DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments, see pages 16-18, filed February 04, 2026, with respect to the rejection(s) of claim(s) 47-68 under 35 USC § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of different interpretation of the previously applied references and new prior art as presented in this Office action. Applicant’s arguments with respect to claim(s) 47-68 are therefore moot.
Claim Rejections - 35 USC § 103
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.
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.
Claims 47-68 are rejected under 35 U.S.C. 103 as being unpatentable over US 20130122913 A1 (Agarwal et al.) (hereinafter Agarwal) in view of US 20220386194 A1 (MWANJE et al.) (hereinafter MWANJE).
In re claims 47, 57 and 67, Agarwal discloses a method (Fig. 9, [0005], “Embodiments of the invention provide a method of automatic state transition in a cluster of cells in a communications network...”) for a first network node of a wireless network (Fig. 6, [0061], “The SON Server (first node) may be responsible for co-ordination of one set of eNB’ s (a single cluster) or multiple sets of eNB’ s (more than one cluster)...The SON server may reside either in one of the radio base stations of the network or within another central network entity like the OAM or the MME/S-GW”. [0106], “The "SON Server" monitors and records the traffic activity for one or more clusters, determines the transition instant and triggers the transition as per the flow shown in FIG. 6”. [0179], “Step 9.1: The SON Server continuously monitors the current traffic activity from all radio base station (eNB) for one or more clusters”), the first network node comprising: communication interface circuitry configured to communicate with at least a second network node in the wireless network (Fig. 5); and processing circuitry operatively coupled to the communication interface circuitry ([0033], “The coordinating entity may include memory and a calculator (processor) used, for example, to calculate whether a threshold for activation of the transition has been crossed”. [0277], “The various features of invention embodiments may be implemented in hardware, or as software modules running on one or more processors”), whereby the processing circuitry and the communication interface circuitry are configured to:
sending, to a second network node in the wireless network, a first message comprising an indication of predicted future adjustments of mobility-related settings, of the first network node ([0268], “A signaling procedure between the SON Server (or other coordinating entity) and radio stations for the transition between states” (sending message to second node by the first node). [0211], “Step 10.7: [Optional] The `micro` mode eNB receives signal from SON server to adjust radio parameters”. [0114], “Step 6.6: The SON server notifies `macro` eNB to perform parameter adjustment”. [0131], “For instance, a 3GPP Rel 9 based eNB can optimize the handover parameter values, for example, certain threshold values for handover trigger events. Such optimization is referred to as Mobility Robustness Optimization. If applicable, it needs to be carried out for both Macro and Micro mode eNB”), for mobility of users between a coverage area of the first network node and an adjacent coverage area of the second network node (Fig. 12, [0248], “FIG. 12 illustrates two neighboring eNB’ s in a transition from a higher capacity to a lower capacity state. The eNB’ s start with initially similar coverage areas, shown as the outer ring of the Micro eNB and the inner ring of the Macro eNB”. [0007], “During the transition, the radio propagation properties within the network will change and this may lead to potential radio link failures or dropped calls for user equipment’s (UEs) in the cluster. Moreover, since the transition also involves a change in the number of available radio stations, it can also lead to an unwanted and uncontrolled surge in handover (HO) activity within the cluster irrespective of the mobility state of active UEs”. [0008], “Invention embodiments provide a solution for the transition mechanism which includes an automatic and coordinated parameter adjustment for the transition in the cluster from off to on (lower state to higher state) or on to off (higher state to lower state) with the aims that the UEs do not lose coverage or experience Radio Link Failure (RLF); and that the UEs undergo a smooth handover without experiencing HO related RLF or RACH conflicts” (handover between adjacent coverage areas)); and receiving, from the second network node in response to the first message, a second message indicating one or more of the following: acknowledgement, confirmation, or rejection of the predicted future adjustments (Fig. 5, [0258], “For example, in case of 3GPP-LTE, the handover process may happen over S1 or X2 interface and for each UE, it would include the time from HO event for reporting measurement for HO, Time to Trigger, HO Command & Acknowledgement, Status/Resource transfer to Target eNB and finally until release of resources at Source eNB”); and corresponding adjustments of mobility-related settings for the second network node ([0262], “Here the macro eNB adjusts one or more radio parameter in a first stage and the micro eNB adjusts one or more radio parameters in a second, subsequent stage, once the first has been completed. The geographic diagram on the left illustrates the first stage and shows the macro eNB increasing its coverage. The corresponding graph shows the macro eNB signal strength increasing, causing handover of UE D and UE C in two steps”. [0263], “In a second stage, the micro eNB adjusts its radio parameters, giving a decrease in signal strength and causing handover of UE B”).
