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 .
DETAILED ACTION
Claims 1-20 are pending.
Double Patenting
I. 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 claims at issue 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); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
II. 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 a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this 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 §§ 706.02(l)(1) - 706.02(l)(3) 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).
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III. CLAIMS 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over U.S. Patent No. 12,088,473. Although the claims at issue are not identical, they are not patentably distinct from each other because as show in the table below:
Instant Application – 18/802,399
Conflicting Patent – 12,088,473
1, 8, 15. A method of a computing network implemented in a data processing device communicatively coupled to a memory, comprising:
detecting a set of point anomalies in real-time data associated with each network entity of a plurality of network entities of the computing network for each feature thereof in sequential time based on determining whether the real-time data falls outside at least one threshold expected value thereof;
computing anomaly scores for the detected set of point anomalies indicative of anomalousness thereof;
determining an event associated with a pattern of change of the real-time data associated with the each network entity for the each feature thereof based on the detected set of point anomalies and the computed anomaly scores;
determining data correlation scores for the point anomalies associated with the event that reflect commonness of the event by way of at least one combination of features that has led to the event;
in accordance with reading the anomaly scores associated with the event as an input feedback to the data processing device, the each feature of the each network entity as a dimension of the input feedback and a category of the event as a label of the event and in accordance with the determination of the data correlation scores, predictively classifying a future event into a predicted category thereof in accordance with subjecting the anomaly scores associated with the event to a binning process and interpreting a severity indicator of the event also input thereto;
refining the predictive classification of the future event based on a subsequent input to the data processing device from another data processing device that modifies a classification model for predictively classifying the future event into the predicted category;
representing each detected point anomaly of the set of point anomalies in a full mesh Q node graph, wherein Q is a number of features applicable for the each network entity;
capturing a transition in the each detected point anomaly associated with a newly detected one of: anomaly and non-anomaly in the real-time data associated with the each feature of the each network entity of the Q number of features via the representation of the full mesh Q node graph;
deriving a current data correlation score for the each detected point anomaly across the captured transition as:
CS=∑i=1APC(1-EWPiTSAC)APC, wherein CS is the current data correlation score for the each detected point anomaly across the captured transition, APC is a count of a total number of pairs of Y current anomalous features in the Q number of features and is given by YC2+YC1, EWPi is a weight of an edge of the ith pair of the Y current anomalous features in the representation of the full mesh Q node graph, and TSAC is a total number of time samples of the each detected point anomaly comprising the captured transition, and
wherein the current data correlation score is indicative of a commonness of a combination of the Y current anomalous features contributing to the each detected point anomaly with respect to an equivalent Y anomalous features contributing to another previously detected point anomaly associated with the each network entity; and
utilizing the current data correlation score to predictively classify the future event into the predicted category thereof.
1, 8, 15. A method comprising:
detecting, through a server of a cloud computing network comprising a plurality of subscribers of application acceleration as a service provided by the cloud computing network at a corresponding plurality of client devices communicatively coupled to the server, a set of point anomalies in real-time data associated with each network entity of a plurality of network entities of the cloud computing network for each feature thereof in sequential time based on determining whether the real-time data falls outside at least one first threshold expected value thereof;
computing, through the server, anomaly scores for the detected set of point anomalies indicative of anomalousness thereof;
determining, through the server, an event associated with a pattern of change of the real-time data associated with the each network entity for the each feature thereof based on the detected set of point anomalies and the computed anomaly scores;
determining, through the server, data correlation scores for the point anomalies associated with the event that reflect commonness of the event by way of at least one combination of features that has led to the event;
in accordance with reading the anomaly scores associated with the event as an input feedback to the server, the each feature of the each network entity as a dimension of the input feedback and a category of the event as a label of the event and in accordance with the determination of the data correlation scores, predictively classifying, through the server, a future event into a predicted category thereof in accordance with subjecting the anomaly scores associated with the event to a binning process and interpreting a severity indicator of the event also input thereto;
refining, through the server, the predictive classification of the future event based on a subsequent input to the server from a client device of the plurality of client devices that modifies a classification model for predictively classifying the future event into the predicted category;
representing, through the server, each detected point anomaly of the set of point anomalies in a full mesh Q node graph, wherein Q is a number of features applicable for the each network entity;
capturing, through the server, a transition in the each detected point anomaly associated with a newly detected one of: anomaly and non-anomaly in the real-time data associated with the each feature of the each network entity of the Q number of features via the representation of the full mesh Q node graph;
deriving, through the server, a current data correlation score for the each detected point anomaly across the captured transition as:
CS=∑i=1APC(1-EWPiTSAC)APC, wherein CS is the current data correlation score for the each detected point anomaly across the captured transition, APC is a count of a total number of pairs of Y current anomalous features in the Q number of features and is given by YC2+YC1, EWPi is a weight of an edge of the ith pair of the Y current anomalous features in the representation of the full mesh Q node graph, and TSAC is a total number of time samples of the each detected point anomaly comprising the captured transition, and
wherein the current data correlation score is indicative of a commonness of a combination of the Y current anomalous features contributing to the each detected point anomaly with respect to an equivalent Y anomalous features contributing to another previously detected point anomaly associated with the each network entity; and
utilizing the current data correlation score to predictively classify, through the server, the future event into the predicted category thereof.
