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 .
DETAILED ACTION
1. Claims 1 - 3, 9, 16 - 26 are pending. Claims 1 - 3, 9 have been amended. Claims 4 - 8, 10 - 15 have been canceled. Claims 16 - 26 are new. Claims 1, 9, 23 are independent. File date on 1-7-2025.
Claim Rejections - 35 USC § 103
2. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
3. Claims 1, 17, 23 are rejected under 35 U.S.C. 103 as being unpatentable over Hahn et al. (US PGPUB No. 2013/032,2325) in view of Bao et al. (Patent No. WO 2022/010910 A1) and further in view of Lekutai et al. (US PGPUB No. 2022/0217704).
Regarding Claims 1, 23, Hahn discloses methods comprising:
a) receiving a request message related to assistance information from a network entity related to application, and b) wherein the request message includes a list of User Equipment (UE) including one or more UEs. (Hahn ¶ 051: The method includes receiving a handover request message containing a list of a plurality of user equipments (UEs) on the mobile relay node, performing an admission control for the plurality of UEs, and transmitting a handover request acknowledge message containing a list of admitted UEs for handover among the plurality of UEs.)
Hahn does not specifically disclose for c) transmitting a notify message (i.e. response message), to network entity related to application, and for d) notify message includes a list of candidate UE for Artificial Intelligence (AI) and Machine Learning (ML) (AI/ML) operation.
However, Bao discloses:
c) transmitting a notify message, to the network entity related to the application, d) wherein the notify message includes the assistance information including a list of candidate UE. (Bao ¶ 139: the target UE 500-1 may generate the candidate list of anchor UEs. The target UE 500-1, for example, may select candidates for anchor UE based on one or more factors, such as a range between the candidate UE and the target UE 500-1 or the quality of signal from the candidate UE. Estimated locations of the candidate UEs and the target UE 500-1 may additionally be used to select candidates UEs for a list of anchor UEs. For example, the locations may be used to determine the Geometric Dilution of Precision (GDOP), which may be used by the target UE 500-1 to select candidate UEs.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hahn for c) transmitting a notify message (response message), to network entity related to application, and for d) notify message includes a list of candidate UEs as taught by Bao. One of ordinary skill in the art would have been motivated to employ the teachings of Bao for the benefits achieved from the flexibility of a system that enables the flexibility of a system that enables the generation of a candidate list of UEs enabling a selection of a UE for data processing. (Bao ¶ 139)
Hahn does not specifically disclose Artificial Intelligence (AI) and Machine Learning (ML) (AI/ML) operation.
However, Lekutai discloses wherein Artificial Intelligence (AI) and Machine Learning (ML) (AI/ML) operation. (Lekutai ¶ 030: artificial intelligence (AI) may be used in selecting the components 116. For instance, the base station 102 may use a computing model to predict conditions of the network, requirements of application(s) 107 being used or to be used by a UE 106, and select the components based on the predicted conditions. The base station 102 may use a computing model, such as a machine learning model, to identify trends in the congestion levels. As used herein, the term “machine learning model” can refer to any computing model that is built or otherwise optimized based on training data. The machine learning model, for example, may be configured to identify features that are indicative of data traffic and/or spectrum trends based on training data indicating previous data traffic metrics associated with the base station 102.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hahn for Artificial Intelligence (AI) and Machine Learning (ML) (AI/ML) operation as taught by Lekutai. One of ordinary skill in the art would have been motivated to employ the teachings of Lekutai for the benefits achieved from the flexibility of a system that enables multiple techniques to be utilized such as artificial intelligence models and machine learning models in the processing of data. (Lekutai ¶ 030)
Regarding Claim 17, Hahn-Bao-Lekutai discloses the method of claim 1.
Hahn does not specifically disclose the list of candidate UEs is derived based on information related to the list of the UEs, collected from a network entity related to session.
