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 .
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.
Claim(s) 1, 2, 4, 6-10, 12, 14-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20190239086 A1 (SHEKALIM et al.) (hereinafter SHEKALIM) in view of US 20180007572 A1 (Chou et al.) (hereinafter Chou) in view of US 20160080248 A1 (Rijnders et al.) (hereinafter Rijnders) and in further view of US 20230072676 A1 (GOMES et al.) (hereinafter GOMES).
In re claims 1, 9 and 17, SHEKALIM discloses a system for dynamic monitoring of coverage level in an area (Fig. 3, [0019], “FIG. 3 illustrates a system architecture for mitigating at least one coverage hole from a coverage area served by at least one serving cell”. [0021], “The input unit [304] may further be configured to transmit the at least one first cell parameter, the at least one second cell parameter, the coverage hole information of the at least one coverage hole (Fig. 1: 108) and the transmission parameter of the at least one serving cell (Fig. 2: 102A to 102L) to the optimizer unit [306]”) and a method (Fig. 4) comprising: a processor, and a memory including a non-transitory computer-readable storage medium storing computer-readable instructions for dynamic monitoring of coverage level in an area, that when executed by a processor, cause the processor to perform actions comprising:
monitoring signals from a plurality of nodes that provide services to the area (Fig. 1-2, [0006], “Embodiments of the present disclosure relate to a method for mitigating at least one coverage hole from a coverage area served by at least one serving cell”. [0017], “plurality of serving cells 102A to 102L”. [0009], “Fig. 1 and FIG. 2 illustrate an exemplary cellular network having a plurality of serving cells and a coverage hole caused either due to obstructions in radio paths of the plurality of serving cells or due to lack of sufficient transmission power of the plurality of serving cells”. [0014], “The “at least one serving cell” as used herein may refer to one or more cells which provide a network coverage to a geographic coverage area, thus the geographic area served by the one or more cells is termed as coverage area of the one or more cells. Further, the coverage area may include at least one coverage hole”), wherein at least some of the pluralities of nodes are associated with a first organization;
determining, based at least in part on the signals, that at least one of the plurality of nodes is out of service ([0017], “More particularly, the coverage hole may be caused not only because of direct obstacles, but also due to a large distance and/or terrain/collateral characteristics of the area and therefore, may lack sufficient transmission power of the plurality of serving cells” (outage). [0020], “Moreover, the at least one first cell parameter comprises at least one of a drive test measurement data, a RF coverage power (RSRP), a Reference Signal (RS) strength, a SINR, a Channel Quality Indicator (CQI), a MCS, a call drop rate, a handover success rate, a number of handovers, a RRC re-establishment, a QOS of the at least one serving cell [102A to 102L] and an antenna height” (signal parameters monitored for cell outage));
determining an explicit coverage level, based at least in part on: population data from multiple sub-areas of the area (Fig. 2, [0020], “The at least one second cell parameter comprises at least one of a number of users (population) served by the at least one serving cell (102A to 102L), a traffic volume, an area size and a number of sessions of user activity, wherein the user activity may correspond to a communication from or towards the user/user equipment” (number of users served by atleast one serving cell is interpreted as population data from multiple subareas of the serving cell), those nodes of the plurality of nodes currently in service in the area, and an indication of a population of the area provided with corresponding services ([0021], “In an exemplary embodiment, the percentage of contribution (POC) for each of the at least one first optimum cell and the at least one second optimum cell is determined using a plurality of parameters including, but not limiting to, a potential traffic load, the number of users, existing of good coverage from competitor companies (services), the percentage of potential contribution area”), wherein the indication is derived by adding a portion of the population data from the multiple sub-areas of the area;
determining that the explicit coverage level in the area is less than a required coverage threshold ([0003], “The 3GPP specification describes a coverage hole as an area with poor coverage where the pilot signal strength is below the threshold which is required by a User Equipment (UE) to maintain an access to the network, or the SINRs of both serving and neighbor cells is below a level needed to maintain a basic service”); and
communicating an outage notification to an emergency service provider, wherein the emergency service provider is a regulatory body mandating the outage notification or a second organization.
