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 Amendment
This communication is considered fully responsive to the amendment filed on 07/24/2026.
Claims 1, 6, 7, 13, 18, 20, 22, and 43 have been amended.
Claims 4, 5, 14-16, 19, 21, and 23-42 have been canceled.
Claims 1-3, 6-13, 17, 18, 20, 22, 43 and 44 are pending in the application
Rejection to claims under 35 USC § 112 is withdrawn since it has been amended accordingly.
Response to Arguments
Applicant’s arguments, see remarks on page12-15, filed 07/24/2026, with respect to the rejection(s) of claim(s) 1 under 102(a)(1) have been considered and regarding the amended feature of “wherein the determination of whether or not the net power consumption of the communications network would be reduced if the allocation of the UE was changed comprises estimating the power consumption due to the handover, the estimate comprising estimating the power consumption if the handover is performed, and estimating the power consumption if the handover is not performed, and wherein the power consumption due to the handover is estimated using a Machine Learning, ML, model that has been trained to estimate power consumptions due to handovers” are persuasive. Therefore, the rejection has been withdrawn.
However, upon further consideration, a new ground(s) of rejection is made in view of previously identified prior art, Song, and newly cited reference, Raymond (U.S. Patent Application Publication No. 20220303855, hereinafter “Raymond”). Raymond explicitly teaches using trained ML models to predict communication network metrics, including power consumption, based on predicted handovers.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 1-3, 6-13, 17, 18, 20, 22, 43 and 44 rejected under 35 U.S.C. 103 as being unpatentable over Song et al. (Korea Patent Publication No. KR1020150068651, hereinafter “Song”) in view of Raymond (U.S. Patent Application Publication No. 20220303855, hereinafter “Raymond”).
Examiner’s note: in what follows, references are drawn to Song (Translated by WIPO translation) unless otherwise mentioned.
With respect to independent claims
Regarding claim 1, Song teaches A method for allocation of User Equipments, UEs (Fig. 1, MTs(mobile terminals)), in a communications network comprising one or more access nodes (Fig. 1)(para [0065]: the vertical handover method according to the present embodiment may be performed by the base station 700, and the operations included in the vertical handover method may be performed by a component included in the base station 700 or the base station 700.), the method comprising:
analysing the power consumption of the one or more access nodes (para [0066]: In operation 810, when one traffic including a plurality of frames is transmitted to the mobile terminal through the downlink, the base station 700 May identify candidate base stations accessible in the plurality of wireless networks every transmission start of each frame. For example, the candidate base station identification unit 720 May perform operation 810 under the control of the at least one processor 710.)(Translated by WIPO translate) (para [0067]: In operation 820, the BS 700 May calculate an expected total cost for total power consumption required for processing one traffic. For example, the expected total cost calculator 730 May perform operation 820 under the control of the at least one processor 710.) ;
determining, for a UE, whether the net power consumption of the communications network would be reduced if the allocation of the UE was changed (para [0068]: In operation 830, the base station 700 May select a candidate base station in which the expected total cost is the lowest from among the candidate base stations. For example, the candidate base station selector 740 May perform operation 830 under the control of the at least one processor 710.) (para [0070]: In another embodiment, the expected total cost may be calculated further using a cost for the power required when executing the handover (interpreted as “if the allocation of the UE was changed”).); and
if it is determined that the net power consumption of the communications network would be reduced if the allocation of the UE was changed, changing the UE allocation (para [0068]: In operation 830, the base station 700 May select a candidate base station in which the expected total cost is the lowest from among the candidate base stations. For example, the candidate base station selector 740 May perform operation 830 under the control of the at least one processor 710.) (para [0070]: In another embodiment, the expected total cost may be calculated further using a cost for the power required when executing the handover (interpreted as “if the allocation of the UE was changed”).) (para [0073]: Operation 840 May optionally be included in a vertical handover method as needed. In operation 840, the BS 700 May determine whether to perform handover for each handover decision epoch before the transmission start of each frame.);
or
if it is determined that the net power consumption of the communications network would not be reduced if the allocation of the UE was changed, retaining the current UE allocation (para [0073]: Operation 840 May optionally be included in a vertical handover method as needed. In operation 840, the BS 700 May determine whether to perform handover for each handover decision epoch before the transmission start of each frame.) (para [0074]: As such, according to embodiments of the present disclosure, it is possible to minimize the overall power consumption required for transmitting one traffic in the heterogeneous wireless network environment through the proposed vertical handover. In addition, the last part of the entire handover decision point required for processing one traffic does not perform the handover determination itself, thereby leading to an additional power saving.)
