DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Publication No.: US 2024/0054357 A1 to Kumar et al. (Kumar) as disclosed in the IDS, in view of Publication No.: US 2025/0008346 A1 to Singh et al. (Singh).
As to Claim 1, Kumar discloses a user equipment (UE) for wireless communication, comprising:
at least one memory (Fig. 3, ‘memory 382’); and
at least one processor coupled with the at least one memory and configured to cause the UE to (Fig. 3, ‘controller/processor 380 connected with memory 382’):
receive one or more of:
an indication, from a first network equipment, of one or more validity criterion for network context information for a second network equipment; or identifiers for the network context information for the second network equipment (‘at 502, the network entity transmits AI/ML data input (e.g., per MLFN, ML model ID, or MS ID) to the first UE. In one example, the network entity may transmit the AI/ML data input to the first UE via system information block (SIB). In another example, the network entity may transmit the AI/ML data input to the first UE via multicast broadcast service (MBS) over MBS channel. In certain aspects, the network entity may store the AI/ML data input request received from the UE (e.g., at least until the geographical information and the validity time remains valid), and then transfer the AI/ML data input request to another network entity (e.g., during UE context setup, modification, and/or retrieve procedures)’, ¶s 0095 and 0108).
Kumar does not expressly disclose portion of network context information.
However, Singh discloses portion of network context information (‘accordingly, the data processing unit 701 (RAN)) may generate the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset. It is to be noted that the AI/ML unit 702 may use the training dataset in predefined portions, namely a first portion of the training data set for training, a second portion of the training dataset for validation and a third portion of the training dataset for testing purpose’, ¶ 0105).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘portion of network context information’ as disclosed by Singh into Kumar so as to effectively manage training artificial intelligence/machine learning models (AI/ML) for resource management
in wireless communication system, Singh ¶ 0024.
As to Claim 2, Kumar further discloses wherein the at least one processor is configured to cause the UE to transmit a request for information regarding the network context information for the second network equipment (‘in some aspects, the method 900 further includes transmitting the request to another network entity when one or more conditions are satisfied. In some cases, the operations of this step refer to, or may be performed by, circuitry for transmitting and/or code for transmitting as described with reference to Fig. 13. In some aspects, the request is transmitted during a handover from one network entity to another network entity; and the one or more conditions are satisfied when the first UE moves from a connected state to an idle or inactive state or vice-versa’, ¶s 0138-0139).
Kumar does not expressly disclose portion of network context information.
However, Singh discloses portion of network context information (‘accordingly, the data processing unit 701 (RAN)) may generate the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset. It is to be noted that the AI/ML unit 702 may use the training dataset in predefined portions, namely a first portion of the training data set for training, a second portion of the training dataset for validation and a third portion of the training dataset for testing purpose’, ¶ 0105).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘portion of network context information’ as disclosed by Singh into Kumar so as to effectively manage training artificial intelligence/machine learning models (AI/ML) for resource management
in wireless communication system, Singh ¶ 0024.
As to Claim 3, Kumar further discloses wherein the at least one processor is configured to cause the UE to perform learning model training based at least in part on the indication of the one or more validity criterion for the network context information for the second network equipment (‘in another example, the AI/ML data input request may include a validity time such as a duration time and/or an interval time at which the AI/ML data input has to be provided to the UE by the network entity. In another example, the AI/ML data input request may include one or more network configurations. In another example, the AI/ML data input request may include one or more network settings. In another example, the AI/ML data input request may include a type of the AI/ML data input such as meta data, training data, and/or inference data’, ¶ 0105).
Kumar does not expressly disclose portion of network context information.
However, Singh discloses portion of network context information (‘accordingly, the data processing unit 701 (RAN)) may generate the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset. It is to be noted that the AI/ML unit 702 may use the training dataset in predefined portions, namely a first portion of the training data set for training, a second portion of the training dataset for validation and a third portion of the training dataset for testing purpose’, ¶ 0105).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘portion of network context information’ as disclosed by Singh into Kumar so as to effectively manage training artificial intelligence/machine learning models (AI/ML) for resource management
in wireless communication system, Singh ¶ 0024.
