DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
This action is responsive to the RCE filed on 12/24/25.
Claim(s) 1-20 is/are presented for examination.
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 of this title, 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, 9-10 & 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, U.S. Pub/Patent No. 2021/0306201 A1 in view of Vankayala, U.S. Patent/Pub. No. US 2024/0022986 A1, and further in view of Ashby, US 2016/0269239 A1.
As to claim 1, Wang teaches a method for forecasting capacity breaches in a mobile network, comprising:
based on the obtained KPI data, identifying critical cells and non-critical cells, the critical cells among cells in the mobile network exhibiting high utilization affecting performance by the critical cells, and the non-critical cells not exhibiting high utilization (Wang, figure 2; page 2, paragraph 43; page 16, paragraph 160; i.e., [0043] communication latencies or throughputs, delays in establishing connections or associations with memory utilization; [0160] the terms (key performance indicator) KPI parameters and SLE parameters should be viewed);
based on the applying the prediction model, generating a report identifying at least one action to execute to configure the mobile network to address capacity issues of the non-critical cells having the forecasted capacity issues within the predetermined forecast time window (Wang, page 2, paragraph 41; i.e., [0041] If a similar defect report is identified, some embodiments update the report to indicate an additional incidence of the defect based on the recent diagnosis. The new defect report is populated with information from the measured operational parameters as well as information derived from the diagnostic process); and
that receives the KPI data of the non-critical cells as input, at least one predetermined forecast time window associated with forecasted capacity issues associated with at least one of the non-critical cells (Wang, page 2, paragraph 28; page 6, paragraph 69; i.e., [0028] embodiments identify possible actions to take to either resolve the system problem or a root cause identification. These actions include one or more of initializing a specific beacon radio, restarting a radio, rebooting a device, restarting a software component, restarting a computer, changing operating parameters of a software or hardware component, querying a system component for status information, requesting a system component to perform a task, or other actions; [0160] the terms (key performance indicator) KPI parameters and SLE parameters should be viewed as interchangeable);
executing the at least one action to configure the mobile network to address the capacity issues of the non-critical cells having the forecasted capacity issues within the predetermined forecast time window (Wang, page 2, paragraph 28; page 6, paragraph 69; i.e., [0028] embodiments identify possible actions to take to either resolve the system problem or obtain additional diagnostic information which can then be applied to increase confidence of a root cause identification. These actions include one or more of initializing a specific beacon radio, restarting a radio, rebooting a device, restarting a software component, restarting a computer, changing operating parameters of a software or hardware component, querying a system component for status information, requesting a system component to perform a task, or other actions; [0160] the terms (key performance indicator) KPI parameters and SLE parameters should be viewed as interchangeable).
But Wang failed to teach the claim limitation wherein accessing a Key Performance Indicators (KPI) database to obtain KPI data associated with each cell, wherein the KPI data shows an amount of communication between a cell and user equipment (UEs) connected to the cell; for the non-critical cells, applying a prediction model to predict a capacity trend for indicating the non-critical cells exceeding a predetermined threshold.
However, Vankayala teaches the limitation wherein accessing a Key Performance Indicators (KPI) database to obtain KPI data associated with each cell, wherein the KPI data shows an amount of communication between a cell and user equipment (UEs) connected to the cell (Vankayala, page 4, paragraph 65; page 5, paragraph 66; page 8, paragraph 130-131; i.e., [0066] The key performance indicators includes a handover/mobility success related information of each cell in the wireless network (1000), a radio resource usage information of each cell in the wireless network (1000), or a cell capability information of each cell in the wireless network (1000); [0131] In one example embodiment, the detecting the plurality of parameters of the current cell associated with the at least one UE (100) in the wireless network (1000) comprises receiving, by the network apparatus (200), a Scheduling Request (SR) from the at least one UE (100) associated with the current cell).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Wang to substitute resource element from Vankayala for CPU utilization from Wang to handle error scenarios which causes latency, accessibility degradation and affects Quality of Experience (QoE) (Vankayala, page 1, paragraph 3).
