DETAILED ACTION
This Office Action is in response to the application filed on 03/11/2025.
Claims 1-12 have been amended prior to examination.
Claims 1-12 are presented for examination.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/11/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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, 5, 7-8, 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cionca et al. (hereinafter Cionca, US 2024/0040396 A1) in view of Fox et al. (hereinafter Fox, US 2012/0129517 A1).
Regarding Claim 1, Cionca discloses A method for use in a wireless communication network (Cionca: para.0064 “The process 400 determines (at 440) load capacities for transport links that connect potential UEs in the geographic area to the telecommunications network…. as well as the transport links 125 … The transport links of some embodiments include wired and wireless links, …” wireless network.), the method comprising:
generating a network traffic prediction indicative of a number of user terminals in a target area under radio coverage by the wireless communication network during a first time period following a second time period (Cionca: para.0062 “The process 400 determines (at 420) a predicted UE population density for the geographic area.” Para.0121 “For example, in some embodiments, a user may provide historical population density data for a region, which can be used outright, or used to generate estimates for current and/or future population density.” Para.0003 “ for a potential deployment of the telecommunications network for a particular geographic area, a model that includes a potential access network, a potential edge network, and a potential core network.” Para.0065 “The process determines (at 450) compute resource allocation for components of the potential deployment of the telecommunications network.” The population density of UEs is predicted for a geographic area, i.e. number of user terminals in target area for modifying the telecommunication network for the area, for a future time, i.e. first time period, after a second time period, i.e. the current time period see also para.0006 “the deployment or Radio Units (RUs) (i.e., access nodes) in the Radio Access Network (RAN)”), the generating comprising:
first generating a first operative prediction of a number of user terminals in the target area during the first time period based on historical user terminal density indicators indicative of a density of user terminals in the target area in a past time period occurred before the second time period (Cionca: Fig. 4 420 para.0062 “The process 400 determines (at 420) a predicted UE population density for the geographic area. In some embodiments, the predicted UE population density is determined based on actual population data for the geographic area (e.g., historical population data obtained through a census),” Para.0121 “For example, in some embodiments, a user may provide historical population density data for a region, which can be used outright, or used to generate estimates for current and/or future population density.” the prediction for future population density, i.e. a first time period, is calculated based on historical population density for the region, that is information from a period before the current time period.);
second generating a second operative prediction of the number of user terminals in the target area during the first time period based on a combination of the historical user terminal density indicators and user terminal position indicators indicative of a movement of user terminals at the target area during the second time period (Cionca: Fig.4 460-480 para.0067 “… When the process determines that the simulation was not successful, the process transitions to modify (at 480) the potential deployment. In some embodiments, a network administrator may modify the quantity of resources allocated, the locations of the allocated resources, the locations of the access nodes, etc. to achieve a desired result (e.g. to meet a latency requirement of a particular application).” para.0116 “FIG. 23 illustrates the UI of some embodiments after modifications to compute resource deployments have been made. In this example, indications of compute resource utilization differ from the graphs illustrated by the examples described above, with some datacenters (or MECs) and edges in the graph 2320 having increased utilization, and others having decreased utilization. In some embodiments, the changes in utilization may also be due to changes in behavior by end-users (e.g., an increased or decreased number of end-users in a particular region). In some such embodiments, these changes may be based on real-time data for a particular region, or based on estimates and predictions for future behavior (e.g., an upcoming event, such as a convention, for which end-user numbers are expected to significantly increase for a region).” Para.0150 “ Like the embodiments described above, the population density of UEs is determined in some embodiments using estimates of current and/or future UEs in the geographic area, historical population density data, current population density data, or a combination of any of these.” A second prediction for density is generated based on predicted future, as described in para.150 this may include historical population density data as well as future UE estimates, and reflected in the updated interface as displayed in Fig. 23. Fig. 23 occurs after modifications are made to a previous simulation result as seen in the loop in Fig. 4 460-480, however is described to have differing utilization in certain areas due to additional prediction of user movement in the area based on upcoming events.); and
providing, at the second time period, the network traffic prediction based on a selected one between the first operative prediction and the second operative prediction (Cionca: para.0067 “ When the process determines that the simulation was not successful, the process transitions to modify (at 480) the potential deployment. …The process then returns to 460 to simulate the performance of the potential deployment. When the simulation is determined to be successful, the process 400 transitions to deploy (at 490) the modeled telecommunications network for the geographic area. Following 490, the process 400 ends.” Upon simulation being successful, i..e in a second iteration using the second prediction, a network traffic prediction, the modeled telecommunications network, is provided, based on the second operative prediction.); and
regulating function parameters of the wireless communication network based on the network traffic prediction (Cionca: para.0067 “ When the process determines that the simulation was not successful, the process transitions to modify (at 480) the potential deployment. In some embodiments, a network administrator may modify the quantity of resources allocated, the locations of the allocated resources, the locations of the access nodes, etc. to achieve a desired result (e.g. to meet a latency requirement of a particular application). The process then returns to 460 to simulate the performance of the potential deployment. When the simulation is determined to be successful, the process 400 transitions to deploy (at 490) the modeled telecommunications network for the geographic area. Following 490, the process 400 ends.” The modeled telecommunications network represents the various function parameters of the network including at least quantities of allocated resources. This allocation is deployed onto the network.).