However, Bao discloses wherein the list of candidate UEs is derived based on information related to the list of the UEs, collected from a network entity related to session. (Bao ¶ 139: the target UE 500-1 may generate the candidate list of anchor UEs. The target UE 500-1, for example, may select candidates for anchor UE based on one or more factors, such as a range between the candidate UE and the target UE 500-1 or the quality of signal from the candidate UE. Estimated locations of the candidate UEs and the target UE 500-1 may additionally be used to select candidates UEs for a list of anchor UEs. For example, the locations may be used to determine the Geometric Dilution of Precision (GDOP), which may be used by the target UE 500-1 to select candidate UEs.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hahn for the list of candidate UEs is derived based on information related to the list of the UEs, collected from a network entity related to session as taught by Bao. One of ordinary skill in the art would have been motivated to employ the teachings of Bao for the benefits achieved from the flexibility of a system that enables the flexibility of a system that enables the generation of a candidate list of UEs enabling a selection of a UE for data processing. (Bao ¶ 139)
4. Claims 2, 9, 19, 22, 24 are rejected under 35 U.S.C. 103 as being unpatentable over Hahn in view of Bao and further in view of Lekutai and Wu et al. (US PGPUB No. 20240121677).
Regarding Claim 2, Hahn-Bao-Lekutai discloses the method of claim 1.
Hahn does not specifically disclose assistance information used by the network entity to select member UE.
However, Wu discloses wherein the assistance information is used by the network entity related to the application to select member UE. (Wu ¶ 088: UE 601A may select the target node from the plurality of candidate nodes based on a priority. The priorities of the candidate nodes may be configured by a BS (e.g., source BS or cell 602) or may be predefined, for example, in standard(s).)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hahn for assistance information used by the network entity to select member UE as taught by Wu. One of ordinary skill in the art would have been motivated to employ the teachings of Wu for the benefits achieved from the flexibility of a system that enables multiple techniques to be utilized to select a UE for data processing. (Wu ¶ 088)
Hahn does not explicitly disclose assistance information used in the AI/ML operation.
However, Lekutai discloses wherein assistance information used in the AI/ML operation. (Lekutai ¶ 030: artificial intelligence (AI) may be used in selecting the components 116. For instance, the base station 102 may use a computing model to predict conditions of the network, requirements of application(s) 107 being used or to be used by a UE 106, and select the components based on the predicted conditions. The base station 102 may use a computing model, such as a machine learning model, to identify trends in the congestion levels. As used herein, the term “machine learning model” can refer to any computing model that is built or otherwise optimized based on training data. The machine learning model, for example, may be configured to identify features that are indicative of data traffic and/or spectrum trends based on training data indicating previous data traffic metrics associated with the base station 102.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hahn for assistance information used in the AI/ML operation as taught by Lekutai. One of ordinary skill in the art would have been motivated to employ the teachings of Lekutai for the benefits achieved from the flexibility of a system that enables multiple techniques to be utilized such as artificial intelligence models and machine learning models in the processing of data. (Lekutai ¶ 030)
Regarding Claim 9, Hahn discloses a device comprising:
a) one or more transceivers; (Hahn ¶ 040: "downlink" refers to communication from the eNB 111 to the UE, from the DeNB 110 to the UE or from the RN 100 to the UE, and "uplink" refers to communication from the UE to the eNB 111, from the UE to the DeNB 110 or from the UE to the RN 100.; (transceiver send and receive communication data over a communication channel))
b) one or more processors; and c) one or more memories that store instructions and are operably coupled to the one or more processors, d) based on the instructions being executed by the at least one processor, perform operations (Hahn ¶ 221: The processor 910 may include an application-specific integrated circuit (ASIC), another chip set, a logical circuit, and/or a data processing unit. The RF unit 920 may include a baseband circuit for processing radio signals. In software implemented, the aforementioned methods can be implemented with a module (i.e., process, function, etc.) for performing the aforementioned functions. The module may be performed by the processor 910.) comprising:
e) receiving a request message related to assistance information from a network entity related to application, and f) wherein the request message includes a list of User Equipment (UE)s including one or more UEs. (Hahn ¶ 051: The method includes receiving a handover request message containing a list of a plurality of user equipments (UEs) on the mobile relay node, performing an admission control for the plurality of UEs, and transmitting a handover request acknowledge message containing a list of admitted UEs for handover among the plurality of UEs.)
Hahn does not specifically disclose for g) transmitting a notify message (i.e. response message) to the network entity related to the application, and for h) notify message includes a list of candidate UEs.