However, Ashby teaches the limitation wherein for the non-critical cells, applying a prediction model to predict a capacity trend for indicating the non-critical cells exceeding a predetermined threshold to identify (Ashby, page 3, paragraph 36, 39-40; page 4, paragraph 43-44; i.e., [0040] The linear regression analysis may produce an output, such as a trend line. The trend line may be projected into the future to determine trended capacity headroom and consumption metrics. The trended metrics are compared against baseline metrics. The trended metrics may additionally be compared against predetermined threshold ranges. The threshold ranges may include a minimum and maximum headroom or capacity consumption level; [0044] Forecasting and modeling process 44 receives system monitoring data for a hosting environment 10 including servers 70a-n and virtual resources 30a-n. Forecasting and modeling process 44 converts this linear component utilization data into non-linear real capacity and consumption and/or headroom data for accurate forecasting and modeling of resource capacities).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Wang to substitute resource utilization from Ashby for CPU utilization from Wang to prevent the first resource from deviating from the predetermined threshold range (Ashby, page 1, paragraph 2).
As to claim 2, Wang-Vankayala-Ashby teaches the method as recited in claim 1, wherein the accessing the KPI database to obtain KPI data associated with the capacity of the cells in the mobile network further includes obtaining the KPI data for a first historical data window and a most recent historical data window, the first historical data window occurring immediately before the most recent historical data window (Wang, page 5, paragraph 61; i.e., [0061] the actions do not rectify the underlying issue and the Ethernet errors continue at the same rate unaffected by the restart action(s). This can be seen at each of time 430a, time 430b, time 430c, time 430d, and time 430e. In some embodiments, the error counts are recorded and stored and are included in historical SLE measurements).
Claim(s) 9-10 & 15-16 is/are directed to a system and non-transitory computer readable medium claims and they do not teach or further define over the limitations recited in claim(s) 1-2. Therefore, claim(s) 9-10 & 15-16 is/are also rejected for similar reasons set forth in claim(s) 1-2.
Claim(s) 3-4, 11, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, U.S. Pub/Patent No. 2021/0306201 A1 in view of Vankayala, U.S. Patent/Pub. No. US 2024/0022986 A1, and Ashby, US 2016/0269239 A1, and further in view of Mansour, U.S. Patent/Pub. No. US 10,085,197 B1.
As to claim 3, Wang-Vankayala-Ashby teaches the method as recited in claim 1, wherein that exceed a predetermined performance threshold based on the KPI data as the critical cells (Wang, page 15, paragraph 147; i.e., [0147] FIG. 14D illustrates another example of a preconfigured rule wherein the network management is guided by a threshold 1420 which is a function of the difference between a confidence of a machine learning model in a determining of a root cause of an underlying issue based on two consecutive invocations of the same action (injection of an action that facilitates collection of additional debugging information). Specifically, as explained in greater details in FIG. 13, when the incremental benefit (cost delta) of repeating injecting (the same) action, collecting current information e.g., information 790, and determining the root cause produces lower cost benefit).
But Wang-Vankayala-Ashby failed to teach the claim limitation wherein determining cells within a predetermined area that are not newly On Air cells; and identifying the cells within the predetermined area that are not newly On Air cells.
However, Mansour teaches the limitation wherein determining cells within a predetermined area that are not newly On Air cells; and identifying the cells within the predetermined area that are not newly On Air cells (Mansour, col 11, lines 4-8 & 33-40; i.e., UEs based on signal strength and/or historical air interface
resource usage, with signal strength being directly proportional to the score and historical air interface resource usage also being directly proportional to the score. Historical air interface resource usage could be defined as a percentage or other measure of resources ( e.g., resource elements or PRBs) used over time, such as a running average of percentage of downlink and/or uplink PRBs).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Wang-Vankayala-Ashby to substitute resource element from Mansour for CPU utilization from Wang-Vankayala-Ashby to enable the base station to communicate with a signaling controller ( e.g., MME), gateway system, other base stations (Mansour, col 2, lines 35-42).
As to claim 4, Wang-Vankayala-Ashby-Mansour teaches the method as recited in claim 3. But Wang-Vankayala-Ashby failed to teach the claim limitation wherein determining, for a first yearly quarter, an average downlink (DL) physical resource block (PRB) utilization exceeding 70% for 63 days out of 90 days in the first yearly quarter, or determining, for a two yearly quarter period, the average downlink (DL) physical resource block (PRB) utilization exceeding 70% for 126 days out of 180 days in the two yearly quarter period.
