positioning data. The measurement information type information may include, for example, at least one of gNB-RxTxTimeDiff, uplink UL-SRS-reference signal received power (RSRP), UL-angle-of-arrival (AoA), UL-relative time of arrival (RTOA), multiple UL-AoA and UL SRS-RSRPP”, as seen in paragraph 266.) 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 Hirzallah to include the feature of sending a collection request for the positioning training data, wherein the collection request comprises an indication of the target area, the list of the network nodes, and the positioning measurement type, thereby ensuring the collected data corresponds to the deployment area of the target AI/ML positioning model, thereby improving the relevance and quality of the positioning training data collected for model training (see paragraphs 262, 264, 266). Regarding claim 2, the combination of Hirzallah and Li teach the network device of claim 1. The combination of Hirzallah and Li further teach wherein the one or more network nodes comprises any combination of one or more Positioning Reference Units (PRUs) or one or more Radio Access Network (RAN) nodes (Hirzallah; paragraph 75 - - teaches FIG. 4 is a diagram 400 illustrating an example of positioning based on reference signal measurements. The wireless device 402 may be a UE, a base station, or a positioning reference unit (PRU)). Regarding claim 3, the combination of Hirzallah and Li teach the network device of claim 1. The combination of Hirzallah and Li further teach wherein the positioning measurement type comprises an uplink (UL) or downlink (DL) Time of Arrival (ToA), an UL or DL Time Difference of Arrival (TDoA), or an Angle of Arrival (AoA). (Hirzallah; paragraph 99 - - teaches the network entity 508 may provide the wireless device 502 with one or more configurations, such as an UL reference signal configuration, types of measurements to be reported by the wireless device 502. Further, paragraph 99 also teaches the request message may include an indication of types of measurement that the wireless device 502 may support (e.g., CIR, CFR, PDP. ToA, RSTD, RSRP, RSRPP, and/or AoD associated with positioning signals transmitted by the wireless device 504). Regarding claim 4, the combination of Hirzallah and Li teach the network device of claim 1. The combination of Hirzallah and Li further teach wherein the environmental characteristics comprise an indication of whether the requested positioning AI/ML model is to be used for a Line-of-Sight (LoS) or a non-LoS scenario, an indication of whether the requested positioning AI/ML model is to be used for an indoor scenario or an outdoor scenario, or an indication of whether the requested positioning AI/ML model is to be used for an urban area or a suburban area. (Hirzallah; paragraph 28 - - teaches in some aspects, the collected training data may include uplink-based (UL-based) basic measurements received and measured by a network node, such as reference signal time difference (RSTD) measurements, reference signal received power (RSRP) measurements, reference signal received power path (RSRPP) measurements, angle of arrival (AoA) measurements, and/or line-of-sight (LOS) identification measurements). Regarding claim 7, the combination of Hirzallah and Li teach the network device of claim 1. The combination of Hirzallah and Li further teach wherein the request for positioning training data comprises the indication of the positioning method type associated with the positioning training data (Hirzallah; paragraph 99 - - teaches the network entity 508 may provide the wireless device 502 with one or more configurations, such as an UL reference signal configuration, types of measurements to be reported by the wireless device 502. Further, paragraph 99 also teaches the request message may include an indication of types of measurement that the wireless device 502 may support (e.g., CIR, CFR, PDP. ToA, RSTD, RSRP, RSRPP, and/or AoD associated with positioning signals transmitted by the wireless device 504 (interpreted as positioning types/ positioning method types as per spec)). Regarding claim 11, Hirzallah teaches the method comprising: receiving a request for a positioning artificial intelligence or machine learning (AI/ML) model, (Hirzallah; paragraph 95- - teaches in some aspects, a network node, such as the wireless device 502 and/or the wireless device 506, may transmit a request to the network entity 508 to assist in collecting training data). wherein the request comprises an indication of environmental characteristics associated with the positioning AI/ML model; (Hirzallah; paragraph 99 - - Hirzallah teaches the network entity 508 making itself available to receive a request from the set of network devices. In other words, the network device 508 offers to provide its capability to be involved in a training data collection session. Wherein device 508 capabilities include receiving request messages that may include an indication of types of measurement that the wireless device 502 may support (e.g., CIR, CFR, PDP. ToA, RSTD, RSRP, RSRPP, and/or AoD associated with positioning signals transmitted