DETAILED ACTION
This Office Action is sent in response to Applicant’s Communication received 3/25/2024 for application number 18/615,121.
Claims 1-20 are pending.
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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim 1 (representative of independent claims 8 and 15) recites:
A computer-implemented method comprising: labeling each timestep comprised in historical multivariate timeseries data logged from a plurality of systems; splitting each labeled timestep into a plurality of training sets, wherein each training set does not overlap with each remaining training set in the plurality of training sets; training a supervised model with the plurality of training sets, wherein the supervised model comprises a one dimensional convolutional layer, a one dimensional max pooling layer, and a dense layer; detecting one or more anomalous timesteps within the new multivariate timeseries data utilizing the train supervised model; and remediating one or more systems associated with the one or more anomalous timesteps.
(2A, prong 1) The underlined portions of the claim recite an abstract idea, specifically a mental process and mathematical calculation. A human can mentally label and split log data into sets. Applicant’s specification discloses training a supervised model and detecting anomalous timesteps is a series of mathematical calculations, see specification at paragraphs 43-48.
(2A, prong 2) This judicial exception is not integrated into a practical application. The claims recite the additional elements of (a) remediating systems associated with anomalous timesteps; and (b) generic computer hardware like a memory and processor (for claims 8 and 15). Element (a) is a mere instruction to apply the exception because it only recites the idea of a solution (that a system is “remediated”) without details of how the solution is accomplished (any details of how to remediate the system). Element (b) is also a mere instruction to apply the exception because it only adds general purpose computer components after-the-fact to the abstract idea. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not integrate the abstract idea into a practical application because they only add mere instructions to apply the exception to the abstract idea.
(2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above, additional elements (a) and (b) are mere instructions to apply the exception. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not amount to significantly more than the abstract idea itself because they only add mere instructions to apply the exception to the abstract idea. In other words, the claim as a whole states implementing a mental process and mathematical calculations on a generic computer, and using the output for remediating a system, which does not amount to significantly more than the abstract idea itself.
For dependent claims 2-3, 9-10, and 16-17 these recite additional mental process steps: a human can label timesteps as anomalous, non-anomalous, or in maintenance and split data into a certain length and categorize training sets.
For dependent claims 4-7, 11-14, and 18-20 these claims recite additional mathematical calculations. training with anomalous or non-anomalous data and not in maintenance data; calculating a composite score that is a monotonically increasing function; and calculating a probability of an anomaly in a labeled timestamp are mathematical calculations (see specification at paragraphs 43-48).
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 (i.e., changing from AIA to pre-AIA ) 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 3, 5-6, 8, 10, 12-13, 15, 17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ohana et al., Hybrid Anomaly Detection and Prioritization for Network Logs at Cloud Scale (NPL [U], see Notice of References Cited) in view of Tchatchoua et al., Application of 1D ResNet for Multivariate Fault Detection on Semiconductor Manufacturing Equipment (NPL [V], see Notice of References Cited).
In reference to claim 1, Ohana teaches a computer-implemented method comprising: labeling each timestep comprised in historical multivariate timeseries data logged from a plurality of systems (multivariate log data is labeled using incident tickets as a ground truth data, pages 240-242); splitting each labeled timestep into a plurality of training sets, wherein each training set does not overlap with each remaining training set in the plurality of training sets (balanced training data sets are created for each alert candidate, page 242); training a supervised model with the plurality of training sets (supervised model trained, page 243-44), wherein the supervised model comprises a … convolutional layer, a … max pooling layer, and a dense layer (CNN is trained with convolutional layers, max poling layer, and fully connected layer, page 243); detecting one or more anomalous timesteps within the new multivariate timeseries data utilizing the train supervised model; and remediating one or more systems associated with the one or more anomalous timesteps (model can then be used to predict anomalies so humans can repair the issue, pages 244, 248).
However, Ohana does not explicitly teach a one dimensional convolutional layer, a one dimensional max pooling layer.
Tchatchoua teaches a one dimensional convolutional layer, a one dimensional max pooling layer, (see pages 7-10 and fig. 4: faults in multivariate time series is detected with 1D convolutional layer and 1D pooling layer).
It would have been obvious to one of ordinary skill in art, having the teachings of Ohana and Tchatchoua before the earliest effective filing date, to modify the CNN of Ohana to include the 1D conv and 1D pooling of Tchatchoua.
One of ordinary skill in the art would have been motivated to modify the CNN of Ohana to include the 1D conv and 1D pooling of Tchatchoua because it can outperform conventional CNN (Tchatchoua, pages 16-17).
In reference to claim 3, Ohana teaches the computer-implemented method of claim 1, wherein splitting each labeled timestep into the plurality of training sets, comprises: splitting each labeled timestep based on an ingestion length associated with the supervised model (split into time window, page 240); and categorizing each training set in the plurality of training sets (categorized by alert candidate types, page 242).
