DETAILED ACTION
The instant application having application No 18/342,807 filed on 06/28/2023 is presented for examination by the examiner.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1-2, 4, 6-10, 13, and 15-20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Parichehrehteroujeni et al. (U.S. 20230115368, Apr. 13, 2023) in view of Park et al. (U.S. 20250088870, Mar. 13, 2025).
Regarding Claim 1, Parichehrehteroujeni discloses select a subset of the plurality of cells based on obtained cell-specific parameters (page 4, par (0091), line 1-10, determining respective values for the input parameters; applying the AI/ML predictive model to the values of the input parameters to determine respective values of the output parameters; and selecting the particular random-access configuration according to the determined values of the output parameters for the network node);
and cause the AI/ML to be trained with radio access network (RAN)-related data of the subset of the plurality of cells (page 16, par (0311), line 1-10, the network node obtains the random-access configurations for the cell based on the trained AI/ML predictive model, the obtained random-access configurations are provided to the one or more UEs via broadcast in the cell).
Parichehrehteroujeni discloses all aspects of the claimed invention, except an artificial intelligence or machine learning model (AI/ML) configured to provide an output used in radio resource management of a plurality of cells; a processor configured to obtain cell-specific parameters of the plurality of cells of a mobile communication network.
Park is the same field of invention teaches an artificial intelligence or machine learning model (AI/ML) configured to provide an output used in radio resource management of a plurality of cells (page 12, par (0206), line 1-10, AI/ML model information provided (wherein provide an output) as information for target cells (plurality of cells) related to Handover and RRM (radio resource management));
a processor configured to obtain cell-specific parameters of the plurality of cells of a mobile communication network (page 12, par (0206), line 1-10, AI/ML model information provided (wherein provide an output) as information for target cells (plurality of cells) related to Handover and RRM (radio resource management) related to a specific parameter for the cell or target cells)).
Parichehrehteroujeni and Park are analogous art because they are from the same field of endeavor of access to a service device.
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify based on the artificial intelligence or machine learning model (AI/ML) configured to provide an output used in radio resource management of a plurality of cells Parichehrehteroujeni the teaching of the AI/ML model information provided wherein provide an output as information for target cells Park because it is providing a time gap for transmission mode-to-reception mode switching or reception mode-to-transmission mode switching at a BS and a UE.
Regarding Claim 2, Parichehrehteroujeni discloses the processor is further configured to selectively cause the AI/ML to be trained with first data including the RAN-related data of the subset of the plurality of cells or cause the AI/ML to be trained with second data including RAN-related data of at least one or more cells that are not within the subset of the plurality of cells(page 16, par (0311), line 1-10, the network node obtain the random-access configurations for the cell based on the trained AI/ML predictive model, the obtained random-access configurations are provided to the one or more UEs via broadcast in the cell).
Regarding Claim 4, Parichehrehteroujeni discloses the processor is further configured to cause the AI/ML to be trained with the first data more frequently than to cause the AI/ML to be trained with the second data (page 16, par (0311), line 1-10, the network node obtains the random-access configurations for the cell based on the trained AI/ML predictive model).
Regarding Claim 6, Parichehrehteroujeni discloses the processor is further configured to aggregate the RAN-related data of the subset of the plurality of cells to obtain training data used to train the AI/ML(page 4, par (0091), line 1-10, determining respective values for the input parameters; applying the AI/ML predictive model to the values of the input parameters to determine respective values of the output parameters; and selecting the particular random-access configuration according to the determined values of the output parameters for the network node).
Regarding Claim 7, Parichehrehteroujeni discloses the processor is further configured to select the subset based on operator information representative of a preference of a mobile network operator; wherein the operator information comprises information representative of at least one of usable priority cells, one or more performance thresholds associated with one or more performance metrics, a number of cells in the subset, one or more cost metrics, or a preference for optimization (page 12, par (0227), line 1-10, artificial intelligence or machine learning based algorithm is provided with a set of input parameters related to random access performance and generates a set of output parameters for optimized and improved random access performance).
Regarding Claim 8, Parichehrehteroujeni discloses cell-specific parameters of each cell comprises information representative of at least one of network traffic, downlink traffic, uplink traffic, physical resource block (PRB) usage, reference signal strength indicator (RSSI), reference signal receive power (RSRP), data throughput, mobility, user density, geolocation, topography, traffic patterns, user equipment (UE) distribution, a number of UEs in an RRC connected state, a number of active users, user channel quality summary, or UE density(page 12, par (0217-0218), line 1-10,. Reporting format: The quantities per cell and per beam that the UE includes in the measurement report ( e.g., RSRP) and other associated information such as the maximum number of cells and the maximum number beams per cell to report, network operators take care that the RACH parameters are set appropriately, considering factors such as the RACH load, UL interference, UL/DL traffic patterns, base station antenna configuration, and population size and density under the cell's coverage, Surrounding cells also affect a particular cell).