by the wireless device 504). Further, Hirzallah teaches the network entity 508 may select the network nodes based on the capability information of the network nodes, for example all network nodes that support CIR of received SRSs). determining a target area to collect positioning training data based on the request for the positioning AI/ML model, wherein the target area is based on (i) the environmental characteristics associated with the positioning AI/ML model or (ii) availability of one or more network nodes that are capable of providing the positioning training data; (Hirzallah; paragraph 99 - - Hirzallah teaches in another example, the network entity 508 may transmit a request to at least one of the set of network nodes, such as the wireless device 502, to provide its capability to be involved in a training data collection session. The request message may include an indication of types of measurement that the wireless device 502 may support (e.g., CIR, CFR, PDP. ToA, RSTD, RSRP, RSRPP, and/or AoD associated with positioning signals transmitted by the wireless device 504). Further, Hirzallah teaches the network entity 508 may select the network nodes based on the capability information of the network nodes, for example all network nodes that support CIR of received SRSs. determining a list of network nodes that are capable of providing the positioning training data; (Hirzallah; paragraph 99 - - teaches the network entity 508 may provide the wireless device 502 with one or more configurations, such as an UL reference signal configuration, types of measurements to be reported by the wireless device 502. Further, paragraph 99 also teaches the request message may include an indication of types of measurement that the wireless device 502 may support (e.g., CIR, CFR, PDP. ToA, RSTD, RSRP, RSRPP, and/or AoD associated with positioning signals transmitted by the wireless device 504). determining a positioning measurement type associated with the positioning AI/ML model; (Hirzallah; paragraph 99 - - teaches the network entity 508 may provide the wireless device 502 with one or more configurations, such as an UL reference signal configuration, types of measurements to be reported by the wireless device 502. Further, paragraph 99 also teaches the request message may include an indication of types of measurement that the wireless device 502 may support (e.g., CIR, CFR, PDP. ToA, RSTD, RSRP, RSRPP, and/or AoD associated with positioning signals transmitted by the wireless device 504). and receiving a positioning training data report, wherein the positioning training data report comprises the positioning training data (Hirzallah; paragraph 118 - - teaches the set of positioning neighbor wireless devices 704 may transmit the positioning feedback 732 at the positioning network entity 706. The positioning network entity 706 may receive the positioning feedback 732 (interpreted as the positioning training data report) from the set of positioning neighbor wireless devices 704 […] The positioning feedback 732 may include measurements and/or training associated information associated with measuring the set of positioning signals 724 received from the positioning target wireless device 702. The positioning feedback 732 may include training associated information. The positioning feedback 732 may include measurements taken at 728). Hirzallah fails to clearly specify sending a collection request for the positioning training data, wherein the collection request comprises an indication of the target area, the list of the network nodes, and the positioning measurement type. However, Li teaches sending a collection request for the positioning training data, wherein the collection request comprises an indication of the target area, the list of the network nodes, and the positioning measurement type (Li; paragraphs 262, 259, 266 - - teaches the request includes indication of the target area, read as “For example, in one embodiment of this disclosure, the cell information for which location data collection is requested may be the Physical Cell Identifier (PCI)” as seen in paragraph 262. Further, Li teaches the request includes the list of network nodes, read as ”the information used to request location data collection includes cell information for which location data collection is requested. The cell information is used to indicate the cells where location data collection is required. This community information is, for example, a list of communities.”, as seen in paragraph 259. Lastly, Li teaches the request “includes positioning measurement type, read as the information for requesting the collection of positioning data includes measurement information type information for requesting the collection of positioning data. The measurement information type information may include, for example, at least one of gNB-RxTxTimeDiff, uplink UL-SRS-reference signal received power (RSRP), UL-angle-of-arrival (AoA), UL-relative time of arrival (RTOA), multiple UL-AoA and UL SRS-RSRPP”, as seen in paragraph 266.) 