However, Bao discloses:
g) transmitting a notify message, to the network entity related to the application; h) wherein the notify message includes the assistance information including a list of candidate UEs for Artificial Intelligence (AI) and Machine Learning (ML) (AI/ML) operation, (Bao ¶ 139: the target UE 500-1 may generate the candidate list of anchor UEs. The target UE 500-1, for example, may select candidates for anchor UE based on one or more factors, such as a range between the candidate UE and the target UE 500-1 or the quality of signal from the candidate UE. Estimated locations of the candidate UEs and the target UE 500-1 may additionally be used to select candidates UEs for a list of anchor UEs. For example, the locations may be used to determine the Geometric Dilution of Precision (GDOP), which may be used by the target UE 500-1 to select candidate UEs.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hahn for g) transmitting a notify message (i.e. response message) to network entity related to application, and for h) notify message includes a list of candidate UEs as taught by Bao. One of ordinary skill in the art would have been motivated to employ the teachings of Bao for the benefits achieved from the flexibility of a system that enables the flexibility of a system that enables the generation of a candidate list of UEs enabling a selection of a UE for data processing. (Bao ¶ 139)
Hahn does not specifically disclose assistance information is used by network entity to select member UE.
However, Wu discloses:
i) wherein the assistance information is used by the network entity related to the application to select member UE. (Wu ¶ 088: UE 601A may select the target node from the plurality of candidate nodes based on a priority. The priorities of the candidate nodes may be configured by a BS (e.g., source BS or cell 602) or may be predefined, for example, in standard(s).)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hahn for assistance information is used by network entity to select member UE as taught by Wu. One of ordinary skill in the art would have been motivated to employ the teachings of Wu for the benefits achieved from the flexibility of a system that enables multiple techniques to be utilized to select a UE for data processing. (Wu ¶ 088)
Hahn does not explicitly disclose for Artificial Intelligence (AI) and Machine Learning (ML) (AI/ML) operation and used in AI/ML operation
However, Lekutai discloses wherein for Artificial Intelligence (AI) and Machine Learning (ML) (AI/ML) operation and used in AI/ML operation. (Lekutai ¶ 030: artificial intelligence (AI) may be used in selecting the components 116. For instance, the base station 102 may use a computing model to predict conditions of the network, requirements of application(s) 107 being used or to be used by a UE 106, and select the components based on the predicted conditions. The base station 102 may use a computing model, such as a machine learning model, to identify trends in the congestion levels. As used herein, the term “machine learning model” can refer to any computing model that is built or otherwise optimized based on training data. The machine learning model, for example, may be configured to identify features that are indicative of data traffic and/or spectrum trends based on training data indicating previous data traffic metrics associated with the base station 102.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hahn for Artificial Intelligence (AI) and Machine Learning (ML) (AI/ML) operation and used in AI/ML operation as taught by Lekutai. One of ordinary skill in the art would have been motivated to employ the teachings of Lekutai for the benefits achieved from the flexibility of a system that enables multiple techniques to be utilized such as artificial intelligence models and machine learning models in the processing of data. (Lekutai ¶ 030)
Regarding Claim 19, Hahn-Bao-Lekutai-Wu discloses the device of claim 9.
Hahn does not specifically disclose assistance information is used by the network entity related to the application to select member UE.
However, Wu discloses wherein the assistance information is used by the network entity related to the application to select member UE. (Wu ¶ 088: UE 601A may select the target node from the plurality of candidate nodes based on a priority. The priorities of the candidate nodes may be configured by a BS (e.g., source BS or cell 602) or may be predefined, for example, in standard(s).)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hahn for assistance information is used by the network entity related to the application to select member UE as taught by Wu. One of ordinary skill in the art would have been motivated to employ the teachings of Wu for the benefits achieved from the flexibility of a system that enables multiple techniques to be utilized to select a UE for data processing. (Wu ¶ 088)
Hahn does not explicitly disclose assistance information used in AI/ML operation.