Agarwal does not explicitly disclose predicted future adjustments.
MWANJE discloses predicted future adjustments (Fig. 22-3, [0035], “The ML decision engine 110 may learn how to choose the right combination of handover parameters for the specific service over the multiple observations it makes in multiple cells at different time instances. This may provide improved handover optimization relative to legacy handover optimization”. [0013], “Cognitive autonomous networks (CANs) may be used in 5G (radio access) networks and/or other future generations of wireless/mobile networks...”. [0020], “In addition, some embodiments may utilize a machine learning engine to account for different contexts in different cells and/or for different services, and/or may select handover settings for a UE under different cell and/or service contexts. This may allow for coordination of optimization among different cells, while considering the performance related to the other service-related metrics, thereby improving operations of a network with respect to handover optimization”. [0023], “The SCTS entity 104 may determine a handover trigger point (e.g., a value) for different kinds of services associated with the UE 100. For example, as illustrated at 116, the link KPIs handler 106 and the QoS/E handler 108 may provide the link performance data and/or the QoS/E data to the ML decision engine 110, and the ML decision engine 110 may process the data to determine the handover trigger point. Utilizing the ML decision engine 110 may improve optimization of handovers, may result in faster handover trigger determinations, and/or the like, which may improve an efficiency of use of network resources, such as bandwidth and/or network node processing resources. As illustrated at 118, the SCTS entity 104 may provide, and the network node 102 may receive, handover trigger points for one or more trigger parameters. For example, the one or more trigger parameters may include a hysteresis value, a time-to-trigger (TTT), or a cell-specific offset (CIO)” (node sends predicted future adjustments based on machine learning algorithm on several factors)).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Agarwal and MWANJE to provide a Mobility Robustness Optimization (MRO) technique for handovers among the radio access points which may include training a machine learning model to predict load or load change for the target radio access point for one or more potential UE handovers from the source radio access point, based on measurement data from historical and current handovers. The advantage of doing so is reliable quality of service for the UE with sufficient coverage, increased energy saving without loss of performance efficiency due to scalable steps and improving ping-pong avoidance in mobility load balance scenarios.
In re claim 48, the combination discloses the method of claim 47, wherein MWANJE discloses the method further comprising determining the predicted future adjustments of mobility-related settings of the first network node based on one or more of the following: measured load and/or traffic during one or more current and/or previous time periods for one or more of the following: one or more cells served by the first network node, one or more cells served by the second network node, one or more beams served by the first network node, and one or more beams served by the second network node; predicted load and/or traffic during one or more subsequent time periods for one or more of the following: the one or more cells served by the first network node, the one or more cells served by the second network node, the one or more beams served by the first network node, and the one or more beams served by the second network node; accuracy, precision, validity, reliability, stability, and/or likelihood associated with the predicted load and/or traffic; measurements made by one or more user equipment, UEs, on one or more of the following: the one or more cells served by the first network node, the one or more cells served by the second network node, the one or more beams served by the first network node, and/or the one or more beams served by the second network node; and current and/or predicted future radio-related conditions in the wireless network ([0020], “In addition, some embodiments may utilize a machine learning engine to account for different contexts in different cells and/or for different services, and/or may select handover settings for a UE under different cell and/or service contexts (e.g., a context may include a mobility profile, a packet size, and/or the like for a service or service class). This may allow for coordination of optimization among different cells, while considering the performance related to the other service-related metrics, thereby improving operations of a network with respect to handover optimization”. [0035], “The ML decision engine 110 may learn how to choose the right combination of handover parameters for the specific service over the multiple observations it makes in multiple cells at different time instances. This may provide improved handover optimization relative to legacy handover optimization”. [0085], “In a variant, the method may include determining the at least one handover (HO) trigger point comprises utilizing at least one machine learning decision engine to determine the at least one handover (HO) trigger point. In a variant, the at least one machine learning decision engine may consider at least one context in at least one cell for at least one service. In a variant, the SCTS entity may operate in conjunction with at least one legacy mobility robustness optimization (MRO)/load balancing (LB)/traffic steering (TS) module”).