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 Hirzallah to include the feature of sending a collection request for the positioning training data, wherein the collection request comprises an indication of the target area, the list of the network nodes, and the positioning measurement type, thereby ensuring the collected data corresponds to the deployment area of the target AI/ML positioning model, thereby improving the relevance and quality of the positioning training data collected for model training (see paragraphs 262, 264, 266). Regarding claim 12, the combination of Hirzallah and Li teach method of claim 11. The combination of Hirzallah and Li further teach wherein the one or more network nodes comprise any combination of one or more Positioning Reference Units (PRUs) or one or more Radio Access Network (RAN) nodes (Hirzallah; paragraph 75 - - teaches FIG. 4 is a diagram 400 illustrating an example of positioning based on reference signal measurements. The wireless device 402 may be a UE, a base station, or a positioning reference unit (PRU)). Regarding claim 13, the combination of Hirzallah and Li teach method of claim 11. The combination of Hirzallah and Li further teach wherein the positioning measurement type comprises an uplink (UL) or downlink (DL) Time of Arrival (ToA), an UL or DL Time Difference of Arrival (TDoA), or an Angle of Arrival (AoA). (Hirzallah; paragraph 99 - - teaches the network entity 508 may provide the wireless device 502 with one or more configurations, such as an UL reference signal configuration, types of measurements to be reported by the wireless device 502. Further, paragraph 99 also teaches the request message may include an indication of types of measurement that the wireless device 502 may support (e.g., CIR, CFR, PDP. ToA, RSTD, RSRP, RSRPP, and/or AoD associated with positioning signals transmitted by the wireless device 504). Regarding claim 14, the combination of Hirzallah and Li teach method of claim 11. The combination of Hirzallah and Li further teach wherein the environmental characteristics comprise an indication of whether the requested positioning AI/ML model is to be used for a Line-of-Sight (LoS) or a non-LoS scenario, an indication of whether the requested positioning AI/ML model is to be used for an indoor scenario or an outdoor scenario, or an indication of whether the requested positioning AI/ML model is to be used for an urban area or a suburban area. (Hirzallah; paragraph 28 - - teaches in some aspects, the collected training data may include uplink-based (UL-based) basic measurements received and measured by a network node, such as reference signal time difference (RSTD) measurements, reference signal received power (RSRP) measurements, reference signal received power path (RSRPP) measurements, angle of arrival (AoA) measurements, and/or line-of-sight (LOS) identification measurements). Regarding claim 17, the combination of Hirzallah and Li teach method of claim 11. The combination of Hirzallah and Li further teach wherein the request for positioning training data comprises the indication of the positioning method type associated with the positioning training data. (Hirzallah; paragraph 99 - - teaches the network entity 508 may provide the wireless device 502 with one or more configurations, such as an UL reference signal configuration, types of measurements to be reported by the wireless device 502. Further, paragraph 99 also teaches the request message may include an indication of types of measurement that the wireless device 502 may support (e.g., CIR, CFR, PDP. ToA, RSTD, RSRP, RSRPP, and/or AoD associated with positioning signals transmitted by the wireless device 504 (interpreted as positioning types/ positioning method types as per spec)) . 07-21-aia AIA Claim s 8, 9, 18, & 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hirzallah, US 20240337721 A1, in view of Li, WO 2025081500 A1 as applied to claims 1 and 11, further in view of Guduru, US 12495275 B2 (hereinafter Guduru) . Regarding claim 8, the combination of Hirzallah and Li teach the network device of claim 1. The combination of Hirzallah and Li fail to teach wherein the request for positioning training data comprises: an indication of a time of day associated with the positioning training data; or an indication of performance requirements, wherein the performance requirements comprise a positioning accuracy or a model interference latency; and wherein the collection request (interpreted as data request 530 in Guduru) comprises the indication of the time of day or the indication of the performance requirements. However, Guduru teaches wherein the request for positioning training data comprises: an indication of a time of day associated with the positioning training data; or an indication of performance requirements, wherein the performance requirements comprise a positioning accuracy or a model interference latency; and wherein the collection request (interpreted as data request 530 in Guduru) comprises the indication of the time of day or the indication of the performance requirements. (Guduru; col. 10, lines 14-16 - - teaches data request 530 may request, for example, exact UE device locations, positioning techniques used, positioning accuracy, positioning latency). 