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Claim Rejections - 35 USC § 103
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.
Claim(s) 1-3, 8, 14-16 and 22 rejected under 35 U.S.C. 103 as being unpatentable over Gao et al. (US 2022/0279394, “Gao”) in view of Alonso-Rubio et al. (US 2010/0273487, “Rubio”).
Examiner’s note: in what follows, references are drawn to Gao unless otherwise mentioned.
Gao comprises the following features:
With respect to independent claims:
Regarding claim 1, a method performed by a first network node for UE prediction flow in a terrestrial network (TN) - non-terrestrial network (NTN), TN-NTN network, the method comprising:
predicting a handover metric (This will be discussed in view of Rubio.) corresponding to respective categories of aggregated user equipment, UEs ([0165] “Based on the group ID and the at least one measurement result, the network determines to move all or a portion of UEs in the group associated to the group ID to other cells.”), for handovers between a source TN or NTN network node and a target NTN or TN network node, respectively ([0130] “when operating in an RRC connected state (i.e. RRC_CONNECTED), the network (e.g. a wireless network node) moves a UE from one cell to another cell by providing a handover command to the UE, e.g., for performing a handover procedure.”),
wherein at least one of the respective categories of aggregated UEs ([0166] “the group ID assigned by the network is determined based on at least one of a location or a service type of the UE.”, and [0171] “In FIG. 6, a wireless network node NN1 (e.g. the network shown in FIG. 5) has a cell CE1 and there are 7 UEs in the cell CE1. Based on the locations and/or service types of the 7 UEs, the wireless network node divides the 7 UEs into 2 groups TG1 and TG2 and assigns group IDs (e.g. indexes 1 and 2) of the groups TG1 and TG2 to the 7 UEs.”) comprises UEs that fluctuate, based on a temporal threshold (This will be discussed in view of Rubio.), between a connection to an NTN network node and a TN network node ([0118] “the UEs may be served by both the NTN and the TN”, and [0129] “he cell selection/reselection procedure of this disclosure may comprise an intra-NTN cell selection/reselection procedure, a TN to NTN cell selection/reselection procedure, and/or an NTN to TN cell selection/reselection procedure.”); and
initiating at least one of (i) modification of a shape of a cell of at least one of the source TN or NTN network node and the target NTN or TN network node, respectively, based on the predicted handover rate and volume (This alternative is not examined.), and (ii) allocation of UEs aggregated to the respective categories to different portions of a bandwidth ([0145] “the at least one frequency FREQ_A of the at least one cell CE_A of the network type NT_A is prioritized by increasing a value of a frequency-level reselection priority associated with each of the at least one frequency FREQ_A. In this embodiment, the frequency-level priority is higher when its value is greater and the UE preferentially selects a frequency associated to the frequency-level priority with higher value during the cell selection/reselection procedure.”).
It is noted that while disclosing mobility handling, Gao does not specifically teach about predicting a handover metric. It, however, had been known in the art before the effective date of the instant application as shown by Rubio as follows;
predicting a handover metric ([Rubio, 0065] “Operation begins with default values for the handover parameters while collecting handover statistics about handovers that have actually occurred between the cells in the cell pair. These actual handover statistics are processed, either in a centralized way or in a distributed way, and a current oscillation rate is determined for each cell pair.”, and [Rubio, 0077] “the handover information is processed to determine a handover oscillation rate and/or handover cost for each of the cell pairs for each direction (step S3).”);
UEs that fluctuate, based on a temporal threshold ([Rubio, 0070] “FIG. 8 shows an example messaging where cell 2 sends a handover oscillation report to cell 1 when the UE call stays in cell 2 for less than one second before being handed over to cell 3. Cell 1 then logs that oscillation rate.”, and [Rubio, 0071] “the target can be set to a 5.sup.th percentile of call durations after a handover above 1 second and a 20.sup.th percentile of call durations after handover above 10 seconds.”)
Gao teaches grouped UE mobility management in a TN-NTN system and recognizes that satellite movement may require moving a large number of UEs, such that individually triggering UEs is inefficient and resource consuming. Gao groups UEs based on location and service type, receives group-associated measurement information and control groups mobility and TN/NTN frequency/cell priority. Rubio teaches that repeated HOs within a relatively short time create undesirable signaling and HO costs, and teaches collecting time-between-HO information, determining an HO oscillation rate/cost, and adjusting HO control based on the determined metric. It would have been obvious to apply Rubio’s time-based HO-oscillation analysis to Gao’s UE groups to identify groups with recurring short-duration HOs and provide differentiated TN/NTN mobility or frequency-priority treatment. The predictable result would be reduced unnecessary HOs and associated signaling/resource consumption in Gao’s large scale satellite mobility scenario.
