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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/21/2024 was filed after the mailing date of the application on 05/21/2024. The submission 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
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3, 9, 15, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Malboubi et al. (US 2020/0127906), hereinafter Malboubi in further view of Dan et al. (US 8174990), hereinafter Dan.
Regarding Claim 1, Malboubi teaches: A method for multi-node analysis, the method comprising: receiving a performance indicator equation: “At step 440, the processing system composes a system of linear equations” (Malboubi ¶ 0039), wherein: the performance indicator equation comprises a corresponding set of measurement parameters: “In one example, the system of linear equations is represented in matrix form (such as illustrated in FIG. 3) equating a vector of the plurality of path measurements with a product of a probe packet routing matrix and a vector of the plurality of link performance indicators” (Malboubi ¶ 0039); and the performance indicator equation defines a relationship between the corresponding set of measurement parameters and a performance indicator corresponding to the performance indicator equation: “each of the linear equations relates to one of the plurality of path measurements between a respective pair of host devices of the plurality of host devices to one or more link performance indicators (broadly at least one link performance indicator) for a corresponding one or more of the plurality of links in the packet network between the respective pair of host devices” (Malboubi ¶ 0039).
Malbubi does not teach: for each node from a set of nodes: receiving, from a database, a set of measurement values for that node, wherein the set of measurement values for that node comprises a current value for each measurement parameter comprised by the performance indicator equation, and determining that node's contribution to a cluster value for the performance indicator corresponding to the performance indicator equation based on the set of measurement values for that node; and reporting performance indicator information for the set of nodes wherein the reported performance indicator information is based on relative contributions of each node from the set of nodes to the cluster value for the performance indicator corresponding to the performance indicator equation.
Regarding Claim 1, Dan teaches: for each node from a set of nodes: receiving, from a database, a set of measurement values for that node, wherein the set of measurement values for that node comprises a current value for each measurement parameter comprised by the performance indicator equation: “assume that an aggregate metric is the average number of invocations of application A across the cluster of nodes 150. The aggregate metric definition that defines the aggregate metric is stored in the metrics definition database 410. The aggregate metric definition describes that the average is calculated by summing the number invocations of the application over a set of the measured nodes executing the application and dividing that sum by the count of that set . . . The metrics sensor unit 450 retrieves the number of invocations of application A for each of the nodes of the cluster of nodes 150 and stores the sensor data in the metrics sensor database 430. The metrics computation unit 160 knows to retrieve the sensor data for nodes 2 and 3 from the metrics sensor database 430 because the metrics state database 420 indicates the current node membership of the aggregate metric AM is {2,3}” (Dan Col 6 Lines 39-60) and determining that node's contribution to a cluster value for the performance indicator corresponding to the performance indicator equation based on the set of measurement values for that node: “Assume that the sensor data for node 2 is an invocation count of 8 and the sensor data for node 3 is an invocation count of 6. The metrics computation unit 160 then calculates the aggregate metric AM to be (8+6)/2, i.e., 7, based on the aggregate metrics definition of the aggregate metric AM in the metrics definition database 410” (Dan Col 6 Lines 60-65); and reporting performance indicator information for the set of nodes wherein the reported performance indicator information is based on relative contributions of each node from the set of nodes to the cluster value for the performance indicator corresponding to the performance indicator equation: “The metrics output unit 440 can then output the computed metric to an interested terminal or workstation 180. The metrics output unit 440 may communicate to the terminal or workstation 180 remotely across a network” (Dan Col 6 Line 67 – Col 7 Line 2).
It would have been obvious to one of ordinary skill in the art to combine the disclosure of Malboubi with Dan for the purpose of providing a measurement system that can compute complex metrics for a varying number of nodes. According to Dan: “there is a need for a measurement system and method which can efficiently compute complex metrics for a dynamic system having a constantly varying number of nodes” (Dan Col 1 Lines 58-60).
Regarding Claim 9, Malboubi teaches: The method of claim 1.