As to Claim 4, Kumar further discloses wherein the at least one processor is configured to cause the UE to perform learning model inference based at least in part on the indication of the one or more validity criterion for the network context information for the second network equipment (‘in another example, the AI/ML data input request may include a validity time such as a duration time and/or an interval time at which the AI/ML data input has to be provided to the UE by the network entity. In another example, the AI/ML data input request may include one or more network configurations. In another example, the AI/ML data input request may include one or more network settings. In another example, the AI/ML data input request may include a type of the AI/ML data input such as meta data, training data, and/or inference data’, ¶ 0105).
Kumar does not expressly disclose portion of network context information.
However, Singh discloses portion of network context information (‘accordingly, the data processing unit 701 (RAN)) may generate the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset. It is to be noted that the AI/ML unit 702 may use the training dataset in predefined portions, namely a first portion of the training data set for training, a second portion of the training dataset for validation and a third portion of the training dataset for testing purpose’, ¶ 0105).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘portion of network context information’ as disclosed by Singh into Kumar so as to effectively manage training artificial intelligence/machine learning models (AI/ML) for resource management
in wireless communication system, Singh ¶ 0024.
As to Claim 5, Kumar further discloses wherein the at least one processor is configured to cause the UE to determine an applicability of a learning model functionality based at least in part on the indication of the one or more validity criterion for the network context information for the second network equipment (‘in certain aspects, the network entity may store the AI/ML data input request received from the UE (e.g., at least until the geographical information and the validity time remains valid), and then transfer the AI/ML data input request to another network entity (e.g., during UE context setup, modification, and/or retrieve procedures). For example, the network entity may transfer the AI/ML data input request to another network entity when one or more conditions are satisfied. In one example, the one or more conditions may be satisfied when the UE moves from a connected state to an idle or inactive state’, ¶ 0108).
Kumar does not expressly disclose portion of network context information.
However, Singh discloses portion of network context information (‘accordingly, the data processing unit 701 (RAN)) may generate the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset. It is to be noted that the AI/ML unit 702 may use the training dataset in predefined portions, namely a first portion of the training data set for training, a second portion of the training dataset for validation and a third portion of the training dataset for testing purpose’, ¶ 0105).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘portion of network context information’ as disclosed by Singh into Kumar so as to effectively manage training artificial intelligence/machine learning models (AI/ML) for resource management
in wireless communication system, Singh ¶ 0024.
As to Claim 6, Kumar further discloses wherein the at least one processor is configured to cause the UE to perform learning model training based at least in part on the identifiers for the network context information for the second network equipment (‘in certain aspects, the network entity may store the AI/ML data input request received from the UE (e.g., at least until the geographical information and the validity time remains valid), and then transfer the AI/ML data input request to another network entity (e.g., during UE context setup, modification, and/or retrieve procedures). For example, the network entity may transfer the AI/ML data input request to another network entity when one or more conditions are satisfied’, ¶ 0108).
Kumar does not expressly disclose portion of network context information.
However, Singh discloses portion of network context information (‘accordingly, the data processing unit 701 (RAN)) may generate the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset. It is to be noted that the AI/ML unit 702 may use the training dataset in predefined portions, namely a first portion of the training data set for training, a second portion of the training dataset for validation and a third portion of the training dataset for testing purpose’, ¶ 0105).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘portion of network context information’ as disclosed by Singh into Kumar so as to effectively manage training artificial intelligence/machine learning models (AI/ML) for resource management
in wireless communication system, Singh ¶ 0024.
As to Claim 7, Kumar further discloses wherein the at least one processor is configured to cause the UE to perform learning model inference based at least in part on the identifiers for the network context information for the second network equipment (‘in another example, the AI/ML data input request may include a validity time such as a duration time and/or an interval time at which the AI/ML data input has to be provided to the UE by the network entity. In another example, the AI/ML data input request may include one or more network configurations. In another example, the AI/ML data input request may include one or more network settings. In another example, the AI/ML data input request may include a type of the AI/ML data input such as meta data, training data, and/or inference data’, ¶ 0105).
Kumar does not expressly disclose portion of network context information.
However, Singh discloses portion of network context information (‘accordingly, the data processing unit 701 (RAN)) may generate the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset. It is to be noted that the AI/ML unit 702 may use the training dataset in predefined portions, namely a first portion of the training data set for training, a second portion of the training dataset for validation and a third portion of the training dataset for testing purpose’, ¶ 0105).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘portion of network context information’ as disclosed by Singh into Kumar so as to effectively manage training artificial intelligence/machine learning models (AI/ML) for resource management
in wireless communication system, Singh ¶ 0024.
