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.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12279002. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of this application are obvious over claims of the US Patent 12279002.
The subject matter claimed in the instant application is fully disclosed in the US Patent 12279002 and the applications are claiming common subject matter, mapping of claims as follows:
Instant Application No. 19/081,245
U.S. Patent No. 12279002
Claims 1-20 maps to
Claims 1-20
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 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.
Claims 1, 4, 5, 7-11, 14-17, and 18 are rejected under U.S.C. 103 as being unpatentable over Thario (US 11089076, part of Information Disclosure Statement filed on 3/17/2025), in view of Lobanov at al. (US 20210176530, part of Information Disclosure Statement filed on 3/17/2025), in further view of Tomkins et al. (US 20190222491, part of Information Disclosure Statement filed on 3/17/2025), in further view of Vasseur et al. (US 20190356533).
Regarding claim 1, Thario discloses, a method comprising:
calculating, by a processing system including a processor, a threshold value of a streaming-media key performance indicator (KPI) based on a level of certainty that a predetermined target portion of a plurality of end-user media processors provide an acceptable end-user experience (column 4, line 50-54, be the maximum number of user devices that are able to simultaneously use the service with in predetermined thresholds for performance characteristics, such as video quality, response time latency, and the like, that represent a suitable streaming experience, i.e. threshold of performance characteristics (i.e. performance indicator) at which group of user devices can have suitable or acceptable streaming experience);
obtaining, by the processing system, access records of a content delivery network (CDN) configured to serve media content requested by the plurality of end-user media processors (column 6, line 25-30 service may store the aggregate values in a new performance data record of an indexed performance data store. The performance metrics may be indexed with respect to various time windows useful for identifying the origin server's response to varying the number of simulated connected user devices. Column 12, line 7-12, media streaming system 110 can include a CDN incorporating nodes that reference endpoints in the network interface 160 to facilitate the delivery of the content streams to one or more end user devices in geographically disparate locations. Column 16, line 15-18, obtain performance data from the performance data store 186 and compare the threshold value of each metric to the stored, collected value of the corresponding metric in the performance data, i.e., obtain server access records of CDN delivery of content streams to end user devices);
by the processing system, a plurality of values of the streaming-media KPI according to the access records of the CDN, to obtain a plurality of values of the streaming-media KPI (Column 16, line 15-18, obtain performance data from the performance data store and compare the threshold value of each metric to the stored, collected value of the corresponding metric in the performance data, i.e., obtain stored record of content access (i.e., for streaming) performance);
comparing, by the processing system, the plurality of values of the streaming-media KPI to the threshold value of the streaming-media KPI to obtain a comparison (column 16, line 16-18, compare the threshold value of each metric to the stored, collected value of the corresponding metric in the performance data); and
identifying, by the processing system, an anomaly according to the comparison (column 16, line 23-26, The signaling system 174 may determine from the comparison whether any of the performance metrics breached the corresponding threshold value. Column 32, line 42-46, compare, for each of the performance metrics, the corresponding calculated value to the corresponding threshold value; and, responsive to a comparison result indicating that one of the threshold values has been breached, produce the indication that the streaming performance is below the acceptable quality of service level, i.e., quality of streaming media failed satisfy threshold level or acceptable level of quality of service).
Thario does not disclose, predicting, by the processing system, a plurality of values of the streaming-media KPI according to the access records of the CDN, to obtain a plurality of predicted values of streaming-media KPI;
comparing, by the processing system, the plurality of predicted values of the streaming- media KPI to the threshold value of the streaming-media KPI within a sequence of anomaly threshold time windows; and
determining, by the processing system, that a threshold number of the plurality of predicted values of the streaming-media KPI exceed the threshold value of the streaming- media KPI in at least one of the anomaly threshold time windows indicating a need for capacity growth in the CDN.
Lobanov discloses, predicting, by the processing system, a plurality of values of the streaming-media KPI according to the access records of the CDN, to obtain a plurality of predicted values of streaming-media KPI (par. 0032, prediction method where neural network may be used. CDN log is provided as input to the Neural Network. Neural Network processes the data and outputs an estimation of QOE metrics, i.e., predicting the QOE or KPI value based on CDN log).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method Thario, by teachings of predicting a plurality of values of the streaming-media KPI according to the access records of the CDN to obtain a plurality of predicted values of streaming-media KPI, as taught by Lobanov, CDN log analysis is preferred to estimate QoE metrics over using telemetry data from user devices to estimate QoE, in cases where access to telemetry data is not available and/or telemetry data is blocked in case of browser based data filtering due to ad blocking, as disclosed in Lobanov, par. 0023-0024.
