CTNF 18/525,042 CTNF 85680 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. Response to Arguments 07-37 AIA Applicant's arguments filed April 2 nd , 2026 have been fully considered but they are not persuasive. Applicant argued on page 11/14 that: PNG media_image1.png 497 691 media_image1.png Greyscale Examiner respectfully disagrees. Although the claim recites a TN mapping information, it is not defined in the claims. Specification, PP88 of the current application, recites “TN mapping information may include a radio coverage map (e.g., an RSRP map) of a respective TN cell.” However, Examiner is not required to incorporate it into the claims. Also, it is reasonable to interpret TN mapping information as UE historical information. Because generating a radio coverage map requires UE signaling information over a geographical area and over a period of time. Kilpatrick, PP102 at least teaches the mobile device mobility parameter is tied to geographical coverage of multiple cells. So, this clearly teaches radio coverage information and therefore TN mapping information. Regarding the claimed machine learning model, the claim does not any detail as to what is in the machine learning model. There is no requirement for any types of neural network, or algorithm. In other words, it is a black box. Since the prior art teaches computer based predictions, it is the closest thing until more qualifications are in the claims. Regarding arguments against the 2ndary reference, Applicant argued: PNG media_image2.png 290 717 media_image2.png Greyscale Examiner respectfully disagrees. Ghimire teaches UE attaching to a network, and a particular cell. When it comes to cell search, the goal is to perform network access, that is attachment to a cell. For all the foregoing reasons, Applicant’s argument is not persuasive. All previous rejections are maintained. However, upon further search, Examiner discover a new reference and will represent it in the form of a 102 rejection over just the independent claims. Since examiner raised a new ground of rejection, this is a 2 nd non-final office action . Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15 AIA Claim s 1, 20, 29 and 30 are rejected under 35 U.S.C. 102( a)(1) and (a)(2 ) as being anticipated by Ma, US 20210194571 . For claim 1, Ma teaches a user equipment (UE), comprising: one or more memories storing processor-executable code; and one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the UE to: (Ma, Fig. 3A shows each of these elements) receive a message indicating terrestrial network mapping information, the terrestrial network mapping information associated with a machine learning model for estimating a signal quality map and coverage information for a plurality of network entities, (Ma, PP231 teaches 3D real time map could be directly obtained by a UE through a network server; PP304 explains the AI/ML aspect) wherein each network entity of the plurality of network entities is associated with a respective terrestrial network (Ma, Fig. 2 shows the terrestrial TRP, see also Fig. 6); establish a communication link between the UE and a first network entity that is associated with a non-terrestrial network (Ma, Fig. 2, connections 192); and search, while connected to the first network entity, for one or more terrestrial network cells associated with at least a subset of network entities of the plurality of network entities (Ma, PP198 teaches UE performing Cell Search), wherein the search is based at least in part on the signal quality map and the coverage information estimated via the machine learning model (Ma, PP230-238). Claims 20, 29 and 30 recites a method claim, a means plus function claim and computer readable medium claim, respective. And they have the same claimed steps as claim 1. Therefore, they are rejected for the same reasons . Claim Rejections - 35 USC § 103 07-103 AIA The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. 07-21-aia AIA Claim s 1-30 are rejected under 35 U.S.C. 103 as being unpatentable over Kilpatrick, II et al. (US20150036663A1), hereinafter KILPATRICK , in view of GHIMIRE et al. (US20240284396A1), hereinafter GHIMIRE . Regarding claim 1, KILPATRICK teaches A user equipment (UE), comprising: one or more memories storing processor-executable code; and one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the UE to: ( KILPATRICK Fig. 11, paragraphs 121-123, teach UE comprising memory 1115 and processors 1110 configured to perform the overall functions claimed.) receive a message indicating terrestrial network mapping information, the terrestrial network mapping information associated with a machine learning model for estimating a signal quality map and coverage information for a plurality of network entities, ( KILPATRICK , Fig. 16, step 1605, paragraphs 171-172, teach receiving historical information associated with mobility patterns based on learning and predicting behavior of a mobile device (i.e. terrestrial network mapping information associated with a machine learning model for estimating a signal quality map and coverage information for a plurality of network entities.) wherein each network entity of the plurality of network entities is associated with a respective terrestrial network; ( KILPATRICK , Fig. 1, paragraphs 42-43, teach cells and base stations 105 comprising a terrestrial network.) KILPATRICK does not describe establish a communication link between the UE and a first network entity that is associated with a non-terrestrial network; and search, while connected to the first network entity, for one or more terrestrial network cells associated with at least a subset of network entities of the plurality of network entities, wherein the search is based at least in part on the signal quality map and the coverage information estimated via the machine learning model. GHIMIRE in the same field of endeavor teaches establish a communication link between the UE and a first network entity that is associated with a non-terrestrial network; ( GHIMIRE , Fig. 5, paragraphs 348-350, teach UE 306 establishing a link (tS2) with non-terrestrial network (NTN) via NTN 1.) and search, while connected to the first network entity, for one or more terrestrial network cells associated with at least a subset of network entities of the plurality of network entities, ( GHIMIRE , Fig. 5, paragraphs 350-353, teach UE 306 searching for one or more terrestrial network cells (e.g. gNB, LMF, TRP).) wherein the search is based at least in part on the signal quality map and the coverage information estimated via the machine learning model. ( GHIMIRE , Fig. 5, paragraphs 350-353, 486, teach the search as based at least in part of the UE position estimated via machine learning model.