As to Claim 8, Kumar further discloses wherein the at least one processor is configured to cause the UE to determine an applicability of learning model functionality based at least in part on the identifiers for the network context information for the second network equipment (‘in certain aspects, the network entity may store the AI/ML data input request received from the UE (e.g., at least until the geographical information and the validity time remains valid), and then transfer the AI/ML data input request to another network entity (e.g., during UE context setup, modification, and/or retrieve procedures). For example, the network entity may transfer the AI/ML data input request to another network entity when one or more conditions are satisfied. In one example, the one or more conditions may be satisfied when the UE moves from a connected state to an idle or inactive state’, ¶ 0108).
Kumar does not expressly disclose portion of network context information.
However, Singh discloses portion of network context information (‘accordingly, the data processing unit 701 (RAN)) may generate the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset. It is to be noted that the AI/ML unit 702 may use the training dataset in predefined portions, namely a first portion of the training data set for training, a second portion of the training dataset for validation and a third portion of the training dataset for testing purpose’, ¶ 0105).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘portion of network context information’ as disclosed by Singh into Kumar so as to effectively manage training artificial intelligence/machine learning models (AI/ML) for resource management
in wireless communication system, Singh ¶ 0024.
As to Claim 9, Kumar discloses a second network equipment for wireless communication, comprising:
at least one memory (Fig. 3, ‘memory 342’); and
at least one processor coupled with the at least one memory and configured to cause the second network equipment to (Fig. 3, ‘controller/processor 340 connected with memory 342’):
transmit one or more of:
an indication, to a first network equipment, of one or more validity criterion for a network context information for the second network equipment; or identifiers for network context information for the second network equipment, to a user equipment (UE) (‘for example, the network entity may transfer the AI/ML data input request to another network entity when one or more conditions are satisfied. In one example, the one or more conditions may be satisfied when the UE moves from a connected state to an idle or inactive state. In another example, the one or more conditions may be satisfied when the UE moves from the idle or inactive state to the connected state’, ¶ 0108).
Kumar does not expressly disclose portion of network context information.
However, Singh discloses portion of network context information (‘accordingly, the data processing unit 701 (RAN)) may generate the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset. It is to be noted that the AI/ML unit 702 may use the training dataset in predefined portions, namely a first portion of the training data set for training, a second portion of the training dataset for validation and a third portion of the training dataset for testing purpose’, ¶ 0105).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘portion of network context information’ as disclosed by Singh into Kumar so as to effectively manage training artificial intelligence/machine learning models (AI/ML) for resource management
in wireless communication system, Singh ¶ 0024.
As to Claim 10, Kumar further discloses wherein the at least one processor is configured to cause the second network equipment to transmit the indication of one or more validity criterion for the network context information for the second network equipment to the first network equipment for forwarding to the UE (‘in another example, the AI/ML data input request may include a validity time such as a duration time and/or an interval time at which the AI/ML data input has to be provided to the UE by the network entity. In another example, the AI/ML data input request may include one or more network configurations. In another example, the AI/ML data input request may include one or more network settings. In another example, the AI/ML data input request may include a type of the AI/ML data input such as meta data, training data, and/or inference data’, ¶ 0105).
Kumar does not expressly disclose portion of network context information.
However, Singh discloses portion of network context information (‘accordingly, the data processing unit 701 (RAN)) may generate the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset. It is to be noted that the AI/ML unit 702 may use the training dataset in predefined portions, namely a first portion of the training data set for training, a second portion of the training dataset for validation and a third portion of the training dataset for testing purpose’, ¶ 0105).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘portion of network context information’ as disclosed by Singh into Kumar so as to effectively manage training artificial intelligence/machine learning models (AI/ML) for resource management
in wireless communication system, Singh ¶ 0024.
As to Claim 11, Kumar further discloses wherein the at least one processor is configured to cause the second network equipment to receive a request for the network context information for the second network equipment, and transmit, to the UE and in response to the request, the identifiers for the network context information for the second network equipment (‘in another example, the AI/ML data input request may include a validity time such as a duration time and/or an interval time at which the AI/ML data input has to be provided to the UE by the network entity. In another example, the AI/ML data input request may include one or more network configurations. In another example, the AI/ML data input request may include one or more network settings. In another example, the AI/ML data input request may include a type of the AI/ML data input such as meta data, training data, and/or inference data’, ¶ 0105).