SHEKALIM does not explicitly disclose a system comprising: a processor, and a memory including a non-transitory computer-readable storage medium storing computer-readable instructions that when executed by a processor cause to perform operations including determining, based at least in part on the signals, that at least one of the plurality of nodes is out of service; determining that the explicit coverage level in the area is less than a required coverage threshold.
Chou discloses a system comprising: a processor (Fig. 8:804), and a memory (Fig. 8:810) including a non-transitory computer-readable storage medium ([0078], “Computer-readable media (including non-transitory computer-readable media), methods, systems and devices for performing...”) storing computer-readable instructions that when executed by a processor ([0071], “In various embodiments, volatile memory (e.g., DRAM 808), non-volatile memory (e.g., ROM 810), flash memory 812, and the mass storage device may include programming instructions configured to enable computing device 800, in response to execution by processor(s) 804”) cause to perform operations including determining, based at least in part on the signals, that at least one of the plurality of nodes is out of service (Fig. 1, [0023], “For example, in FIG. 1, UEs reporting acceptable performance metrics may be indicated by solid dots 106 (for clarity, only a few solid dots are labeled in the figure). UEs reporting unacceptable performance metrics may be indicated by “x” marks 108 in FIG. 1”. [0024], “multiple eNB’ s (nodes) (Fig. 2A: 204a-204g) provide service to one or more UEs located in their associated coverage cells”. [0003], “Service in an E-UTRAN may be compromised when a coverage hole arises due to, e.g., signal propagation attenuation, shadowing effects, signal interference, and object obstructions”. [0023], “In some embodiments, unacceptable performance is signaled by an RLF report from a UE. By analyzing the locations at which unacceptable performance occurs, a network management (NM) apparatus or other component of the network may identify the approximate boundaries of coverage hole 110”. [0014], “In particular, the data may be representative of service performance at a plurality of geographic locations covered by one or more cells of the E- UTRAN” (determining based on signal strengths, a coverage hole in a region). [0013], “In some embodiments, a network management (NM) apparatus may receive data representative of first and second radio link failure (RLF) reports including information related to respective disconnections of first and second UEs from an E-UTRAN. The NM apparatus may identify a hole in a coverage area of the E-UTRAN based at least in part on the first and second RLF reports, and may perform an automated coverage adjustment action (such as a coverage and capacity optimization (CCO) action) to reconfigure cell resources of the E-UTRAN based on the identified hole” (Fig. 4:406)); determining that the explicit coverage level in the area is less than a required coverage threshold (Fig. 3:326, [0031], “The data may be representative of service performance at a plurality of geographic locations covered by one or more cells of the E-UTRAN or other RAT network. For example, the data may include, for one or more of the pluralities of geographic locations, information such as a number of active UEs, upload or download physical resource block usage, internet protocol (IP) throughput, packet delay, drop rate, and/or loss rate” (data that can impact coverage level in a certain geographic location). [0023], “Unacceptable performance may include, for example, failure to achieve a desired level of signal strength (a threshold value) or the failure to successfully provide service to UE devices within a certain number of access attempts”. Claim 1, “Wherein the coverage area is service-deficient when the coverage area has insufficient signal strength to provide threshold connectivity to a Long-Term Evolution (LTE) network” (determine an explicit coverage level in the area below a threshold)).
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 SHEKALIM with Chou to provide a method and system for dynamic monitoring of service outage in a geographical area covered by multiple service nodes wherein the determination of explicit coverage level incorporates a population from various sub areas in the region of service. The advantage of doing so is having an optimized model to better estimate coverage level for a certain area so there is adequate coverage and signal strength sufficient to maintain connectivity in the network and the quality of service.
SHEKALIM and Chou do not explicitly disclose wherein at least some of the pluralities of nodes are associated with a first organization; communicating an outage notification to an emergency service provider, wherein the emergency service provider is a regulatory body mandating the outage notification or a second organization.
