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 .
Claims 21-35 have been examined.
Response to Argument
Applicant’s arguments in the Remarks, filed on 8/4/26 have been fully considered but they are not persuasive.
In the Remarks, Applicant argues that:
Salunke fails to teach "determine a prediction error of the unsupervised machine learning model based on a difference between the predicted measure of occurrences and an observed measure of occurrences of network events of the first event type based on the observed network event data during the observation period; and adapt parameters of the unsupervised machine learning model based on a function of the prediction error being outside of a threshold range of prediction error," as recited by amended independent claim 21.
In response to point (i), Examiner respectfully disagree. Salunke teaches determine a prediction error of the unsupervised machine learning model based on a difference between the predicted measure of occurrences and an observed measure of occurrences of network events of the first event type based on the observed network event data during the observation period ([53][80][82][86], e.g., determine predicted deviation of the unsupervised machine learning model based on a difference between the representation of expected pattern (expected occurrences of event [58]) and the observed/monitored time series data); and
adapt parameters of the unsupervised machine learning model based on a function of the prediction error being outside of a threshold range of prediction error ([82][83][91][98]; fig. 5, e.g., adjust/train the baseline model used (i.e., unsupervised baselining includes generating baseline model with parameters [35][36][45]) to detect anomalous data based on evaluation of new time series data with the predicted deviation being outside of the predicted deviation threshold).
Double Patenting
A rejection based on double patenting of the "same invention" type finds its support in the language of 35 U.S.C. 101 which states that "whoever invents or discovers any new and useful process ... may obtain a patent therefor ..." (Emphasis added). Thus, the term "same invention," in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957); and In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970). A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the conflicting claims so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101. 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 obviousness-type 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); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
"A later patent claim is not patentably distinct from an earlier patent claim if the later claim is obvious over, or anticipated by, the earlier claim. In re Longi, 759 F.2d at 896, 225 USPQ at 651 (affirming a holding of obviousness-type double patenting because the claims at issue were obvious over claims in four prior art patents); In re Berg, 140 F.3d at 1437, 46 USPQ2d at 1233 (Fed. Cir. 1998) (affirming a holding of obviousness type double patenting where a patent application claim to a genus is anticipated by a patent claim to a species within that genus). " ELI LILLY AND COMPANY v BARR LABORATORIES, INC., United States Court of Appeals for the Federal Circuit, ON PETITION FOR REHEARING EN BANC (DECIDED: May 30, 2001).
"Claim 12 and Claim 13 are generic to the species of invention covered by claim 3 of the patent. Thus, the generic invention is "anticipated" by the species of the patented invention. Cf., Titanium Metals Corp. v. Banner, 778 F.2d 775, 227 USPQ 773 (Fed. Cir. 1985) (holding that an earlier species disclosure in the prior art defeats any generic claim) 4. This court's predecessor has held that, without a terminal disclaimer, the species claims preclude issuance of the generic application. In re Van Ornum, 686 F.2d 937, 944, 214 USPQ 761, 767 (CCPA 1982). Accordingly, absent a terminal disclaimer, claims 12 and 13 were properly rejected under the doctrine of obviousness-type double patenting." (In re Goodman (CA FC) 29 USPQ2d 2010 (12/3/1993).
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 conflicting 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. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Instant Application
26. A method comprising:
applying, by a computing system, an unsupervised machine learning model to network event data to determine a predicted measure of occurrences of network events of a first event type of one or more event types during an observation period and a threshold range of occurrences for network events of the first event type;
detecting, by the computing system and based on the threshold range, whether observed network event data during the observation period is indicative of abnormal network behavior;
determining, by the computing system, a prediction error of the unsupervised machine learning model based on a difference between the predicted measure of occurrences and an observed measure of occurrences of network events of the first event type based on observed network event data during the observation period; and
adapting, by the computing system, parameters of the unsupervised machine learning model based on a function of the prediction error being outside of a threshold range of prediction error.