Malboubi does not teach: the cluster value for the performance indicator corresponding to the performance indicator equation is a value equal to a result of calculating the performance indicator equation with measurement values for all nodes from the set of nodes for each measurement parameter comprised by the performance indicator equation.
Regarding Claim 9, Dan teaches: the cluster value for the performance indicator corresponding to the performance indicator equation is a value equal to a result of calculating the performance indicator equation with measurement values for all nodes from the set of nodes for each measurement parameter comprised by the performance indicator equation: “assume that an aggregate metric is the average number of invocations of application A across the cluster of nodes 150. The aggregate metric definition that defines the aggregate metric is stored in the metrics definition database 410. The aggregate metric definition describes that the average is calculated by summing the number invocations of the application over a set of the measured nodes executing the application and dividing that sum by the count of that set . . . The metrics sensor unit 450 retrieves the number of invocations of application A for each of the nodes of the cluster of nodes 150 and stores the sensor data in the metrics sensor database 430. The metrics computation unit 160 knows to retrieve the sensor data for nodes 2 and 3 from the metrics sensor database 430 because the metrics state database 420 indicates the current node membership of the aggregate metric AM is {2,3}” (Dan Col 6 Lines 39-60).
It would have been obvious to one of ordinary skill in the art to combine the disclosure of Malboubi with Dan for the purpose of providing a measurement system that can compute complex metrics for a varying number of nodes. According to Dan: “there is a need for a measurement system and method which can efficiently compute complex metrics for a dynamic system having a constantly varying number of nodes” (Dan Col 1 Lines 58-60).
Regarding Claim 15, Malboubi teaches: : An apparatus for multi-node analysis, the apparatus comprising one or more processors: “The network controller 150 may comprise a computing system or server, such as computing system 500 depicted in FIG. 5, and may be configured to provide one or more operations or functions for determining a plurality of link performance indicators from a plurality of path measurements, as described herein. It should be noted that as used herein, the terms “configure,” and “reconfigure” may refer to programming or loading a processing system with computer-readable/computer-executable instructions, code, and/or programs, e.g., in a distributed or non-distributed memory, which when executed by a processor, or processors, of the processing system within a same device or within distributed devices, may cause the processing system to perform various functions” (Malboubi ¶ 0022) configured with instructions operable to, when executed, perform a set of acts comprising: receiving a performance indicator equation: “At step 440, the processing system composes a system of linear equations” (Malboubi ¶ 0039), wherein: the performance indicator equation comprises a corresponding set of measurement parameters: “In one example, the system of linear equations is represented in matrix form (such as illustrated in FIG. 3) equating a vector of the plurality of path measurements with a product of a probe packet routing matrix and a vector of the plurality of link performance indicators” (Malboubi ¶ 0039); and the performance indicator equation defines a relationship between the corresponding set of measurement parameters and a performance indicator corresponding to the performance indicator equation: “each of the linear equations relates to one of the plurality of path measurements between a respective pair of host devices of the plurality of host devices to one or more link performance indicators (broadly at least one link performance indicator) for a corresponding one or more of the plurality of links in the packet network between the respective pair of host devices” (Malboubi ¶ 0039).
Malboubi does not teach: for each node from a set of nodes: receiving, from a database, a set of measurement values for that node, wherein the set of measurement values for that node comprises a current value for each measurement parameter comprised by the performance indicator equation, and determining that node's contribution to a cluster value for the performance indicator corresponding to the performance indicator equation based on the set of measurement values for that node; and reporting performance indicator information for the set of nodes wherein the reported performance indicator information is based on relative contributions of each node from the set of nodes to the cluster value for the performance indicator corresponding to the performance indicator equation.