However, Lekutai discloses wherein assistance information used in the AI/ML operation. (Lekutai ¶ 030: artificial intelligence (AI) may be used in selecting the components 116. For instance, the base station 102 may use a computing model to predict conditions of the network, requirements of application(s) 107 being used or to be used by a UE 106, and select the components based on the predicted conditions. The base station 102 may use a computing model, such as a machine learning model, to identify trends in the congestion levels. As used herein, the term “machine learning model” can refer to any computing model that is built or otherwise optimized based on training data. The machine learning model, for example, may be configured to identify features that are indicative of data traffic and/or spectrum trends based on training data indicating previous data traffic metrics associated with the base station 102.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hahn for assistance information used in AI/ML operation as taught by Lekutai. One of ordinary skill in the art would have been motivated to employ the teachings of Lekutai for the benefits achieved from the flexibility of a system that enables multiple techniques to be utilized such as artificial intelligence models and machine learning models in the processing of data. (Lekutai ¶ 030)
Regarding Claim 22, Hahn-Bao-Lekutai-Wu discloses the device of claim 9.
Hahn does not explicitly disclose list of candidate UEs derived based on information related to list of UEs.
However, Bao discloses wherein the list of candidate UE is derived based on information related to the list of the UEs, collected from a network entity related to session. (Bao ¶ 139: the target UE 500-1 may generate the candidate list of anchor UEs. The target UE 500-1, for example, may select candidates for anchor UE based on one or more factors, such as a range between the candidate UE and the target UE 500-1 or the quality of signal from the candidate UE. Estimated locations of the candidate UEs and the target UE 500-1 may additionally be used to select candidates UEs for a list of anchor UEs. For example, the locations may be used to determine the Geometric Dilution of Precision (GDOP), which may be used by the target UE 500-1 to select candidate UEs.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hahn for list of candidate UEs derived based on information related to list of UEs as taught by Bao. One of ordinary skill in the art would have been motivated to employ the teachings of Bao for the benefits achieved from the flexibility of a system that enables the flexibility of a system that enables the generation of a candidate list of UEs enabling a selection of a UE for data processing. (Bao ¶ 139)
Regarding Claim 24, Hahn-Bao-Lekutai discloses the method of claim 23.
Hahn does not explicitly disclose selecting member UE, based on the assistance information.
However, Wu discloses wherein further comprising: selecting member UE, based on the assistance information. (Wu ¶ 088: UE 601A may select the target node from the plurality of candidate nodes based on a priority. The priorities of the candidate nodes may be configured by a BS (e.g., source BS or cell 602) or may be predefined, for example, in standard(s).)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hahn for selecting member UE, based on the assistance information as taught by Wu. One of ordinary skill in the art would have been motivated to employ the teachings of Wu for the benefits achieved from the flexibility of a system that enables multiple techniques to be utilized to select a UE for data processing. (Wu ¶ 088)
Hahn does not explicitly disclose assistance information used in AI/ML operation.
However, Lekutai discloses wherein assistance information used in the AI/ML operation.
(Lekutai ¶ 030: artificial intelligence (AI) may be used in selecting the components 116. For instance, the base station 102 may use a computing model to predict conditions of the network, requirements of application(s) 107 being used or to be used by a UE 106, and select the components based on the predicted conditions. The base station 102 may use a computing model, such as a machine learning model, to identify trends in the congestion levels. As used herein, the term “machine learning model” can refer to any computing model that is built or otherwise optimized based on training data. The machine learning model, for example, may be configured to identify features that are indicative of data traffic and/or spectrum trends based on training data indicating previous data traffic metrics associated with the base station 102.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hahn for assistance information used in AI/ML operation as taught by Lekutai. One of ordinary skill in the art would have been motivated to employ the teachings of Lekutai for the benefits achieved from the flexibility of a system that enables multiple techniques to be utilized such as artificial intelligence models and machine learning models in the processing of data. (Lekutai ¶ 030)
5. Claims 3, 16, 18, 25, 26 are rejected under 35 U.S.C. 103 as being unpatentable over Hahn in view of Bao and further in view of Lekutai and Kim et al. (US Patent No. 10,869,236).
Regarding Claim 3, Hahn-Bao-Lekutai discloses the method of claim 1.
Hahn does not specifically disclose request message includes preferred access type and/or Radio Access Technology (RAT) type.