In re claim 49, the combination discloses the method of claim 47, wherein MWANJE discloses wherein the first message also includes one or more of the following: an identifier associated with the predicted future adjustments; an indication of accuracy, precision, validity, reliability, stability, and/or likelihood associated with the predicted future adjustments or with information used by the first network node to determine the predicted future adjustments; an indication of load and/or traffic that is expected to be transferred from the first network node to the second network node after the predicted future adjustments are applied; an indication of load and/or traffic that is expected to be transferred from the first network node to the second network node before the predicted future adjustments are applied; and an indication of a validity time for the predicted future adjustments ([0085], “In a variant, the method may include determining the at least one handover (HO) trigger point comprises utilizing at least one machine learning decision engine to determine the at least one handover (HO) trigger point”. [0084], “In a variant, the at least one handover (HO) trigger parameter may include at least one of a hysteresis value, a time-to-trigger (TTT), or a cell-specific offset (CIO)”) (machine learning for accuracy for future adjustments)).
In re claim 50, the combination discloses the method of claim 47, wherein Agarwal discloses wherein the indication of the predicted future adjustments in the first message comprises one or more of the following indications: that the predicted future adjustments will be applied by the first network node based on one or more first conditions; that the predicted future adjustments will not be applied by the first network node based on one or more second conditions; and that corresponding adjustments of mobility-related settings should be made by the second network node for mobility of users between the coverage area of the second network node and the adjacent coverage area of the first network node ([0109], “Step 6.2: The SON Server checks the condition if (Transition Instant=True). If false, the logic returns to Step 1. If true, the SON server identifies the `macro` & `micro` mode radio base station (eNB) within the cluster where the transition can be applied”. [0105], “This transition happens as a result of the traffic/throughput demand falling below the set threshold so that a trigger is invoked to switch off some of the radio base stations within the cluster”. [0248], “FIG. 12 illustrates two neighboring eNB’ s in a transition from a higher capacity to a lower capacity state. The eNB’ s start with initially similar coverage areas, shown as the outer ring of the Micro eNB and the inner ring of the Macro eNB”).
In re claim 51, the combination discloses the method of claim 50, wherein Agarwal discloses wherein when the first message indicates that corresponding adjustments of mobility-related settings should be made by the second network node ([0114], “Step 6.6: The SON server notifies `macro` eNB to perform parameter adjustment for micro...”), the second message also indicates one or more of the following: confirmation or rejection of the corresponding adjustments indicated in the first message ([0148], “The only feature that is desirable is that the Micro base-station notifies the SON server of its Switch OFF time instant so that the server is aware whether the transition procedure has completed (which is when it receives the confirmation from all Micro stations from within the cluster). This allows the SON server to make a decision whether to issue a CANCEL command or invoke a reverse transition procedure for the next cycle”); and the corresponding adjustments that should be made by the second network node, which are different than the corresponding adjustments indicated in the first message ([0094], “The values defined for State 0 for a Micro eNB may be same as those for State 2 (that is no parameter adjustment before switching off), or it may be different than those at State n=2”).
In re claim 52, the combination discloses the method of claim 51, wherein Agarwal discloses wherein one or more of the following applies: when the second message includes a rejection of the predicted future adjustments indicated in the first message, the second message also includes a cause value indicating a reason why the predicted future adjustments are rejected by the second network node; and when the second message includes a rejection of the corresponding adjustments indicated in the first message, the second message also includes a cause value indicating a reason why the corresponding adjustments are rejected by the second network node ([0007], “During the transition, the radio propagation properties within the network will change and this may lead to potential radio link failures or dropped calls for user equipment’s (UEs) in the cluster”. [0006], “in the transition more than one radio station should adjust its transmission parameters and that the adjustment in the radio stations should be coordinated, so that for example coordinated steps will take place in a plurality of radio stations (although each step may take place in one radio station only). For instance, at least the neighboring radio stations in the cluster to a radio station which is switching on or off may have to adapt their own radio parameters in coordination with the switch” (adjusting the parameters by the second node and rejecting with a cause for coordinating with other stations)).
