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 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).
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 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.
III. CLAIMS 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims of U.S. Patent No. 12,095,639. Although the claims at issue are not identical, they are not patentably distinct from each other as indicated below.
Conflicting Patent – 12,095,639
(US Appl No. 18/088,806)
Instant Application – 18/797,557
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;
determining, through the server, at least a subset of the set of point anomalies as a sequential series of continuous anomalies based on a separation in time between immediately next point anomalies thereof in the sequential time being equal to or below a second threshold value in time;
incrementally adding, through the server, a point anomaly of the set of point anomalies in an order of the sequential time to the sequential series of continuous anomalies until the point anomaly to be added is separated in time from a last added point anomaly to the sequential series of continuous anomalies for a duration above the second threshold value in time to determine a current longest occurring sequence of anomalies in the set of point anomalies;
in light of new point anomalies of the set of point anomalies in the real-time data detected via the server for the each network entity for the each feature thereof, improving performance of determination of a subsequent longest occurring sequence of anomalies in the set of point anomalies based on combining, through the server, the determined current longest occurring sequence of anomalies incrementally with at least one new point anomaly of the new point anomalies as compared to iteration therefor through an entirety of the sequence in time; and
detecting, through the server, at least one anomaly in the real-time data associated with the each network entity for the each feature thereof including at least one point anomaly of the set of point anomalies in accordance with computing a score for the at least one anomaly indicative of anomalousness thereof, the computation of the score involving both relative scoring and absolute deviation scoring, and the absolute deviation scoring being based on previous data deviations from reference data bands.
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 first threshold expected value thereof;
determining at least a subset of the set of point anomalies as a sequential series of continuous anomalies based on a separation in time between immediately next point anomalies thereof in the sequential time being equal to or below a second threshold value in time;
incrementally adding a point anomaly of the set of point anomalies in an order of the sequential time to the sequential series of continuous anomalies until the point anomaly to be added is separated in time from a last added point anomaly to the sequential series of continuous anomalies for a duration above the second threshold value in time to determine a current longest occurring sequence of anomalies in the set of point anomalies;
in light of new point anomalies of the set of point anomalies in the real-time data detected for the each network entity for the each feature thereof, improving performance of determination of a subsequent longest occurring sequence of anomalies in the set of point anomalies based on combining the determined current longest occurring sequence of anomalies incrementally with at least one new point anomaly of the new point anomalies as compared to iteration therefor through an entirety of the sequence in time; and
detecting at least one anomaly in the real-time data associated with the each network entity for the each feature thereof including at least one point anomaly of the set of point anomalies in accordance with computing a score for the at least one anomaly indicative of anomalousness thereof, the computation of the score involving both relative scoring and absolute deviation scoring, and the absolute deviation scoring being based on previous data deviations from reference data bands.
2, 9, 16. The method of claim 1, further comprising determining, through the server, an event associated with a pattern of change of the real-time data associated with the each network entity based on determining at least one of: the current longest occurring sequence and the subsequent longest occurring sequence of anomalies in the set of point anomalies.
2, 9, 16. The method of claim 1, further comprising determining an event associated with a pattern of change of the real-time data associated with the each network entity based on determining at least one of: the current longest occurring sequence and the subsequent longest occurring sequence of anomalies in the set of point anomalies.
3, 10, 17. The method of claim 1, further comprising, through the server, at least one of:
clearing out, from a memory associated with the server, a point anomaly of the set of point anomalies that is detected after the second threshold value in time elapses with respect to an immediately previous detected point anomaly of the set of point anomalies; and in accordance with determining that two continuous detected point anomalies of the set of point anomalies are separated in time by more than the second threshold value in time, restarting the determining of the at least the subset of the set of point anomalies as the sequential series of continuous anomalies from a most recently detected point anomaly of the two continuous detected point anomalies.
3, 10, 17. The method of claim 1, further comprising at least one of:
clearing out, from a memory associated with the data processing device, a point anomaly of the set of point anomalies that is detected after the second threshold value in time elapses with respect to an immediately previous detected point anomaly of the set of point anomalies; and in accordance with determining that two continuous detected point anomalies of the set of point anomalies are separated in time by more than the second threshold value in time, restarting the determining of the at least the subset of the set of point anomalies as the sequential series of continuous anomalies from a most recently detected point anomaly of the two continuous detected point anomalies.
4, 11, 18. The method of claim 1, comprising implementing, through the server, at least one of: the current longest occurring sequence and the subsequent longest occurring sequence as an object comprising information pertaining to at least one of: a number of point anomalies therein and a length in time of the point anomalies therein.
4, 11, 18. The method of claim 1, comprising implementing at least one of: the current longest occurring sequence and the subsequent longest occurring sequence as an object comprising information pertaining to at least one of: a number of point anomalies therein and a length in time of the point anomalies therein.
5, 12, 18. The method of claim 4, comprising the information further comprising at least one of: a start time stamp and an end time stamp of each of the point anomalies in the at least one of: the current longest occurring sequence and the subsequent longest occurring sequence.
5, 12, 18. The method of claim 4, comprising the information further comprising at least one of: a start time stamp and an end time stamp of each of the point anomalies in the at least one of: the current longest occurring sequence and the subsequent longest occurring sequence.
6, 13, 19. The method of claim 1, further comprising:
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; and
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.
6, 13, 19. The method of claim 1, further comprising:
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; and
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.
7, 14, 20. The method of claim 3, further comprising, through the server, discarding, from the memory associated with the server, the current longest occurring sequence in the determination of the subsequent longest occurring sequence based on determining that a new point anomaly of the new point anomalies immediately following a last point anomaly of the current longest occurring sequence is separated in time therefrom by more than the second threshold value in time.
7, 14, 20. The method of claim 3, further comprising discarding, from the memory associated with the data processing device, the current longest occurring sequence in the determination of the subsequent longest occurring sequence based on determining that a new point anomaly of the new point anomalies immediately following a last point anomaly of the current longest occurring sequence is separated in time therefrom by more than the second threshold value in time.
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. The prior art most relevant and applicable to the claimed scope have common inventors/assignee. Other than the above Double Patenting rejection, no additional prior art has been found to explicitly anticipate, teach or suggest the detailed features of the claimed inventive scope.
Conclusion
V. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: USPN 11916765; USPN 11070440.
VI. 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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/KRISTIE D SHINGLES/
Primary Examiner, Art Unit 2453