Thario in view of Lobanov does not disclose, comparing, by the processing system, the plurality of predicted values of the streaming- media KPI to the threshold value of the streaming-media KPI within a sequence of anomaly threshold time windows;
determining, by the processing system, that a threshold number of the plurality of predicted values of the streaming-media KPI exceed the threshold value of the streaming- media KPI in at least one of the anomaly threshold time windows indicating a need for capacity growth in the CDN.
Tomkins discloses, comparing, by the processing system, the plurality of predicted values of the streaming- media KPI to the threshold value of the streaming-media KPI within a sequence of anomaly threshold time windows (Par. 0034, video streaming service with QoE, Par. 0072, optimization platform identify the users experiencing a QOE and preventing the QoE from crossing a policy defined threshold for minimum quality of experience, i.e. comparing streaming media QoE to the minimum required threshold to determine if it is crossing (i.e. if it is lower than threshold) to prevent from going lower than threshold. Par. 0080, the adaptive systems and methods include forecasting (i.e. predicting value of KPI) QOS and QOE via collection of application measurements and network measurements, correlate (i.e. Comparing) QOS and QOE when both are measured, identify violations of target QOE, The exogenous data can include network failures, scheduled equipment outages, traffic generating events (sporting events, popular shows), par. 0101, fig. 7, comparing predicted discloses, QOE (i.e. predicted) versus time as well as target (i.e. threshold),comparing predicted QOE versus Target QOE, if it is above or within target QOE measured over time windows, from T0 to T1, T1 to T2 and T2 to T3) i.e. compare forecasted QOE data with target QOE over the sequence of time windows to see if it violates value of target QOE and generates exogenous data (i.e. anomalies). i.e. to determine if anomalies occurs within sequence of time window = a sequence of anomaly threshold time windows);
determining, by the processing system, that predicted values of the streaming-media KPI exceed the threshold value of the streaming- media KPI in at least one of the anomaly threshold time windows indicating a need for capacity growth in the CDN (Par. 0106, A check is made to see if the QoE at time to violates (i.e. exceeds threshold value of) target QoE. If such a violation occurs, the block Develop (reactive) remediation plan determines steps to mitigate immediate QoE violations which are enacted by Implement remediation plan. Par. 0107, candidate mediation options (including the time of application): these can be capacity changes, content cache changes, stream routing changes, par. 0130, The remedial actions can include any of capacity changes in the network, content cache changes in the network, routing changes for streams i.e. capacity of content distribution network as shown in fig. 2)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method Thario in view of Lobanov, by teachings of comparing, the plurality of predicted values of the streaming- media KPI to the threshold value of the streaming-media KPI within a sequence of anomaly threshold time windows and determining, that predicted values of the streaming-media KPI exceed the threshold value of the streaming- media KPI in at least one of the anomaly threshold time windows indicating a need for capacity growth in the CDN, as taught by Tomkins, to adaptively use machine learning tools to predict QOE to ensure that target or threshold QoE is maintained in ongoing fashion for ensuring a mitigation and determine remedial actions in the network to repair the poor QoE, and cause implementation of the remedial actions in the network, as disclosed in Tomkins, par. 0007 and 0034.
Thario in view of Lobanov in further view of Tomkins does not disclose, determining a threshold number of the KPI exceed the threshold value of the streaming- media KPI indicating a need for capacity growth.
Vasseur discloses, determining a threshold number of the KPI exceed the threshold value of the streaming- media KPI indicating a need for capacity growth (Par. 0068, par. 0070, fig. 8A and 8B, the anomaly detector(s) 406 may calculate anomaly scores for the KPIs over time and, if an anomaly score exceeds an anomaly detection threshold, anomaly detector(s) 406 may raise an anomaly detection alert, fig. 8A and 8B shows number or count of KPI exceeding threshold value. Par. 0043, predictive analytics (e.g., models used to predict user experience, etc.), troubleshooting with root cause analysis, and/or trending analysis for capacity planning, i.e. KPI analysis result is used for capacity planning and growth).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method Thario in view of Lobanov in further view of Tomkins, by teachings of determining a threshold number of the KPI exceed the threshold value of the streaming- media KPI indicating a need for capacity growth, as taught by Vasseur, to determine number of KPI that produced anomaly in generating anomaly score for period of time to determine anomaly severity, as disclosed in Vasseur, par. 0068 and 0072.