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to search for a base station while connected to a non-terrestrial network entity. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 2, KILPATRICK in view of GHIMIRE teaches the UE of claim 1, wherein, to receive the message indicating the terrestrial network mapping information, the one or more processors are individually or collectively operable to execute the code to cause the UE to: receive the message indicating the machine learning model that is associated with the terrestrial network mapping information, the machine learning model being trained for generating the signal quality map and the coverage information for the one or more terrestrial network cells, wherein searching for the one or more terrestrial network cells is based at least in part on one or more outputs of the machine learning model. ( GHIMIRE , paragraph 488 teaches UE position comprising one or more machine learning models and/or a deep learning models, and the particular model chosen by the network may be subject to capabilities of network elements and/or UE-capabilities. To have a common understanding of the used model between one or more network nodes, the parameters used for classification may need to be communicated between the entities.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to receive the message indicating the machine learning model that is associated with the terrestrial network mapping information, the machine learning model being trained for generating the signal quality map and the coverage information for the one or more terrestrial network cells, wherein searching for the one or more terrestrial network cells is based at least in part on one or more outputs of the machine learning model. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 3, KILPATRICK in view of GHIMIRE teaches the UE of claim 1, wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to: train the machine learning model using a set of training data at the UE, wherein the search is based at least in part on one or more outputs of the machine learning model and in accordance with training the machine learning model. ( GHIMIRE , paragraph 532 teaches utilizing training data to train the data based on a combination of outputs.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to train the machine learning model using a set of training data at the UE, wherein the search is based at least in part on one or more outputs of the machine learning model and in accordance with training the machine learning model. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 4, KILPATRICK in view of GHIMIRE teaches the UE of claim 3, wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to: transmit, to a network entity, a second message indicating the machine learning model for generating the signal quality map and the coverage information for the one or more terrestrial network cells. ( GHIMIRE , paragraphs 538-541, teach deploying the training models to selected network entities.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to transmit, to a network entity, a second message indicating the machine learning model for generating the signal quality map and the coverage information for the one or more terrestrial network cells. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 5, KILPATRICK in view of GHIMIRE teaches the UE of claim 4, wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to: transmit a capability message indicating that the UE is capable of transmitting the machine learning model, wherein transmitting the second message indicating the machine learning model in accordance with the capability message. ( GHIMIRE , paragraphs 555-556, teach transmitting capability information indicating UE is AI capable.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to transmit a capability message indicating that the UE is capable of transmitting the machine learning model, wherein transmitting the second message indicating the machine learning model in accordance with the capability message. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 6, KILPATRICK in view of GHIMIRE teaches the UE of claim 3, wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to: transmit, to a network entity, a second message indicating, per component carrier, one or more signal quality maps for the one or more terrestrial network cells, wherein the one or more signal quality maps are based at least in part on the machine learning model. ( GHIMIRE , paragraphs 555-556, teach deploying the models indicating component carrier and signal quality maps for the terrestrial cells.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to transmit, to a network entity, a second message indicating, per component carrier, one or more signal quality maps for the one or more terrestrial network cells, wherein the one or more signal quality maps are based at least in part on the machine learning model. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 7, KILPATRICK in view of GHIMIRE teaches the UE of claim 1, wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to: transmit a capability message indicating that the UE is capable of receiving the terrestrial network mapping information, wherein receiving the message indicating the terrestrial network mapping information is in accordance with the capability message. ( GHIMIRE , paragraphs 488, 565, teach transmitting a capability message to indicate UE capabilities.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to transmit a capability message indicating that the UE is capable of receiving the terrestrial network mapping information, wherein receiving the message indicating the terrestrial network mapping information is in accordance with the capability message. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 8, KILPATRICK in view of GHIMIRE teaches the UE of claim 1, wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to: receive a first set of reference signals from the plurality of network entities associated with respective terrestrial networks; and perform one or more signal quality measurements on a subset of reference signals from the first set of reference signals, wherein searching for the one or more terrestrial network cells is based at least in part on the one or more signal quality measurements comprising an input to the machine learning model. ( GHIMIRE , paragraphs 356-357, teach receiving sets of reference signals from a plurality of network entities associated with respective terrestrial networks.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to receive a first set of reference signals from the plurality of network entities associated with respective terrestrial networks; and perform one or more signal quality measurements on a subset of reference signals from the first set of reference signals, wherein searching for the one or more terrestrial network cells is based at least in part on the one or more signal quality measurements comprising an input to the machine learning model. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 9, KILPATRICK in view of GHIMIRE teaches the UE of claim 1, wherein, to receive the message indicating the terrestrial network mapping information, the one or more processors are individually or collectively operable to execute the code to cause the UE to: receive the message indicating, for respective component carriers, one or more signal quality maps, wherein the machine learning model associated with the terrestrial network mapping information is associated with the one or more signal quality maps. ( GHIMIRE , paragraphs 555-556, teach deploying the models indicating component carrier and signal quality maps for the terrestrial cells.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to receive the message indicating, for respective component carriers, one or more signal quality maps, wherein the machine learning model associated with the terrestrial network mapping information is associated with the one or more signal quality maps. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 10, KILPATRICK in view of GHIMIRE teaches the UE of claim 1, wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to: receive, from a network entity of the plurality of network entities that is associated with a terrestrial network, a request message indicating a request to apply an offset to a first component carrier for estimating coverage information for a second carrier component different from the first component carrier. ( GHIMIRE , paragraphs 356-357, teach receiving sets of reference signals from a plurality of network entities associated with respective terrestrial networks.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to receive, from a network entity of the plurality of network entities that is associated with a terrestrial network, a request message indicating a request to apply an offset to a first component carrier for estimating coverage information for a second carrier component different from the first component carrier. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 11, KILPATRICK in view of GHIMIRE teaches the UE of claim 1, wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to: obtain, via the machine learning model, a cell search decision for each terrestrial network cell associated with each network entity of the plurality of network entities, wherein searching for the one or more terrestrial network cells is based at least in part on the cell search decision. ( GHIMIRE , Fig. 1, paragraphs 4, 47, teach obtaining serving cell via the machine learning model.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to obtain, via the machine learning model, a cell search decision for each terrestrial network cell associated with each network entity of the plurality of network entities, wherein searching for the one or more terrestrial network cells is based at least in part on the cell search decision. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 12, KILPATRICK in view of GHIMIRE teaches the UE of claim 11, wherein the cell search decision comprises a binary indication of whether to search for a respective terrestrial network cell. ( GHIMIRE , Fig. 1, paragraphs 4, 47, teach obtaining serving cell via the machine learning model.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to configure the cell search decision comprises a binary indication of whether to search for a respective terrestrial network cell. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 13, KILPATRICK in view of GHIMIRE teaches the UE of claim 11, wherein searching for the one or more terrestrial network cells is based at least in part on the cell search decision satisfying a threshold search parameter. ( GHIMIRE , Fig. 1, paragraphs 4, 47, teach the cell search satisfying quality parameters.