Rijnders discloses wherein at least some of the pluralities of nodes are associated with a first organization ([0018], “However, the network service provider may provide a solution to those customers who are not willing to contract up-front for network service redundancy. This solution is to allow the customer to purchase restoration-on-demand service, immediately upon the occurrence of a service outage...This is beneficial not only for the customers but also for the network service provider because it potentially relieves the burden of rushing the repair of the wired network connection” (service provider providing some relief to customers while they repair for disruption in their services). [0067], “the automation engine server 110 may communicate with the billing server to charge the account of the customer for using the cellular network connection 139 for the network services, only during the restoration period provided in the SLA” (billing the customers based on their contract with the service provider). [0022], “For example, during a network connection outage, the customer may receive a notification, from an automation engine server, that a trouble ticket has been created for the network outage and the network carrier is working on resolving the issue” (the nodes/cells are associated with the network service provider as they communicate with the customers during outage as they fix the issue)); communicating an outage notification to an emergency service provider, wherein the emergency service provider is a regulatory body mandating the outage notification or a second organization ([0057], “In addition, automation engine server 110 may communicate with local and national emergency servers (e.g., national hurricane center) to determine if the likely cause of the outage is a weather event. Based on the cause of the outage, automation engine server 110 may further estimate the required time to restore the wired network connection 120. Alternatively, automation engine server 110 may consult a database (stored in the automation engine server 110) that stores history of multiple outages and their corresponding responding periods to determine the restoration period for a network outage” (discloses scenarios where the service provider communicates to a second organization or agency regarding the outage for various reasons such as updating their database or to find restoration time in case of emergency such as hurricanes)).
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 SHEKALIM and Chou with Rijnders to provide a method and system for dynamic monitoring of service outage in a geographical area covered by multiple service nodes wherein the determination of explicit coverage level incorporates a population from various sub areas in the region of service. The advantage of doing so is having an optimized model to better estimate coverage level for a certain area so there is adequate coverage and signal strength sufficient to maintain connectivity in the network and the quality of service.
SHEKALIM, Chou and Rijnders do not explicitly disclose determining an explicit coverage level, based at least in part on: population data from multiple sub-areas of the area, those nodes of the plurality of nodes currently in service in the area, and an indication of a population of the area provided with corresponding services wherein the indication is derived by adding a portion of the population data from the multiple sub-areas of the area.
GOMES discloses determining an explicit coverage level, based at least in part on: population data from multiple sub-areas of the area ([0041], “One of the main issues faced by mobile telecommunication operators today is how best to divide an area of mobile coverage (e.g., area covered by radio access nodes 102, 103, 104) into most relevant segments, due to dynamics of the mobile phone usage that keep changing. These “relevant segments,” (sub areas) can be used to custom apply mobile performance improvement strategies to provide better service and user experience in the network”. [0002], “geo-segmenting a terrestrial region covered by a wireless communications network into clusters based on communication derived criteria or data” (sub areas receiving service through a network or nodes). [0003], “Some examples are the use of telephone country codes or area codes, and postal zip codes. In metropolitan areas, a city or town may be segmented into neighborhoods, zones, wards, districts, etc. In wireless communications, a network provider generally segments a region based on communication coverage. The segmentation may be dependent on cell coverage in the area and/or the volume of wireless communication traffic a certain node or cell tower handles”. [0064], “The subdividing module 803 can perform operations corresponding to the operation 302 for subdividing the Geohash area into subareas... The cluster generation module 805 can perform operations ... generate grouped Geohash subareas and further subareas, which are placed into cluster groupings based on threshold limits set for the parameter” (grouping different subareas into clusters based on threshold limit set for coverage levels in those subareas), those nodes of the plurality of nodes currently in service in the area (Fig. 1: 102, 103, 104, [0031], “The disclosure describes in detail the Geohash segmentation scheme in reference to a wireless communication network and devices wirelessly connected to the various transmission/receiving points (e.g., towers)”. [0038], “Within each area, wireless devices communicate with the respective radio access node(s) to provide services to users of the devices”. [0003], “For example, when wireless communications traffic increases to such a point for a region, a network provider of the wireless communications network may need to perform systematic analysis to determine how and where to deploy additional towers” (discloses the plurality of nodes in service in the area as may need to add more towers depending on traffic)), and an indication of a population of the area provided with corresponding services wherein the indication is derived by adding a portion of the population data from the multiple sub-areas of the area ([0042], “A self-organizing network (SON) associated with the network node 101 may use an optimization manager to automate network optimization”. [0038], “FIG. 1 shows the density of terminal devices 112, 113, 114 to correspond to the density of the radio access nodes for the groupings”. [0039], “FIG. 1 shows three grouping of radio access nodes 102, 103, 104 in order to illustrate that network 100 coverage areas of terrestrial regions can vary significantly, such as due to population density of people using the terminal devices (e.g., urban, suburban, remote)”. [0044], “In areas of higher density of the radio access nodes, the Geohash grid(s) can each be segmented into further 32 smaller sub-grids...By covering smaller areas, each such area will have lesser number of radio access nodes. Thus, in reference to the example of FIG. 1, the radio access nodes 102 may undergo a number of iterations of segmentation until a desired density of access nodes is reached for a sub-divided grid”. [0062], “Other than metrics related to wireless (radio) access nodes and related data, Geohash based auto segmentation can be applied to many other inputs, including, but not limited to, identifying and clustering: areas with high disease or pandemic density, areas with high fire incidence, areas of tornado activity, automotive traffic, emergency calls, population density, power outages...” (through this Geohash technique and SON automated data collection and analysis, population data from multiple sub areas of the area are considered for coverage analysis and is broken into grids to find optimized number of cells to provide service in that area)).