Regarding Claim 15, Dan teaches: for each node from a set of nodes: receiving, from a database, a set of measurement values for that node, wherein the set of measurement values for that node comprises a current value for each measurement parameter comprised by the performance indicator equation: “assume that an aggregate metric is the average number of invocations of application A across the cluster of nodes 150. The aggregate metric definition that defines the aggregate metric is stored in the metrics definition database 410. The aggregate metric definition describes that the average is calculated by summing the number invocations of the application over a set of the measured nodes executing the application and dividing that sum by the count of that set . . . The metrics sensor unit 450 retrieves the number of invocations of application A for each of the nodes of the cluster of nodes 150 and stores the sensor data in the metrics sensor database 430. The metrics computation unit 160 knows to retrieve the sensor data for nodes 2 and 3 from the metrics sensor database 430 because the metrics state database 420 indicates the current node membership of the aggregate metric AM is {2,3}” (Dan Col 6 Lines 39-60) and determining that node's contribution to a cluster value for the performance indicator corresponding to the performance indicator equation based on the set of measurement values for that node: “Assume that the sensor data for node 2 is an invocation count of 8 and the sensor data for node 3 is an invocation count of 6. The metrics computation unit 160 then calculates the aggregate metric AM to be (8+6)/2, i.e., 7, based on the aggregate metrics definition of the aggregate metric AM in the metrics definition database 410” (Dan Col 6 Lines 60-65); and reporting performance indicator information for the set of nodes wherein the reported performance indicator information is based on relative contributions of each node from the set of nodes to the cluster value for the performance indicator corresponding to the performance indicator equation: “The metrics output unit 440 can then output the computed metric to an interested terminal or workstation 180. The metrics output unit 440 may communicate to the terminal or workstation 180 remotely across a network” (Dan Col 6 Line 67 – Col 7 Line 2).
It would have been obvious to one of ordinary skill in the art to combine the disclosure of Malboubi with Dan for the purpose of providing a measurement system that can compute complex metrics for a varying number of nodes. According to Dan: “there is a need for a measurement system and method which can efficiently compute complex metrics for a dynamic system having a constantly varying number of nodes” (Dan Col 1 Lines 58-60).
Regarding Claim 27, Malboubi teaches: A non-transitory machine-readable storage medium having program instructions thereon, which are configured to, when executed by one or more processors: “The network controller 150 may comprise a computing system or server, such as computing system 500 depicted in FIG. 5, and may be configured to provide one or more operations or functions for determining a plurality of link performance indicators from a plurality of path measurements, as described herein. It should be noted that as used herein, the terms “configure,” and “reconfigure” may refer to programming or loading a processing system with computer-readable/computer-executable instructions, code, and/or programs, e.g., in a distributed or non-distributed memory, which when executed by a processor, or processors, of the processing system within a same device or within distributed devices, may cause the processing system to perform various functions” (Malboubi ¶ 0022) perform a set of acts comprising: “At step 440, the processing system composes a system of linear equations” (Malboubi ¶ 0039), wherein: the performance indicator equation comprises a corresponding set of measurement parameters: “In one example, the system of linear equations is represented in matrix form (such as illustrated in FIG. 3) equating a vector of the plurality of path measurements with a product of a probe packet routing matrix and a vector of the plurality of link performance indicators” (Malboubi ¶ 0039); and the performance indicator equation defines a relationship between the corresponding set of measurement parameters and a performance indicator corresponding to the performance indicator equation: “each of the linear equations relates to one of the plurality of path measurements between a respective pair of host devices of the plurality of host devices to one or more link performance indicators (broadly at least one link performance indicator) for a corresponding one or more of the plurality of links in the packet network between the respective pair of host devices” (Malboubi ¶ 0039).
Malboubi does not teach: for each node from a set of nodes: receiving, from a database, a set of measurement values for that node, wherein the set of measurement values for that node comprises a current value for each measurement parameter comprised by the performance indicator equation, and determining that node's contribution to a cluster value for the performance indicator corresponding to the performance indicator equation based on the set of measurement values for that node; and reporting performance indicator information for the set of nodes wherein the reported performance indicator information is based on relative contributions of each node from the set of nodes to the cluster value for the performance indicator corresponding to the performance indicator equation.