Regarding claim 14, it is a first network node claim corresponding to the method claim 1, except the limitations, “processing circuitry”, “memory coupled with the processing circuitry”, “the memory includes instructions that when executed by the processing circuitry” ([0124] “The wireless network node 30 may be a satellite, a base station (BS), a network entity, a Mobility Management Entity (MME), Serving Gateway (S-GW), Packet Data Network (PDN) Gateway (P-GW), or Radio Network Controller (RNC), and is not limited herein. The wireless network node 30 may include a processor 300 such as a microprocessor or ASIC, a storage unit 310 and a communication unit 320. The storage unit 310 may be any data storage device that stores a program code 312, which is accessed and executed by the processor 300.”), and is therefore rejected for the similar reasons set forth in the rejection of claim 1.
Regarding claim 16, it is a first network node claim corresponding to the method claim 1, and is therefore rejected for the similar reasons set forth in the rejection of claim 1.
Regarding claim 22, it is a system claim (See Fig. 1 for the system.) corresponding to the method claim 1, except “at least one additional TN or NTN network node” (See [0171 and Fig. 6] “In FIG. 6, one of UEs in the group TG1 transmits a measurement report comprising the group ID of the group TG1 and at least one measurement result to the wireless network node NN1 when determining at least one measurement report condition is fulfilled, to inform the wireless network node NN1 that the UEs of the group TG1 can be moved (e.g. handover) to a cell CE2 of a wireless network node NN2.”), and is therefore rejected for the similar reasons set forth in the rejection of claim 1.
With respect to dependent claims:
Regarding claim 2, the method of Claim 1, wherein the handover metric comprises at least one of a handover rate (See aforesaid [Rubio, 0065 and 0077]) and a handover volume (This alternative is not examined.).
Regarding claim 3, the method of Claim 1, wherein the temporal threshold is included in a set of temporal thresholds ([Rubio, 0071] “the target handover oscillation rate may be set as one or more targets on the connection duration percentiles. For example, the target can be set to a 5.sup.th percentile of call durations after a handover above 1 second and a 20.sup.th percentile of call durations after handover above 10 seconds.”) and the aggregated UEs are categorized based on the set of temporal thresholds ([0166] “the network divides at least one UE camping in its service cell into at least one group based on the location and/or the service type of each of the at least one UE.”).
Regarding claim 8, the method of any of Claim 1,wherein the predicted handover rate and volume comprises one or more of (i) a volume of handovers at a next time period (This alternative is not examined.), (ii) a number of aggregated UEs in the respective category of aggregated UEs for the source TN or NTN network node and the target NTN or TN network node, respectively ([0171] “In FIG. 6, a wireless network node NN1 (e.g. the network shown in FIG. 5) has a cell CE1 and there are 7 UEs in the cell CE1. Based on the locations and/or service types of the 7 UEs, the wireless network node divides the 7 UEs into 2 groups TG1 and TG2 and assigns group IDs (e.g. indexes 1 and 2) of the groups TG1 and TG2 to the 7 UEs.”), and (iii) a fluctuation of the operating load at the next time period for the source TN or NTN network node and the target NTN or TN network node, respectively (This alternative is not examined.).
Regarding claim 15, the first network node of Claim 14, the operations further comprising the operations of
predicting a handover metric corresponding to respective categories of aggregated user equipment, UEs, for handovers between a source TN or NTN network node and a target NTN or TN network node, respectively, wherein at least one of the respective categories of aggregated UEs comprises UEs that fluctuate, based on a temporal threshold, between a connection to an NTN network node and a TN network node; and initiating at least one of (i) modification of a shape of a cell of at least one of the source TN or NTN network node and the target NTN or TN network node, respectively, based on the predicted handover rate and volume, and (ii) allocation of UEs aggregated to the respective categories to different portions of a bandwidth (See aforesaid [0118, 0130, 0145, 0165, 0166, 0171] and [Rubio, 0065, 0070]),
wherein the handover metric comprises at least one of a handover rate (See aforesaid [Rubio, 0065 and 0077]) and a handover volume (This alternative is not examined.).
Claim(s) 5-7 rejected under 35 U.S.C. 103 as being unpatentable over Gao et al. (US 2022/0279394, “Gao”) in view of Alonso-Rubio et al. (US 2010/0273487, “Rubio”) and further in view of Orhan et al. (US 2022/0124543, “Orhan”).