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 SHEKALIM, Chou and Rijnders with GOMES to provide a method and system for dynamic monitoring of service outage in a geographical area covered by multiple service nodes wherein the determination of explicit coverage level incorporates a population from various sub areas in the region of service. The advantage of doing so is having an optimized model to better estimate coverage level for a certain area so there is adequate coverage and signal strength sufficient to maintain connectivity in the network and the quality of service.
In re claims 2 and 10, the combination discloses the method of claim 1 and the system of claim 9, wherein GOMES discloses wherein the sub-areas are a plurality of blocks (Fig. 3:306, [0062], “The resulting output is a list of Geohashed identified terrestrial areas divided into logically grouped sections (e.g., clusters), where the grouping is based on the density of the input parameter” (here density parameter maybe data usage threshold such as coverage level of that block based on the population of that block)), and the method further comprises: determining, based at least in part on individual population data of each block of the plurality of blocks, an individual coverage level of each block (Fig. 4, [0047], “Once the method 300 completes the iterations of segmenting subareas (grids), the method 300 generates (operation 306) a plurality of clusters for the node, where the clusters are identified according to Geohash coordinates and the size of the terrestrial area covered by each cluster is dependent on the selected parameter”. [0048], “In some embodiments, the number of terminals devices connected to each radio access node, or amount of traffic generated by the terminal devices for each radio access node, or some other metric, determines the parameter for each radio access node. The threshold number not to exceed is chosen as 10 in the example” (here the selected parameter for grouping may be a coverage level for the area exceeding a threshold number)); and determining, based at least in part on the individual coverage level of each block, the explicit coverage level of the area (Fig. 5:507, [0048], “In some embodiments, the number of terminals devices connected to each radio access node, or amount of traffic generated by the terminal devices for each radio access node, or some other metric, determines the parameter for each radio access node. The threshold number not to exceed is chosen as 10 in the example”. [0044], “In reference to FIG. 1, the radio access nodes 102, 103, 104 can be placed into one or more Geohash grid(s)...Then, based on some selected parameter, the network node 101, or some other node of network 100, can segment this region into 32 smaller areas...Thus, in reference to the example of FIG. 1, the radio access nodes 102 may undergo a number of iterations of segmentation until a desired density of access nodes is reached for a sub-divided grid”. [0049], “list of subareas makes up the cluster” (number of devices connected to each node is interpreted as population data for each node using the service and determining an optimized number of nodes to put in that grid is determining coverage level of the area based on coverage level of individual subareas)).