However, Mansour teaches the limitation wherein determining, for a first yearly quarter, an average downlink (DL) physical resource block (PRB) utilization exceeding 70% for 63 days out of 90 days in the first yearly quarter, or determining, for a two yearly quarter period, the average downlink (DL) physical resource block (PRB) utilization exceeding 70% for 126 days out of 180 days in the two yearly quarter period (Mansour, col 8, lines 45-63; col 11, lines 32-40; i.e., a percentage or other measure of resources ( e.g., resource elements or PRBs) used over time, such as a running average of percentage of downlink and/or uplink PRBs that base station 12 has allocated for use and/or a percentage of control channel resource elements used. For instance, the threshold could be a percentage of resource use, such as a percentage between 70% and 100% or the like, a number of served UEs, also perhaps as a percentage of a maximum limit).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Wang-Vankayala-Ashby to substitute resource element from Mansour for CPU utilization from Wang-Vankayala-Ashby to enable the base station to communicate with a signaling controller ( e.g., MME), gateway system, other base stations (Mansour, col 2, lines 35-42).
Claim(s) 11 & 17 is/are directed to a system and non-transitory computer readable medium claims and they do not teach or further define over the limitations recited in claim(s) 3-4. Therefore, claim(s) 11 & 17 is/are also rejected for similar reasons set forth in claim(s) 3-4.
Claim(s) 5-6, 12, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, U.S. Pub/Patent No. 2021/0306201 A1 in view of Vankayala, U.S. Patent/Pub. No. US 2024/0022986 A1, Ashby, US 2016/0269239 A1, and Mansour, U.S. Patent/Pub. No. US 10,085,197 B1, and Martinnson, US 2022/0108246 A1, and further in view of Vatto US 2014/0289386 A1.
As to claim 5, Wang-Vankayala-Ashby teaches the method as recited in claim 1. But Wang-Vankayala-Ashby failed to teach the claim limitation wherein the identifying the non-critical cells includes: determining cells within a predetermined area that are not newly On Air cells; identifying the cells within the predetermined area that are not newly On Air cells and that do not exceed a predetermined performance threshold based on the KPI data as the non- critical cells; filling in invalid data values by copying a last available valid data for a day with invalid data values to generate adjusted data for the non-critical cells; calculating a moving average for the adjusted data to generate averaged data for the non-critical cells; and applying linear regression to the averaged data to identify a trend associated with the averaged data for the non-critical cells.
However, Mansour teaches the limitation wherein determining cells within a predetermined area that are not newly On Air cells; identifying the cells within the predetermined area that are not newly On Air cells and that do not exceed a predetermined performance threshold based on the KPI data as the non-critical cells (Mansour, col 9, lines 3-32; i.e., threshold number to determine whether the number of UEs is threshold high. The threshold number could be a percentage of a maximum limit of served UEs, such as a percentage between 80% and 100% or the like. the number of UEs being served by relay base station 32 or that indicates whether or not the number of UEs being served by relay base station 32 is threshold high. For example, responsive to determining that the air interface is threshold highly congested, base station 12 may send to relay base station).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Wang-Vankayala-Ashby to substitute resource element from Mansour for CPU utilization from Wang-Vankayala-Ashby to enable the base station to communicate with a signaling controller ( e.g., MME), gateway system, other base stations (Mansour, col 2, lines 35-42).
However, Martinnson teaches the limitation wherein filling in invalid data values by copying a last available valid data for a day with invalid data values to generate adjusted data for the non-critical cells (Martinnson, page 6, paragraph 59; i.e., machine learning models may include a seasonal autoregressive integrated moving average (SARIMA) machine learning model; a seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) machine learning model).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Wang-Vankayala-Ashby to substitute historical transaction from Martinnson for CPU utilization from Wang-Vankayala-Ashby to increase an accuracy of the prediction (Martinnson, page 1, paragraph 2).
However, Vatto teaches the limitation wherein calculating a moving average for the adjusted data to generate averaged data for the non-critical cells; and applying linear regression to the averaged data to identify a trend associated with the averaged data for the non-critical cell (Vatto, page 3, paragraph 26; i.e., data utilizing at least one technique selected from the group consisting of: simple linear regression, linear regression, a least squares method, weighted least squares estimation, autoregressive modeling, regression analysis, autoregressive moving average modeling, and generalized linear modeling. Again, social amplification may be utilized in the procedure of trend detection. Trend( s) may be basically detected relative to any performance indicia such as KPIs).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Wang-Vankayala-Ashby to substitute resource allocation from Vatto for CPU utilization from Wang-Vankayala-Ashby to react rapidly to changing conditions but even them usually utilize overly simplistic, numeric decision-making logic and associated criteria (Vatto, page 1, paragraph 5).