Examiner’s note: in what follows, references are drawn to Gao unless otherwise mentioned.
Regarding claim 5, it is noted that while disclosing mobility handling, Gao does not specifically teach about a ML model. It, however, had been known in the art before the effective date of the instant application as shown by Orhan as follows;
the method of Claim 1,wherein the predicting is performed by a machine learning, ML, model ([Orhan, 0070] “the GNN can be trained using different reinforcement learning (RL) techniques. In embodiments, a deep Q-learning approach (see e.g., [Sutton]) is used to learn a Q-function Q(⋅) from NAN 231 and UE 221 deployment instances and the corresponding reward is obtained from the network environment. One advantage of the GNN formulation as an NN for Q-function is that GNN is scalable to different graph sizes and can capture local network features with variable numbers of cells/NANs 231 and UEs 221.”).
It would have been obvious to implement the Gao-Rubio HO metric determination/prediction using Orhan’s ML framework to improve scalable handling of multiple UEs, connection-management decisions, and mobility control performance.
Regarding claim 6, the method of Claim 5, wherein the ML model learns a function F(u, v)=I, wherein u and v are each an edge in a graph and I is the volume of handovers ([Orhan, 0047] “graph custom-character also includes data elements for virtual connections 241 between two or more NANs 231 (also referred to as “virtual edges 241”). Although NANs 231 may be directly connected to a RAN controller/CMF 136 (e.g., an O-RAN RIC) with a tree structure, virtual edges 241 between cells/NANs 231 convey information about their UE connectivity and local graph structure. The use of the virtual edges 241 allows for relationships between different cells/NANs 231 to be accounted for when making connection management decisions for individual UEs 221. Additionally or alternatively, the virtual edges 241 and edges between individual UEs 221 and individual NANs 231 (not shown) can be associated with respective weights or features.”).
Regarding claim 7, the method of Claim 6, wherein u and v are expressed as vectors containing features of a cell ([Orhan, 0061] “The initial nodal features of the cells/NANs 231 and UEs 221 are defined as (X.sub.cl,1.sup.(0), X.sub.cl,2.sup.(0)) and X.sub.ue.sup.(0), respectively. Here, X.sub.cl,1.sup.(0) is a feature vector of initial nodal features related to the graph between individual cells/NANs 231, X.sub.cl,2.sup.(0) is a feature vector of initial nodal features related to the graph between UEs 221 and cells/NANs 231, and X.sub.ue.sup.(0) is a feature vector of initial nodal features related to individual UEs 221.”), the features comprising
(i) a topology of the respective source TN or NTN network nodes and the respective target NTN or TN network nodes in the graph ([Orhan, 0043] “The network 200 includes N NANs 231 or N cells (the terms “NAN”, “RU”, “DU”, and “cell” may be used interchangeably throughout the present disclosure, although these terms refer to different concepts) and M UEs 221 represented as a graph data structure (referred to as “graph custom-character”).”, and [Orhan, 0097] “After making a connection decision for a UE 221, the connection management xApp 400 sends its decision as a target cell ID to the UE 221 (e.g., for an HO or cell (re)selection procedure).”),
(ii) an actual operating load of the respective source TN or NTN network nodes and the respective target NTN or TN network nodes in the graph ([Orhan, 0040] “To capture the load-awareness, edge and node labels reflecting features, such as instantaneous load conditions, channel quality, average UE rates, etc. are considered and the proposed joint GNN-RL framework is applied to enable intelligent user handover decisions.”), and
(iii) a capacity range for the operating load of the respective source network nodes and the respective target network nodes in the graph ([Orhan, 0062] “The initial nodal features are functions of reported channel capacities and data rates at the cells/NANs 231 and UEs 221. Where a channel capacity matrix is defined as Cϵcustom-character.sup.N×M with elements c(v.sub.i.sup.cl, v.sub.j.sup.ue) and a user rate matrix is defined as Rϵcustom-character.sup.N×M with elements”).
Claim(s) 9 rejected under 35 U.S.C. 103 as being unpatentable over Gao et al. (US 2022/0279394, “Gao”) in view of Alonso-Rubio et al. (US 2010/0273487, “Rubio”) and further in view of Comsa et al. (US 2019/0124667, “Comsa”).
Examiner’s note: in what follows, references are drawn to Gao unless otherwise mentioned.