However while Cionca discloses using real time user location information, and information related to user movement based on upcoming events, Cionca does not explicitly disclose second generating a second operative prediction of the number of user terminals in the target area during the first time period based on a combination of the historical user terminal density indicators and real time user terminal position indicators indicative of a movement of user terminals at the target area during the second time period, as Cionca discloses real time OR based on estimates…upcoming event.
Fox discloses second generating a second operative prediction of the number of user terminals in the target area during the first time period based on a combination of the historical user terminal density indicators and real time user terminal position indicators indicative of a movement of user terminals at the target area during the second time period (Fox: para.0084 “The controller may be operable to predict future location of mobile terminals based on measured location, or linkage of mobile terminal and known route of vehicle.” para.0509 “ This may predict future movement and speed of a mobile device based on historical location, speed and direction information.” para.0526-0527 “At step 14D the speed, density and direction of travel of terminal served by the platform are calculated. If the location of a particular terminal is calculated at two spaced apart time intervals, then the speed and direction of travel can be calculated. If the location of each terminal (or a selection of the terminals) served by the platform 700 can be detected, then the density of mobile terminals at particular locations served by the platform 700 can be calculated. At step 14E the platform may predict the future speed, density and direction of travel of terminals served by the platform.” Historical information, and current information are used to calculate speed, direction ,and ultimately predicted density of devices served by the telecommunication network at a location.).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Cionca with Fox in order to incorporate second generating a second operative prediction of the number of user terminals in the target area during the first time period based on a combination of the historical user terminal density indicators and real time user terminal position indicators indicative of a movement of user terminals at the target area during the second time period, such that the real time position information is used in conjunction with movement information to predict density.
One of ordinary skill in the art would have been motivated to combine because of the expected benefit of properly servicing the number of terminals at a location based on movement information (Fox: para.0525-0528, para.0102 “radio capacity…radio resource”).
Regarding Claim 2, Cionca-Fox discloses Claim 1 as set forth above.
However Cionca does not explicitly disclose wherein the historical user terminal density indicators comprise at least one among: -a number of user terminals connected to the wireless communication network; -a number of user terminals whose last interaction with the wireless communication network occurred in a cell of the wireless communication network corresponding to the target area; -a number of active user terminals; and -traffic volumes caused by user terminals connected to the wireless communication network, collected in the past time period which occurred before the second time period.