(Fig. 5 and para [0063] by Google Translate: Figure 5 is a graph illustrating the total number of handovers as a function of varying handover costs. Figure 5 demonstrates that the total number of handovers decreases as the handover cost increases. This implies that, from the perspective of power saving, maintaining the connection with the current base station (BS)—even if the link quality with that BS is relatively inferior to that of other BSs—can be an effective strategy, provided that the nominal network service rate is guaranteed through dynamic power control. The rationale for this is that performing a handover incurs a specific cost, and this handover cost (`power consumption’) may potentially exceed the transmission power savings realized through the handover itself.)
(Examiner’s note: Due to inaccuracies found in the WIPO machine translation of para [0063] of Song included in this Office Action, the Google Translate version has been used instead.) The Original Korean text of para [0063] is reproduced herein below for reference.)
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(para [0063] in original publication of Song)
Song teaches a method for deciding whether to perform a handover (“changing UE allocation”) by comparing the potential power savings against the `handover cost’ at Operation 840 (see Fig. 6 and paras [0073], [0074] of Song).
Furthermore, Fig. 5 and para [0063] discloses that “The rationale for this is that performing a handover incurs a specific cost, and this handover cost may potentially exceed the transmission power savings realized through the handover itself.” This comparison determines whether the “net power consumption” of the network would actually be reduced. Song explicitly teaches that even if the link quality of the current base station is relatively poor compared to other BSs, “maintaining the connection with the current base station (BS) can be an effective strategy” from the perspective of power saving (see para [0063] of Song). The handover cost refers power consumption (see para [0033]).
Song, therefore, teaches the claimed feature “if it is determined that the net power consumption of the communications network would not be reduced if the allocation of the UE was changed, retaining the current UE allocation” of claim 1.
Song, however, fails to teach or disclose:
wherein the determination of whether or not the net power consumption of the communications network would be reduced if the allocation of the UE was changed comprises estimating the power consumption due to the handover, the estimate comprising estimating the power consumption if the handover is performed, and estimating the power consumption if the handover is not performed, and wherein the power consumption due to the handover is estimated using a Machine Learning, ML, model that has been trained to estimate power consumptions due to handovers.
Raymond teaches digital wireless communications and, more particularly, to facilitating intelligent mobility including session and handover management within a 5G communications network (see Abstract and para [0001] of Raymond).
Specifically, Raymond teaches: wherein… estimating the power consumption due to the handover, the estimate comprising estimating the power consumption if the handover is performed, and estimating the power consumption if the handover is not performed, and wherein the power consumption due to the handover is estimated using a Machine Learning, ML, model that has been trained to estimate power consumptions due to handovers (para [0065] of Raymond: At block 604, the process 600 includes determining predicted factors that affect pre-allocation decisions. The predicted factors can include any predicted activity, metric, or status regarding the communication network 110 (including that of the cells or user devices). Illustratively, the predicted factors can include handover predictions for individual or groups of user devices (e.g., the estimated time, location, source cell, target cell(s), and confidence level associated with predicted handovers). The predicted factors can include predicted communication network metrics (e.g., predicted resource utilization in various cells including the use of processor, memory, wave bandwidth, or power consumption; predicted resource utilization by AS and MEC platform serving users in that cell; predicted number and type of user devices served by different cells; predicted network performance including latency, packet drop rate, or communication quality; etc.). The predicted factors can be generated using one or more machine learning models or other AI techniques, based on past, current, and/or predicted data. For example, a predicted handover can be a partial basis for determining corresponding predicted change in resource utilization of source or target cells related to the predicted handover.)
Raymond also teaches that the machine learning models are trained using past and current network state data (see para [0053]: “The historical information is stored in the data repository 310. This data can later be used by the training engine 302 to build machine learning or other AI models (e.g., artificial neural networks, support vector machines, hierarchical statistical models, or the like) that can be used to perform inference” and para [0057] of Raymond: “The prediction engine 304 can receive data corresponding to past state 402 and current state 404 of the communication network 110 (e.g., including monitored travel patterns of user devices and associated handovers, quality of service metrics, cell resource utilization, or the like), and use trained machine learning or other AI model(s) to output predicted state 406 of the communication network 110 (e.g., including estimated travel patterns of user devices and associated handovers, estimated quality of service metrics, estimated cell resource utilization, or the like)”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify the method of Song by incorporating the trained machine learning models of Raymond to predict the change in power consumption due to handovers. The motivation to combine would be to improve the prediction accuracy of handover costs and to ensure a more optimal resource allocation and power saving in a dynamic communication network environment, as taught by Raymone.