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 Guduru into the invention of Hirzallah and Li to include the feature of request for positioning training data comprises: an indication of a time of day associated with the positioning training data; or an indication of performance requirements, wherein the performance requirements comprise a positioning accuracy or a model interference latency; and wherein the collection request comprises the indication of the time of day or the indication of the performance requirements, in order to enable the network device to specify the required accuracy and latency constraints for the positioning AI/ML model when requesting training data collection, ensuring that the collected training data is sufficient to meet the model’s performance targets, thereby improving the quality of the collected positioning training data for training an AI/ML positioning model that satisfies defined accuracy and latency measurements (see col. 10, lines 14-16). Regarding claim 9, the combination of Hirzallah and Li teach network device of claim 1. The combination of Hirzallah and Li fail to teach wherein the network device comprises a network data analytics function (NWDAF), and wherein the collection request is sent to a Location Management Function (LMF). However, Guduru teaches wherein the network device comprises a network data analytics function (NWDAF), and wherein the collection request is sent to a Location Management Function (LMF). (Guduru; col. 2, lines 47-49 & fig. 5 - - teaches the NWDAF may send to the LMF a data request based on the information request, and may receive, from the LMF, event data responsive to the data request). 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 Guduru into the invention of Hirzallah and Li to include the feature of wherein the network device comprises a network data analytics function (NWDAF), and wherein the collection request is sent to a Location Management Function (LMF) in order to implement the 5G core network architecture in which the NWDAF serves as the analytics and model training function that delegates positioning data collection to the LMF, which has direct access to the positioning data collection to the LMF, which has direct access to the positioning infrastructure including PRUs and gNB’s, thereby improving the efficiency of positioning protocol interfaces to collect measurements from network nodes on behalf of the NWDAF, rather than requiring the NDWAF to directly interface with each single positioning node (see lines 47-49 & fig. 5). Regarding claim 18, the combination of Hirzallah and Li teach the network device of claim 11. The combination of Hirzallah and Li fail to teach wherein the request for positioning training data comprises: an indication of a time of day associated with the positioning training data; or an indication of performance requirements, wherein the performance requirements comprise a positioning accuracy or a model interference latency; and wherein the collection request comprises the indication of the time of day or the indication of the performance requirements. However, Guduru teaches wherein the request for positioning training data comprises: an indication of a time of day associated with the positioning training data; or an indication of performance requirements, wherein the performance requirements comprise a positioning accuracy or a model interference latency; and wherein the collection request (interpreted as data request 530 in Guduru) comprises the indication of the time of day or the indication of the performance requirements. (Guduru; col. 10, lines 14-16 - - teaches data request 530 may request, for example, exact UE device locations, positioning techniques used, positioning accuracy, positioning latency). 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 Guduru into the invention of Hirzallah and Li to include the feature of request for positioning training data comprises: an indication of a time of day associated with the positioning training data; or an indication of performance requirements, wherein the performance requirements comprise a positioning accuracy or a model interference latency; and wherein the collection request comprises the indication of the time of day or the indication of the performance requirements, in order to enable the network device to specify the required accuracy and latency constraints for the positioning AI/ML model when requesting training data collection, ensuring that the collected training data is sufficient to meet the model’s performance targets, thereby improving the quality of the collected positioning training data for training an AI/ML positioning model that satisfies defined accuracy and latency measurements (see col. 10, lines 14-16). Regarding claim 19 , the combination of Hirzallah and Li teach the method of claim 11. The combination of Hirzallah and Li fail to teach wherein the method is performed by a network data analytics function (NWDAF), and wherein the collection request is sent to a Location Management Function (LMF). However, Guduru teaches wherein the method is performed by a network data analytics function (NWDAF), and wherein the collection request is sent to a Location Management Function (LMF). (Guduru; col. 2, lines 47-49 & fig. 5 - - teaches the NWDAF may send to the LMF a data request based on the information request, and may receive, from the LMF, event data responsive to the data request). 