Kumar does not expressly disclose portion of network context information.
However, Singh discloses portion of network context information (‘accordingly, the data processing unit 701 (RAN)) may generate the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset. It is to be noted that the AI/ML unit 702 may use the training dataset in predefined portions, namely a first portion of the training data set for training, a second portion of the training dataset for validation and a third portion of the training dataset for testing purpose’, ¶ 0105).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘portion of network context information’ as disclosed by Singh into Kumar so as to effectively manage training artificial intelligence/machine learning models (AI/ML) for resource management
in wireless communication system, Singh ¶ 0024.
As to Claim 12, Kumar further discloses wherein the at least one processor is configured to cause the second network equipment to jointly transmit the indication of the one or more validity criterion for the network context information for the second network equipment and the identifiers for the network context information for the second network equipment (‘in another example, the AI/ML data input request may include a validity time such as a duration time and/or an interval time at which the AI/ML data input has to be provided to the UE by the network entity. In another example, the AI/ML data input request may include one or more network configurations. In another example, the AI/ML data input request may include one or more network settings. In another example, the AI/ML data input request may include a type of the AI/ML data input such as meta data, training data, and/or inference data’, ¶ 0105).
Kumar does not expressly disclose portion of network context information.
However, Singh discloses portion of network context information (‘accordingly, the data processing unit 701 (RAN)) may generate the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset. It is to be noted that the AI/ML unit 702 may use the training dataset in predefined portions, namely a first portion of the training data set for training, a second portion of the training dataset for validation and a third portion of the training dataset for testing purpose’, ¶ 0105).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘portion of network context information’ as disclosed by Singh into Kumar so as to effectively manage training artificial intelligence/machine learning models (AI/ML) for resource management
in wireless communication system, Singh ¶ 0024.
As to Claim 13, Kumar further discloses wherein the at least one processor is configured to cause the second network equipment to transmit the identifiers for the network context information for the second network equipment independent of a request (‘in certain aspects, the network entity may store the AI/ML data input request received from the UE (e.g., at least until the geographical information and the validity time remains valid), and then transfer the AI/ML data input request to another network entity (e.g., during UE context setup, modification, and/or retrieve procedures). For example, the network entity may transfer the AI/ML data input request to another network entity when one or more conditions are satisfied’, ¶ 0108).
Kumar does not expressly disclose portion of network context information.
However, Singh discloses portion of network context information (‘accordingly, the data processing unit 701 (RAN)) may generate the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset. It is to be noted that the AI/ML unit 702 may use the training dataset in predefined portions, namely a first portion of the training data set for training, a second portion of the training dataset for validation and a third portion of the training dataset for testing purpose’, ¶ 0105).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘portion of network context information’ as disclosed by Singh into Kumar so as to effectively manage training artificial intelligence/machine learning models (AI/ML) for resource management
in wireless communication system, Singh ¶ 0024.
As to Claim 14, Kumar discloses a user equipment (UE) for wireless communication, comprising:
at least one memory (Fig. 3, ‘memory 382’); and
at least one processor coupled with the at least one memory and configured to cause the UE to (Fig. 3, ‘controller/processor 380 connected with memory 382’):
receive, from a first network equipment, configuration information comprising a request for information to be sent to a second network equipment (‘at 502, the network entity transmits AI/ML data input (e.g., per MLFN, ML model ID, or MS ID) to the first UE. In one example, the network entity may transmit the AI/ML data input to the first UE via system information block (SIB). In another example, the network entity may transmit the AI/ML data input to the first UE via multicast broadcast service (MBS) over MBS channel. In certain aspects, the network entity may store the AI/ML data input request received from the UE (e.g., at least until the geographical information and the validity time remains valid), and then transfer the AI/ML data input request to another network entity (e.g., during UE context setup, modification, and/or retrieve procedures)’, ¶s 0095 and 0108);
receive a request for UE context information (‘in certain aspects, the network entity may store the AI/ML data input request received from the UE (e.g., at least until the geographical information and the validity time remains valid), and then transfer the AI/ML data input request to another network entity (e.g., during UE context setup, modification, and/or retrieve procedures). For example, the network entity may transfer the AI/ML data input request to another network entity when one or more conditions are satisfied. In one example, the one or more conditions may be satisfied when the UE moves from a connected state to an idle or inactive state’, ¶ 0108); and
transmit, to the second network equipment, identifiers for the UE context information (‘in certain aspects, the network entity may store the AI/ML data input request received from the UE (e.g., at least until the geographical information and the validity time remains valid), and then transfer the AI/ML data input request to another network entity (e.g., during UE context setup, modification, and/or retrieve procedures). For example, the network entity may transfer the AI/ML data input request to another network entity when one or more conditions are satisfied’, ¶ 0108).