In re claims 4, 12 and 18, the combination discloses the method of claim 2, the system of claim 10 and the non-transitory computer-readable storage medium of claim 17, wherein GOMES discloses wherein the method further comprising: obtaining individual population data of an individual block ([0046], “In reference to FIG. 1, the selected parameter can be the radio access node, the terminal devices connected to the radio access nodes (population data for that block), communications traffic generated by the terminal devices, or any other metric related to the operation of the radio access nodes 102, 103, 104” (based on coverage threshold optimizing the number of nodes in each block based on population and usage)); based at least on data describing the plurality of nodes (Fig. 1: 102, 103, 104), at least one of: determining that the individual block is covered by at least one of the plurality of nodes and setting the individual population data of the individual block as the individual coverage level of the individual block (Fig. 10, [0048], “In some embodiments, the number of terminals devices connected to each radio access node, or amount of traffic generated by the terminal devices for each radio access node, or some other metric, determines the parameter for each radio access node”. [0046], “The method 300 subdivides (operation 302) the Geohash area represented by the node into subareas by increasing the Geohash string by +1. The selected parameter can be any input variable of interest. In reference to FIG. 1, the selected parameter can be the radio access node, the terminal devices connected to the radio access nodes...” (parameter maybe subarea covered by atleast one node)); or determining that the individual block is not covered by at least one of the plurality of nodes and setting zero as the individual coverage level of the individual block ([0047], “sequencing through the further subareas to place the further subareas into grouped clusters without exceeding the threshold number...until all parameters are placed into clusters, where no cluster exceeds the threshold number of the selected parameter. In this manner, grid areas can combine to reform as a cluster, where each cluster contains no more than the selected threshold number of whatever metric used for the parameter” (if a sub area is not covered by any node implies zero coverage in that sub area and Geohashing in this way to optimize coverage for the area).
In re claims 6 and 14, the combination discloses the method of claim 1 and the system of claim 9, wherein GOMES discloses wherein the monitoring signals further comprises receiving a prior explicit coverage level, based at least in part on historically adding a portion of the population data from the multiple sub-areas of the area ([0038], “As shown, the radio access nodes 102 are more in number for a given area (density), such as with a compacted urban area (e.g., city, town, etc.). The radio access nodes 103 are less in number, indicating an area of less density (e.g., suburban area, etc.)”. [0039], “network 100 coverage areas of terrestrial regions can vary significantly, such as due to population density of people using the terminal devices (e.g., urban, suburban, remote)” (estimate coverage based on historical population or traffic in different sub areas like urban, remote and use as input parameter for coverage analysis); and wherein Rijnders discloses wherein determining the explicit coverage level further comprises updating the prior explicit coverage level based at least in part on determining that the at least one of the plurality of nodes that is out of service was, at a time associated with the prior explicit coverage level, in service ([0057], “Alternatively, automation engine server 110 may consult a database (stored in the automation engine server 110) that stores history of multiple outages and their corresponding responding periods to determine the restoration period for a network outage...In another example, automation engine server 110 may determine that an outage of wired network connection 120 has likely been caused by a lightning strike, and the restoration period for such an event is 1-2 hours (i.e., shorter than the restoration period provided in the SLA). Automation engine server 110 may be further configured to compare the restoration period for the wired network connection 120, provided in the SLA, with the actual repair period to update its statistics” (updating statistics in database on information related to outages)).
In re claims 7 and 15, the combination discloses the method of claim 1 and the system of claim 9, wherein GOMES discloses wherein the area indicates a geographic area defined by at least one of a city boundary, a census tract, a county boundary, or a state boundary ([0003], “In metropolitan areas, a city or town may be segmented into neighborhoods, zones, wards, districts, etc.”).
In re claims 8 and 16, the combination discloses the method of claim 1 and the system of claim 9, wherein Chou discloses wherein the plurality of nodes is configured with radio transceivers and the signals include radio frequency (RF) signals ([0030], “Receiver circuitry 322 may be configured to receive signals from or transmit signals to an element manager (EM) component of an eNB (such as any of eNB’s 308-312) or other portion of system 300. Receiver circuitry 322 may include, for example, one or more directional or omni-directional antennas (transceivers) suitable for reception of radio frequency (RF) or other wireless communication signals”).
Claim(s) 3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over US 20190239086 A1 (SHEKALIM et al.) (hereinafter SHEKALIM) in view of US 20180007572 A1 (Chou et al.) (hereinafter Chou) in view of US 20160080248 A1 (Rijnders et al.) (hereinafter Rijnders) in view of US 20230072676 A1 (GOMES et al.) (hereinafter GOMES) and in further view of US 20110119375 A1 (Beeco et al.) (hereinafter Beeco).