Regarding Claim 9, Parichehrehteroujeni discloses the processor is further configured to select the subset of the plurality of cells based on AI/ML information representative of features of the AI/ML; wherein the AI/ML information comprises information representative of at least one of exemplary cells of the plurality of cells, a performance requirement of the AI/ML model, a computation requirement of the AI/ML model, a data aggregation requirement to train the AI/ML model, a weighting parameter associated with performance and cost of operation, a mapping associated with the performance of the AI/ML and the cost of operation of the AI/ML, one or more requirements associated with input data of the AI/ML(page 14, par (0261), line 1-10, the RAN node train the MU/AI algorithm using inputs, the execution of the trained AI/ML algorithm performed by the UE, upon training the AI/ML model, RAN node can send the model to the UE, the UE then use the model to select the initial preamble transmission power when performing random access in a cell to which the model relates).
Regarding Claim 10, Parichehrehteroujeni discloses the processor is further configured to determine exemplary cells of the plurality of cells based on a cell selection criterion; wherein the processor is further configured to calculate similarity scores for multiple subsets of the cells, each calculated similarity score is representative of a similarity between one or more cell-specific parameters of cells of a respective subset and one or more cell-specific parameters of the exemplary cells (page 6, par (0133), line 1-10, gNB includes gNB-CU and gNB-Dus, CUs (e.g., gNB-CU) are logical nodes that host higher-layer protocols and perform various gNB functions such controlling the operation of DUs. Each DU is a logical node that hosts lower-layer
protocols and can include, depending on the functional split, various subsets of the gNB functions).
Regarding Claim 13, Parichehrehteroujeni discloses all aspects of the claimed invention, except the processor is further configured to select the subset of the plurality of cells based on exemplary cells using a reinforcement learning model; wherein a reward of the reinforcement learning (RL) model is based on a performance metric of the AI/ML and a cost metric of the AI/ML.
Park is the same field of invention teaches the processor is further configured to select the subset of the plurality of cells based on exemplary cells using a reinforcement learning model; wherein a reward of the reinforcement learning (RL) model is based on a performance metric of the AI/ML and a cost metric of the AI/ML (page 9, par (0133), line 1-10, In reinforcement learning (RL), an agent aims to optimize a long-term goal by interacting with an environment based on a trial-and-error process, which is goal-oriented learning based on an interaction with the environment of an RL algorithm include (i) Q-learning, (ii) Multi-armed bandit learning, (iii) Deep Q Network, State-Action-Reward-State-Action (SARSA), (iv) Temporal Difference Learning, (v) Actorcritic reinforcement learning).
Regarding Claim 15, Parichehrehteroujeni discloses the processor is further configured to implement the AI/MLpage 4, par (0091), line 1-10, determining respective values for the input parameters; applying the AI/ML predictive model to the values of the input parameters to determine respective values of the output parameters).
Regarding Claim 16, Parichehrehteroujeni discloses the mobile communication network comprises an open radio access network (O-RAN); wherein the device is configured to implement a radio access network intelligent controller (RIC); wherein the RIC comprises a near real-time RIC or a non-real time RIC(page 4, par (0091), line 1-10, determining respective values for the input parameters; applying the AI/ML predictive model to the values of the input parameters to determine respective values of the output parameters; and selecting the particular random-access configuration according to the determined values of the output parameters for the network node).
Regarding Claim 17, Parichehrehteroujeni discloses a device comprising a memory; a processor configured to determine, using a trained artificial intelligence or machine learning model (AI/ML) (page 16, par (0311), line 1-10, the network node obtain the random-access configurations for the cell based on the trained AI/ML predictive model),
wherein the AI/ML has been trained using training input data comprising radio access network (RAN)-related data of network access nodes of one or more second cells of the plurality of cells (page 16, par (0311), line 1-10, the network node obtains the random-access configurations for the cell based on the trained AI/ML predictive model, the obtained random-access configurations are provided to the one or more UEs via broadcast in the cell).
Parichehrehteroujeni discloses all aspects of the claimed invention, except a parameter of radio resource management of one or more first cells of a plurality of cells, and encode information representative of the determined parameter for a transmission to network access nodes of the one or more first cells.
Park is the same field of invention teaches a parameter of radio resource management of one or more first cells of a plurality of cells(page 12, par (0206), line 1-10, AI/ML model information provided (wherein provide an output) as information for target cells (plurality of cells) related to Handover and RRM (radio resource management)), and encode information representative of the determined parameter for a transmission to network access nodes of the one or more first cells (page 2, par (0040), line 1-10, Signal encoding transmission of the transmitting side, understood as signal monitoring decoding of the receiving side, when a UE performs a specific operation, this interpreted as that a BS expects that the UE performs the specific operation. When that a BS performs a specific operation, this interpreted as that a UE expects that the BS performs the specific operation).