Kumar does not expressly disclose portion of UE context information.
However, Singh discloses portion of UE context information (‘accordingly, the data processing unit 701 (device 350) may generate the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset. It is to be noted that the AI/ML unit 702 may use the training dataset in predefined portions, namely a first portion of the training data set for training, a second portion of the training dataset for validation and a third portion of the training dataset for testing purpose’, ¶s 0060 and 0105).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘portion of UE context information’ as disclosed by Singh into Kumar so as to effectively manage training artificial intelligence/machine learning models (AI/ML) for resource management
in wireless communication system, Singh ¶ 0024.
As to Claim 15, Kumar further discloses wherein the at least one processor is configured to cause the UE to transmit one or more validity criterion for the portion of the UE context information (‘in certain aspects, the network entity may store the AI/ML data input request received from the UE (e.g., at least until the geographical information and the validity time remains valid), and then transfer the AI/ML data input request to another network entity (e.g., during UE context setup, modification, and/or retrieve procedures). For example, the network entity may transfer the AI/ML data input request to another network entity when one or more conditions are satisfied’, ¶ 0108).
Kumar does not expressly disclose portion of UE context information.
However, Singh discloses portion of UE context information (‘accordingly, the data processing unit 701 (device 350) may generate the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset. It is to be noted that the AI/ML unit 702 may use the training dataset in predefined portions, namely a first portion of the training data set for training, a second portion of the training dataset for validation and a third portion of the training dataset for testing purpose’, ¶ 0105).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘portion of UE context information’ as disclosed by Singh into Kumar so as to effectively manage training artificial intelligence/machine learning models (AI/ML) for resource management
in wireless communication system, Singh ¶ 0024.
As to Claim 16, Kumar further discloses wherein the at least one processor is configured to cause the UE to jointly transmit, to the second network equipment, the identifiers for the UE context information and the one or more validity criterion for the portion of the UE context information (‘in certain aspects, the network entity may store the AI/ML data input request received from the UE (e.g., at least until the geographical information and the validity time remains valid), and then transfer the AI/ML data input request to another network entity (e.g., during UE context setup, modification, and/or retrieve procedures). For example, the network entity may transfer the AI/ML data input request to another network entity when one or more conditions are satisfied’, ¶ 0108).
Kumar does not expressly disclose portion of UE context information.
However, Singh discloses portion of UE context information (‘accordingly, the data processing unit 701 (device 350) may generate the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset. It is to be noted that the AI/ML unit 702 may use the training dataset in predefined portions, namely a first portion of the training data set for training, a second portion of the training dataset for validation and a third portion of the training dataset for testing purpose’, ¶ 0105).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘portion of UE context information’ as disclosed by Singh into Kumar so as to effectively manage training artificial intelligence/machine learning models (AI/ML) for resource management
in wireless communication system, Singh ¶ 0024.
As to Claim 18, Kumar discloses a second network equipment for wireless communication, comprising:
at least one memory (Fig. 3, ‘memory 342’); and
at least one processor coupled with the at least one memory and configured to cause the second network equipment to (Fig. 3, ‘controller/processor 340 connected with memory 342’):
transmit a request message for user equipment (UE) context information (‘in certain aspects, the network entity may store the AI/ML data input request received from the UE (e.g., at least until the geographical information and the validity time remains valid), and then transfer the AI/ML data input request to another network entity (e.g., during UE context setup, modification, and/or retrieve procedures). For example, the network entity may transfer the AI/ML data input request to another network entity when one or more conditions are satisfied. In one example, the one or more conditions may be satisfied when the UE moves from a connected state to an idle or inactive state’, ¶ 0108); and
receive identifiers for a UE context information (‘in certain aspects, the network entity may store the AI/ML data input request received from the UE (e.g., at least until the geographical information and the validity time remains valid), and then transfer the AI/ML data input request to another network entity (e.g., during UE context setup, modification, and/or retrieve procedures). For example, the network entity may transfer the AI/ML data input request to another network entity when one or more conditions are satisfied. In one example, the one or more conditions may be satisfied when the UE moves from a connected state to an idle or inactive state’, ¶ 0108).