In reference to claim 5, Ohana teaches the computer-implemented method of claim 1, further comprising: creating a composite score to evaluate the trained supervised model (model evaluated using composite score F-score of precision and recall, pages 244-45).
In reference to claim 6, Ohana teaches the computer-implemented method of claim 5, wherein the composite score is a monotonically increasing function with respect to a timeframe preceding a raised ticket (F-score would increase over time, like Applicant’s disclosed F-score, pages 244-45).
In reference to claim 8, this claim is directed to a computer-readable medium associated with the method claimed in claim 1 and is therefore rejected under a similar rationale. (The Examiner notes Applicant’s definition of “computer readable storage media” excludes transitory embodiments, see spec. para. 17 as filed).
In reference to claim 10, this claim is directed to a computer-readable medium associated with the method claimed in claim 3 and is therefore rejected under a similar rationale.
In reference to claim 12, this claim is directed to a computer-readable medium associated with the method claimed in claim 5 and is therefore rejected under a similar rationale.
In reference to claim 13, this claim is directed to a computer-readable medium associated with the method claimed in claim 6 and is therefore rejected under a similar rationale.
In reference to claim 15, this claim is directed to a system associated with the method claimed in claim 1 and is therefore rejected under a similar rationale.
In reference to claim 17, this claim is directed to a system associated with the method claimed in claim 3 and is therefore rejected under a similar rationale.
In reference to claim 19, this claim is directed to a system associated with the method claimed in claim 6 and is therefore rejected under a similar rationale.
In reference to claim 20, this claim is directed to a system associated with the method claimed in claim 7 and is therefore rejected under a similar rationale.
Claim(s) 2, 4, 7, 9, 11, 14, 16, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ohana et al., Hybrid Anomaly Detection and Prioritization for Network Logs at Cloud Scale (NPL [U], see Notice of References Cited) in view of Tchatchoua et al., Application of 1D ResNet for Multivariate Fault Detection on Semiconductor Manufacturing Equipment (NPL [V], see Notice of References Cited) as applied to claims 1, 8, and 15 above, and in further view of Caffrey (US 2018/0053117 A1).
In reference to claim 2, Ohana teaches the computer-implemented method of claim 1, wherein labeling each timestep, comprises: labeling each timestep as a selection from the group consisting of anomalous, non-anomalous, … (relevance labels for alert being related to incident or not, page 242).
However, Ohana and Tchatchoua do not explicitly teach labeling in maintenance.
Caffrey teaches labeling in maintenance (para. 0023).
It would have been obvious to one of ordinary skill in art, having the teachings of Ohana, Tchatchoua, and Caffrey before the earliest effective filing date, to modify the labels of Ohana to include the in maintenance label of Caffrey.
One of ordinary skill in the art would have been motivated to modify the labels of Ohana to include the in maintenance label of Caffrey because it can be used to filter out expected disruptions from periods of scheduled maintenance (Caffrey para. 0023).
In reference to claim 4, Ohana teaches the computer-implemented method of claim 2, wherein training the supervised model with the plurality of training sets, comprises: training the supervised model with timesteps labeled as a selection from the group consisting of anomalous or non-anomalous … (supervised model trained with at least anomalous data, page 243-44).
However, Ohana and Tchatchoua do not explicitly teach preventing training the supervised model with timesteps labeled as in maintenance.
Caffrey teaches preventing training the supervised model with timesteps labeled as in maintenance (maintenance time periods removed from detection model dataset, para. 0023).
It would have been obvious to one of ordinary skill in art, having the teachings of Ohana, Tchatchoua, and Caffrey before the earliest effective filing date, to modify the labels of Ohana to include the in maintenance label of Caffrey.
One of ordinary skill in the art would have been motivated to modify the labels of Ohana to include the in maintenance label of Caffrey because it can be used to filter out expected disruptions from periods of scheduled maintenance (Caffrey para. 0023).
In reference to claim 7, Ohana teaches the computer-implemented method of claim 2, further comprising: encoding a calculated probability of an anomaly within one or more labeled timesteps (strong or weak probability, page 242).
In reference to claim 9, this claim is directed to a computer-readable medium associated with the method claimed in claim 2 and is therefore rejected under a similar rationale.
In reference to claim 11, this claim is directed to a computer-readable medium associated with the method claimed in claim 4 and is therefore rejected under a similar rationale.
In reference to claim 14, this claim is directed to a computer-readable medium associated with the method claimed in claim 7 and is therefore rejected under a similar rationale.
In reference to claim 16, this claim is directed to a system associated with the method claimed in claim 2 and is therefore rejected under a similar rationale.
In reference to claim 18, this claim is directed to a system associated with the method claimed in claim 4 and is therefore rejected under a similar rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew T. Chiusano whose telephone number is (571)272-5231. The examiner can normally be reached M-F, 10am-6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571-272-4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW T CHIUSANO/Primary Examiner, Art Unit 2144
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