As to claim 6, Wang-Vankayala-Ashby-Mansour-Martinnson-Vatto teaches the method as recited in claim 5. But Wang-Vankayala-Ashby-Mansour failed to teach the claim limitation wherein determining whether the non-critical cells have a negative trend associated with the averaged data for the non-critical cells for an immediately previous yearly quarter; in response to determining the non-critical cells have the negative trend associated with the averaged data for the non-critical cells for the immediately previous yearly quarter, determining whether the non-critical cells have the negative trend associated with the averaged data for the non-critical cells for an immediately previous two yearly quarters; in response to determining the non-critical cells have the negative trend associated with the averaged data for the non-critical cells for an immediately previous quarter and for the immediately previous two yearly quarters, saving the average data for the non-critical cells and performing root cause analysis using the average data for the non-critical cells to identify a reason for the negative trend for the immediately previous quarter and for the immediately previous two yearly quarters; wherein the prediction model is applied to the non-critical cells to identify the predicted at least one predetermined forecast time window associated with capacity issues associated with the forecasted at least one of the non-critical cells in response to determining the non-critical cells do not have the negative trend for the immediately previous quarter and for the immediately previous two yearly quarters.
However, Martinnson teaches the limitation wherein determining whether the non-critical cells have a negative trend associated with the averaged data for the non-critical cells for an immediately previous yearly quarter; in response to determining the non-critical cells have the negative trend associated with the averaged data for the non-critical cells for the immediately previous yearly quarter, determining whether the non-critical cells have the negative trend associated with the averaged data for the non-critical cells for an immediately previous two yearly quarters (Martinnson, page 3, paragraph 24; i.e., [0024] In some implementations, the transaction forecast system determines the previous period of time based on the forecast information. The transaction forecast system may determine a length of the future period of time (e.g., a number of days, a number of weeks, a number of months, and/or the like) and may determine the previous period of time based on the length of the future period of time. The transaction forecast system may determine a beginning date for the period of time to cause a length of the previous period of time to be equal to a length of the future period of time, twice the length of the future period of time).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Wang-Vankayala-Ashby-Mansour to substitute historical transaction from Martinnson for CPU utilization from Wang-Vankayala-Ashby-Mansour to increase an accuracy of the prediction (Martinnson, page 1, paragraph 2).
However, Vatto teaches the limitation wherein in response to determining the non-critical cells have the negative trend associated with the averaged data for the non-critical cells for an immediately previous quarter and for the immediately previous two yearly quarters, saving the average data for the non-critical cells and performing root cause analysis using the average data for the non-critical cells to identify a reason for the negative trend for the immediately previous quarter and for the immediately previous two yearly quarters (Vatto, page 3, paragraph 26; page 9, paragraph 119; i.e., [0026] trends may be detected in the data utilizing at least one technique selected from the group consisting of: simple linear regression, linear regression, a least squares method, weighted least squares estimation, autoregressive modeling, regression analysis, autoregressive moving average modeling, and generalized linear modeling. Trend( s) may be basically detected relative to any performance indicia such as KPIs (key performance indicators) based on the raw data or derived utilizing the raw data; [0119] whether the reported trend is positive or negative. For example, a trend indicating decreasing satisfaction may have a "thumb down" icon next to it, whereas a trend indicating increasing satisfaction would be paired with a "thumb up" icon).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Wang-Vankayala-Ashby-Mansour to substitute resource allocation from Vatto for CPU utilization from Wang-Vankayala-Ashby-Mansour to react rapidly to changing conditions but even them usually utilize overly simplistic, numeric decision-making logic and associated criteria (Vatto, page 1, paragraph 5).
Claim(s) 12 & 18 is/are directed to a system and non-transitory computer readable medium claims and they do not teach or further define over the limitations recited in claim(s) 5. Therefore, claim(s) 12 & 18 is/are also rejected for similar reasons set forth in claim(s) 5.