Regarding claim 22, it is a communications network 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 43, it is a communications network claim corresponding to the method claim 1, except limitations “an analyser configured to…” “a determinator configured to” and “an allocator configured to” (Fig. 7 and para [0065]) and is therefore rejected for the similar reasons set forth in the rejection of claim 1. (See Fig. 7 and para [0065]: FIG. 7 is a block diagram illustrating an internal configuration of a base station according to an embodiment of the present invention, and FIG. 8 is a flowchart illustrating a vertical handover method according to an embodiment of the present invention. As illustrated in FIG. 7, the base station 700 according to the present embodiment may include at least one processor 710, a candidate base station identification unit 720, an expected total cost calculation unit 730, and a candidate base station selection unit 740. In addition, the vertical handover method according to the present embodiment may be performed by the base station 700, and the operations included in the vertical handover method may be performed by a component included in the base station 700 or the base station 700.)
With respect to dependent claims:
Regarding claim 2, Song and Raymond teach The method of claim 1, wherein changing the allocation of the UE comprises allocating the UE to a different access node and/or allocating the UE to a different radio access technology, RAT (para [0006]: A vertical handover method in heterogeneous wireless networks, …) (para [0068]: In operation 830, the base station 700 May select a candidate base station in which the expected total cost is the lowest from among the candidate base stations. ) (para [0073]: Operation 840 May optionally be included in a vertical handover method as needed. In operation 840, the BS 700 May determine whether to perform handover for each handover decision epoch before the transmission start of each frame.).
Regarding claim 3, Song and Raymond teach The method of claim 1, Song further teaches wherein the changing of the UE allocation comprises executing a handover from the current UE allocation to the new UE allocation (Fig. 1: Vertical handoff decision). Fig. 1 of Song is reproduced herein below.
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(Fig. 1 of Song)
Regarding claim 6, Song and Raymond teach The method of claim 1, Raymond further teaches wherein the ML model is a neural network (para [0053] of Raymond: This data can later be used by the training engine 302 to build machine learning or other AI models (e.g., artificial neural networks, support vector machines, hierarchical statistical models, or the like) that can be used to perform inference.).
Regarding claim 7, Song and Raymond teach The method of claim 1, Raymond further teaches wherein the ML model is trained using data from the one or more access nodes (Fing. 1 and para [0053] of Raymond: llustratively, the IME 102 gathers input from the communication network 110 for observing user devices as they move around. The historical information is stored in the data repository 310. This data can later be used by the training engine 302 to build machine learning or other AI models (e.g., artificial neural networks, support vector machines, hierarchical statistical models, or the like) that can be used to perform inference.)
Regarding claim 8, Song and Raymond teach The method of claim 1, Song further teaches wherein the determination of whether or not the net power consumption of the communications network would be reduced if the allocation of the UE was changed comprises estimating a traffic profile of the UE and/or a traffic profile of the class of UE and/or a mobility profile of the UE (para [0059]: The MT is located within a colored area formed from two BSs, and considers a situation in which the MT is selectively connectable to two BSs during a traffic processing time. For simplicity of simulation, it is assumed that the remaining parameters except for the service rate are the same. In addition, two simulation scenarios may be considered in order to check a difference in simulation results according to a difference in cell size. Scenario 1 considers a case of a large cell, and scenario 2 considers a case of a small cell.) (para [0060]: A vertical handover method to be proposed through simulation (hereinafter, referred to as a power-optimized VHO algorithm) and a performance between an SNR-based algorithm and a NO-VHO algorithm are compared. A signal-to-noise ratio-based algorithm is a method of determining a handover execution by comparing a signal-to-noise ratio from a currently connected BS with a signal-to-noise ratio value of other BSs, and is the most commonly used method due to the advantage of a simple hardware configuration. In addition, the NO-VHO algorithm may not generate the handover cost as a method of not performing handover during one traffic processing period.) (Fig. 4 and para [0062]: FIG. 4 is a graph illustrating expected overall power consumption according to different handover costs. FIG. 4 illustrates expected overall power consumption according to a change in handover cost. As the handover cost increases, FIG. 4 shows that the power-optimal VHO algorithm obtains an expected overall cost lower than the signal-to-noise ratio-based algorithm and the NO-VHO algorithm regardless of the cell size.) (Fig. 6 and para [0064]: FIG. 6 is a graph illustrating the number of handovers generated at each handover decision point according to a change in a dispatch factor when K is 9 in a scenario 1 environment. In this case, it is assumed that the number of frames constituting one traffic is 10. The fact that it can be seen through FIG. 6 is that a handover does not occur at the time of the last two consecutive handover decision regardless of the dispatch factor. Further, as the handover cost increases, it is possible to easily predict that the number of determination times at which the handover does not continuously occur at the end will increase. Therefore, the vertical handover method to be additionally proposed through the result of FIG. 6 is to reflect the traffic size and the network characteristics to which the BS belongs, so that the handover determination itself is not considered at the time of determining the handover between the last alignment (interpreted as “estimating a traffic profile of the UE and/or a traffic profile of the class of UE and/or a mobility profile of the UE”). Each of the BSs should transmit and receive various information to neighboring BSs for handover determination, and perform handover decision based on the received information. This is very complex, but since the process itself is not performed at the time of determining the handover, the additional power saving may be derived.) (para [0070]: In another embodiment, the expected total cost may be calculated further using a cost for the power required when executing the handover (interpreted as “if the allocation of the UE was changed”).).