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 Guduru into the invention of Hirzallah and Li to include the feature of wherein the network device comprises a network data analytics function (NWDAF), and wherein the collection request is sent to a Location Management Function (LMF) in order to implement the 5G core network architecture in which the NWDAF serves as the analytics and model training function that delegates positioning data collection to the LMF, which has direct access to the positioning data collection to the LMF, which has direct access to the positioning infrastructure including PRUs and gNB’s, thereby improving the efficiency of positioning protocol interfaces to collect measurements from network nodes on behalf of the NWDAF, rather than requiring the NDWAF to directly interface with each single positioning node (see lines 47-49 & fig. 5) . 07-21-aia AIA Claim s 5, 6, 15, & 16 are rejected under 35 U.S.C. 103 as being unpatentable over Hirzallah, US 20240337721 A1, in view of Li, WO 2025081500 A1 as applied to claims 1 and 11, further in view of Kim, US 20260012397 A1 (hereinafter Kim) . Regarding claim 5 , the combination of Hirzallah and Li teach the network device of claim 1. The combination of Hirzallah and Li fails to teach wherein the one or more processors are configured to determine the list of WTRUs based on a distribution of the WTRUs within the target area. However, Kim teaches wherein the one or more processors are configured to determine the list of WTRUs based on a distribution of the WTRUs within the target area (Kim; page 23, paragraphs 284 & 293 - - receiving a request message related to assistance information from a network entity related to application, wherein the request message includes a list of User Equipment (UE) including one or more UEs (also see Kim claim 1). Further, Kim teaches Area of Interest (AIML operations for UEs within the Area of Interest) in paragraph 293). 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 Kim into the invention of Hirzallah and Li to include the feature of determining the list of WTRUs based on a distribution of the WTRUs within the target area in order to ensure that the WTRUs selected to provide positioning training data are spatially distributed across the target area such that the collected training data adequately represents the full range of positioning conditions within that area, thereby improving the coverage and representativeness of the positioning training data collected for AI/ML model training by avoiding over-reliance on clustered WTRUs that would introduce geographic bias into the training dataset (see page 23, paragraphs 284 & 293). Regarding claim 6 , the combination of Hirzallah and Li teach the network device of claim 1. The combination of Hirzallah and Li fail to teach wherein the request for positioning AI/ML model comprises: an indication of mobility characteristics associated with the positioning training data, wherein the mobility characteristics comprise an indication of whether the positioning AI/ML model is associated with stationary wireless transmit/receive units (WTRUs) or moving WTRUs; wherein the list of the network nodes is based on the indication of mobility characteristics associated with the positioning AI/ML model. However, Kim teaches wherein the request for positioning AI/ML model comprises: an indication of mobility characteristics associated with the positioning training data, wherein the mobility characteristics comprise an indication of whether the positioning AI/ML model is associated with stationary wireless transmit/receive units (WTRUs) or moving WTRUs; wherein the list of the network nodes is based on the indication of mobility characteristics associated with the positioning AI/ML model. (Kim; paragraphs 284, 292 - - teaches the request for AIML includes expected UE moving trajectories for each UE in the UE list). 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 Kim into the invention of Hirzallah and Li to include the feature of an indication of mobility characteristics associated with the positioning training data, wherein the mobility characteristics comprise an indication of whether the positioning AI/ML model is associated with stationary wireless transmit/receive units (WTRUs) or moving WTRUs; and wherein the list of the network nodes is based on the indication of mobility characteristics associated with the positioning AI/ML model in order to enable the network device to select network nodes for positioning training data collection, ensuring that training data is collected from WTRUs whose movement characteristics correspond to the deployment scenario the model is intended for, thereby improving the accuracy and applicability of the trained AI/ML positioning model by