Claim(s) 7-8, 13-14 & 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, U.S. Pub/Patent No. 2021/0306201 A1 in view of Vankayala, U.S. Patent/Pub. No. US 2024/0022986 A1, and Ashby, US 2016/0269239 A1, and further in view of Martinnson, US 2022/0108246 A1.
As to claim 7, Wang-Vankayala-Ashby teaches the method as recited in claim 1. But Wang-Vankayala-Ashby failed to teach the claim limitation wherein the applying the prediction model to predict the at least one predetermined forecast time window associated with the capacity issues associated with the forecasted at least one of the non-critical cells includes: applying a Seasonal AutoRegressive Integrated Moving Average (SARIMA) prediction model to the at least one of the non-critical cells; based on the applying the SARIMA prediction model to the at least one of the non- critical cells, identifying a first predetermined forecast window for capacity issues of the non- critical cells forecasted to occur in 0 to 3 months, a second predetermined forecast window for capacity issues of the non-critical cells forecasted to occur in 3 to 6 months, a third predetermined forecast window for capacity issues of the non-critical cells forecasted to occur in 6 to 9 months, and a fourth predetermined forecast window for capacity issues of the non-critical cells forecasted to occur in 9 to 12 months; and applying a first priority to the non-critical cells forecasted to have capacity issues in 0 to 3 months, a second priority to the non-critical cells forecasted to have capacity issues in 3 to 6 months, a third priority to the non-critical cells forecasted to have capacity issues in 6 to 9 months, and a fourth priority to the non-critical cells forecasted to have capacity issues in 9 to 12 months.
However, Martinnson teaches the limitation wherein applying a Seasonal AutoRegressive Integrated Moving Average (SARIMA) prediction model to the at least one of the non-critical cells (Martinnson, page 6, paragraph 59; i.e., [0059] A the one or more machine learning models may include a seasonal autoregressive integrated moving average (SARIMA) machine learning model; a seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) machine learning model; a long short-term memory (LSTM) machine learning model; an exponential smoothing machine learning model; a business logic machine learning model); based on the applying the SARIMA prediction model to the at least one of the non- critical cells, identifying a first predetermined forecast window for capacity issues of the non- critical cells forecasted to occur in 0 to 3 months, a second predetermined forecast window for capacity issues of the non-critical cells forecasted to occur in 3 to 6 months, a third predetermined forecast window for capacity issues of the non-critical cells forecasted to occur in 6 to 9 months, and a fourth predetermined forecast window for capacity issues of the non-critical cells forecasted to occur in 9 to 12 months (Martinnson, page 3, paragraph 24 & 33; i.e., [0024] The transaction data may include historical transaction data associated with a plurality of financial transactions occurring during a previous period of time ( e.g., the past day, the past week, the past month, the past year, and/or the like). The transaction forecast system may determine a length of the future period of time (e.g., a number of days, a number of weeks, a number of months, and/or the like) and may determine the previous period of time based on the length of the future period of time. The transaction forecast system may determine a beginning date for the period of time to cause a length of the previous period of time to be equal to a length of the future period of time, twice the length of the future period of time; [0033] In some implementations, the transaction forecast system identifies the one or more recurrent transactions based on the transaction data. The transaction forecast system may analyze the transaction data); and applying a first priority to the non-critical cells forecasted to have capacity issues in 0 to 3 months, a second priority to the non-critical cells forecasted to have capacity issues in 3 to 6 months, a third priority to the non-critical cells forecasted to have capacity issues in 6 to 9 months, and a fourth priority to the non-critical cells forecasted to have capacity issues in 9 to 12 months (Martinnson, page 3, paragraph 33; i.e., [0033] A recurrent transaction may be a financial transaction that occurs periodically ( e.g., daily, weekly, monthly, quarterly, yearly, and/or the like). In some implementations, the transaction forecast system identifies the one or more recurrent transactions based on the transaction data. The transaction forecast system may analyze the transaction data).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Wang-Vankayala-Ashby to substitute historical transaction from Martinnson for CPU utilization from Wang-Vankayala-Ashby to increase an accuracy of the prediction (Martinnson, page 1, paragraph 2).