2, 9, 16. The method of claim 1, comprising predictively classifying the future event into the predicted category using one of: a decision tree and at least one non-linear classification algorithm based on an amount of feedback data available to the data processing device.
2. The method of claim 1, comprising predictively classifying, through the server, the future event into the predicted category using one of: a decision tree and at least one non-linear classification algorithm based on an amount of feedback data available to the server.
3, 10, 17. The method of claim 1, comprising one of: boosting and scaling down one or more of the anomaly scores associated with the event to impact prediction of a severity thereof.
3. The method of claim 1, comprising, through the server, one of: boosting and scaling down one or more of the anomaly scores associated with the event to impact prediction of a severity thereof.
4, 11. The method of claim 1, further comprising building the classification model based on feedback data to the data processing device during each cycle of a set of periodic cycles.
4. The method of claim 1, further comprising the server building the classification model based on feedback data thereto during each cycle of a set of periodic cycles.
5, 12, 18. The method of claim 1, comprising the computation of the anomaly scores involving both relative scoring and absolute deviation scoring, and the absolute deviation scoring being based on previous data deviations from reference data bands.
5. The method of claim 1, comprising the computation of the anomaly scores involving both relative scoring and absolute deviation scoring, and the absolute deviation scoring being based on previous data deviations from reference data bands.
6, 13, 20. The method of claim 1, comprising the subsequent input to the data processing device from the another data processing device modifying the classification model in accordance with at least one of:
reclassifying at least one of: the event and the future event under another at least one of: category and predicted category;
reclassifying the at least one of: the event and the future event under at least one of: a new category and a new predicted category; and one of:
nullifying and modifying one or more elements of the classification model.
6. The method of claim 1, comprising the subsequent input to the server from the client device modifying the classification model in accordance with at least one of:
reclassifying at least one of: the event and the future event under another at least one of: category and predicted category;
reclassifying the at least one of: the event and the future event under at least one of: a new category and a new predicted category; and
one of:
nullifying and modifying one or more elements of the classification model.
7, 14, 19. The method of claim 1, comprising: the data processing device predictively classifying the future event into the predicted category thereof further in accordance with at least one graph image generated as a time series also input thereto, the at least one graph image representing at least one point anomaly of the set of point anomalies.
7. The method of claim 1, comprising:
the server predictively classifying the future event into the predicted category thereof further in accordance with at least one graph image generated as a time series also input thereto, the at least one graph image representing at least one point anomaly of the set of point anomalies.
Allowable Subject Matter
IV. CLAIMS 1-20 have been deemed allowable over the 35 U.S.C. 102 and 35 U.S.C. 103 statutes. Other than the above Double Patenting rejection, no prior art has been found to anticipate, teach or suggest the detailed features of the claimed inventive scope.
Conclusion
V. Any inquiry concerning this communication or earlier communications from the examiner should be directed to KRISTIE D. SHINGLES whose telephone number is (571) 272-3888. The examiner can normally be reached on Monday-Thursday 10am-7pm.
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VI. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamal Divecha can be reached on 571-272-5863. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KRISTIE D SHINGLES/Primary Examiner, Art Unit 2453