However, Kim discloses wherein the request message further includes preferred access type and/or Radio Access Technology (RAT) type. (Kim col 2: determining to initiate a secondary node addition procedure for establishing a user equipment context at a secondary node to provide radio resources from the secondary node to the user equipment, transmitting, to the secondary node, a secondary base station addition request message for requesting to allocate the radio resources for a specific E-UTRAN radio access bearer (E-RAB), and receiving, from the secondary node, secondary cell group radio resource configuration information for additionally providing secondary cell group radio resources to the user equipment.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hahn for request message includes preferred access type and/or Radio Access Technology (RAT) type as taught by Kim. One of ordinary skill in the art would have been motivated to employ the teachings of Kim for the benefits achieved from the flexibility of a system that enables multiple communication access techniques such as designated access type and/or RAT type related communication access. (Kim col 2)
Regarding Claim 16, Hahn-Bao-Lekutai discloses the method of claim 1.
Hahn does not specifically disclose assistance information includes access type and/or RAT type related to one or more UEs.
However, Kim discloses wherein the assistance information further includes access type and/or RAT type related to one or more UEs included in the list of the candidate UE. (Kim col 2: determining to initiate a secondary node addition procedure for establishing a user equipment context at a secondary node to provide radio resources from the secondary node to the user equipment, transmitting, to the secondary node, a secondary base station addition request message for requesting to allocate the radio resources for a specific E-UTRAN radio access bearer (E-RAB), and receiving, from the secondary node, secondary cell group radio resource configuration information for additionally providing secondary cell group radio resources to the user equipment.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hahn for assistance information includes access type and/or RAT type related to one or more UEs as taught by Kim. One of ordinary skill in the art would have been motivated to employ the teachings of Kim for the benefits achieved from the flexibility of a system that enables multiple communication access techniques such as designated access type and/or RAT type related communication access. (Kim col 2)
Regarding Claim 18, Hahn-Bao-Lekutai discloses the method of claim 1,
Hahn does not explicitly disclose for a) transmitting a request message for an event related to access type change, to a network entity related to session, and b) receiving information related to access type from the network entity related to the session, and for c) information related to access type includes two access type information being used for Multi Access (MA) Protocol Data Unit (PDU) session.
However, Kim discloses further comprising:
a) transmitting a subscription request message for an event related to access type change, to a network entity related to session; and b) receiving information related to access type from the network entity related to the session, (Kim col 2: determining to initiate a secondary node addition procedure for establishing a user equipment context at a secondary node to provide radio resources from the secondary node to the user equipment, transmitting, to the secondary node, a secondary base station addition request message for requesting to allocate the radio resources for a specific E-UTRAN radio access bearer (E-RAB), and receiving, from the secondary node, secondary cell group radio resource configuration information for additionally providing secondary cell group radio resources to the user equipment.) and
c) wherein the information related to the access type includes two access type information being used for Multi Access (MA) Protocol Data Unit (PDU) session. (Kim col 7: In case of the dual connectivity, a user equipment may transmit/receive data through a plurality of cells provided by two or more base stations. In the present disclosure, a main base station establishing an RRC connection with the user equipment and acting as a reference of a handover is referred to as a master base station (MeNB) or a master node, and a base station supporting an additional cell to the user equipment is referred to as a secondary base station (SeNB) or a secondary node.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hahn for a) transmitting a request message for an event related to access type change, to a network entity related to session, and b) receiving information related to access type from the network entity related to the session, and for c) information related to access type includes two access type information being used for Multi Access (MA) Protocol Data Unit (PDU) session as taught by Kim. One of ordinary skill in the art would have been motivated to employ the teachings of Kim for the benefits achieved from the flexibility of a system that enables multiple communication access techniques such as designated access type and/or RAT type related communication access. (Kim col 2)
Regarding Claim 25, Hahn-Bao-Lekutai discloses the method of claim 23.
Hahn does not explicitly disclose request message further includes preferred access type and/or Radio Access Technology (RAT) type.