In re claim 53, the combination discloses the method of claim 50, wherein Agarwal discloses wherein: the mobility-related settings for the first network node include a first mobility trigger point ([0012], “Thus the switch off is likely to be the only step in the transition to a lower capacity state which is not necessarily coordinated between radio stations. However, as indicated above, the switch on step could conceivably immediately trigger UE handover and is more likely to be coordinated”. [0016], “For example, in case of 3GPP-LTE, the handover process may happen over the S1 or X2 interface and for each UE, `t` would include the time from the HO event for reporting measurement for HO, Time to Trigger, HO Command & Acknowledgement...”); a coverage area of the first network node corresponds to a difference between a previous or current value of the first mobility trigger point and an adjusted value of the first mobility trigger point ([0105], “This transition happens as a result of the traffic/throughput demand falling below the set threshold so that a trigger is invoked to switch off some of the radio base stations within the cluster”); the one or more first conditions include any of the following: load and/or load variation in the coverage area of the first network node being above, below, or between one or more first thresholds ([0131], “For instance, a 3GPP Rel 9 based eNB can optimize the handover parameter values, for example, certain threshold values for handover trigger events”. [0176], “This transition happens as a result of the traffic/throughput demand rising above the set threshold so that the trigger is invoked to switch on some or all of the radio base stations within the cluster”); and a first time when or after which the predicted future adjustments will be applied, the first time being indicated in the first message, and the one or more second conditions include any of the following: load and/or load variation in the coverage area of the first network node being above, below, or between one or more second thresholds; and a second time when or after which the predicted future adjustments have not been or will not be applied, the second time being indicated in the first message ([0147], “This is because for the final step, there is no more coverage impact and if different Micro base-stations switch off at different time instants within the cluster, it does not affect the operation as long as each Micro base-station hands over all its UEs to a Macro base-station before it switches off”. [0156], “Step 8.4: The `macro` and `micro` mode eNB’ s receive signal from SON Server to adjust radio parameters for micro mode transition at time Ts1”).
In re claim 54, the combination discloses the method of claim 53, wherein Agarwal discloses wherein: the mobility-related settings for the second network node include a second mobility trigger point; a coverage area of the second network node corresponds to a difference between a previous or current value of the second mobility trigger point and an adjusted value of the second mobility trigger point; the corresponding adjustments of mobility-related settings, indicated in the second message, are based on one or more of the following third conditions: load and/or load variation in the coverage area of the second network node being above, below, or between one or more third thresholds; and the first time (Design variation. See “In re claim 53”. All features are disclosed in claim 53).
In re claim 55, the combination discloses the method of claim 50, wherein MWANJE discloses wherein the predicted future adjustments indicated in the first message and the corresponding adjustments indicated in the second message include at least one of the following: adjustments to one or more of the following mobility-related settings: trigger points, hysteresis, time to trigger, and measurement offsets ([0084], “In a variant, the at least one handover (HO) trigger parameter may include at least one of a hysteresis value, a time-to-trigger (TTT), or a cell-specific offset (CIO). In a variant, the quality of service or quality of experience (QoS/E) data may comprise data related to at least one of an achieved throughputs for the user equipment (UE), a radio-specific or end-to-end latency for the user equipment (UE), or a packet loss associated with user equipment (UE). In a variant, the data may further comprise mobility pattern prediction (MPP) data”); and wherein Agarwal discloses adjustments specific to one or more of the following: one or more cells; one or more SSB beams; one or more CSI-RS beams; one or more network slices; one or more carrier frequencies; one or more services; one or more public land mobile networks, PLMNs; and one or more user equipment, UEs ([0011], “The number of coordinated steps in the transition may be determined by the expected number of UE handovers expected to be produced by the transition”).
In re claim 56, the combination discloses the method of claim 50, wherein Agarwal discloses wherein the second message also includes one or more of the following: an identifier associated with the corresponding adjustments; one or more limitations at the second network node with respect to the predicted future adjustments of the first network node and/or the corresponding adjustments of the second network node indicated in the first message; actual or estimated amount of traffic and/or load to be transferred from second network node to first network node after one or more of the following: the predicted future adjustments indicated in the first message are applied by the first network node; the corresponding adjustments indicated in the first message are applied by the second network node; and the corresponding adjustments indicated in the second message are applied by the second network node ([0022], “Such a change may be determined by the traffic load and could occur when the change in traffic load (capacity requirement) is rapid rather than gradual”. [0029], “The method may be triggered by traffic load or traffic demand crossing a particular threshold or by any other suitable trigger”).