Regarding Claim 27, Dan teaches: for each node from a set of nodes: receiving, from a database, a set of measurement values for that node, wherein the set of measurement values for that node comprises a current value for each measurement parameter comprised by the performance indicator equation: “assume that an aggregate metric is the average number of invocations of application A across the cluster of nodes 150. The aggregate metric definition that defines the aggregate metric is stored in the metrics definition database 410. The aggregate metric definition describes that the average is calculated by summing the number invocations of the application over a set of the measured nodes executing the application and dividing that sum by the count of that set . . . The metrics sensor unit 450 retrieves the number of invocations of application A for each of the nodes of the cluster of nodes 150 and stores the sensor data in the metrics sensor database 430. The metrics computation unit 160 knows to retrieve the sensor data for nodes 2 and 3 from the metrics sensor database 430 because the metrics state database 420 indicates the current node membership of the aggregate metric AM is {2,3}” (Dan Col 6 Lines 39-60) and determining that node's contribution to a cluster value for the performance indicator corresponding to the performance indicator equation based on the set of measurement values for that node: “Assume that the sensor data for node 2 is an invocation count of 8 and the sensor data for node 3 is an invocation count of 6. The metrics computation unit 160 then calculates the aggregate metric AM to be (8+6)/2, i.e., 7, based on the aggregate metrics definition of the aggregate metric AM in the metrics definition database 410” (Dan Col 6 Lines 60-65); and reporting performance indicator information for the set of nodes wherein the reported performance indicator information is based on relative contributions of each node from the set of nodes to the cluster value for the performance indicator corresponding to the performance indicator equation: “The metrics output unit 440 can then output the computed metric to an interested terminal or workstation 180. The metrics output unit 440 may communicate to the terminal or workstation 180 remotely across a network” (Dan Col 6 Line 67 – Col 7 Line 2).
It would have been obvious to one of ordinary skill in the art to combine the disclosure of Malboubi with Dan for the purpose of providing a measurement system that can compute complex metrics for a varying number of nodes. According to Dan: “there is a need for a measurement system and method which can efficiently compute complex metrics for a dynamic system having a constantly varying number of nodes” (Dan Col 1 Lines 58-60).
Claims 2, 4, 5, 16-17, and 28-29 are rejected under 35 U.S.C. 103 as being unpatentable over Malboubi and Dan as applied to claims 1, 15, and 27 above, and further in view of Meredith et al. (US 2013/0051239), hereinafter Meredith.
Regarding Claim 2, Malboubi and Dan teach: The method of claim 1.
Malboubi and Dan do not teach: the method comprises prioritizing remediation activities among the set of nodes based on the nodes' contributions to the cluster value for the performance indicator corresponding to the performance indicator equation.
Regarding Claim 2, Meredith teaches: the method comprises prioritizing remediation activities among the set of nodes based on the nodes' contributions to the cluster value for the performance indicator corresponding to the performance indicator equation: “In addition to the foregoing, ticketing priority system 102 can comprise a decision engine 114 configured to quantify a repair priority for respective base stations of a mobile network, based at least in part on an estimate of network impact generated by analysis component 112 for respective base stations. Repair scores can be generated for these base stations and stored in database 104, in a base station repair score file 116. Various algorithms for generating the repair score are considered within the scope of the subject application. For instance, decision engine 114 can employ an algorithm that separately scores and then aggregates a set of performance metrics (aggregate or individual) that comprise the overall estimate of network impact” (Meredith ¶ 0041).
It would have been obvious to one of ordinary skill in the art to combine the disclosure of Malboubi and Dan with Meredith for the purpose of providing a metric to prioritize network repairs. According to Meredith: “Where the volume of electronic failure tickets exceeds service resources, a common tendency is to increase the out of service time that triggers creation of a ticket. This of course doesn't cure the underlying failure, but merely masks magnitude of a given network problem. Accordingly, mechanisms for determining overall impact on network services and impact to subscriber activity can help to provide a better deployment of finite maintenance resources for correcting network service outage” (Meredith ¶ 0007).