In re claims 3 and 11, the combination discloses the method of claim 2 and the system of claim 10, wherein the method further comprising: determining, based on a sum of the individual coverage levels of the plurality of blocks, the explicit coverage level of the area; but does not explicitly disclose setting the coverage threshold as fifty percent of the explicit coverage level of the area.
Beeco discloses setting the coverage threshold as fifty percent of the coverage level of the area ([0086], “According to another embodiment, one or more device inactivity percentage thresholds may be defined against which the number of network devices that are local to a node are compared to classify the status of that node and/or the status of the corresponding portion of the network. For example, a predefined inactivity percentage threshold may be set to 50 percent, such that when more than 50 percent of the local network devices for a single node are reporting as inactive, the node itself may be classified as not achieving performance standards”).
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 SHEKALIM, Chou, Rijnders and GOMES with Beeco to provide a method and system for dynamic monitoring of service outage in a geographical area covered by multiple service nodes wherein the determination of explicit coverage level incorporates a population from various sub areas in the region of service. The advantage of doing so is having an optimized model to better estimate coverage level for a certain area so there is adequate coverage and signal strength sufficient to maintain connectivity in the network and the quality of service.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over US 20190239086 A1 (SHEKALIM et al.) (hereinafter SHEKALIM) in view of US 20180007572 A1 (Chou et al.) (hereinafter Chou) in view of US 20160080248 A1 (Rijnders et al.) (hereinafter Rijnders) in view of US 20230072676 A1 (GOMES et al.) (hereinafter GOMES) in view of US 20110119375 A1 (Beeco et al.) (hereinafter Beeco) and in further view of AU 2013317995 A1 (SOMA et al.) (hereinafter SOMA).
In re claim 5, the combination discloses the method of claim 3, but does not explicitly disclose the method further comprising: determining a signal strength at a centroid of an individual block; wherein Chou discloses determining that the signal strength is equal to or greater than a signal threshold ([0023], “Unacceptable performance may include, for example, failure to achieve a desired level of signal strength”. Claim 1, “Wherein the coverage area is service-deficient when the coverage area has insufficient signal strength to provide threshold connectivity to a Long-Term Evolution (LTE) network”); and determining that the individual block is covered by at least one of the plurality of nodes (if the signal is equal or greater than the threshold would mean that the block is covered by atleast one of the pluralities of the nodes).
SOMA discloses the method further comprising: determining a signal strength at a centroid of an individual block (Fig. 14, [0139], “shown on the latitude 1401 and longitude 1402 map, the serving and neighbor cells are based on two different base stations 1403 1404. The service area (block) of the serving cell 1405 and the service area of the neighbor cell overlap. The center of area 1407 (centroid) of the overlap area 1408 is reported as the estimated location while the dimensions of the overlap area 1408 are used to describe the error area”. [0130], “In many low accuracy location techniques, the reported location is the merely the center or centroid of an area of equal location probability. When a single serving cell identifier is only reported, the location estimate is computed as the centroid of serving cell's serving geographic area”. [0075], “The received power is given as the transmitter power multiplied by the gains of the transmit antenna in the direction of the receiver multiplied by the effective area of the receive antenna in the direction of the transmitter. Additionally, we divide this quantity by the area of a sphere of radius r to account for the reduction in the power density of the RF signal at a distance r from the transmitter (received power measured at a center of a block), or source, due to spherical spreading of the radio wave as it propagates from the transmitter to the receiver”).
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 SHEKALIM, Chou, Rijnders, GOMES with Beeco and SOMA to provide a method and system for dynamic monitoring of service outage in a geographical area covered by multiple service nodes wherein the determination of explicit coverage level incorporates a population from various sub areas in the region of service. The advantage of doing so is having an optimized model to better estimate coverage level for a certain area so there is adequate coverage and signal strength sufficient to maintain connectivity in the network and the quality of service.
Claims 13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US 20190239086 A1 (SHEKALIM et al.) (hereinafter SHEKALIM) in view of US 20180007572 A1 (Chou et al.) (hereinafter Chou) in view of US 20160080248 A1 (Rijnders et al.) (hereinafter Rijnders) in view of US 20230072676 A1 (GOMES et al.) (hereinafter GOMES) and in further view of AU 2013317995 A1 (SOMA et al.) (hereinafter SOMA).