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to search for the one or more terrestrial network cells is based at least in part on the cell search decision satisfying a threshold search parameter. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 14, KILPATRICK in view of GHIMIRE teaches the UE of claim 1, wherein the message indicating the terrestrial network mapping information is received from the first network entity or a second network entity from the plurality of network entities that is associated with a terrestrial network. ( GHIMIRE , paragraphs 555-556, teach deploying the models indicating component carrier and signal quality maps for the terrestrial cells.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to configure the message indicating the terrestrial network mapping information as received from the first network entity or a second network entity from the plurality of network entities that is associated with a terrestrial network. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 15, KILPATRICK in view of GHIMIRE teaches the UE of claim 1, wherein the coverage information for the plurality of network entities comprises coverage information for at least one network entity that is based at least in part on a network energy savings mode of the at least one network entity, a terrestrial network configuration of the at least one network entity, or any combination thereof. ( GHIMIRE , paragraph 348-349, teach coverage information based on a terrestrial network configuration.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to configure the coverage information for the plurality of network entities comprises coverage information for at least one network entity that is based at least in part on a network energy savings mode of the at least one network entity, a terrestrial network configuration of the at least one network entity, or any combination thereof. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 16, KILPATRICK in view of GHIMIRE teaches the UE of claim 1, wherein the terrestrial network mapping information includes one or more inputs for the machine learning model, the one or more inputs comprising positioning information of the plurality of network entities, an indication of a configuration of each respective terrestrial network, an indication of a configuration of the non-terrestrial network, or any combination thereof. ( GHIMIRE , paragraph 532 teaches one the inputs comprising positioning information.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to configure the terrestrial network mapping information as including one or more inputs for the machine learning model, the one or more inputs comprising positioning information of the plurality of network entities, an indication of a configuration of each respective terrestrial network, an indication of a configuration of the non-terrestrial network, or any combination thereof. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 17, KILPATRICK in view of GHIMIRE teaches the UE of claim 16, wherein the configuration of each respective terrestrial network, the configuration of the non-terrestrial network, or both, comprise a transmission power, an indication of an energy savings mode, or both. ( GHIMIRE , paragraph 459 teaches configuration comprising relative transmit power values.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to configure the configuration of each respective terrestrial network, the configuration of the non-terrestrial network, or both, comprise a transmission power, an indication of an energy savings mode, or both. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 18, KILPATRICK in view of GHIMIRE teaches the UE of claim 1, wherein the machine learning model that is received by the UE is trained by a second UE. ( GHIMIRE , paragraph 502 teaches the machine learning mode trained by a second UE.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to configure the machine learning model that is received by the UE is trained by a second UE. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 19, KILPATRICK in view of GHIMIRE teaches the UE of claim 1, wherein the signal quality map comprises a reference signal received power map, a reference signal received quality map, a signal interference noise ratio map, or any combination thereof. ( GHIMIRE , paragraphs 356-357, teach receiving sets of reference signals from a plurality of network entities associated with respective terrestrial networks.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to configure the signal quality map comprises a reference signal received power map, a reference signal received quality map, a signal interference noise ratio map, or any combination thereof. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 20, KILPATRICK teaches A method for wireless communications by a user equipment (UE), comprising: receiving a message indicating terrestrial network mapping information, the terrestrial network mapping information associated with a machine learning model for estimating a signal quality map and coverage information for a plurality of network entities, ( KILPATRICK , Fig. 16, step 1605, paragraphs 171-172, teach receiving historical information associated with mobility patterns based on learning and predicting behavior of a mobile device (i.e. terrestrial network mapping information associated with a machine learning model for estimating a signal quality map and coverage information for a plurality of network entities.) wherein each network entity of the plurality of network entities is associated with a respective terrestrial network; ( KILPATRICK , Fig. 1, paragraphs 42-43, teach cells and base stations 105 comprising a terrestrial network.) KILPATRICK does not describe establishing a communication link between the UE and a first network entity that is