Regarding claim 9, Song and Raymond teach The method of claim 8, Raymond further teaches wherein the traffic profile of the UE and/or of the class of UE is estimated using a further Machine Learning, ML, model that has been trained to estimate traffic profiles of UEs and/or traffic profiles of classes of UEs (Fing. 1 and para [0053] of Raymond: llustratively, the IME 102 gathers input from the communication network 110 for observing user devices as they move around. The historical information is stored in the data repository 310. This data can later be used by the training engine 302 to build machine learning or other AI models (e.g., artificial neural networks, support vector machines, hierarchical statistical models, or the like) that can be used to perform inference.).
Regarding claim 10, Song and Raymond teach The method of claim 9, Raymond further teaches wherein the further ML model is a neural network (Fing. 1 and para [0053] of Raymond: llustratively, the IME 102 gathers input from the communication network 110 for observing user devices as they move around. The historical information is stored in the data repository 310. This data can later be used by the training engine 302 to build machine learning or other AI models (e.g., artificial neural networks, support vector machines, hierarchical statistical models, or the like) that can be used to perform inference.).
Regarding claim 11, Song and Raymond teach The method of claim 9, Raymond further teaches wherein the further ML model is trained using data from a plurality of UEs (Fing. 1 and para [0053] of Raymond: llustratively, the IME 102 gathers input from the communication network 110 for observing user devices as they move around. The historical information is stored in the data repository 310. This data can later be used by the training engine 302 to build machine learning or other AI models (e.g., artificial neural networks, support vector machines, hierarchical statistical models, or the like) that can be used to perform inference.).
Regarding claim 12, Song and Raymond teach The method of claim 8, Raymond further teaches wherein the mobility profile of the UE is obtained from a Core Network Node, CNN, of the communications network (para [0049] of Raymond: the IME 102 can include one or more computing devices for performing the intelligent mobility functions described herein. The IME 102 can interface or otherwise communicate with multiple elements (e.g., AS, AMF (‘Access and Mobility Management Function (as defined in 3GPP 5G core specifications’, see para [0018 of Raymond)., CU, DU, RU, Data Lake, SMF, UDR, UPF, UE) via the communication network 110 (interpreted as “CNN”).)(Fing. 1 and para [0053] of Raymond: llustratively, the IME 102 gathers input from the communication network 110 for observing user devices as they move around.) (para [0057] of Raymond: The prediction engine 304 can receive data corresponding to past state 402 and current state 404 of the communication network 110 (e.g., including monitored travel patterns of user devices and associated handovers, quality of service metrics, cell resource utilization, or the like), and use trained machine learning or other AI model(s) to output predicted state 406 of the communication network 110 (e.g., including estimated travel patterns of user devices and associated handovers, estimated quality of service metrics, estimated cell resource utilization, or the like).
Regarding claim 13, Song and Raymond teach The method of claim 1, Song further teaches wherein the method is repeated each time a UE in the communications network provides measurements that trigger a handover (para [0066]: In operation 810, when one traffic including a plurality of frames is transmitted to the mobile terminal through the downlink (interpreted as “the method is repeated each time a UE in the communications network provides measurements that trigger a handover”, the one traffic may trigger a handover), the base station 700 May identify candidate base stations accessible in the plurality of wireless networks every transmission start of each frame. …) (para [0067]: In operation 820, the BS 700 May calculate an expected total cost for total power consumption required for processing one traffic. For example, the expected total cost calculator 730 May perform operation 820 under the control of the at least one processor 710.).