Fox discloses wherein the historical user terminal density indicators comprise at least one among:
-a number of user terminals connected to the wireless communication network (Fox: para.0513 “The location data of the mobile devices may be used to determine the density of mobile terminals at a particular location. For example, the number of mobile terminals in a particular street may be determined. … The platform 700 may further be operable to predict the future density of mobile terminals at a particular location, and to provide this information to application 1410 for controlling street lights or other external resources based on the predicted mobile terminal density.” Para.0102 “ The external resource may additionally or alternatively be radio capacity/capability availability (for example, depending upon radio interference and cell congestion). The resource availability may include radio load of cell, radio resource available to the terminal; the resources available on backhaul or application servers in the network.” Para.0526-0527 “At step 14D the speed, density and direction of travel of terminal served by the platform are calculated. If the location of a particular terminal is calculated at two spaced apart time intervals, then the speed and direction of travel can be calculated….At step 14E the platform may predict the future speed, density and direction of travel of terminals served by the platform.” A number of active terminals serviced by the platform, i.e. connected to the wireless network is used to predict the density);
-a number of user terminals whose last interaction with the wireless communication network occurred in a cell of the wireless communication network corresponding to the target area;
-a number of active user terminals (Fox: para.0513 “The location data of the mobile devices may be used to determine the density of mobile terminals at a particular location. For example, the number of mobile terminals in a particular street may be determined. … The platform 700 may further be operable to predict the future density of mobile terminals at a particular location, and to provide this information to application 1410 for controlling street lights or other external resources based on the predicted mobile terminal density.” Para.0102 “ The external resource may additionally or alternatively be radio capacity/capability availability (for example, depending upon radio interference and cell congestion). The resource availability may include radio load of cell, radio resource available to the terminal; the resources available on backhaul or application servers in the network.” Para.0526-0527 “At step 14D the speed, density and direction of travel of terminal served by the platform are calculated. If the location of a particular terminal is calculated at two spaced apart time intervals, then the speed and direction of travel can be calculated….At step 14E the platform may predict the future speed, density and direction of travel of terminals served by the platform.” A number of active terminals serviced by the platform, i.e. connected to the wireless network is used to predict the density); and
-traffic volumes caused by user terminals connected to the wireless communication network, collected in the past time period which occurred before the second time period.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Cionca with Fox in order to incorporate wherein the historical user terminal density indicators comprise at least one among: -a number of user terminals connected to the wireless communication network; -a number of user terminals whose last interaction with the wireless communication network occurred in a cell of the wireless communication network corresponding to the target area; -a number of active user terminals; and -traffic volumes caused by user terminals connected to the wireless communication network, collected in the past time period which occurred before the second time period.
One of ordinary skill in the art would have been motivated to combine because of the expected benefit of properly servicing the number of terminals at a location based on current location and movement information (Fox: para.0525-0528, para.0102 “radio capacity…radio resource”).
Regarding Claim 3, Cionca-Fox discloses claim 1 as set forth above.
However Cionca does not explicitly disclose wherein the real time user terminal position indicators comprise at least one among: -GNSS records each containing at least a GNSS position of a user terminal located at the target area during the second time period; and -user terminal position indicators generated by interactions of user terminals at the target area with the wireless communication network during the second time period .
Fox discloses wherein the real time user terminal position indicators comprise at least one among: -GNSS records each containing at least a GNSS position of a user terminal located at the target area during the second time period; and -user terminal position indicators generated by interactions of user terminals at the target area with the wireless communication network during the second time period (Fox: para.0079 “The information collected by the controller may provide an indication of the location of the mobile terminals. Such information might be the time it takes for a data packet sent to a mobile terminal to be acknowledged by that mobile terminal. It could be other timing information. For example, the location of the mobile terminal could be determined by cell triangulation or any other suitable technique. Information from a GPS receiver of the mobile terminal may also be used to determine the location of the mobile terminal.” User interaction at the location and GPS information are used to determine location of the user in the second time period.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Cionca with Fox in order to incorporate wherein the real time user terminal position indicators comprise at least one among: -GNSS records each containing at least a GNSS position of a user terminal located at the target area during the second time period; and -user terminal position indicators generated by interactions of user terminals at the target area with the wireless communication network during the second time period.
One of ordinary skill in the art would have been motivated to combine because of the expected benefit of properly servicing the number of terminals at a location based on current location and movement information (Fox: para.0525-0528, para.0102 “radio capacity…radio resource”).
Regarding Claim 5, Cionca-Fox discloses claim 1 as set forth above.
However Cionca does not explicitly disclose wherein the second generating the second operative prediction comprises comparing the historical user terminal density indicators with the real time user terminal position indicators.