associated with a non-terrestrial network; and searching, while connected to the first network entity, for one or more terrestrial network cells associated with at least a subset of network entities of the plurality of network entities, wherein the searching is based at least in part on the signal quality map and the coverage information estimated via the machine learning model. GHIMIRE in the same field of endeavor teaches establishing a communication link between the UE and a first network entity that is associated with a non-terrestrial network; ( GHIMIRE , Fig. 5, paragraphs 348-350, teach UE 306 establishing a link (tS2) with non-terrestrial network (NTN) via NTN 1.) and searching, while connected to the first network entity, for one or more terrestrial network cells associated with at least a subset of network entities of the plurality of network entities, ( GHIMIRE , Fig. 5, paragraphs 350-353, teach UE 306 searching for one or more terrestrial network cells (e.g. gNB, LMF, TRP).) wherein the searching is based at least in part on the signal quality map and the coverage information estimated via the machine learning model. ( GHIMIRE , Fig. 5, paragraphs 350-353, 486, teach the search as based at least in part of the UE position estimated via machine learning model.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to search for a base station while connected to a non-terrestrial network entity. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 21, KILPATRICK in view of GHIMIRE teaches the method of claim 20, wherein receiving the message indicating the terrestrial network mapping information comprises: receiving the message indicating the machine learning model that is associated with the terrestrial network mapping information, the machine learning model being trained for generating the signal quality map and the coverage information for the one or more terrestrial network cells, wherein searching for the one or more terrestrial network cells is based at least in part on one or more outputs of the machine learning model. ( GHIMIRE , paragraph 488 teaches UE position comprising one or more machine learning models and/or a deep learning models, and the particular model chosen by the network may be subject to capabilities of network elements and/or UE-capabilities. To have a common understanding of the used model between one or more network nodes, the parameters used for classification may need to be communicated between the entities.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to receive the message indicating the machine learning model that is associated with the terrestrial network mapping information, the machine learning model being trained for generating the signal quality map and the coverage information for the one or more terrestrial network cells, wherein searching for the one or more terrestrial network cells is based at least in part on one or more outputs of the machine learning model. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 22, KILPATRICK in view of GHIMIRE teaches the method of claim 20, further comprising: training the machine learning model using a set of training data at the UE, wherein the searching is based at least in part on one or more outputs of the machine learning model and in accordance with training the machine learning model. ( GHIMIRE , paragraph 488 teaches UE position comprising one or more machine learning models and/or a deep learning models, and the particular model chosen by the network may be subject to capabilities of network elements and/or UE-capabilities. To have a common understanding of the used model between one or more network nodes, the parameters used for classification may need to be communicated between the entities.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to receive the message indicating the machine learning model that is associated with the terrestrial network mapping information, the machine learning model being trained for generating the signal quality map and the coverage information for the one or more terrestrial network cells, wherein searching for the one or more terrestrial network cells is based at least in part on one or more outputs of the machine learning model. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 23, KILPATRICK in view of GHIMIRE teaches the method of claim 22, further comprising: transmitting, to a network entity, a second message indicating the machine learning model for generating the signal quality map and the coverage information for the one or more terrestrial network cells. ( GHIMIRE , paragraphs 538-541, teach deploying the training models to selected network entities.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to transmit, to a network entity, a second message indicating the machine learning model for generating the signal quality map and the coverage information for the one or more terrestrial network cells. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 24, KILPATRICK in view of GHIMIRE teaches the method of claim 22, further comprising: transmitting, to a network entity, a second message indicating, per component carrier, one or more signal quality maps for the one or more terrestrial network cells, wherein the one or more signal quality maps are based at least in part on the machine learning model. ( GHIMIRE , paragraphs 555-556, teach deploying the models indicating component carrier and signal quality maps for the terrestrial cells.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to transmit, to a network entity, a second message indicating, per component carrier, one or more signal quality maps for the one or more terrestrial network cells, wherein the one or more signal quality maps are based at least in part on the machine learning model. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 25, KILPATRICK in view of GHIMIRE teaches the method of claim 20, further comprising: transmitting a capability message indicating that the UE is capable of receiving the terrestrial network mapping information, wherein receiving the message indicating the terrestrial network mapping information is in accordance with the capability message. ( GHIMIRE , paragraphs 488, 565, teach transmitting a capability message to indicate UE capabilities.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to transmit a capability message indicating that the UE is capable of receiving the terrestrial network mapping information, wherein receiving the message indicating the terrestrial network mapping information is in accordance with the capability message. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 26, KILPATRICK in view of GHIMIRE teaches the method of claim 20, further comprising: receiving a first set of reference signals from the plurality of network entities associated with respective terrestrial networks; and performing one or more signal quality measurements on a subset of reference signals from the first set of reference signals, wherein searching for the one or more terrestrial network cells is based at least in part on the one or more signal quality measurements comprising an input to the machine learning model. ( GHIMIRE , paragraphs 356-357, teach receiving sets of reference signals from a plurality of network entities associated with respective terrestrial networks.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to receive a first set of reference signals from the plurality of network entities associated with respective terrestrial networks; and perform one or more signal quality measurements on a subset of reference signals from the first set of reference signals, wherein searching for the one or more terrestrial network cells is based at least in part on the one or more signal quality measurements comprising an input to the machine learning model. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 27, KILPATRICK in view of GHIMIRE teaches the method of claim 20, wherein receiving the message indicating the terrestrial network mapping information comprises: receiving the message indicating, for respective component carriers, one or more signal quality maps, wherein the machine learning model associated with the terrestrial network mapping information is associated with the one or more signal quality maps. ( GHIMIRE , paragraphs 555-556, teach deploying the models indicating component carrier and signal quality maps for the terrestrial cells.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to receive the message indicating, for respective component carriers, one or more signal quality maps, wherein the machine learning model associated with the terrestrial network mapping information is associated with the one or more signal quality maps. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 28, the method of claim 20, further comprising: obtaining, via the machine learning model, a cell search decision for each terrestrial network cell associated with each network entity of the plurality of network entities, wherein searching for the one or more terrestrial network cells is based at least in part on the cell search decision. ( GHIMIRE , Fig. 1, paragraphs 4, 47, teach obtaining serving cell via the machine learning model.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to obtain, via the machine learning model, a cell search decision for each terrestrial network cell associated with each network entity of the plurality of network entities, wherein searching for the one or more terrestrial network cells is based at least in part on the cell search decision. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 29, KILPATRICK teaches A user equipment (UE) for wireless communications, comprising: ( KILPATRICK Fig. 11, paragraphs 121-123, teach UE comprising memory 1115 and processors 1110 configured to perform the means for as claimed below.) means for receiving a message indicating terrestrial network mapping information, the terrestrial network mapping information associated with a machine learning model for estimating a signal quality map and coverage information for a plurality of network entities, ( KILPATRICK , Fig. 16, step 1605, paragraphs 171-172, teach receiving historical information associated with mobility patterns based on learning and predicting behavior of a mobile device (i.e. terrestrial network mapping information associated with a machine learning model for estimating a signal quality map and coverage information for a plurality of network entities.) wherein each network entity of the plurality of network entities is associated with a respective terrestrial network; ( KILPATRICK , Fig. 1, paragraphs 42-43, teach cells and base stations 105 comprising a terrestrial network.) KILPATRICK does not describe means for establishing a communication link between the UE and a first network entity that is associated with a non-terrestrial network; and means for searching, while connected to the first network entity, for one or more terrestrial network cells associated with at least a subset of network entities of the plurality of network entities, wherein the searching is based at least in part on the signal quality map and the coverage information estimated via the machine learning model. GHIMIRE in the same field of endeavor teaches means for establishing a communication link between the UE and a first network entity that is associated with a non-terrestrial network; ( GHIMIRE , Fig. 5, paragraphs 348-350, teach UE 306 establishing a link (tS2) with non-terrestrial network (NTN) via NTN 1.) and means for searching, while connected to the first network entity, for one or more terrestrial network cells associated with at least a subset of network entities of the plurality of network entities, ( GHIMIRE , Fig. 5, paragraphs 350-353, teach UE 306 searching for one or more terrestrial network cells (e.g. gNB, LMF, TRP).) wherein the searching is based at least in part on the signal quality map and the coverage information estimated via the machine learning model. ( GHIMIRE , Fig. 5, paragraphs 350-353, 486, teach the search as based at least in part of the UE position estimated via machine learning model.