Regarding claim 4, The method of claim 1,
Thario in view of Lobanov in further view Tomkins in further view of Vasseur further discloses, wherein the predicting the plurality of values of the streaming-media KPI further comprises:
providing, by the processing system and to a neural network, training data comprising a training time-series of streaming-media KPI values and corresponding training access records of the CDN (Lobanov Par. 0031, CDN log is input to Neural network model, Par. 0033, the Neural Network model is presented with multiple examples of CDN log segments each covering an N minute interval (i.e. time series of) and corresponding actual values of the QoE metrics collected from user device for the same N minute interval);
training, by the processing system, the neural network according to the training data to obtain a trained neural network (Lobanov par. 0051, The model was trained, using the training data set); and
providing, by the processing system, current access records of the CDN to the trained neural network, wherein the trained neural network, in response, determines the plurality of predicted values of the streaming-media KPI (Lobanov par. 0034, training may be repeated periodically with one or more new sets of CDN logs, i.e., providing current or new CDN log to neural network, par. 0053, using a training data set, which includes these new elements. After training or retraining the model, new QoE parameters may be predicted, i.e., trained model generates the predication of new QoE or KPI values).
Regarding claim 5, The method of claim 4,
Thario in view of Lobanov in further view Tomkins in further view of Vasseur further discloses, further comprising:
obtaining, by the processing system, the training time-series of streaming-media KPI values via a KPI monitoring service (Lobanov, par. 0012, Monitoring viewer’s QoE of streaming. Par. 0033, training of machine learning model based on CDN log that represents different time of day, i.e., obtain CDN log (i.e., that represents QoS obtained over time period) to provide as input to training mode. Par. 0040, time series sequence as input for predication of buffering, i.e., using time series data of QoE (i.e., KPI) as input to training model as part of QoS monitoring system).
Regarding claim 7, The method of claim 1,
Thario in view of Lobanov in further view Tomkins in further view of Vasseur further discloses, wherein the streaming-media KPI comprise one of a media start time, a media playback stall rate, a media failure to start rate, an exit before media start rate, a media rebuffering ratio, or any combination thereof (Tomkins Par. 0053, QOE data includes, video start time, re-buffering ratio).
Regarding claim 8, The method of claim 1,
Thario in view of Lobanov in further view Tomkins in further view of Vasseur further discloses,
further comprising: associating, by the processing system, access records of the CDN with the plurality of end-user media processors (Lobanov, par. 0062 discloses, CDN log contains log records that includes IP address of requesting device. Also, can include MAC address may be used as unique ID to associate with CDN log).
Regarding claim 9, The method of claim 8,
Thario in view of Lobanov in further view Tomkins in further view of Vasseur further discloses,
wherein the associating further comprises:
obtaining, by the processing system, one of a transaction identifier, a geolocation, or both, of an end-user media processor of the plurality of end-user media processors (Tomkins Par. 0053, QOE data includes client geolocation, i.e., as part of obtained QoE data, geolocation of client device is obtained); and
associating, by the processing system, the plurality of streaming-media KPI values with the end-user media processor according to the transaction identifier, the geolocation, or both (Tomkins Par. 0053, QOE data includes client geolocation, i.e., as part of obtained QoE data, geolocation of client device is obtained and associated with QoE data).
Regarding claim 10, The method of claim 1,
Thario in view of Lobanov in further view Tomkins in further view of Vasseur discloses, wherein the identifying the anomaly further comprises:
counting, by the processing system, plurality of predicted values of the streaming- media KPI that exceed the threshold value of the streaming-media KPI to obtain a sum (Thario column 30, line 37-49, is calculated value is not within the threshold value, (i.e., breach occurred), system may increment (i.e., add one to the current value of) the breach counter for the corresponding metric);
comparing, by the processing system, the sum to an acceptable number limit to obtain a comparison result (Thario column 30 line, 52-57 the system may evaluate whether, including the breach of the current window, the corresponding metric has been in breach for the maximum number of consecutive windows (e.g., the new value for the metric's breach counter is equal to the sustained breach setting) and should be considered a sustained breach, i.e. breach counter is compared to sustained breach setting to be considered as sustained breach)); and
initiating, by the processing system, an alarm responsive to the comparison result indicating the sum exceeds the acceptable number limit (Thario if the breach counter is equals sustained breach setting, system sends a scale-down signal to the load control module to reduce the load on the origin server, i.e., sending scale-down signal to reduce load on origin server = sending alarm to original server when breach counter is higher than normal).