ensuring the training data reflects the actual mobility conditions under which the model will be used (see paragraphs 284 & 292). Regarding claim 15 , the combination of Hirzallah and Li teach the method of claim 11. The combination of Hirzallah and Li fail to teach wherein the list of WTRUs is determined based on a distribution of the WTRUs within the target area. However, Kim teaches wherein the list of WTRUs is determined based on a distribution of the WTRUs within the target (Kim; page 23, paragraphs 284 & 293 - - receiving a request message related to assistance information from a network entity related to application, wherein the request message includes a list of User Equipment (UE) including one or more UEs (also see Kim claim 1). Further, Kim teaches Area of Interest (AIML operations for UEs within the Area of Interest) in paragraph 293). 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 Kim into the invention of Hirzallah and Li to include the feature of determining the list of WTRUs based on a distribution of the WTRUs within the target area in order to ensure that the WTRUs selected to provide positioning training data are spatially distributed across the target area such that the collected training data adequately represents the full range of positioning conditions within that area, thereby improving the coverage and representativeness of the positioning training data collected for AI/ML model training by avoiding over-reliance on clustered WTRUs that would introduce geographic bias into the training dataset (see page 23, paragraphs 284 & 293). Regarding claim 16 , the combination of Hirzallah and Li teach the network device of claim 11. The combination of Hirzallah and Li fail to teach wherein the request for positioning AI/ML model comprises: an indication of mobility characteristics associated with the positioning training data, wherein the mobility characteristics comprise an indication of whether the positioning AI/ML model is associated with stationary wireless transmit/receive units (WTRUs) or moving WTRUs; and wherein the list of the network nodes is based on the indication of mobility characteristics associated with the positioning AI/ML model. However, Kim teaches wherein the request for positioning AI/ML model comprises: an indication of mobility characteristics associated with the positioning training data, wherein the mobility characteristics comprise an indication of whether the positioning AI/ML model is associated with stationary wireless transmit/receive units (WTRUs) or moving WTRUs; and wherein the list of the network nodes is based on the indication of mobility characteristics associated with the positioning AI/ML model. (Kim; paragraphs 284, 292 - - teaches the request for AIML includes expected UE moving trajectories for each UE in the UE list). 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 Kim into the invention of Hirzallah and Li to include the feature of an indication of mobility characteristics associated with the positioning training data, wherein the mobility characteristics comprise an indication of whether the positioning AI/ML model is associated with stationary wireless transmit/receive units (WTRUs) or moving WTRUs; and wherein the list of the network nodes is based on the indication of mobility characteristics associated with the positioning AI/ML model in order to enable the network device to select network nodes for positioning training data collection, ensuring that training data is collected from WTRUs whose movement characteristics correspond to the deployment scenario the model is intended for, thereby improving the accuracy and applicability of the trained AI/ML positioning model by ensuring the training data reflects the actual mobility conditions under which the model will be used (see paragraphs 284 & 292) . 07-21-aia AIA Claim s 10 & 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hirzallah, US 20240337721 A1, in view of Li, WO 2025081500 A1 as applied to claims 1 and 11, further in view of Feng, US 20260143457 A1 (hereinafter Feng) . Regarding claim 10, the combination of Hirzallah and Li teach the network device of claim 1. The combination of Hirzallah and Li fail teach wherein the network device comprises a Location Management Function (LMF), and wherein the request for positioning AI/ML model is received from a network data analytics function (NWDAF); or wherein the network device comprises a Model Training logical function (MTLF), and wherein the request for positioning AI/ML model is received from a LMF. However, Feng teaches wherein the network device comprises a Location Management Function (LMF), and wherein the request for positioning AI/ML model is received from a network data analytics function (NWDAF); or wherein the network device comprises a Model Training logical function (MTLF), and wherein the request for positioning AI/ML model is received from a LMF. (Feng; paragraph 27 & 138 - - teaches a location management function network element receives a second request message from a network data analytics function network element; and the location management function network element sends a second response message to the network data analytics function network element, where the second request message is for requesting location information of a terminal device, wherein this location information corresponds to the AI positioning model). 