As to claim 8, Wang-Vankayala-Ashby teaches the method as recited in claim 1. But Wang-Vankayala-Ashby failed to teach the claim limitation wherein the identifying the critical cells includes: determining whether the critical cells have a negative trend associated with the averaged data for the critical cells for an immediately previous yearly quarter, in response to determining whether the critical cells have the negative trend associated with the averaged data for the critical cells for an immediately previous two yearly quarters, marking the critical cells; in response to determining the critical cells have the negative trend associated with the averaged data for the critical cells for the immediately previous quarter and for the immediately previous two yearly quarters, saving the cell data associated with the critical cells and performing root cause analysis to identify a reason for the negative trend for the immediately previous quarter and for the immediately previous two yearly quarters; and in response to determining the critical cells have the negative trend associated with the averaged data for the critical cells for the immediately previous quarter and for the immediately previous two yearly quarters, marking the critical cells.
However, Martinnson teaches the limitation wherein determining whether the critical cells associated with the averaged data for the critical cells for an immediately previous yearly quarter, in response to determining whether the critical cells have associated with the averaged data for the critical cells for an immediately previous two yearly quarters, marking the critical cells; in response to determining the critical cells have the associated with the averaged data for the critical cells for the immediately previous quarter and for the immediately previous two yearly quarters, saving the cell data associated with the critical cells and performing root cause analysis to identify a reason for the negative trend for the immediately previous quarter and for the immediately previous two yearly quarters; and in response to determining the critical cells associated with the averaged data for the critical cells for the immediately previous quarter and for the immediately previous two yearly quarters, marking the critical cells (Martinnson, page 3, paragraph 24; i.e., [0024] The transaction data may include historical transaction data associated with a plurality of financial transactions occurring during a previous period of time ( e.g., the past day, the past week, the past month, the past year, and/or the like). The transaction forecast system may determine a length of the future period of time (e.g., a number of days, a number of weeks, a number of months, and/or the like) and may determine the previous period of time based on the length of the future period of time. The transaction forecast system may determine a beginning date for the period of time to cause a length of the previous period of time to be equal to a length of the future period of time, twice the length of the future period of time).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Wang-Vankayala-Ashby to substitute historical transaction from Martinnson for CPU utilization from Wang-Vankayala-Ashby to increase an accuracy of the prediction (Martinnson, page 1, paragraph 2).
However, Vatto teaches the limitation wherein determining a negative trend; in response to determining the negative trend, determining whether the negative trend; calculating a moving average for the adjusted data to generate averaged data for the non-critical cells; and applying linear regression to the averaged data to identify a trend associated with the averaged data for the non-critical cell (Vatto, page 3, paragraph 26; page 9, paragraph 119; i.e., [0026] trends may be detected in the data
utilizing at least one technique selected from the group consisting of: simple linear regression, linear regression, a least squares method, weighted least squares estimation, autoregressive modeling, regression analysis, autoregressive moving average modeling, and generalized linear modeling. Trend( s) may be basically detected relative to any performance indicia such as KPIs (key performance indicators) based on the raw data or derived utilizing the raw data; [0119] whether the reported trend is positive or negative. For example, a trend indicating decreasing satisfaction may have a "thumb down" icon next to it, whereas a trend indicating increasing satisfaction would be paired with a "thumb up" icon).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Wang-Vankayala-Ashby to substitute resource allocation from Vatto for CPU utilization from Wang-Vankayala-Ashby to react rapidly to changing conditions but even them usually utilize overly simplistic, numeric decision-making logic and associated criteria (Vatto, page 1, paragraph 5).
Claim(s) 13-14 & 19-20 is/are directed to a system and non-transitory computer readable medium claims and they do not teach or further define over the limitations recited in claim(s) 7-8. Therefore, claim(s) 13-14 & 19-20 is/are also rejected for similar reasons set forth in claim(s) 7-8.
Response to Arguments
Applicant's arguments with respect to claim(s) 1-20 have been considered but are moot in view of the new ground(s) of rejection.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Listing of Relevant Arts
Loftus, U.S. Patent/Pub. No. US 20210119878 A1 discloses performance issue and implementing remedial actions.
Youtz, U.S. Patent/Pub. No. US 20180184412 A1 discloses reduce network resources during periods and the KPIs.
Contact Information
The present application is being examined under the pre-AIA first to invent provisions.
THUONG NGUYEN whose telephone number is (571)272-3864. The examiner can normally be reached on Monday-Friday 9:00-6:00.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Noel Beharry can be reached on 571-270-5630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THUONG NGUYEN/Primary Examiner, Art Unit 2416
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