In re claims 57, Agarwal discloses a method (Fig. 9, [0005], “Embodiments of the invention provide a method of automatic state transition in a cluster of cells in a communications network...”), for a second network node of a wireless network ([0061], “The SON Server may be responsible for co-ordination of one set of eNBs (a single cluster) or multiple sets of eNBs (more than one cluster)”), the method comprising:
receiving, from a first network node of the wireless network, a first message comprising an indication of predicted future adjustments of mobility-related settings, of the first network node ([0268], “A signaling procedure between the SON Server (or other coordinating entity) and radio stations for the transition between states” (sending message to second node by the first node). [0211], “Step 10.7: [Optional] The `micro` mode eNB receives signal from SON server to adjust radio parameters”. [0114], “Step 6.6: The SON server notifies `macro` eNB to perform parameter adjustment”. [0131], “For instance, a 3GPP Rel 9 based eNB can optimize the handover parameter values, for example, certain threshold values for handover trigger events. Such optimization is referred to as Mobility Robustness Optimization. If applicable, it needs to be carried out for both Macro and Micro mode eNB”), for mobility of users between a coverage area of the first network node and an adjacent coverage area of the second network node (Fig. 12, [0248], “FIG. 12 illustrates two neighboring eNB’ s in a transition from a higher capacity to a lower capacity state. The eNB’ s start with initially similar coverage areas, shown as the outer ring of the Micro eNB and the inner ring of the Macro eNB”. [0007], “During the transition, the radio propagation properties within the network will change and this may lead to potential radio link failures or dropped calls for user equipment’s (UEs) in the cluster. Moreover, since the transition also involves a change in the number of available radio stations, it can also lead to an unwanted and uncontrolled surge in handover (HO) activity within the cluster irrespective of the mobility state of active UEs”. [0008], “Invention embodiments provide a solution for the transition mechanism which includes an automatic and coordinated parameter adjustment for the transition in the cluster from off to on (lower state to higher state) or on to off (higher state to lower state) with the aims that the UEs do not lose coverage or experience Radio Link Failure (RLF); and that the UEs undergo a smooth handover without experiencing HO related RLF or RACH conflicts” (handover between adjacent coverage areas)); and
sending, to the first network node in response to the first message, a second message indicating one or more of the following: acknowledgement, confirmation, or rejection of the predicted future adjustments (Fig. 5, [0215], “In some embodiments, the exemplary method can also include the operations of block 1950, where the target node can transmit, to the source node, a response including an acknowledgement that the one or more first handover offsets will be applied, or one or more second handover offsets (e.g., determined in block 1940) to be applied instead of the one or more first handover offsets”. [0060], “In some embodiments, the Handover Request Acknowledge message can include measurement configuration information, for the subset of the first plurality of UEs, with respect to one or more beams of the target node”); and corresponding adjustments of mobility-related settings for the second network node ([0262], “Here the macro eNB adjusts one or more radio parameter in a first stage and the micro eNB adjusts one or more radio parameters in a second, subsequent stage, once the first has been completed. The geographic diagram on the left illustrates the first stage and shows the macro eNB increasing its coverage. The corresponding graph shows the macro eNB signal strength increasing, causing handover of UE D and UE C in two steps”. [0263], “In a second stage, the micro eNB adjusts its radio parameters, giving a decrease in signal strength and causing handover of UE B”).
Agarwal does not explicitly disclose predicted future adjustments.