Parichehrehteroujeni and Park are analogous art because they are from the same field of endeavor of access to a service device.
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify based on the artificial intelligence or machine learning model (AI/ML) configured to provide an output used in radio resource management of a plurality of cells Parichehrehteroujeni the teaching of the AI/ML model information provided wherein provide an output as information for target cells Park because it is providing a time gap for transmission mode-to-reception mode switching or reception mode-to-transmission mode switching at a BS and a UE.
Regarding Claim 18, Parichehrehteroujeni discloses all aspects of the claimed invention, except a transceiver configured to communicate the encoded information to the network access nodes of the one or more first cells.
Park is the same field of invention teaches a transceiver configured to communicate the encoded information to the network access nodes of the one or more first cells (page 5, par (0067), line 1-10, A TB is encoded into a codeword, the PDSCH deliver up to two codewords. Scrambling and modulation mapping performed on a codeword basis, and modulation symbols generated from each codeword mapped to layers).
Regarding Claim 19, Parichehrehteroujeni discloses select a subset of the plurality of cells based on obtained cell-specific parameters (page 4, par (0091), line 1-10, determining respective values for the input parameters; applying the AI/ML predictive model to the values of the input parameters to determine respective values of the output parameters; and selecting the particular random-access configuration according to the determined values of the output parameters for the network node);
and cause an artificial intelligence or machine learning model (AI/ML) to be trained with radio access network (RAN)-related data of the subset of the plurality of cells(page 16, par (0311), line 1-10, the network node obtain the random-access configurations for the cell based on the trained AI/ML predictive model, the obtained random-access configurations are provided to the one or more UEs via broadcast in the cell).
Parichehrehteroujeni discloses all aspects of the claimed invention, except obtain cell-specific parameters of a plurality of cells of a mobile communication network; wherein radio resources of the plurality of cells are managed based on output of the AI/ML.
Park is the same field of invention teaches a non-transitory computer-readable medium comprising one or more instructions which, if executed by a processor, cause the processor to obtain cell-specific parameters of a plurality of cells(page 12, par (0206), line 1-10, AI/ML model information provided (wherein provide an output) as information for target cells (plurality of cells) related to Handover and RRM (radio resource management)) of a mobile communication network(page 12, par (0206), line 1-10, AI/ML model information provided (wherein provide an output) as information for target cells (plurality of cells) related to Handover and RRM (radio resource management) related to a specific parameter for the cell or target cells));
wherein radio resources of the plurality of cells are managed based on output of the AI/ML(page 12, par (0206), line 1-10, AI/ML model information provided (wherein provide an output) as information for target cells (plurality of cells) related to Handover and RRM (radio resource management)).
Parichehrehteroujeni and Park are analogous art because they are from the same field of endeavor of access to a service device.
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify based on the artificial intelligence or machine learning model (AI/ML) configured to provide an output used in radio resource management of a plurality of cells Parichehrehteroujeni the teaching of the AI/ML model information provided wherein provide an output as information for target cells Park because it is providing a time gap for transmission mode-to-reception mode switching or reception mode-to-transmission mode switching at a BS and a UE.
Regarding Claim 20, Parichehrehteroujeni discloses cell-specific parameters of each cell comprises information representative of at least one of network traffic, downlink traffic, uplink traffic, physical resource block (PRB) usage, reference signal strength indicator (RSSI), reference signal receive power (RSRP), data
throughput, mobility, user density, geolocation, topography, traffic patterns, user equipment (UE) distribution, a number of UEs in an RRC connected state, a number of active users, user channel quality summary, or UE density (page 12, par (0217-0218), line 1-10, Reporting format: The quantities per cell and per beam that the UE includes in the measurement report ( e.g., RSRP) and other associated information such as the maximum number of cells and the maximum number beams per cell to report, network operators take care that the RACH parameters are set appropriately, considering factors such as the RACH load, UL interference, UL/DL traffic patterns, base station antenna configuration, and population size and density under the cell's coverage, Surrounding cells also affect a particular cell).
Examiner Notice
Claim 1 would be allowable if (i) claims 3 or 5 or 11 or 12 or 14 are incorporated into the independent claim 1.
Claim 17 would be allowable if (i) claims 4 or 9 or 13 or 16 or 17 or 18 are incorporated into the independent claim 17.
Claim 19 would be allowable if (i) claims 4 or 9 or 13 or 16 or 17 or 18 are incorporated into the independent claim 19.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure are:
SHI et al. (US 20250056372, Feb. 13, 2025) teaches Method, Apparatus and System For Managing Network Resources.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IQBAL ZAIDI whose telephone number is (571)270-3943. The examiner can normally be reached on M to Thu 8.a.m to 6.p.m..
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, NGO RICKY can be reached on 571-272-3139. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/IQBAL ZAIDI/
Primary Examiner, Art Unit 2464
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