Regarding claim 9, it is noted that while disclosing mobility handling, Gao does not specifically teach about an RL. It, however, had been known in the art before the effective date of the instant application as shown by Comsa as follows;
the method of Claim 1, wherein the initiating at least one of modification and allocation comprises signaling a TN or NTN network nodes that comprises reinforcement learning, RL, agent to process an input comprising the predicted handover rate and volume to decide at least one of the modification to the shape of the cell and the allocation of the portion of the bandwidth based on maximizing a reward value ([Comsa, 0042] “determining, by iterations of a reinforcement learning, an allocation of resource maximising a reward sum.”, and [Comsa, 0048] “to make it possible to fulfil requirements in terms of QoS of heterogeneous data flow categories, the invention uses a reinforcement learning to learn the optimum resource allocation plan to be implemented by the planner. Within the scope of the favoured embodiment, the reinforcement learning makes it possible to determine the hierarchisation rule and/or optimum scheduling rules to be implemented respectively by the time classifier which dynamically hierarchises the different data flow categories and by the frequency classifier which selects the most appropriate scheduling rule to allocate transmission resources to the activated data flow category”).
It would have been obvious to configure the Gao’s TN-NTN network node to use the Rubio-derived HO rate/cost and Gao’s UE group information as state inputs to the reward-maximizing RL resource-allocation technique of Comsa. The RL agent would thereby select differentiated RB or frequency-resource allocations for the respective UE groups based on the predicted and determined mobility load. This would predictably reduce resource consumption and mobility-related congestion while improving use of limited radio resources.
Claim(s) 12 rejected under 35 U.S.C. 103 as being unpatentable over Gao et al. (US 2022/0279394, “Gao”) in view of Alonso-Rubio et al. (US 2010/0273487, “Rubio”) and further in view of Chitrapu (US 2003/0114196).
Examiner’s note: in what follows, references are drawn to Gao unless otherwise mentioned.
Regarding claim 12, it is noted that while disclosing mobility handling, Gao does not specifically teach about reconfiguring beam for a cell size. It, however, had been known in the art before the effective date of the instant application as shown by Chitrapu as follows;
the method of Claim 1, wherein the modification to the shape of the cell comprises at least one of (i) adding or removing a beam within the cell (This alternative is not examined.), (ii) scaling a beam in the cell to increase or decrease in size ([Chitrapu, 0072] “a base station can have an antenna system with two antenna arrays, one which produces relatively narrow beams in a given direction and one that produces relatively wide beam in a given direction.”), and (iii) shifting a beam in the cell in a lateral direction or in elevation direction (This alternative is not examined.).
It would have been obvious to modify the Gao-Rubio TN-NTN system to dynamically scale a beam between wide and narrow beam modes and steer the beam laterally in response to the Rubio-derived Ho metric. Such a modification would predictably adjust cell coverage to reduce mobility-related interference, repeated handovers, and service degradation for Gao’s grouped UEs.
Allowable Subject Matter
Claim(s) 4, 10-11 and 13 objected to as being dependent upon a rejected base claim, but be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The claims contain the following underlined features which, when combined with other features of the claim, prior art of record failed to anticipate or render obvious before the effective filing date of the instant application was filed:
4. The method of Claim 3, wherein the categorization comprises
(i) a first category of aggregated UEs that stay attached to a cell of a serving network node for more than a first defined time period,
(ii) a second category of aggregated UEs that handover to a target network node from a source network node within a second defined time period and do not reassociate with the source network node before a third defined time period, and
(iii) a third category of aggregated UEs that handover to a target network node from the source network node within a fourth defined time period and reassociate to the source network node before a fifth defined time period.
Regarding claim 13, said claim depends from claim 4 and thus is objected.
10. The method of Claim 9, wherein the reward value comprises a weighted sum of a plurality of metrics, the plurality of metrics comprising (i) a number of aggregated UEs, or a utilization of a number of aggregated UEs, per category of the plurality of categories of aggregated UEs, (ii) a cost of switching the portion of the bandwidth on a UE and on a TN or NTN network node, and (iii) a value for a key performance indicator, KPI.
11. The method of Claim 9, wherein the input to the RL agent further comprises (i) a location of individual UEs in a respective category of aggregated UEs from the plurality of categories of aggregated UEs, (iii) a statistical representation of a key performance indicator, KPI, (ii) whether a TN or NTN network node includes a capability to modify the shape of the cell, and (iii) a buffer status report, BSR, of individual UEs in a respective category of aggregated UEs.
Conclusion
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/HARRY H KIM/ Primary Examiner, Art Unit 2411
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