MWANJE discloses predicted future adjustments (Fig. 22-3, [0035], “The ML decision engine 110 may learn how to choose the right combination of handover parameters for the specific service over the multiple observations it makes in multiple cells at different time instances. This may provide improved handover optimization relative to legacy handover optimization”. [0013], “Cognitive autonomous networks (CANs) may be used in 5G (radio access) networks and/or other future generations of wireless/mobile networks...”. [0020], “In addition, some embodiments may utilize a machine learning engine to account for different contexts in different cells and/or for different services, and/or may select handover settings for a UE under different cell and/or service contexts. This may allow for coordination of optimization among different cells, while considering the performance related to the other service-related metrics, thereby improving operations of a network with respect to handover optimization”. [0023], “The SCTS entity 104 may determine a handover trigger point (e.g., a value) for different kinds of services associated with the UE 100. For example, as illustrated at 116, the link KPIs handler 106 and the QoS/E handler 108 may provide the link performance data and/or the QoS/E data to the ML decision engine 110, and the ML decision engine 110 may process the data to determine the handover trigger point. Utilizing the ML decision engine 110 may improve optimization of handovers, may result in faster handover trigger determinations, and/or the like, which may improve an efficiency of use of network resources, such as bandwidth and/or network node processing resources. As illustrated at 118, the SCTS entity 104 may provide, and the network node 102 may receive, handover trigger points for one or more trigger parameters. For example, the one or more trigger parameters may include a hysteresis value, a time-to-trigger (TTT), or a cell-specific offset (CIO)” (node sends predicted future adjustments based on machine learning algorithm on several factors)).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Agarwal and MWANJE to provide a Mobility Robustness Optimization (MRO) technique for handovers among the radio access points which may include training a machine learning model to predict load or load change for the target radio access point for one or more potential UE handovers from the source radio access point, based on measurement data from historical and current handovers. The advantage of doing so is reliable quality of service for the UE with sufficient coverage, increased energy saving without loss of performance efficiency due to scalable steps and improving ping-pong avoidance in mobility load balance scenarios.
In re claim 58, the combination discloses the method of claim 57, wherein MWANJE discloses the method further comprising determining the corresponding adjustments of mobility-related settings of the second network node, indicated in the second message, based on one or more of the following: the predicted future adjustments of mobility-related settings of the first network node, indicated in the first message; measured load and/or traffic during one or more current and/or previous time periods for one or more of the following: one or more cells served by the first network node, one or more cells served by the second network node, one or more beams served by the first network node, and one or more beams served by the second network node; predicted load and/or traffic during one or more subsequent time periods for one or more of the following: the one or more cells served by the first network node, the one or more cells served by the second network node, the one or more beams served by the first network node, and the one or more beams served by the second network node; accuracy, precision, validity, reliability, stability, and/or likelihood associated with the predicted load and/or traffic; measurements made by one or more user equipment, UEs, on one or more of the following: the one or more cells served by the first network node, the one or more cells served by the second network node, the one or more beams served by the first network node, and the one or more beams served by the second network node; and current and/or predicted future radio-related conditions in the wireless network ([0020], “In addition, some embodiments may utilize a machine learning engine to account for different contexts in different cells and/or for different services, and/or may select handover settings for a UE under different cell and/or service contexts (e.g., a context may include a mobility profile, a packet size, and/or the like for a service or service class). This may allow for coordination of optimization among different cells, while considering the performance related to the other service-related metrics, thereby improving operations of a network with respect to handover optimization”. [0035], “The ML decision engine 110 may learn how to choose the right combination of handover parameters for the specific service over the multiple observations it makes in multiple cells at different time instances. This may provide improved handover optimization relative to legacy handover optimization”. [0085], “In a variant, the method may include determining the at least one handover (HO) trigger point comprises utilizing at least one machine learning decision engine to determine the at least one handover (HO) trigger point. In a variant, the at least one machine learning decision engine may consider at least one context in at least one cell for at least one service. In a variant, the SCTS entity may operate in conjunction with at least one legacy mobility robustness optimization (MRO)/load balancing (LB)/traffic steering (TS) module”).
In re claim 59, the combination discloses the method of claim 57, wherein MWANJE discloses wherein the first message also includes one or more of the following: an identifier associated with the predicted future adjustments; an indication of accuracy, precision, validity, reliability, stability, and/or likelihood associated with the predicted future adjustments or with information used by the first network node to determine the predicted future adjustments; an indication of load and/or traffic that is expected to be transferred from the first network node to the second network node after the predicted future adjustments are applied; an indication of load and/or traffic that is expected to be transferred from the first network node to the second network node before the predicted future adjustments are applied; and an indication of a validity time for the predicted future adjustments ([0085], “In a variant, the method may include determining the at least one handover (HO) trigger point comprises utilizing at least one machine learning decision engine to determine the at least one handover (HO) trigger point”. [0084], “In a variant, the at least one handover (HO) trigger parameter may include at least one of a hysteresis value, a time-to-trigger (TTT), or a cell-specific offset (CIO)”) (machine learning for accuracy for future adjustments)).