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to search for a base station while connected to a non-terrestrial network entity. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Regarding claim 30, KILPATRICK teaches A non-transitory computer-readable medium storing code for wireless communications, the code comprising instructions executable by one or more processors to: ( KILPATRICK Fig. 11, paragraphs 121-123, teach UE comprising non-transitory computer readable medium executed by processors 1110 configured to perform the overall functions claimed.) receive a message indicating terrestrial network mapping information, the terrestrial network mapping information associated with a machine learning model for estimating a signal quality map and coverage information for a plurality of network entities, ( KILPATRICK , Fig. 16, step 1605, paragraphs 171-172, teach receiving historical information associated with mobility patterns based on learning and predicting behavior of a mobile device (i.e. terrestrial network mapping information associated with a machine learning model for estimating a signal quality map and coverage information for a plurality of network entities.) wherein each network entity of the plurality of network entities is associated with a respective terrestrial network; ( KILPATRICK , Fig. 1, paragraphs 42-43, teach cells and base stations 105 comprising a terrestrial network.) KILPATRICK does not describe establish a communication link between a user equipment (UE) and a first network entity that is associated with a non-terrestrial network; and search, while connected to the first network entity, for one or more terrestrial network cells associated with at least a subset of network entities of the plurality of network entities, wherein the search is based at least in part on the signal quality map and the coverage information estimated via the machine learning model. GHIMIRE in the same field of endeavor teaches establish a communication link between a user equipment (UE) and a first network entity that is associated with a non-terrestrial network; ( GHIMIRE , Fig. 5, paragraphs 348-350, teach UE 306 establishing a link (tS2) with non-terrestrial network (NTN) via NTN 1.) and search, while connected to the first network entity, for one or more terrestrial network cells associated with at least a subset of network entities of the plurality of network entities, ( GHIMIRE , Fig. 5, paragraphs 350-353, teach UE 306 searching for one or more terrestrial network cells (e.g. gNB, LMF, TRP).) wherein the search is based at least in part on the signal quality map and the coverage information estimated via the machine learning model. ( GHIMIRE , Fig. 5, paragraphs 350-353, 486, teach the search as based at least in part of the UE position estimated via machine learning model.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of GHIMIRE with the teachings of KILPATRICK to search for a base station while connected to a non-terrestrial network entity. The motivation would be to determine the UE position using a combination of NTN satellites and terrestrial TRPs ( GHIMIRE , paragraph 240). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Charles C Jiang whose telephone number is (571)270-7191. The examiner can normally be reached Monday to Thursday 7 am to 5 pm Eastern Time. 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, TC Group director, Deborah Reynolds can be reached at (571) 272-0734. 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. /CHARLES C JIANG/Supervisory Patent Examiner, Art Unit 2412 Application/Control Number: 18/525,042 Page 2 Art Unit: 2412 Application/Control Number: 18/525,042 Page 3 Art Unit: 2412 Application/Control Number: 18/525,042 Page 4 Art Unit: 2412 Application/Control Number: 18/525,042 Page 5 Art Unit: 2412 Application/Control Number: 18/525,042 Page 6 Art Unit: 2412 Application/Control Number: 18/525,042 Page 7 Art Unit: 2412 Application/Control Number: 18/525,042 Page 8 Art Unit: 2412 Application/Control Number: 18/525,042 Page 9 Art Unit: 2412 Application/Control Number: 18/525,042 Page 10 Art Unit: 2412 Application/Control Number: 18/525,042 Page 11 Art Unit: 2412 Application/Control Number: 18/525,042 Page 12 Art Unit: 2412 Application/Control Number: 18/525,042 Page 13 Art Unit: 2412 Application/Control Number: 18/525,042 Page 14 Art Unit: 2412 Application/Control Number: 18/525,042 Page 15 Art Unit: 2412 Application/Control Number: 18/525,042 Page 16 Art Unit: 2412 Application/Control Number: 18/525,042 Page 17 Art Unit: 2412 Application/Control Number: 18/525,042 Page 18 Art Unit: 2412 Application/Control Number: 18/525,042 Page 19 Art Unit: 2412 Application/Control Number: 18/525,042 Page 20 Art Unit: 2412 Application/Control Number: 18/525,042 Page 21 Art Unit: 2412 Application/Control Number: 18/525,042 Page 22 Art Unit: 2412 Application/Control Number: 18/525,042 Page 23 Art Unit: 2412 Application/Control Number: 18/525,042 Page 24 Art Unit: 2412 Application/Control Number: 18/525,042 Page 25 Art Unit: 2412 Application/Control Number: 18/525,042 Page 26 Art Unit: 2412 Application/Control Number: 18/525,042 Page 27 Art Unit: 2412 Application/Control Number: 18/525,042 Page 28 Art Unit: 2412 Application/Control Number: 18/525,042 Page 29 Art Unit: 2412 Application/Control Number: 18/525,042 Page 30 Art Unit: 2412
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