Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments, see page 11, filed 08/07/2026, with respect to the rejection(s) of claims 1, 20, 29, 30 under 35 USC 102 with Ma have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of 35 USC 103 (single reference 103 rejection) based on Ma, US 20210194571.
The main rebuttal is that: it would be obvious to adapt a system for signal quality mapping of non-terrestrial system for terrestrial systems. Because use a terrestrial systems are stationary, whereas non-terrestrial system is moving. Terrestrial networks, for example, 5G, relies are base station or access points that are grounded. Non-terrestrials systems are based on moving satellites, drones, and low orbit flying objects that acts as base stations or TRPs. So non-terrestrial networks are more complicated to map. Since Ma teaches using AI/ML to create a signal quality map for a moving (non-terrestrial) network, it would be far easier to modify it to create a signal quality map for stationary terrestrial networks.
Applicant’s arguments, see page 13, filed 08/07/2026, with respect to the rejection(s) of claims 1-30 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of 35 USC 103 based on Ma, US 20210194571 in view of GHIMIRE et al. (US20240284396A1).
Response to Amendment
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 20, 29, 30 are rejected under 35 U.S.C. 103 as being unpatentable over 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, wherein the terrestrial network mapping information includes one or more inputs for 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), and wherein the signal quality map estimated via the machine learning model (this limitation is previously addressed) comprises one or more signal quality valued for one or more terrestrial network cells (Ma, PP265 appears to say UE based on instruction from a base station, in other words, terrestrial, collect signaling information; it could be said then signal quality data for terrestrial network cells are collected into this mapping; see also PP307-314).
It would be obvious for one of ordinary skill in the art before the effective filing of the current application to modify non-terrestrial mapping of Ma to mapping the signa quality of terrestrial systems. Terrestrial systems are stationary, whereas non-terrestrial system are moving. Because terrestrial networks, for example, 5G, relies are base station or access points that are grounded. Non-terrestrials systems are based on moving satellites, drones, and low orbit flying objects that acts as base stations or TRPs. So non-terrestrial networks are more complicated. Since Ma teaches using AI/ML to create a signal quality map for a moving (non-terrestrial) network, it would be far easier to modify it to create a signal quality map for stationary terrestrial networks.
Examiner recommends applicant to amend the claims to capture some technical detail of AI/ML features of the current application, to overcome the prior art (See Ma, PP307-314).
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.
Claims 1-30 are rejected under 35 U.S.C. 103 as being unpatentable over Ma, US 20210194571, in view of GHIMIRE et al. (US20240284396A1).
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, wherein the terrestrial network mapping information includes one or more inputs for 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)
and 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), and wherein the signal quality map estimated via the machine learning model (this limitation is previously addressed) comprises one or more signal quality valued for one or more terrestrial network cells (Ma, PP265 appears to say UE based on instruction from a base station, in other words, terrestrial, collect signaling information; it could be said then signal quality data for terrestrial network cells are collected into this mapping; see also PP307-314).
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.) and wherein the signal quality map estimated via the machine learning model (this limitation is previously addressed) comprises one or more signal quality valued for one or more terrestrial network cells (GHIMIRE, PP5 teaches mapping physical channel resources, PDSCH, PUSCH, PSSCH are well-known 5G signaling channels)
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 MA 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, MA 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.)
Regarding claim 3, MA 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.)
Regarding claim 4, MA 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.)
Regarding claim 5, MA 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.)
Regarding claim 6, MA 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.)
Regarding claim 7, MA 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.)
Regarding claim 8, MAin 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.)
Regarding claim 9, MA 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.)
Regarding claim 10, MA 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.)
Regarding claim 11, MA 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.)
Regarding claim 12, MAin 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.)
Regarding claim 13, MAin 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.)
Regarding claim 14, MA 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.)
Regarding claim 15, MA 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.)
Regarding claim 16, MA in view of GHIMIRE teaches the UE of claim 1, wherein the one or more inputs comprisie 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.)
Regarding claim 17, MA 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.)
Regarding claim 18, MA 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.)
Regarding claim 19, MA 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.)
Regarding claim 20, Ma teaches A method for wireless communications by a user equipment (UE), comprising:
receiving a message indicating terrestrial network mapping information, wherein the terrestrial network mapping information includes one or more inputs for 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);
establishing 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
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 (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), and wherein the signal quality map estimated via the machine learning model (this limitation is previously addressed) comprises one or more signal quality valued for one or more terrestrial network cells (Ma, PP265 appears to say UE based on instruction from a base station, in other words, terrestrial, collect signaling information; it could be said then signal quality data for terrestrial network cells are collected into this mapping; see also PP307-314).
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.) and wherein the signal quality map estimated via the machine learning model (this limitation is previously addressed) comprises one or more signal quality valued for one or more terrestrial network cells (GHIMIRE, PP5 teaches mapping physical channel resources, PDSCH, PUSCH, PSSCH are well-known 5G signaling channels)
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, MA teaches A user equipment (UE) for wireless communications, comprising:
means for receiving a message indicating terrestrial network mapping information, wherein the terrestrial network mapping information includes one or more inputs for 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);
means for establishing 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
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 (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), and wherein the signal quality map estimated via the machine learning model (this limitation is previously addressed) comprises one or more signal quality valued for one or more terrestrial network cells (Ma, PP265 appears to say UE based on instruction from a base station, in other words, terrestrial, collect signaling information; it could be said then signal quality data for terrestrial network cells are collected into this mapping; see also PP307-314).
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.) and wherein the signal quality map estimated via the machine learning model (this limitation is previously addressed) comprises one or more signal quality valued for one or more terrestrial network cells (GHIMIRE, PP5 teaches mapping physical channel resources, PDSCH, PUSCH, PSSCH are well-known 5G signaling channels)
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 MA 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, MA teaches A non-transitory computer-readable medium storing code for wireless communications, the code comprising instructions executable by one or more processors to: (MA, pp 38)
receive a message indicating terrestrial network mapping information, wherein the terrestrial network mapping information includes one or more inputs for 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), and wherein the signal quality map estimated via the machine learning model (this limitation is previously addressed) comprises one or more signal quality valued for one or more terrestrial network cells (Ma, PP265 appears to say UE based on instruction from a base station, in other words, terrestrial, collect signaling information; it could be said then signal quality data for terrestrial network cells are collected into this mapping; see also PP307-314).
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.) and wherein the signal quality map estimated via the machine learning model (this limitation is previously addressed) comprises one or more signal quality valued for one or more terrestrial network cells (GHIMIRE, PP5 teaches mapping physical channel resources, PDSCH, PUSCH, PSSCH are well-known 5G signaling channels)
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 MA 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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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