In re claim 60, the combination discloses the method of claim 57, wherein Agarwal discloses wherein the indication of the predicted future adjustments in the first message comprises one or more of the following indications: that the predicted future adjustments will be applied by the first network node based on one or more first conditions; that the predicted future adjustments will not be applied by the first network node based on one or more second conditions; and that corresponding adjustments of mobility-related settings should be made by the second network node for mobility of users between the coverage area of the second network node and the adjacent coverage area of the first network node ([0109], “Step 6.2: The SON Server checks the condition if (Transition Instant=True). If false, the logic returns to Step 1. If true, the SON server identifies the `macro` & `micro` mode radio base station (eNB) within the cluster where the transition can be applied”. [0105], “This transition happens as a result of the traffic/throughput demand falling below the set threshold so that a trigger is invoked to switch off some of the radio base stations within the cluster”. [0248], “FIG. 12 illustrates two neighboring eNB’ s in a transition from a higher capacity to a lower capacity state. The eNB’ s start with initially similar coverage areas, shown as the outer ring of the Micro eNB and the inner ring of the Macro eNB”).
In re claim 61, the combination discloses the method of claim 60, wherein Agarwal discloses wherein when the first message indicates that corresponding adjustments of mobility-related settings should be made by the second network node ([0114], “Step 6.6: The SON server notifies `macro` eNB to perform parameter adjustment for micro...”), the second message also indicates one or more of the following: confirmation or rejection of the corresponding adjustments indicated in the first message ([0148], “The only feature that is desirable is that the Micro base-station notifies the SON server of its Switch OFF time instant so that the server is aware whether the transition procedure has completed (which is when it receives the confirmation from all Micro stations from within the cluster). This allows the SON server to make a decision whether to issue a CANCEL command or invoke a reverse transition procedure for the next cycle”); and the corresponding adjustments that should be made by the second network node, which are different than the corresponding adjustments indicated in the first message ([0094], “The values defined for State 0 for a Micro eNB may be same as those for State 2 (that is no parameter adjustment before switching off), or it may be different than those at State n=2”).
In re claim 62, the combination discloses the method of claim 61, wherein Agarwal discloses wherein one or more of the following applies: when the second message includes a rejection of the predicted future adjustments indicated in the first message, the second message also includes a cause value indicating a reason why the predicted future adjustments are rejected by the second network node; or when the second message includes a rejection of the corresponding adjustments indicated in the first message, the second message also includes a cause value indicating a reason why the corresponding adjustments are rejected by the second network node ([0007], “During the transition, the radio propagation properties within the network will change and this may lead to potential radio link failures or dropped calls for user equipment’s (UEs) in the cluster”. [0006], “in the transition more than one radio station should adjust its transmission parameters and that the adjustment in the radio stations should be coordinated, so that for example coordinated steps will take place in a plurality of radio stations (although each step may take place in one radio station only). For instance, at least the neighboring radio stations in the cluster to a radio station which is switching on or off may have to adapt their own radio parameters in coordination with the switch” (adjusting the parameters by the second node and rejecting with a cause for coordinating with other stations)).
In re claim 63, the combination discloses the method of claim 60, wherein Agarwal discloses wherein: the mobility-related settings for the first network node include a first mobility trigger point ([0012], “Thus the switch off is likely to be the only step in the transition to a lower capacity state which is not necessarily coordinated between radio stations. However, as indicated above, the switch on step could conceivably immediately trigger UE handover and is more likely to be coordinated”. [0016], “For example, in case of 3GPP-LTE, the handover process may happen over the S1 or X2 interface and for each UE, `t` would include the time from the HO event for reporting measurement for HO, Time to Trigger, HO Command & Acknowledgement...”); a coverage area of the first network node corresponds to a difference between a previous or current value of the first mobility trigger point and an adjusted value of the first mobility trigger point ([0105], “This transition happens as a result of the traffic/throughput demand falling below the set threshold so that a trigger is invoked to switch off some of the radio base stations within the cluster”); the one or more first conditions include any of the following: load and/or load variation in the coverage area of the first network node being above, below, or between one or more first thresholds ([0131], “For instance, a 3GPP Rel 9 based eNB can optimize the handover parameter values, for example, certain threshold values for handover trigger events”. [0176], “This transition happens as a result of the traffic/throughput demand rising above the set threshold so that the trigger is invoked to switch on some or all of the radio base stations within the cluster”); and a first time when or after which the predicted future adjustments will be applied, the first time being indicated in the first message, and the one or more second conditions include any of the following: load and/or load variation in the coverage area of the first network node being above, below, or between one or more second thresholds; and a second time when or after which the predicted future adjustments have not been or will not be applied, the second time being indicated in the first message ([0147], “This is because for the final step, there is no more coverage impact and if different Micro base-stations switch off at different time instants within the cluster, it does not affect the operation as long as each Micro base-station hands over all its UEs to a Macro base-station before it switches off”. [0156], “Step 8.4: The `macro` and `micro` mode eNB’ s receive signal from SON Server to adjust radio parameters for micro mode transition at time Ts1”).