Regarding claim 11, Thario in view of Lobanov in further view Tomkins in further view of Vasseur meets claim limitation as set forth in claim 1, respectively, Thario further discloses, a device, comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations (Column 11, line, 47-55, the media streaming system 110 (and each of the components therein) may include one or more processors, memory that stores instructions executed by the one or more processors, network interfaces, application-specific hardware, or other hardware components that allow the system 110 to perform the functionality).
Regarding claim 14, Thario in view of Lobanov in further view Tomkins in further view of Vasseur meets the claim limitations as set forth in claim 4.
Regarding claim 15, Thario in view of Lobanov in further view Tomkins in further view of Vasseur meets the claim limitations as set forth in claim 7.
Regarding claim 16, Thario in view of Lobanov in further view Tomkins in further view of Vasseur meets the claim limitations as set forth in claim 8.
Regarding claim 17, Thario in view of Lobanov in further view Tomkins in further view of Vasseur meets the claim limitations as set forth in claim 9.
Regarding claim 18, Thario in view of Lobanov in further view Tomkins in further view of Vasseur meets claim limitation as set forth in claim 1, respectively, Thario further discloses, a non-transitory, machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations (Column 23, line, 20-25).
Claims 2, 12, and 19 are rejected under U.S.C. 103 as being unpatentable over Thario (US 11089076, part of Information Disclosure Statement filed on 3/17/2025), in view of Lobanov at al. (US 20210176530, part of Information Disclosure Statement filed on 3/17/2025), in further view of Tomkins et al. (US 20190222491, part of Information Disclosure Statement filed on 3/17/2025), in further view of Vasseur et al. (US 20190356533), in further view of Embarmannar et al. (US 20200401936, part of Information Disclosure Statement filed on 3/17/2025).
Regarding claim 2, The method of claim 1,
Thario in view of Lobanov in further view Tomkins in further view of Vasseur does not disclose, wherein the calculating the threshold value of the streaming-media KPI further comprises:
accessing, by the processing system, a time series of actual streaming-media KPI values associated a serving of media content to the plurality of end-user media processors, wherein the calculating of the threshold value of the streaming-media KPI is further based on the time series of actual streaming-media KPI values.
Embarmannar discloses, wherein the calculating the threshold value of the streaming-media KPI further comprises:
accessing, by the processing system, a time series of actual streaming-media KPI values associated a serving of media content to the plurality of end-user media processors, wherein the calculating of the threshold value of the streaming-media KPI is further based on the time series of actual streaming-media KPI values (Par. 0107, the learning algorithm can use information from that include a history of time-series KPIs to use for pattern recognition and establishing dynamic thresholds against which anomalies can be detected, par. 0080, KPI for video services).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method Thario in view of Lobanov in further view Tomkins in further view of Vasseur, by teaching of accessing and calculating the threshold value of KPI by accessing a time series of KPI values associated with serving of media content to plurality of end user media processors, as taught by Embarmannar, to allow the ML engine to recognize patterns over time and establish dynamic threshold against which anomalies can be detected, as disclosed in Embarmannar, par. 0076 and 0107.
Regarding claim 12, Thario in view of Lobanov in further view Tomkins in further view of Vasseur in further view of Embarmannar meets the claim limitations as set forth in claim 2.
Regarding claim 19, Thario in view of Lobanov in further view Tomkins in further view of Vasseur in further view of Embarmannar meets the claim limitations as set forth in claim 2.
Claims 3, 13, and 20 are rejected under U.S.C. 103 as being unpatentable over Thario (US 11089076, part of Information Disclosure Statement filed on 3/17/2025), in view of Lobanov at al. (US 20210176530, part of Information Disclosure Statement filed on 3/17/2025), in further view of Tomkins et al. (US 20190222491, part of Information Disclosure Statement filed on 3/17/2025), in further view of Vasseur et al. (US 20190356533), in further view of Embarmannar et al. (US 20200401936, part of Information Disclosure Statement filed on 3/17/2025), in further view of Farag (US 20210410029, part of Information Disclosure Statement filed on 3/17/2025).
Regarding claim 3, The method of claim 2,
Thario in view of Lobanov in further view Tomkins in further view of Vasseur in further view of Embarmannar does not disclose, wherein the threshold value of the streaming-media KPI comprises one of a maximum value, a minimum value, an average maximum value, an average minimum value, an average value or any combination thereof, of the time series of actual streaming-media KPI values reported for the plurality of end-user media processors.