Patent 12,170,600
1.A method comprising:
obtaining network event data indicative of operational behavior of a network, wherein the network event data defines a series of network events of one or more event types; dynamically determining, based on the network event data, corresponding minimum (MIN) and maximum (MAX) thresholds that define a range of expected counts of occurrences of network events for each event type of the one or more event types; constructing an unsupervised machine learning model based on the network event data and the corresponding MIN and MAX thresholds for each event type of the one or more event types without requiring labelling of each of the network events of the network event data, wherein constructing the unsupervised machine learning model comprises: dividing the network event data into at least two time series subgroups, each of the time series subgroups comprising a portion of the series of network events of the one or more event types, training the unsupervised machine learning model to predict counts of occurrences of network events for each event type using a first one of the time series subgroups, and validating the unsupervised machine learning model using a second one of the time series subgroups;
after constructing the unsupervised machine learning model, processing additional network event data with the unsupervised machine learning model to determine predicted counts of occurrences of network events of the additional network event data for each event type of the one or more event types; and identifying, based on the predicted counts of occurrences and the corresponding MIN and MAX thresholds for each event type of the one or more event types, one or more of the network events of the additional network event data as indicative of abnormal network behavior.
6. The method of claim 1, wherein identifying the one or more of the network events of the additional network event data as indicative of abnormal network behavior comprises:
determining a prediction error indicative of a difference between the predicted counts of occurrences of the network events of the additional network event data and counts of actual network events of the additional network event data for an event type of the one or more event types; and
detecting the abnormal network behavior when the prediction error is out of bounds of the corresponding MIN and MAX thresholds for the event type of the one or more event types.
Claim 26 is non provisionally rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1 and 6 of Patent 12170600 in view of Salunke et al, US 2019/0373007 (hereinafter Salunke). Claims 1 and 6 of Patent 12170600 do not specifically claim during an observation period; prediction error of the unsupervised machine learning model, observed measure of occurrences of network events of the first event type, and adapting, by the computing system, parameters of the unsupervised machine learning model based on a function of the prediction error being outside of a threshold range of prediction error. Salunke teaches applying an unsupervised machine learning model to network event data to determine a predicted measure of occurrences of network events of a first event type of one or more event types during an observation period and a threshold range of occurrences for network events of the first event type([35][58][76][78][80], e.g., apply unsupervised machine learning to time series data to learn pattern to determine predictability of observed resource behavior (e.g., learned patterns that repeat daily, weekly, … and/or over seasonal periods) to represent expected pattern (i.e., predicted occurrences of events));
determining a prediction error of the unsupervised machine learning model based on a difference between the predicted measure of occurrences and an observed measure of occurrences of network events of the first event type based on the observed network event data during the observation period ([53][80][82][86], e.g., determine predicted deviation of the unsupervised machine learning model based on a difference between the representation of expected pattern (expected occurrences of event [58]) and the observed/monitored time series data); and adapting parameters of the unsupervised machine learning model based on a function of the prediction error being outside of a threshold range of prediction error ([82][83][91][98]; fig. 5, e.g., adjust/train the baseline model used (i.e., unsupervised baselining includes generating baseline model with parameters [35][36][45]) to detect anomalous data based on evaluation of new time series data with the predicted deviation being outside of the predicted deviation threshold). Except for the identified elements above, Claims 1 and 6 of U.S. Patent 12170600 contains every elements of claim 26 in the instant application and thus anticipate the claims of the instant application.
Claims of the instant application therefore are not patently distinct from the earlier claims and as such are unpatentable over non provisional obvious-type double patenting.