In re claim 64, the combination discloses the method of claim 63, wherein Agarwal discloses wherein: the mobility-related settings for the second network node include a second mobility trigger point; a coverage area of the second network node corresponds to a difference between a previous or current value of the second mobility trigger point and an adjusted value of the second mobility trigger point; the corresponding adjustments of mobility-related settings, indicated in the second message, are based on one or more of the following third conditions: load and/or load variation in the coverage area of the second network node being above, below, or between one or more third thresholds; and the first time (Design variation. See “In re claim 63”. All features are disclosed in claim 53).
In re claim 65, the combination discloses the method of claim 60, wherein Agarwal discloses wherein the predicted future adjustments indicated in the first message and the corresponding adjustments indicated in the second message include at least one of the following: adjustments to one or more of the following mobility-related settings: trigger points, hysteresis, time to trigger, and measurement offsets ([0154], “In some embodiments, a dedicated procedure for handover offset exchange can be used prior to performing the mobility (e.g., handover) procedure. For example, this can include the source node triggering a signaling exchange (referred to as “Mobility Settings Change” or “Handover Setting Change”) with the potential target node, by which the source node communicates a change in one or more handover offsets ΔHO applied by the source node for mobility of UE(s) from its serving cell or beam to the target node beams that are considered most likely handover candidates”); and adjustments specific to one or more of the following: one or more cells; one or more SSB beams; one or more CSI-RS beams; one or more network slices; one or more carrier frequencies; one or more services; one or more public land mobile networks, PLMNs; and one or more user equipment, UEs ([0154], “Such handover offsets ΔHO can be beam- or beam-group-specific, for the target node to use in relation to one or more beams of the source node”. [0157], “Expected load to be handed over, the expected resources or capacity to be needed at the target node to serve UEs being handed over, the type of traffic to be handed over, the network slice(s) associated with the traffic/UEs to be handed over, etc.”).
In re claims 66, the combination discloses the method of claim 60, wherein Agarwal discloses wherein the second message also includes one or more of the following: an identifier associated with the corresponding adjustments; one or more limitations at the second network node with respect to the predicted future adjustments of the first network node and/or the corresponding adjustments of the second network node indicated in the first message; actual or estimated amount of traffic and/or load to be transferred from second network node to first network node; after one or more of the following: the predicted future adjustments indicated in the first message are applied by the first network node, the corresponding adjustments indicated in the first message are applied by the second network node; and the corresponding adjustments indicated in the second message are applied by the second network node ([0022], “Such a change may be determined by the traffic load and could occur when the change in traffic load (capacity requirement) is rapid rather than gradual”. [0029], “The method may be triggered by traffic load or traffic demand crossing a particular threshold or by any other suitable trigger”).
In re claim 68, the combination discloses a second network node configured to operate in a wireless network (Fig. 12, Fig. 15: “network node 1230), the second network node comprising: communication interface circuitry configured to communicate with at least a first network node in the wireless network ([0247], “Interface 2090 is used in the wired or wireless communication of signaling and/or data between network node 2060, network 2006, and/or WDs 2010. As illustrated, interface 2090 comprises port(s)/terminal(s) 2094 to send and receive data, for example to and from network 2006 over a wired connection. Interface 2090 also includes radio front end circuitry 2092 that can be coupled to, or in certain embodiments a part of, antenna 2062”); and processing circuitry operatively coupled to the communication interface circuitry, whereby the processing circuitry and the communication interface circuitry are configured to perform the method of claim 57 ([0239], “In FIG. 20, network node 2060 includes processing circuitry 2070, device readable medium 2080, interface 2090, auxiliary equipment 2084, power source 2086, power circuitry 2087, and antenna 2062”)
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/SWATI JAIN/Examiner, Art Unit 2649 /YUWEN PAN/Supervisory Patent Examiner, Art Unit 2649
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