Farag discloses, wherein the threshold value of the streaming-media KPI comprises one of a maximum value, a minimum value, an average maximum value, an average minimum value, an average value or any combination thereof, of the time series of actual streaming-media KPI values reported for the plurality of end-user media processors (Par. 0047, the threshold level of service quality may be an average QoS measured over a relevant area and/or time period, i.e. threshold value of QoS is determined based on average QoS measured over a time period, collecting QoS data over time period = time series of QoS).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method Thario in view of Lobanov in further view Tomkins in further view of Vasseur in further view of Embarmannar, by teaching of threshold value KPI comprises average value of QoS or time series of actual Qos, as taught by Farag, averaging of QoS over time helps to predict the variation of quality of services against the certain threshold level averaged from actual KPI, as disclosed in Farag, par. 0047.
Regarding claim 13, Thario in view of Lobanov in further view Tomkins in further view of Vasseur in further view of Embarmannar in further view of Farag meets the claim limitations as set forth in claim 3.
Regarding claim 20, Thario in view of Lobanov in further view Tomkins in further view of Vasseur in further view of Embarmannar in further view of Farag meets the claim limitations as set forth in claim 3.
Claim 6 is rejected under U.S.C. 103 as being unpatentable over Thario (US 11089076, part of Information Disclosure Statement filed on 3/17/2025), in view of Lobanov at al. (US 20210176530, part of Information Disclosure Statement filed on 3/17/2025), in further view of Tomkins et al. (US 20190222491, part of Information Disclosure Statement filed on 3/17/2025), in further view of Vasseur et al. (US 20190356533), in further view of Farag (US 20210410029, part of Information Disclosure Statement filed on 3/17/2025), in further view of BIRD (US 20220036259, part of Information Disclosure Statement filed on 3/17/2025).
Regarding claim 6, The method of claim 1,
Thario in view of Lobanov in further view Tomkins in further view of Vasseur does not disclose, further comprising:
Identifying, by the processing system, an alarm threshold value;
determining, by the processing system, a time-averaged, threshold value of the streaming-media KPI; and
calculating, by the processing system, a difference value between the time- averaged, threshold value of the streaming-media KPI and the alarm threshold value, wherein the difference value provides an estimate of growth capacity.
Farag discloses, determining, by the processing system, a time-averaged, threshold value of the streaming-media KPI (Par. 0017, Qos for maintaining user’s streaming experience. Par. 0047, the threshold level of service quality may be an average QoS measured over a relevant area and/or time period, i.e., threshold value of QoS is determined based on average QoS measured over a time period).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method Thario in view of Lobanov in further view Tomkins in further view of Vasseur, by teaching of determining time averaged threshold value of streaming media KPI, as taught by Farag, averaging of QoS over time helps to predict the variation of quality of services against the certain threshold level averaged from actual KPI, as disclosed in Farag, par. 0047.
Thario in view of Lobanov in further view Tomkins in further view of Vasseur in further view of Farag does not disclose, Identifying, by the processing system, an alarm threshold value;
calculating, by the processing system, a difference value between the threshold and the alarm threshold value, wherein the difference value provides an estimate of growth capacity.
Bird discloses, further comprising: Identifying, by the processing system, an alarm threshold value (par. 0042, setting a threshold value at which an alert is issued, i.e., alarm threshold is set);
calculating, by the processing system, a difference value between the threshold value of the KPI and the alarm threshold value, wherein the difference value provides an estimate of growth capacity (par. 0042, setting a threshold value at which an alert is issued relative to the difference between a maximum capacity (i.e., alert threshold) and an approaching maximum capacity value (i.e., threshold). Par. 0086, If the capacity threshold is set at 85% for capacity used, an alert is issued, indicating that the application is now coming close to a maximum capacity and would be unable to “absorb” much additional impact from a network latency increase or spike in transaction volume, i.e., when difference between alert threshold and maximum capacity is very close then estimated that growth in or additional volume will not be accommodated).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method Thario in view of Lobanov in further view Tomkins in further view of Vasseur in further view of Farag, by teaching of identifying alarm threshold value calculating a difference between threshold value of KPI and alarm threshold value to estimate of growth capacity, as taught by Bird, to track the capacity change with efficient monitoring to avoid degradation of system, as disclosed in Bird, par. 0002.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AKSHAY DOSHI whose telephone number is (571)272-2736. The examiner can normally be reached M-F 9:30 AM to 6:00 PM.
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/A.D./Examiner, Art Unit 2422
/JOHN W MILLER/Supervisory Patent Examiner, Art Unit 2422
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