Instant Application
21. A system comprising:
one or more processors; and
one or more non-transitory computer-readable storage media storing instructions executable by the one or more processors to cause the system to:
apply an unsupervised machine learning model to network event data to determine a predicted measure of occurrences of network events of a first event type of one or more event types during an observation period and a threshold range of occurrences for network events of the first event
detect, based on the threshold range, whether observed network event data during the observation period is indicative of abnormal network behavior
determine a prediction error of the unsupervised machine learning model based on a difference between the predicted measure of occurrences and an observed measure of occurrences of network events of the first event type based on observed network event data during the observation period; and
adapt parameters of the unsupervised machine learning model based on a function of the prediction error being outside of a threshold range of prediction error
Patent 11743151
1.A system comprising: a plurality of access point (AP) devices in a wireless network; and a network management system comprising: a memory storing network event data received from the AP devices, wherein the network event data is indicative of operational behavior of the wireless network, and wherein the network event data defines a series of network events of one or more event types over a plurality of observation time periods; and one or more processors coupled to the memory and configured to:
apply an unsupervised machine learning model to the network event data to dynamically determine, for a most recent one of the observation time periods: (i) predicted counts of occurrences of the network events for each of the event types, and (ii) an estimated minimum (MIN) threshold and an estimated maximum (MAX) threshold for each event type, wherein the MIN thresholds and MAX thresholds define ranges of expected occurrences for the network events of the corresponding event types; and identify, based on the estimated minimum thresholds and estimated maximum thresholds and actual network event data for the most recent one of the observation time periods, one or more of the network events as indicative of abnormal network behavior.
5. The system of claim 1, wherein the processors are configured to identify the one or more of the network events as indicative of abnormal network behavior by: determining a prediction error indicative of a difference between the predicted counts of the occurrences for the network events as generated by the machine learning model and actual network events for the event data; and detecting the abnormal network behavior when the prediction error is out of the bounds of the dynamically-determined minimum threshold and maximum threshold for any of the event types.
Claim 21 is non provisionally rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1 and 5 of Patent 11743151 in view of Salunke. Claims 1 and 5 of Patent 11743151 do not specifically claim during an observation period; prediction error of the unsupervised machine learning model, observed measure of occurrences of network events of the first event type, and adapt parameters of the unsupervised machine learning model based on a function of the prediction error being outside of a threshold range of prediction error. Salunke teaches apply an unsupervised machine learning model to network event data to determine a predicted measure of occurrences of network events of a first event type of one or more event types during an observation period and a threshold range of occurrences for network events of the first event type([35][58][76][78][80], e.g., apply unsupervised machine learning to time series data to learn pattern to determine predictability of observed resource behavior (e.g., learned patterns that repeat daily, weekly, … and/or over seasonal periods) to represent expected pattern (i.e., predicted occurrences of events)); determine a prediction error of the unsupervised machine learning model based on a difference between the predicted measure of occurrences and an observed measure of occurrences of network events of the first event type based on the observed network event data during the observation period ([53][80][82][86], e.g., determine predicted deviation of the unsupervised machine learning model based on a difference between the representation of expected pattern (expected occurrences of event [58]) and the observed/monitored time series data); and adapt parameters of the unsupervised machine learning model based on a function of the prediction error being outside of a threshold range of prediction error ([82][83][91][98]; fig. 5, e.g., adjust/train the baseline model used (i.e., unsupervised baselining includes generating baseline model with parameters [35][36][45]) to detect anomalous data based on evaluation of new time series data with the predicted deviation being outside of the predicted deviation threshold). Except for the identified elements above, Claims 1 and 5 of U.S. Patent 11743151 contains every elements of claim 21 in the instant application and thus anticipate the claims of the instant application.
Claims of the instant application therefore are not patently distinct from the earlier claims and as such are unpatentable over non provisional obvious-type double patenting.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 21-24, 26-29 and 31-34 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Salunke et al, U.S. Patent Application Publication 2019/0373007 (hereinafter Salunke).
As per claim 21, Salunke teaches the invention as claimed comprising:
one or more processors (fig. 11); and
one or more non-transitory computer-readable storage media storing instructions executable by the one or more processors to cause the system to (fig. 11):
apply an unsupervised machine learning model to network event data to determine a predicted measure of occurrences of network events of a first event type of one or more event types during an observation period and a threshold range of occurrences for network events of the first event type([35][58][76][78][80], e.g., apply unsupervised machine learning to time series data to learn pattern to determine predictability of observed resource behavior (e.g., learned patterns that repeat daily, weekly, … and/or over seasonal periods) to represent expected pattern (i.e., predicted occurrences of events));
detect, based on the threshold range, whether observed network event data during the observation period is indicative of abnormal network behavior ([81], e.g., detect, based on the lower and upper limits, whether monitored time series data have been crossed is indicative of anomalous behavior);
determine a prediction error of the unsupervised machine learning model based on a difference between the predicted measure of occurrences and an observed measure of occurrences of network events of the first event type based on the observed network event data during the observation period ([53][80][82][86], e.g., determine predicted deviation of the unsupervised machine learning model based on a difference between the representation of expected pattern (expected occurrences of event [58]) and the observed/monitored time series data); and
adapt parameters of the unsupervised machine learning model based on a function of the prediction error being outside of a threshold range of prediction error ([82][83][91][98]; fig. 5, e.g., adjust/train the baseline model used (i.e., unsupervised baselining includes generating baseline model with parameters [35][36][45]) to detect anomalous data based on evaluation of new time series data with the predicted deviation being outside of the predicted deviation threshold).