Regarding Claim 4, Malboubi and Dan teach: The method of claim 1.
Malboubi and Dan do not teach: the method comprises, after receiving the performance indicator equation, determining a contribution equation, wherein the contribution equation relates the corresponding set of measurement parameters comprised by the performance indicator equation and a contribution to the performance indicator corresponding to the performance indicator equation; and for each node from the set of nodes, determining that node's contribution to the performance indicator corresponding to the performance indicator equation based on the set of measurement values for that node is performed based on calculating the contribution equation with the set of measurement values for that node.
Regarding Claim 4, Meredith teaches: the method comprises, after receiving the performance indicator equation, determining a contribution equation, wherein the contribution equation relates the corresponding set of measurement parameters comprised by the performance indicator equation and a contribution to the performance indicator corresponding to the performance indicator equation: ”the term "network impact" can include aggregate performance metrics for groups of terminals within a sector(s), or performance metrics for individual terminals therein. Aggregate performance metrics can include average quality of service, call connectivity rate, call drop rate, base station loading, percent of resource capacity, or the like. Individual performance metrics can include data throughput, quality of service, and other metrics observable for a single device. In addition, it should be appreciated that analysis component 112 can be configured to calculate network impact metrics utilizing different functions of aggregate and individual performance metrics. This can provide flexibility for different mobile service providers, enabling repair ticket priorities to reflect varying customer service goals of those providers. Thus, one service provider might establish a network impact metric weighted more heavily toward aggregate performance metrics, whereas another might weight more heavily toward individual performance metrics, while yet another could weight evenly between the two, and so on” (Meredith ¶ 0040); and for each node from the set of nodes, determining that node's contribution to the performance indicator corresponding to the performance indicator equation based on the set of measurement values for that node is performed based on calculating the contribution equation with the set of measurement values for that node: “In addition to the foregoing, ticketing priority system 102 can comprise a decision engine 114 configured to quantify a repair priority for respective base stations of a mobile network, based at least in part on an estimate of network impact generated by analysis component 112 for respective base stations. Repair scores can be generated for these base stations and stored in database 104, in a base station repair score file 116. Various algorithms for generating the repair score are considered within the scope of the subject application. For instance, decision engine 114 can employ an algorithm that separately scores and then aggregates a set of performance metrics (aggregate or individual) that comprise the overall estimate of network impact” (Meredith ¶ 0041).
It would have been obvious to one of ordinary skill in the art to combine the disclosure of Malboubi and Dan with Meredith for the purpose of providing a metric to prioritize network repairs. According to Meredith: “Where the volume of electronic failure tickets exceeds service resources, a common tendency is to increase the out of service time that triggers creation of a ticket. This of course doesn't cure the underlying failure, but merely masks magnitude of a given network problem. Accordingly, mechanisms for determining overall impact on network services and impact to subscriber activity can help to provide a better deployment of finite maintenance resources for correcting network service outage” (Meredith ¶ 0007).
Regarding Claim 5, Malboubi and Dan teach: The method of claim 4.
Malboubi and Dan do not teach: determining the contribution equation comprises: classifying the performance indicator equation, wherein classifying the performance indicator equation comprises identifying the performance indicator equation as belonging to a class from a plurality of classes; and determining the contribution equation based on the class.