Fox discloses wherein the second generating the second operative prediction comprises comparing the historical user terminal density indicators with the real time user terminal position indicators (Fox: Para.0526-0527 “At step 14D the speed, density and direction of travel of terminal served by the platform are calculated. If the location of a particular terminal is calculated at two spaced apart time intervals, then the speed and direction of travel can be calculated….At step 14E the platform may predict the future speed, density and direction of travel of terminals served by the platform.” A number of active terminals serviced by the platform, i.e. connected to the wireless network is used to predict the density. This is performed by comparing 2 locations of the terminal, one past and one current to calculate the predicted location).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Cionca with Fox in order to incorporate wherein the second generating the second operative prediction comprises comparing the historical user terminal density indicators with the real time user terminal position indicators.
One of ordinary skill in the art would have been motivated to combine because of the expected benefit of properly servicing the number of terminals at a location based on current location and movement information (Fox: para.0525-0528, para.0102 “radio capacity…radio resource”).
Regarding Claim 7, Cionca-Fox discloses claim 1 as set forth above.
Cionca further discloses wherein the providing further comprises providing, at the second time period, the network traffic prediction based on the second operative prediction if the first operative prediction is indicative of a peak in the number of user terminals in the target area (Cionca: Fig. 4 420 para.0062 “The process 400 determines (at 420) a predicted UE population density for the geographic area. In some embodiments, the predicted UE population density is determined based on actual population data for the geographic area (e.g., historical population data obtained through a census),” Para.0121 “For example, in some embodiments, a user may provide historical population density data for a region, which can be used outright, or used to generate estimates for current and/or future population density.” para.0116 “FIG. 23 illustrates the UI of some embodiments after modifications to compute resource deployments have been made. … In some embodiments, the changes in utilization may also be due to changes in behavior by end-users (e.g., an increased or decreased number of end-users in a particular region). In some such embodiments, these changes may be based on real-time data for a particular region, or based on estimates and predictions for future behavior (e.g., an upcoming event, such as a convention, for which end-user numbers are expected to significantly increase for a region).” the prediction for future population density, i.e. a first time period, is calculated based on historical population density for the region. This is the peak number of devices predicted to be at that location. Based on Fig. 4, this simulation then loops top step 480 after determining the simulation failed, i.e. based on the peak number of users as predicted, and the second result may be used in the second iteration in para.0067, and step 490 in Fig. 4.).
Regarding Claim 8, Cionca-Fox discloses claim 1 as set forth above.
Cionca further discloses receiving calendar data providing indications of scheduled increases in the number of user terminals in the target area (Cionca: para.0116 “FIG. 23 illustrates the UI of some embodiments after modifications to compute resource deployments have been made. In this example, indications of compute resource utilization differ from the graphs illustrated by the examples described above, with some datacenters (or MECs) and edges in the graph 2320 having increased utilization, and others having decreased utilization. In some embodiments, the changes in utilization may also be due to changes in behavior by end-users (e.g., an increased or decreased number of end-users in a particular region). In some such embodiments, these changes may be based on real-time data for a particular region, or based on estimates and predictions for future behavior (e.g., an upcoming event, such as a convention, for which end-user numbers are expected to significantly increase for a region).” When modifications are made, and over time, the UI of figures 20-23 update based on new information that affect density of users. This includes obtaining calendar data indicative of scheduled events.) and
providing, at the second time period, said the network traffic prediction based on the second operative prediction if the calendar data provides an indication of an increase in the number of user terminals in the target area scheduled for the first time period (Cionca: para.0067 “ When the process determines that the simulation was not successful, the process transitions to modify (at 480) the potential deployment. …The process then returns to 460 to simulate the performance of the potential deployment. When the simulation is determined to be successful, the process 400 transitions to deploy (at 490) the modeled telecommunications network for the geographic area. Following 490, the process 400 ends.” Upon simulation being successful, i..e in a second iteration using the second prediction after modifications are made in Fig. 4 460-480, a network traffic prediction, the modeled telecommunications network, is provided, based on the second operative prediction based on an increase due to upcoming event.).
Regarding Claim 10, Cionca-Fox discloses claim 1 as set forth above.