As per claim 22, Salunke teaches the invention as claimed in claim 21 above. Salunke further teach wherein the unsupervised machine learning model has been trained using unlabeled training data comprising network event data observed during a plurality of overlapping time windows having different time origins ([99][100], fig. 9, e.g., 920, fig. 9 overlapping 900, fig. 9 having different time)and augmented with threshold ranges of occurrences for network events of the one or more event types ([58][87]).
As per claim 23, Salunke teaches the invention as claimed in claim 22 above. Salunke further teach wherein the one or more processors are further configured to determine minimum thresholds and maximum thresholds that define the threshold ranges of occurrences for network events of the one or more event types based on the network event data ([80][81][78][86][88], e.g., determine lowest/MIN and highest/MAX values/limits for transaction or CPU utilization rate, wherein the MIN/MAX defined range of expected occurrences for the transaction or CPU utilization) observed during the plurality of overlapping time windows having different time origins ([99][100], fig. 9, e.g., 920, fig. 9 overlapping 900, fig. 9 having different time).
As per claim 24, Salunke teaches the invention as claimed in claim 23 above. Salunke further teach wherein to determine the minimum thresholds and the maximum thresholds, the one or more processors are further configured to:
for each time window of the plurality of overlapping time windows, determine a measure of occurrences of network events for each event type of the one or more event types ([99][100][58], fig. 9, 900, 920);
determine, for each event type, the maximum threshold as a largest measure of occurrences of the network events for the respective event type during any time window of the plurality of overlapping time windows ([99][100][58], fig. 9, 900, 920, e.g., determine the upper limit of data value in 900, 920);; and
determine, for each event type, the minimum threshold as a smallest measure of occurrences of the network events for the respective event type during any time window of the plurality of overlapping time windows ([99][100][58], fig. 9, 900, 920, e.g., determine the lower limit of data value in 900, 920).
As per claims 26 and 31, they are rejected for the same reason as set forth in claim 21 above.
As per claims 27 and 32, they are rejected for the same reason as set forth in claim 22 above.
As per claims 28 and 33, they are rejected for the same reason as set forth in claim 23 above.
As per claims 29 and 34, they are rejected for the same reason as set forth in claim 24 above.
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, 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 25, 30 and 35 are rejected under 35 U.S.C. 103 as being unpatentable over Salunke in view of Jiang et al, U.S. Patent Application Publication 2021/0160263 (hereinafter Jiang).
As per claim 25, Salunke teaches the invention as claimed in claim 21 above. Salunke is silent in regards to root cause analysis. Jiang teaches perform root cause analysis of the observed network event data based on detecting the abnormal network behavior ([65]-[67]); and determine a remedial action based on one or more identified root causes of the abnormal network behavior [68].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jiang’s teaching with Salunke’s system in order to allow detection and remediation of the source of an issue, thus improving the anomaly detection of Salunke’s system.
As per claims 30 and 35, they are rejected for the same reason as set forth in claim 25 above.
Conclusion
THIS ACTION IS MADE FINAL. 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should
be directed to Philip Lee whose telephone number is (571)272-3967. The examiner can normally be
reached on 6a-3p M-F.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor,
Glenton Burgess can be reached on 571-272-3949. The fax phone number for the organization where this
application or proceeding is assigned is 571-273-8300.
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/PHILIP C LEE/Primary Examiner, Art Unit 2454