Regarding Claim 5, Meredith teaches: determining the contribution equation comprises: classifying the performance indicator equation, wherein classifying the performance indicator equation comprises identifying the performance indicator equation as belonging to a class from a plurality of classes; and determining the contribution equation based on the class: ” The following operational example is provided to illustrate operation of ticketing priority system 602 given a particular set of impact algorithms 612. For this example, executing impact algorithms 612 can comprise: estimating a percentage of the dynamic population of terminals that will receive coverage from nearby base stations in response to a service error, and assigning a priority score to this percentage, estimating change in voice quality to the dynamic population of terminals in response to the service error, and assigning a second priority score to the change in voice quality, estimating a change in data throughput to the dynamic population of terminals in response to the service error and assigning a third priority score to the change in data throughput, and estimating changes in loading at the related base station in response to the service error, and assigning a fourth priority score to the changes in loading. Respective priority scores can be obtained by comparing the estimated changes in the above metrics to metric scoring 616 (e.g., a look-up table) stored in memory 604. The impact on network performance is obtained by combining the priority score, second priority score, third priority score and fourth priority score. In at least one aspect of the subject disclosure, one or more of the priority scores can be given service provider-configurable score weights 618 in estimating the impact on network performance” (Meredith ¶ 0076).
It would have been obvious to one of ordinary skill in the art to combine the disclosure of Malboubi and Dan with Meredith for the purpose of providing a metric to prioritize network repairs. According to Meredith: “Where the volume of electronic failure tickets exceeds service resources, a common tendency is to increase the out of service time that triggers creation of a ticket. This of course doesn't cure the underlying failure, but merely masks magnitude of a given network problem. Accordingly, mechanisms for determining overall impact on network services and impact to subscriber activity can help to provide a better deployment of finite maintenance resources for correcting network service outage” (Meredith ¶ 0007).
Regarding Claim 16, Malboubi and Dan teach: The apparatus of claim 15.
Malboubi and Dan do not teach: the set of acts comprises, after receiving the performance indicator equation, determining a contribution equation, wherein the contribution equation relates the corresponding set of measurement parameters comprised by the performance indicator equation and a contribution to the performance indicator corresponding to the performance indicator equation; and for each node from the set of nodes, determining that node's contribution to the performance indicator corresponding to the performance indicator equation based on the set of measurement values for that node is performed based on calculating the contribution equation with the set of measurement values for that node.
Regarding Claim 16, Meredith teaches: the set of acts comprises, after receiving the performance indicator equation, determining a contribution equation, wherein the contribution equation relates the corresponding set of measurement parameters comprised by the performance indicator equation and a contribution to the performance indicator corresponding to the performance indicator equation: ”the term "network impact" can include aggregate performance metrics for groups of terminals within a sector(s), or performance metrics for individual terminals therein. Aggregate performance metrics can include average quality of service, call connectivity rate, call drop rate, base station loading, percent of resource capacity, or the like. Individual performance metrics can include data throughput, quality of service, and other metrics observable for a single device. In addition, it should be appreciated that analysis component 112 can be configured to calculate network impact metrics utilizing different functions of aggregate and individual performance metrics. This can provide flexibility for different mobile service providers, enabling repair ticket priorities to reflect varying customer service goals of those providers. Thus, one service provider might establish a network impact metric weighted more heavily toward aggregate performance metrics, whereas another might weight more heavily toward individual performance metrics, while yet another could weight evenly between the two, and so on” (Meredith ¶ 0040); and for each node from the set of nodes, determining that node's contribution to the performance indicator corresponding to the performance indicator equation based on the set of measurement values for that node is performed based on calculating the contribution equation with the set of measurement values for that node: “In addition to the foregoing, ticketing priority system 102 can comprise a decision engine 114 configured to quantify a repair priority for respective base stations of a mobile network, based at least in part on an estimate of network impact generated by analysis component 112 for respective base stations. Repair scores can be generated for these base stations and stored in database 104, in a base station repair score file 116. Various algorithms for generating the repair score are considered within the scope of the subject application. For instance, decision engine 114 can employ an algorithm that separately scores and then aggregates a set of performance metrics (aggregate or individual) that comprise the overall estimate of network impact” (Meredith ¶ 0041).
It would have been obvious to one of ordinary skill in the art to combine the disclosure of Malboubi and Dan with Meredith for the purpose of providing a metric to prioritize network repairs. According to Meredith: “Where the volume of electronic failure tickets exceeds service resources, a common tendency is to increase the out of service time that triggers creation of a ticket. This of course doesn't cure the underlying failure, but merely masks magnitude of a given network problem. Accordingly, mechanisms for determining overall impact on network services and impact to subscriber activity can help to provide a better deployment of finite maintenance resources for correcting network service outage” (Meredith ¶ 0007).