Cionca further discloses wherein the providing further comprises providing, at the second time period, said the network traffic prediction based on the second operative prediction if the second operative prediction is indicative of a peak in the number of user terminals in the target area (Cionca: para.0067 “ When the process determines that the simulation was not successful, the process transitions to modify (at 480) the potential deployment. …The process then returns to 460 to simulate the performance of the potential deployment. When the simulation is determined to be successful, the process 400 transitions to deploy (at 490) the modeled telecommunications network for the geographic area. Following 490, the process 400 ends.” para.0116 “FIG. 23 illustrates the UI of some embodiments after modifications to compute resource deployments have been made. In this example, indications of compute resource utilization differ from the graphs illustrated by the examples described above, with some datacenters (or MECs) and edges in the graph 2320 having increased utilization, and others having decreased utilization. In some embodiments, the changes in utilization may also be due to changes in behavior by end-users (e.g., an increased or decreased number of end-users in a particular region). In some such embodiments, these changes may be based on real-time data for a particular region, or based on estimates and predictions for future behavior (e.g., an upcoming event, such as a convention, for which end-user numbers are expected to significantly increase for a region).” Upon simulation being successful, i.e. in a second iteration, a network traffic prediction, the modeled telecommunications network, is provided, based on the second operative prediction. The second density predicted is a peak number of users to be at that area during the upcoming time period.).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cionca et al. (hereinafter Cionca, US 2024/0040396 A1) in view of Fox et al. (hereinafter Fox, US 2012/0129517 A1) in view of Tran et al. (hereinafter Tran, US 2023/0421994 A1).
Regarding Claim 4, Cionca-Fox disclose claim 1 as set forth above.
However Cionca-Fox does not explicitly disclose wherein said the first generating the first operative prediction comprises processing the historical user terminal density indicators through a machine learning algorithm.
Tran discloses wherein said the first generating the first operative prediction comprises processing the historical user terminal density indicators through a machine learning algorithm (Tran: para.0012 “The method can include predicting a real-time distribution of the multiple wireless devices in the geographic area based on the ML model. The real-time distribution can be predicted based on a pattern of network activities and locations of wireless devices at prior points in time that are analogous to a current point in time. The method can also include dynamically generating an estimate of a distribution density of the multiple wireless devices in the geographic area based on the predicted real-time distribution, adjusted by the real-time count of network activities.” Historical location information is fed to a machine learning model to determine the distribution density of devices in an area).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Cionca-Fox with that of Tran in order to incorporate wherein said the first generating the first operative prediction comprises processing the historical user terminal density indicators through a machine learning algorithm.
One of ordinary skill in the art would have been motivated to combine because of the expected benefit of improved accuracy and precision in predicting density (Tran: para.0002).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cionca et al. (hereinafter Cionca, US 2024/0040396 A1) in view of Fox et al. (hereinafter Fox, US 2012/0129517 A1) in view of Zhou et al. (hereinafter Zhou, US 2017/0278382 A1) in view of Onishi et al. (hereinafter Onishi, US 2023/0412303 A1).
Regarding Claim 9, Cionca-Fox discloses claim 1 as set forth above.
However Cionca-Fox does not explicitly disclose wherein the first generating comprises: - subdividing a range of possible values for the first operative prediction into a set of sub-intervals, and - calculating a prediction probability distribution providing for each sub-interval an indication of a reliability of the first operative prediction corresponding to the values of the sub-interval, wherein the providing, at the second time period, said the network traffic prediction is based on the second operative prediction if the prediction probability distribution is lower than a reliability threshold for all the sub-intervals.