Regarding claim 17, Song and Raymond teach The method of claim 1, wherein: Song further teaches:
the method determines, for a plurality of UEs, whether the net power consumption of the communications network would be reduced if the allocations of the plurality of UEs was changed (para [0025]: Each active MT (interpreted as “plurality of UEs”) may be expressed as one traffic flow composed of homogeneous K +1 frames as shown in FIG. 2. After transmitting K +1 frames, the active MT is in an inactive state and leaves the system until again activated. Since each MT can only connect to only one BS, a handover decision is required before transmission of every frame after transmission of the first frame (presence of a total K handover decision epoch).) In operation 810,) (Fig. 6 and para [0064]: FIG. 6 is a graph illustrating the number of handovers generated at each handover decision point (interpreted as “if the allocations of the plurality of UEs was changed”) according to a change in a dispatch factor when K is 9 in a scenario 1 environment.) ; and
if it is determined that the net power consumption of the communications network would be reduced if the allocation of the plurality of UEs was changed, the allocation of the plurality of UEs is changed (Fig. 6 and para [0064]: Each of the BSs should transmit and receive various information to neighboring BSs for handover determination, and perform handover decision based on the received information.).
Regarding claim 18, Song and Raymond teach The method of claim 1, Song further teaches: further comprising determining whether controlling the UE allocation based on the determined effect on the net power consumption of the communications network has resulted in UE connection loss or terminating of existing UE data sessions (Fig. 5 and para [0063] by Google Translate: Figure 5 is a graph illustrating the total number of handovers as a function of varying handover costs. Figure 5 demonstrates that the total number of handovers decreases as the handover cost increases. This implies that, from the perspective of power saving, maintaining the connection with the current base station (BS)—even if the link quality with that BS is relatively inferior to that of other BSs—can be an effective strategy, provided that the nominal network service rate is guaranteed through dynamic power control. The rationale for this is that performing a handover incurs a specific cost, and this handover cost (`power consumption’) may potentially exceed the transmission power savings realized through the handover itself.) (Examiner’s note: Due to inaccuracies found in the WIPO machine translation of para [0063] of Song included in this Office Action, the Google Translate version has been used instead.) The Original Korean text of para [0063] is reproduced herein below for reference.)
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Regarding claim 20, Song and Raymond teach The method of claim 1, Raymond further teaches: further comprising, , if it is determined that UE connection loss or termination of existing UE data sessions occurred above a predetermined frequency, ceasing control of the UE allocation based on the determined effect on the net power consumption of the communications network (para [0043] of Raymond: when the mobile device moves outside of the area that the current UPF can serve. This typically implies a change of IP address, the session may be interrupted for many seconds and many packets could be lost.) (para [0064] of Raymond: Illustratively, the current factors can include currently measured communication network metrics … packet drop rate, or communication quality;) (interpreted as “UE connection loss or termination of existing UE data sessions occurred above a predetermined frequency, ceasing control of the UE allocation”) (para [0054] of Raymond: The training engine 302 can do this by analyzing past events to enable or improve the handover predictions by the prediction engine 304. It can also analyze quality of service data, to build other models for resource pre-allocation that can better decide if the cost of an early handover resource allocation is justified.) (para [0058] of Raymond: Based on the predicted state, the policy engine 306 can weigh the tradeoff between resource allocation for early handover (e.g., having implications associated with using some resources on a target cell) and maintaining or improving quality of service and experience. This tradeoff can be decided based on pre-defined logic, output of other machine learning or AI model(s) trained on past data, combination of the same or the like. The more contextual information is available to the policy engine 306, the more optimal pre-allocation decision 408 can be made based on the tradeoff analysis (interpreted as “based on the determined effect on the net power consumption of the communications network”). Once the desired resource pre-allocation 408 is determined by the policy engine 306, it controls the execution engine 308 to implement corresponding resource configuration, setting, or other utilization in related cells in anticipation of predicted handovers.) (para [0066] of Raymond: The IME 102 can analyze the current and/or predicted factors in accordance with various user-defined rules or based on output from applicable machine learning or other AI models for resource pre-allocation. The user-defined rules or pre-allocation models can take into account certain selected subset of the current and/or predicted factors, determine a tradeoff between the pre-allocation cost and quality of service gain, and make optimized resource pre-allocation decisions in accordance with the tradeoff determination.)
Regarding claim 44, Song and Raymond teach A computer program product comprising a non-transitory computer-readable medium comprising instructions which, when executed on processing circuitry, cause the processing circuitry to perform the method according to claim 1 (para [0077]: The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination.).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WON JUN CHOI whose telephone number is (703)756-1695. The examiner can normally be reached MON-FRI 08:00 - 17:00.
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/WON JUN CHOI/Examiner, Art Unit 2411 /DERRICK W FERRIS/Supervisory Patent Examiner, Art Unit 2411
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