DETAILED ACTION
This action is in response to claims filed 19 April 2023 for application 18302939 filed 19 April 2023. Currently 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. In step 1, claims 1, 8 and 15 are directed to the statutory category of a method, an article of manufacture and a system.
In step 2a prong 1, claims 1, 8 and 15 recite, in part, embedding a sequence of events in low dimension space, employing a feature exactor and representation learning to convert log data from the sequence to time-series data, and detecting root cases of failures or fault activities from the data. The limitations of embedding, employing and detecting are processes that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “computer-readable storage medium”, “computer”, “processor” and “memory” in the context of the claims, the limitations encompass converting data, applying a model to it and then identifying abnormal data in the mind or with aid. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
In step 2a prong 2, this judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of “computer-readable storage medium”, “computer”, “processor” and “memory”. The computer components in the claim are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Please see MPEP §2106.04.(a)(2).III.C.
In step 2b, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, either alone or in combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “computer-readable storage medium”, “computer”, “processor” and “memory” to perform the steps of the claims amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claims 2-7, 9-14 and 16-20 recite further limitations of using an LSTM encoder and decoder, mathematical concepts for how an LSTM functions, mathematical concepts for a decoder reconstructions using an LSTM, mathematical concepts for a LSTM objective function, predicting a next event with the language model, and mathematical concepts for predicting a next event. These limitations amount to the same abstract idea above and either amount to mental processes or mathematical concepts in step 2a prong 1. In step 2a prong 2 and step 2b, no further additional elements have been presented and thus the claims are similarly rejected as the independent claims.
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 (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 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.
Claim(s) 1, 2, 6, 8, 9, 13, 15, and 16 is/are rejected under 35 U.S.C. 102(A)(1) as being anticipated by Wang et al. (Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection).
Regarding claims 1, 8 and 15, Wang discloses:
A method for employing root cause analysis, the method comprising:
embedding, by an embedding layer, a sequence of events into a low-dimension space (Fig 1 shows sequential events in the log key sequence, “Therefore, we design an embedding layer to embed events into a low-dimension space that can preserve relations between events.” P3728 §4.1
employing a feature extractor and representation learner to convert log data from the sequence of events to time series data, the feature extractor including an auto-encoder model and a language model (“To apply OC-SVM, we first need to extract features from each sequence. In this work, we tried two models to extract features: sequence auto-encoder [10] and bagof-words [38].” P3731 ¶2, note: the autoencoder used also uses LSTMs and generates a time sequential output interpreted as time-series data, the autoencoder and LSTM RNN are interpreted as the autoencoder and language model); and
detecting root causes of failure or fault activities from the time series data (“In this section, we introduce OC4Seq, a multi-scale one-class recurrent neural network framework for event sequence anomaly detection.” P3728 §4).
Regarding claims 2, 9 and 16, Wang discloses: The method of claim 1, wherein the auto-encoder model includes a long short-term memory (LSTM) encoder network and an LSTM decoder network (“To apply OC-SVM, we first need to extract features from each sequence. In this work, we tried two models to extract features: sequence auto-encoder [10] and bagof-words [38].” P3731 ¶2, note: the autoencoder used also uses LSTMs and generates a time sequential output).
Regarding claims 6 and 13, Wang discloses: The method of claim 1, wherein the language model is trained to predict a next event given previous events in a categorical sequence (“Specifically, by training with normal sequences, it learns to predict the next token given the previously seen tokens in a sequence. During the test stage, for each time step in a sequence, DeepLog will output a probability distribution over all the log keys.” P3731 ¶3).
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) 3, 5, 10, 12, 17 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection) in view of Pankajashan et al. (Hybrid approach with Deep Auto-Encoder and optimized LSTM based Deep Learning approach to detect anomaly in cloud logs)(hereinafter “Panka”).
Regarding claims 3, 10 and 17, Wang does not explicitly disclose, however, Panka teaches:
The method of claim 2, wherein the LSTM encoder network learns a representation of a whole sequence as follows:
f t=σg(W f x t i +U f h t−1 +b f)
i t=σg(W i x t i +U i h t−1 +b i)
o t =o g(W o x t i +U o h t−1 +b o)
c t=tanh(W c x t +U c h t−1 +b c)
c t =f t ⊙c t−1 +i t +c t
h t =o t ⊙c t,
where xt is an input embedding of the tth element in a training set Si, ft, it, ot are a forget gate, an input gate, and an output gate, respectively, W*, U*, and b*(*∈{f, i, o, c}) are all trainable parameters of the LSTM, and hN i is a final hidden state obtained by the LSTM (Fig 7, equations 1-5 disclose exemplary LSTM construction).
Wang and Panka are in the same field of endeavor of autoencoder- and LSTM- based log analysis for find faults and are analogous. Wang discloses a system that uses LSTM autoencoders for analyzing sequential data. Panka teaches an autoencoder and LSTM model. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the known LSTM autoencoder used by Wang with the known LSTM equations and objective functions as taught by Panka to yield predictable results.
Regarding claims 5, 12 and 19, Wang discloses: The method of claim 2, wherein parameters of the LSTM encoder network and the LSTM decoder network are optimized by an objective function defined as:
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192
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where ej i T is a predicted event and pj i is a probability distribution over all possible events (
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496
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p3729 §4.3).
However, Panka teaches a logarithmic cross entropy loss (“In this trial, the logarithmic loss function is adopted, mentioned as “categorical cross-entropy” in Keras, as the examination means to handle problems related to multi-classification.” P6266 ¶2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Wang et al. (US 20230039740), Fan et al. (US 20210049452), and Agarwal et al. (US 20200125992) all disclose analyzing logs using Autoencoders and/or LSTMs. No art has been found the discloses the limitations of claims 4, 7, 10, 14, 18 and 20 either alone or in combination.
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/ERIC NILSSON/ Primary Examiner, Art Unit 2151