Regarding Claim 17, Malboubi and Dan teach: The apparatus of claim 17.
Malboubi and Dan do not teach: determining the contribution equation comprises: classifying the performance indicator equation, wherein classifying the performance indicator equation comprises identifying the performance indicator equation as belonging to a class from a plurality of classes; and determining the contribution equation based on the class.
Regarding Claim 17, Meredith teaches: determining the contribution equation comprises: classifying the performance indicator equation, wherein classifying the performance indicator equation comprises identifying the performance indicator equation as belonging to a class from a plurality of classes; and determining the contribution equation based on the class: ” The following operational example is provided to illustrate operation of ticketing priority system 602 given a particular set of impact algorithms 612. For this example, executing impact algorithms 612 can comprise: estimating a percentage of the dynamic population of terminals that will receive coverage from nearby base stations in response to a service error, and assigning a priority score to this percentage, estimating change in voice quality to the dynamic population of terminals in response to the service error, and assigning a second priority score to the change in voice quality, estimating a change in data throughput to the dynamic population of terminals in response to the service error and assigning a third priority score to the change in data throughput, and estimating changes in loading at the related base station in response to the service error, and assigning a fourth priority score to the changes in loading. Respective priority scores can be obtained by comparing the estimated changes in the above metrics to metric scoring 616 (e.g., a look-up table) stored in memory 604. The impact on network performance is obtained by combining the priority score, second priority score, third priority score and fourth priority score. In at least one aspect of the subject disclosure, one or more of the priority scores can be given service provider-configurable score weights 618 in estimating the impact on network performance” (Meredith ¶ 0076).
It would have been obvious to one of ordinary skill in the art to combine the disclosure of Malboubi and Dan with Meredith for the purpose of providing a metric to prioritize network repairs. According to Meredith: “Where the volume of electronic failure tickets exceeds service resources, a common tendency is to increase the out of service time that triggers creation of a ticket. This of course doesn't cure the underlying failure, but merely masks magnitude of a given network problem. Accordingly, mechanisms for determining overall impact on network services and impact to subscriber activity can help to provide a better deployment of finite maintenance resources for correcting network service outage” (Meredith ¶ 0007).
Regarding Claim 28, Malboubi and Dan teach: The non-transitory machine-readable storage medium of claim 27.
Malboubi and Dan do not teach: the set of acts comprises, after receiving the performance indicator equation, determining a contribution equation, wherein the contribution equation relates the corresponding set of measurement parameters comprised by the performance indicator equation and a contribution to the performance indicator corresponding to the performance indicator equation; and for each node from the set of nodes, determining that node's contribution to the performance indicator corresponding to the performance indicator equation based on the set of measurement values for that node is performed based on calculating the contribution equation with the set of measurement values for that node.
Regarding Claim 28, Meredith teaches: the set of acts comprises, after receiving the performance indicator equation, determining a contribution equation, wherein the contribution equation relates the corresponding set of measurement parameters comprised by the performance indicator equation and a contribution to the performance indicator corresponding to the performance indicator equation: ”the term "network impact" can include aggregate performance metrics for groups of terminals within a sector(s), or performance metrics for individual terminals therein. Aggregate performance metrics can include average quality of service, call connectivity rate, call drop rate, base station loading, percent of resource capacity, or the like. Individual performance metrics can include data throughput, quality of service, and other metrics observable for a single device. In addition, it should be appreciated that analysis component 112 can be configured to calculate network impact metrics utilizing different functions of aggregate and individual performance metrics. This can provide flexibility for different mobile service providers, enabling repair ticket priorities to reflect varying customer service goals of those providers. Thus, one service provider might establish a network impact metric weighted more heavily toward aggregate performance metrics, whereas another might weight more heavily toward individual performance metrics, while yet another could weight evenly between the two, and so on” (Meredith ¶ 0040); and for each node from the set of nodes, determining that node's contribution to the performance indicator corresponding to the performance indicator equation based on the set of measurement values for that node is performed based on calculating the contribution equation with the set of measurement values for that node: “In addition to the foregoing, ticketing priority system 102 can comprise a decision engine 114 configured to quantify a repair priority for respective base stations of a mobile network, based at least in part on an estimate of network impact generated by analysis component 112 for respective base stations. Repair scores can be generated for these base stations and stored in database 104, in a base station repair score file 116. Various algorithms for generating the repair score are considered within the scope of the subject application. For instance, decision engine 114 can employ an algorithm that separately scores and then aggregates a set of performance metrics (aggregate or individual) that comprise the overall estimate of network impact” (Meredith ¶ 0041).