In re claims 13 and 19, the combination discloses the system of claim 10 and the non-transitory computer-readable storage medium of claim 18, but does not explicitly disclose the method further comprising: determining a signal strength at a centroid of an individual block; wherein Chou discloses determining that the signal strength is equal to or greater than a signal threshold ([0023], “Unacceptable performance may include, for example, failure to achieve a desired level of signal strength”. Claim 1, “Wherein the coverage area is service-deficient when the coverage area has insufficient signal strength to provide threshold connectivity to a Long-Term Evolution (LTE) network”); and determining that the individual block is covered by at least one of the plurality of nodes (if the signal is equal or greater than the threshold would mean that the block is covered by atleast one of the pluralities of the nodes).
SOMA discloses the method further comprising: determining a signal strength at a centroid of an individual block (Fig. 14, [0139], “shown on the latitude 1401 and longitude 1402 map, the serving and neighbor cells are based on two different base stations 1403 1404. The service area (block) of the serving cell 1405 and the service area of the neighbor cell overlap. The center of area 1407 (centroid) of the overlap area 1408 is reported as the estimated location while the dimensions of the overlap area 1408 are used to describe the error area”. [0130], “In many low accuracy location techniques, the reported location is the merely the center or centroid of an area of equal location probability. When a single serving cell identifier is only reported, the location estimate is computed as the centroid of serving cell's serving geographic area”. [0075], “The received power is given as the transmitter power multiplied by the gains of the transmit antenna in the direction of the receiver multiplied by the effective area of the receive antenna in the direction of the transmitter. Additionally, we divide this quantity by the area of a sphere of radius r to account for the reduction in the power density of the RF signal at a distance r from the transmitter (received power measured at a center of a block), or source, due to spherical spreading of the radio wave as it propagates from the transmitter to the receiver”).
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 SHEKALIM, Chou, Rijnders, GOMES and SOMA to provide a method and system for dynamic monitoring of service outage in a geographical area covered by multiple service nodes wherein the determination of explicit coverage level incorporates a population from various sub areas in the region of service. The advantage of doing so is having an optimized model to better estimate coverage level for a certain area so there is adequate coverage and signal strength sufficient to maintain connectivity in the network and the quality of service.
In re claim 20, the combination discloses the non-transitory computer-readable storage medium of claim 17, wherein SHEKALIM discloses wherein the actions further comprise: determining a relationship between the plurality of nodes and an individual sub-area of the multiple sub-areas, wherein the relationship comprises at least one of a node priority ([0007], “determine a final set of target cells from the at least one first optimum cell and the at least one second optimum cell, wherein the final set of target cells includes at least one final cell arranged in one of an ascending and a descending order of priority, and the ascending and the descending order of priority is based on at least one of the percentage of contribution (POC) (priority based on contribution to the area)...and mitigate the at least one coverage hole by modifying the transmission parameter of the at least one final cell, wherein the modification is based on the coverage hole information”), a node distance, a binary coverage status, historical signal data, wherein Rijnders discloses a no-coverage duration parameter ([0057], “Based on the cause of the outage, automation engine server 110 may further estimate the required time to restore the wired network connection 120” (no coverage duration), or a signal strength at one or more reference points within the individual sub-area; and wherein Rijnders discloses communicating an outage notification to a second organization is based at least in part on the explicit coverage level in the area being less than the required coverage threshold for a period of time ([0057], “In addition, automation engine server 110 may communicate with local and national emergency servers (e.g., national hurricane center) to determine if the likely cause of the outage is a weather event...In one example, automation engine server 110 may determine that an outage of wired network connection 120 has likely been caused by a major hurricane, and the restoration period for such an event is 48-72 hours (i.e., longer than the restoration period provided in the SLA)” (for example, in case of a hurricane, when the outage is longer than the restoration period provided in the customer contract, the service provider may contact a second organization such as hurricane center to confirm that the cause of outage is a weather event and determine the time for repair)).
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/SWATI JAIN/Examiner, Art Unit 2649
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