Zhou discloses wherein the first generating comprises: - subdividing a range of possible values for the first operative prediction into a set of sub-intervals (Zhou: para.0038 “For example, the electronic device may select a weight coefficient a of a mean square error according to a series of historical crowd gathering abnormal events, such that the crowd gathering abnormal event will occur in a relatively large probability within a period after the historical search traffic for the occurrence location of a crowd gathering abnormal event on the online map exceeds a historical search traffic threshold w′.sub.m. The historical search traffic threshold w′.sub.m satisfies w′.sub.m=.sub.pm+α* σ.sub.pm, wherein u.sub.pm is the mean of the probability distribution, and σ.sub.pm is the mean square error of the probability distribution….Assuming that the peak value of the number of positioning devices at a location within a set period (for example, an hour) every day is pq(d) and the mean and variance of the probability distribution p(d) to which pq(d) conforms are respectively u.sub.pq and σ.sub.pq.sup.2” Fig. 3, a range of possible values is subdivided by hour as seen in Fig. 3.), and
- calculating a prediction probability distribution providing for each sub-interval an indication of a reliability of the first operative prediction corresponding to the values of the sub-interval (Zhou: para.0038 “Then, the sum of a product of the weight coefficient and the mean square error and the mean of the probability distribution is set as the search traffic threshold. That is, the search traffic threshold can be calculated by a formula pm w.sub.m=u.sub.pm+α*σ.sub.pm, wherein w.sub.m is the search traffic threshold, u.sub.pm is the mean of the probability distribution, σ.sub.pm is the mean square error of the probability distribution, and α is the weight coefficient of the mean square error of the probability distribution. … Assuming that the peak value of the number of positioning devices at a location within a set period (for example, an hour) every day is pq(d) and the mean and variance of the probability distribution p(d) to which pq(d) conforms are respectively u.sub.pq and σ.sub.pq.sup.2, wherein the positioning devices may be portable mobile devices, it is set that the crowd gathering abnormal event occurs once the number of the positioning devices at the location within the set period is larger than u.sub.pq+3σ.sub.pq. Step 203, send early warning information of a crowd gathering risk if the search traffic exceeds the preset search traffic threshold.” For each time period, a probability distribution is calculated, indicating a reliability of the crowd gathering exceeding a value in a location. Wherein W is calculated based on probability distribution for the time frames, and when a time frame of the set of time frames exceed traffic threshold, i.e. is reliable for that time frame, it indicates the high density of people at that location for each time frame of the set of time frames.);
not providing, at the second time period, said the network traffic prediction if the prediction probability distribution is lower than a reliability threshold for all the sub-intervals (Zhou: para.0038 “Assuming that the peak value of the number of positioning devices at a location within a set period (for example, an hour) every day is pq(d) and the mean and variance of the probability distribution p(d) to which pq(d) conforms are respectively u.sub.pq and σ.sub.pq.sup.2, wherein the positioning devices may be portable mobile devices, it is set that the crowd gathering abnormal event occurs once the number of the positioning devices at the location within the set period is larger than u.sub.pq+3σ.sub.pq. Step 203, send early warning information of a crowd gathering risk if the search traffic exceeds the preset search traffic threshold.” If the sum of the probability distributions is lower than a threshold for each time period, i.e. all of the sub intervals, then no warning would be sent out, as there is no prediction of a crowd gathering risk.)
Therefore it would have been obvious to one of ordinary skill in the art before ethe effective filing date of the claimed invention to combine Cionca-Fox with that of Zhou in order to incorporate wherein the first generating comprises: - subdividing a range of possible values for the first operative prediction into a set of sub-intervals, and - calculating a prediction probability distribution providing for each sub-interval an indication of a reliability of the first operative prediction corresponding to the values of the sub-interval; not providing, at the second time period, said the network traffic prediction if the prediction probability distribution is lower than a reliability threshold for all the sub-intervals
One of ordinary skill in the art would have been motivated to combine because of the expected benefit of improved accuracy when calculating user density in a location (Zhou para.0038, para.0051).
However Cionca-Fox-Zhou does not explicitly disclose wherein the providing, at the second time period, said the network traffic prediction is based on the second operative prediction if the prediction probability distribution is lower than a reliability threshold for all the sub-intervals.
Onishi discloses wherein the providing, at the second time period, said the network traffic prediction is based on the second operative prediction if the prediction probability distribution is lower than a reliability threshold (Onishi: para.0116 “the expected value includes a plurality of expected values corresponding to the plurality of probability distributions, and the at least one processor is configured to select a value of the second parameter from among one or more candidate values corresponding to one or more probability distributions corresponding to one or more expected values that satisfy the BLER or reliability requirement.” Fig. 5, from a plurality of distributions, one that satisfies a reliability requirement is selected. For example if there are 2 predictions, a second prediction may be selected that meets the reliability requirement.).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Cionca-Fox-Zhou with that of Onishi in order to incorporate wherein the providing, at the second time period, said the network traffic prediction is based on the second operative prediction if the prediction probability distribution is lower than a reliability threshold, and apply this concept to the probability distributions generated in Cionca-Fox-Zhou.
One of ordinary skill in the art would have been motivated to combine because of the expected benefit of selecting the most reliability result of a plurality of results (Onishi: para.0116).