However, Kim discloses wherein the request message includes preferred access type and/or Radio Access Technology (RAT) type. (Kim col 2: determining to initiate a secondary node addition procedure for establishing a user equipment context at a secondary node to provide radio resources from the secondary node to the user equipment, transmitting, to the secondary node, a secondary base station addition request message for requesting to allocate the radio resources for a specific E-UTRAN radio access bearer (E-RAB), and receiving, from the secondary node, secondary cell group radio resource configuration information for additionally providing secondary cell group radio resources to the user equipment.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hahn for request message includes preferred access type and/or Radio Access Technology (RAT) type as taught by Kim. One of ordinary skill in the art would have been motivated to employ the teachings of Kim for the benefits achieved from the flexibility of a system that enables multiple communication access techniques such as designated access type and/or RAT type related communication access. (Kim col 2)
Regarding Claim 26, Hahn-Bao-Lekutai discloses the method of claim 23.
Hahn does not explicitly disclose assistance information includes access type and/or RAT type related to one or more UEs.
However, Kim discloses wherein the assistance information further includes access type and/or RAT type related to one or more UEs included in the list of the candidate UE. (Kim col 2: determining to initiate a secondary node addition procedure for establishing a user equipment context at a secondary node to provide radio resources from the secondary node to the user equipment, transmitting, to the secondary node, a secondary base station addition request message for requesting to allocate the radio resources for a specific E-UTRAN radio access bearer (E-RAB), and receiving, from the secondary node, secondary cell group radio resource configuration information for additionally providing secondary cell group radio resources to the user equipment.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hahn for assistance information includes access type and/or RAT type related to one or more UEs as taught by Kim. One of ordinary skill in the art would have been motivated to employ the teachings of Kim for the benefits achieved from the flexibility of a system that enables multiple communication access techniques such as designated access type and/or RAT type related communication access. (Kim col 2)
6. Claims 20, 21 are rejected under 35 U.S.C. 103 as being unpatentable over Hahn in view of Bao and further in view of Lekutai and Wu and Kim.
Regarding Claim 20, Hahn-Bao-Lekutai-Wu discloses the device of claim 9.
Hahn does not explicitly disclose request message includes preferred access type and/or Radio Access Technology (RAT) type.
However, Kim discloses wherein the request message further includes preferred access type and/or Radio Access Technology (RAT) type. (Kim col 2: determining to initiate a secondary node addition procedure for establishing a user equipment context at a secondary node to provide radio resources from the secondary node to the user equipment, transmitting, to the secondary node, a secondary base station addition request message for requesting to allocate the radio resources for a specific E-UTRAN radio access bearer (E-RAB), and receiving, from the secondary node, secondary cell group radio resource configuration information for additionally providing secondary cell group radio resources to the user equipment.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hahn for request message includes preferred access type and/or Radio Access Technology (RAT) type as taught by Kim. One of ordinary skill in the art would have been motivated to employ the teachings of Kim for the benefits achieved from the flexibility of a system that enables multiple communication access techniques such as designated access type and/or RAT type related communication access. (Kim col 2)
Regarding Claim 21, Hahn-Bao-Lekutai-Wu discloses the device of claim 9.
Hahn does not explicitly disclose assistance information includes access type and/or RAT type related to one or more UEs.
However, Kim discloses wherein the assistance information further includes access type and/or RAT type related to one or more UEs included in the list of the candidate UE. (Kim col 2: determining to initiate a secondary node addition procedure for establishing a user equipment context at a secondary node to provide radio resources from the secondary node to the user equipment, transmitting, to the secondary node, a secondary base station addition request message for requesting to allocate the radio resources for a specific E-UTRAN radio access bearer (E-RAB), and receiving, from the secondary node, secondary cell group radio resource configuration information for additionally providing secondary cell group radio resources to the user equipment.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hahn for assistance information includes access type and/or RAT type related to one or more UEs as taught by Kim. One of ordinary skill in the art would have been motivated to employ the teachings of Kim for the benefits achieved from the flexibility of a system that enables multiple communication access techniques such as designated access type and/or RAT type related communication access. (Kim col 2)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kyung H Shin whose telephone number is (571)272-3920. The examiner can normally be reached M - F: 12pm - 8pm.
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/KYUNG H SHIN/ 6-19-2026 Primary Examiner, Art Unit 2447
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