It would have been obvious to one of ordinary skill in the art to combine the disclosure of Malboubi and Dan with Meredith for the purpose of providing a metric to prioritize network repairs. According to Meredith: “Where the volume of electronic failure tickets exceeds service resources, a common tendency is to increase the out of service time that triggers creation of a ticket. This of course doesn't cure the underlying failure, but merely masks magnitude of a given network problem. Accordingly, mechanisms for determining overall impact on network services and impact to subscriber activity can help to provide a better deployment of finite maintenance resources for correcting network service outage” (Meredith ¶ 0007).
Regarding Claim 29, Malboubi and Dan teach: The non-transitory machine-readable storage medium of claim 28.
Malboubi and Dan do not teach: determining the contribution equation comprises: classifying the performance indicator equation, wherein classifying the performance indicator equation comprises identifying the performance indicator equation as belonging to a class from a plurality of classes; and determining the contribution equation based on the class.
Regarding Claim 29, Meredith teaches: determining the contribution equation comprises: classifying the performance indicator equation, wherein classifying the performance indicator equation comprises identifying the performance indicator equation as belonging to a class from a plurality of classes; and determining the contribution equation based on the class: ” The following operational example is provided to illustrate operation of ticketing priority system 602 given a particular set of impact algorithms 612. For this example, executing impact algorithms 612 can comprise: estimating a percentage of the dynamic population of terminals that will receive coverage from nearby base stations in response to a service error, and assigning a priority score to this percentage, estimating change in voice quality to the dynamic population of terminals in response to the service error, and assigning a second priority score to the change in voice quality, estimating a change in data throughput to the dynamic population of terminals in response to the service error and assigning a third priority score to the change in data throughput, and estimating changes in loading at the related base station in response to the service error, and assigning a fourth priority score to the changes in loading. Respective priority scores can be obtained by comparing the estimated changes in the above metrics to metric scoring 616 (e.g., a look-up table) stored in memory 604. The impact on network performance is obtained by combining the priority score, second priority score, third priority score and fourth priority score. In at least one aspect of the subject disclosure, one or more of the priority scores can be given service provider-configurable score weights 618 in estimating the impact on network performance” (Meredith ¶ 0076).
It would have been obvious to one of ordinary skill in the art to combine the disclosure of Malboubi and Dan with Meredith for the purpose of providing a metric to prioritize network repairs. According to Meredith: “Where the volume of electronic failure tickets exceeds service resources, a common tendency is to increase the out of service time that triggers creation of a ticket. This of course doesn't cure the underlying failure, but merely masks magnitude of a given network problem. Accordingly, mechanisms for determining overall impact on network services and impact to subscriber activity can help to provide a better deployment of finite maintenance resources for correcting network service outage” (Meredith ¶ 0007).
Allowable Subject Matter
Claims 6-8 and 10-14 are 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
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/B.D.L./Examiner, Art Unit 2473
/BRADLEY D LYTLE JR./Examiner, Art Unit 2473
/KWANG B YAO/Supervisory Patent Examiner, Art Unit 2473
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