Claim(s) 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cionca et al. (hereinafter Cionca, US 2024/0040396 A1) in view of Fox et al. (hereinafter Fox, US 2012/0129517 A1) in view of Mahdi et al. (hereinafter Madhi, US 2020/0336376 A1).
Regarding Claim 11, Cionca-Fox discloses claim 1 as set forth above.
However Cionca-Fox does not explicitly disclose wherein the regulating is performed by a self-organizing-network module.
Mahdi discloses wherein the regulating is performed by a self-organizing-network module (Mahdi: para.0002 “More particularly, the present disclosure relates to a next-generation network architecture for connect, compute, sense, store, and act resources, and specifically a Self-Optimizing Fabric (SOF) architecture, a self-assembling network that utilizes the SOF architecture, and the like.” Para.0274 “FIG. 22 is a network diagram of a self-assembling wireless network 400. The self-assembling wireless network 400 includes a self-assembling grouping of flying wireless nodes 402 (e.g., LTE/5G edge nodes, access points, etc.). The network can assemble in as efficient a configuration as possible, including rerouting around failed nodes, adjusting topology to handle loads including the movement of nodes, etc. One or more of the flying nodes can include a fiber tether and/or satellite uplink 404 for backhaul. In an embodiment, the flying wireless nodes 402 can be drone-based.” Para.0274 “The self-assembling wireless network 400 includes a self-assembling grouping of flying wireless nodes 402 (e.g., LTE/5G edge nodes, access points, etc.). The network can assemble in as efficient a configuration as possible, including rerouting around failed nodes, adjusting topology to handle loads including the movement of nodes, etc.” See also para.0280, 0285-0286. The system is a self assembling network, i.e. self-organizing, wherein drones are flown to areas that need additional processing or access points to distribute load.).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Cionca-Fox-Mahdi in order to incorporate wherein the regulating is performed by a self-organizing-network module, such that the regulating as performed in Cionca-Fox, that changes network operation and deploys access points as part of the modeled communication network in para.0128, is performed by a self organizing network module that performs a similar function but using drones.
One of ordinary skill in the art would have been motivated to combine because of the expected benefit of improved efficiency of solving network issues (Mahdi: para.0274).
Regarding Claim 12, Cionca-Fox discloses claim 1 as set forth above.
However Ciona-Fox does not explicitly disclose driving a drone equipped with a cell site of the wireless communication network to the target area based on the network traffic prediction.
Mahdi discloses driving a drone equipped with a cell site of the wireless communication network to the target area to handle loads including the movement of nodes (Mahdi: Para.0274 “FIG. 22 is a network diagram of a self-assembling wireless network 400. The self-assembling wireless network 400 includes a self-assembling grouping of flying wireless nodes 402 (e.g., LTE/5G edge nodes, access points, etc.). The network can assemble in as efficient a configuration as possible, including rerouting around failed nodes, adjusting topology to handle loads including the movement of nodes, etc. One or more of the flying nodes can include a fiber tether and/or satellite uplink 404 for backhaul. In an embodiment, the flying wireless nodes 402 can be drone-based.” Para.0274 “The self-assembling wireless network 400 includes a self-assembling grouping of flying wireless nodes 402 (e.g., LTE/5G edge nodes, access points, etc.). The network can assemble in as efficient a configuration as possible, including rerouting around failed nodes, adjusting topology to handle loads including the movement of nodes, etc.” See also para.0280, 0285-0286. The system is a self assembling network, i.e. self-organizing, wherein drones are flown to areas that need additional processing or access points to distribute load.).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Cionca-Fox-Mahdi in order to incorporate driving a drone equipped with a cell site of the wireless communication network to the target area to handle loads including the movement of nodes, and apply this concept to Cionca-Fox that solves issues based on network traffic prediction.
One of ordinary skill in the art would have been motivated to combine because of the expected benefit of improved efficiency of solving network issues (Mahdi: para.0274).
Allowable Subject Matter
Claim 6 is objected to as being dependent upon a rejected base claim but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tofighbakhsh et al. US 2020/0322814 A1, para.0017, para.0023, para.0030 Fig. 7 predicting areas of lower and higher densities of users in a location for providing additional network coverage.
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/EUI H KIM/Examiner, Art Unit 2453
/KAMAL B DIVECHA/Supervisory Patent Examiner, Art Unit 2453