Kumar does not expressly disclose portion of UE context information.
However, Singh discloses portion of UE context information (‘accordingly, the data processing unit 701 (device 350) may generate the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset. It is to be noted that the AI/ML unit 702 may use the training dataset in predefined portions, namely a first portion of the training data set for training, a second portion of the training dataset for validation and a third portion of the training dataset for testing purpose’, ¶ 0105).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘portion of UE context information’ as disclosed by Singh into Kumar so as to effectively manage training artificial intelligence/machine learning models (AI/ML) for resource management
in wireless communication system, Singh ¶ 0024.
As to Claim 19, Kumar further discloses wherein the at least one processor is configured to cause the second network equipment to receive one or more validity criterion for the UE context information (‘in certain aspects, the network entity may store the AI/ML data input request received from the UE (e.g., at least until the geographical information and the validity time remains valid), and then transfer the AI/ML data input request to another network entity (e.g., during UE context setup, modification, and/or retrieve procedures). For example, the network entity may transfer the AI/ML data input request to another network entity when one or more conditions are satisfied’, ¶ 0108).
Kumar does not expressly disclose portion of UE context information.
However, Singh discloses portion of UE context information (‘accordingly, the data processing unit 701 (device 350) may generate the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset. It is to be noted that the AI/ML unit 702 may use the training dataset in predefined portions, namely a first portion of the training data set for training, a second portion of the training dataset for validation and a third portion of the training dataset for testing purpose’, ¶ 0105).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘portion of UE context information’ as disclosed by Singh into Kumar so as to effectively manage training artificial intelligence/machine learning models (AI/ML) for resource management
in wireless communication system, Singh ¶ 0024.
As to Claim 20, Kumar further discloses wherein the at least one processor is configured to cause the second network equipment to utilize, for at least one of one or more learning model life cycle management phases or one or more learning model functionality life cycle management phases, one or more of the identifiers for the portion of the UE context information or the one or more validity criterion for the portion of the UE context information (‘in certain aspects, the network entity may store the AI/ML data input request received from the UE (e.g., at least until the geographical information and the validity time remains valid), and then transfer the AI/ML data input request to another network entity (e.g., during UE context setup, modification, and/or retrieve procedures). For example, the network entity may transfer the AI/ML data input request to another network entity when one or more conditions are satisfied. In one example, the one or more conditions may be satisfied when the UE moves from a connected state to an idle or inactive state’, ¶ 0108).
Kumar does not expressly disclose portion of UE context information.
However, Singh discloses portion of UE context information (‘accordingly, the data processing unit 701 (device 350) may generate the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset. It is to be noted that the AI/ML unit 702 may use the training dataset in predefined portions, namely a first portion of the training data set for training, a second portion of the training dataset for validation and a third portion of the training dataset for testing purpose’, ¶ 0105).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘portion of UE context information’ as disclosed by Singh into Kumar so as to effectively manage training artificial intelligence/machine learning models (AI/ML) for resource management
in wireless communication system, Singh ¶ 0024.
Claim Rejections - 35 USC § 103
4. 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.
5. Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Kumar, in view of Publication No.: US 2021/0168603 A1 to Ryoo et al. (Ryoo).
As to Claim 17, Kumar does not expressly disclose wherein the at least one processor is configured to cause the UE to receive the configuration information via a radio resource control (RRC) reconfiguration message.
However, Ryoo discloses wherein the at least one processor is configured to cause the UE to receive the configuration information via a radio resource control (RRC) reconfiguration message. (‘more specifically, in the RRC connected state 1910, at step 1920, the base station 120 may transmit an RRC connection reconfiguration message to the terminal 110. Here, the RRC connection reconfiguration message may include mobility control information, security information, and UE context identity information’, ¶ 0272).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘wherein the at least one processor is configured to cause the UE to perform learning model training based at least in part on the indication of the one or more validity criterion for the portion of network context information for the second network equipment’ as disclosed by Ryoo into Kumar so as to effectively mitigate power consumption in wireless communication system, Ryoo ¶ 0024.
Conclusion
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to GBEMILEKE J ONAMUTI whose telephone number is (571)270-5619. The examiner can normally be reached 8:00 AM - 5:00 PM.
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/GBEMILEKE J ONAMUTI/Primary Examiner, Art Unit 2463
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