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 Feng into the invention of Hirzallah and Li to include a features of a network device comprising a Location Management Function (LMF), and wherein the request for positioning AI/ML model is received from a network data analytics function (NWDAF); or wherein the network device comprises a Model Training logical function (MTLF), and wherein the request for positioning AI/ML model is received from a LMF, in order to implement the standardized 5G core network architecture for AI/ML positioning model training in which the LMF initiates model training requests to the NWDAF containing the MTLF, thereby improving interoperability between the location management and model training functions of the 5G core network by establishing a defined interface and subscription mechanism for the AI/ML positioning model training services (see paragraph 27 and 138). Regarding claim 20, the combination of Hirzallah and Li teach method of claim 11. The combination of Hirzallah and Li fail teach wherein the method is performed by a Location Management Function (LMF), and wherein the request for positioning AI/ML model is received from a network data analytics function (NWDAF); or wherein the network device comprises a Model Training logical function (MTLF), and wherein the request for positioning AI/ML model is received from a LMF. However, Feng teaches wherein the method is performed by a Location Management Function (LMF), and wherein the request for positioning AI/ML model is received from a network data analytics function (NWDAF); or wherein the network device comprises a Model Training logical function (MTLF), and wherein the request for positioning AI/ML model is received from a LMF. (Feng; paragraph 27 & 138 - - teaches a location management function network element receives a second request message from a network data analytics function network element; and the location management function network element sends a second response message to the network data analytics function network element, where the second request message is for requesting location information of a terminal device, wherein this location information corresponds to the AI positioning model). 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 Feng into the invention of Hirzallah and Li to include a features of a network device comprising a Location Management Function (LMF), and wherein the request for positioning AI/ML model is received from a network data analytics function (NWDAF); or wherein the network device comprises a Model Training logical function (MTLF), and wherein the request for positioning AI/ML model is received from a LMF, in order to implement the standardized 5G core network architecture for AI/ML positioning model training in which the LMF initiates model training requests to the NWDAF containing the MTLF, thereby improving interoperability between the location management and model training functions of the 5G core network by establishing a defined interface and subscription mechanism for the AI/ML positioning model training services (see paragraph 27 and 138). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRES RAFAEL SANCHEZ whose telephone number is (571)272-8776. The examiner can normally be reached 7:30-9:00. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anthony Addy can be reached at 571-272-7795. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ANDRES RAFAEL SANCHEZ/Examiner, Art Unit 2645 /ANTHONY S ADDY/Supervisory Patent Examiner, Art Unit 2645 Application/Control Number: 18/798,270 Page 2 Art Unit: 2645 Application/Control Number: 18/798,270 Page 3 Art Unit: 2645 Application/Control Number: 18/798,270 Page 4 Art Unit: 2645 Application/Control Number: 18/798,270 Page 5 Art Unit: 2645 Application/Control Number: 18/798,270 Page 6 Art Unit: 2645 Application/Control Number: 18/798,270 Page 7 Art Unit: 2645 Application/Control Number: 18/798,270 Page 8 Art Unit: 2645 Application/Control Number: 18/798,270 Page 9 Art Unit: 2645 Application/Control Number: 18/798,270 Page 10 Art Unit: 2645 Application/Control Number: 18/798,270 Page 11 Art Unit: 2645 Application/Control Number: 18/798,270 Page 12 Art Unit: 2645 Application/Control Number: 18/798,270 Page 13 Art Unit: 2645 Application/Control Number: 18/798,270 Page 14 Art Unit: 2645 Application/Control Number: 18/798,270 Page 15 Art Unit: 2645 Application/Control Number: 18/798,270 Page 16 Art Unit: 2645 Application/Control Number: 18/798,270 Page 17 Art Unit: 2645 Application/Control Number: 18/798,270 Page 18 Art Unit: 2645 Application/Control Number: 18/798,270 Page 19 Art Unit: 2645 Application/Control Number: 18/798,270 Page 20 Art Unit: 2645 Application/Control Number: 18/798,270 Page 21 Art Unit: 2645 Application/Control Number: 18/798,270 Page 22 Art Unit: 2645
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