Notice of Pre-AIA or AIA Status
This Final communication is in response to Application No. 18/214,340 filed 06/26/2023. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendments filed 6/2/2026 have been entered. They provide amendments to claims 1-16, 18, 20, and cancel claims 17 and 19. Claims 21-22 are also added. Claims 1-16, 18, 20-22 are pending.
Response to Arguments
Applicant’s arguments with respect to 35 U.S.C § 101 filed 6/2/2026 (pages 9-13 of applicant’s arguments) have been fully considered but they are not persuasive.
Applicant argues that the claimed invention is integrated into a practical application through specific technical improvements. The applicant states the claimed invention “improves the system performance by significantly reducing the size of the dataset needed to detect anomalies, and by reducing the storage size for the reconstruction machine learning model and associated dataset”. The examiner respectfully disagrees. MPEP 2016.05(a) states the improvement cannot come from the judicial exception (mental process in this case) alone. In the amended independent claims the limitations “sampling the time-series dataset to generate an anomaly preserving version of the time-series dataset”, “generating, …, a reconstructed version of the time-series dataset based on the anomaly preserving version of the time-series dataset, wherein the reconstructed version of the time-series dataset is a smaller dataset than the time-series dataset that oversamples under-represented values in the time-series dataset” and “determining, one or more anomalies in a further time-series dataset” are all considered mental processes. These leaves “obtaining a distribution of values of a time-series dataset”, “via a reconstruction machine learning model” and “using an anomaly detecting machine learning model, …, wherein the anomaly detecting machine learning model was trained using the reconstructed version of the time-series dataset” as the additional elements. It is unclear how these additional elements demonstrates the improvements mentioned above and in the specification.
Applicant also argues that the claims do amount to significantly more because “the combination of a reconstruction machine learning model and anomaly detection machine learning model represents a significant departure from standard anomaly detection”. The examiner respectfully disagrees. Combining two separate machine learning models in common in machine learning. Thus the 101 rejection is maintained.
Applicant’s arguments with respect to 35 U.S.C § 103 filed 6/2/2026 (pages 13-15 of applicant’s arguments) have been fully considered but they are not persuasive.
Applicant argues Horry does not disclose “wherein the reconstructed version of the time-series dataset is a smaller dataset than the time-series dataset”. The examiner respectfully disagrees. While Horry does not create smaller dataset when creating a reconstructed version, Ahmed does sample a smaller version. Using Horry’s reconstruction techniques on Ahmed’s sampled data would result in a smaller reconstructed version than the original time-series dataset.
The rest of applicant’s arguments with respect to 35 U.S.C § 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-16, 18, 20-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
101 Subject Matter Eligibility Analysis
Step 1: Claims 1-16, 18, 20-22 are within the four statutory (a process, machine, manufacture or composition of matter.) Claims 1-10, 21-22 describe a process and 10-16, 18, 20 describes a machine.
With respect to claim 1:
Step 2A Prong 1: The claim recites an abstract idea enumerated in the 2019 PEG.
sampling the time-series dataset to generate an anomaly preserving version of the time-series dataset; (This is an abstract idea of a "Mental Process." The "generate" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
generating, …, a reconstructed version of the time- series dataset based on the anomaly preserving version of the time-series data wherein the reconstructed version of the time-series dataset is a smaller dataset than the time-series dataset that oversamples under-represented values in the time-series dataset; and (This is an abstract idea of a "Mental Process." The "generating" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
determining, …, one or more anomalies in a further time-series dataset, (This is an abstract idea of a "Mental Process." The "determining" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application
Additional elements:
obtaining a distribution of values of a time-series dataset; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
via a reconstruction machine learning model (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
using an anomaly detecting machine learning model, …, wherein the anomaly detecting machine learning model was trained using the reconstructed version of the time-series dataset. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception
The additional element “obtaining a distribution of values of a time-series dataset” adds insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
The additional elements “via a reconstruction machine learning model” and “using an anomaly detecting machine learning model…” are recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
When considered in combination, these additional elements represent insignificant extra-solution activity and mere instructions to apply an expectation, which do not provide an inventive concept.
Therefore, claim 1 is ineligible.
With respect to claim 2:
Step 2A Prong 1: claim 2, which incorporates the rejection of claim 1, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
storing the anomaly preserving version of the time-series dataset. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element adds insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
Therefore, claim 2 is ineligible.
With respect to claim 3:
Step 2A Prong 1: claim 3, which incorporates the rejection of claim 1, recites an additional abstract idea:
analyzing a property of the time-series dataset including by using the reconstructed version of the time-series dataset. (This is an abstract idea of a "Mental Process." The "analyzing" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The analysis could be done manually by an individual.)
Step 2A Prong 2: claim 3 does not recite any additional elements and thus cannot be integrated into a practical application.
Step 2B: claim 3 does not recite an additional element.
Therefore, claim 3 is ineligible.
With respect to claim 4:
Step 2A Prong 1: claim 4, which incorporates the rejection of claim 1, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
the anomaly preserving version of the time-series dataset is a smaller size than the time-series dataset. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element adds insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
Therefore, claim 4 is ineligible.
With respect to claim 5:
Step 2A Prong 1: claim 5, which incorporates the rejection of claim 1, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
generating the reconstructed version of the time-series dataset includes providing the anomaly preserving version of the time-series dataset to the reconstruction machine learning model. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 5 is ineligible.
With respect to claim 6:
Step 2A Prong 1: claim 6, which incorporates the rejection of claim 1, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
The reconstruction machine learning model is trained using the time-series dataset and the anomaly preserving version of the time-series dataset to train the trained machine learning model. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 6 is ineligible.
With respect to claim 7:
Step 2A Prong 1: claim 7, which incorporates the rejection of claim 1, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
At least one of the reconstruction machine learning model or the anomaly detecting machine learning model is a multi-variate machine learning model. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 7 is ineligible.
With respect to claim 8:
Step 2A Prong 1: claim 8, which incorporates the rejection of claim 1, recites an additional abstract idea:
Generating a mapping for a configuration item type of a plurality of different configuration item types to the pairing of the anomaly preserving version of the time-series dataset and the reconstruction machine learning model; and (This is an abstract idea of a "Mental Process." The "mapping" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The mapping could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
Generating a pairing of the anomaly preserving version of the time-series dataset and the reconstruction machine learning model; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
storing the mapping for the configuration item type. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
Therefore, claim 8 is ineligible.
With respect to claim 9:
Step 2A Prong 1: claim 9, which incorporates the rejection of claim 8, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
receiving an identifier of a configuration item of the configuration item type; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
retrieving the pairing of the anomaly preserving version of the time-series dataset and the reconstruction machine learning model and the mapping for the configuration item type; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
retrieving the anomaly preserving version of the time-series dataset; and (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
retrieving the reconstruction machine learning model. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
Therefore, claim 9 is ineligible.
With respect to claim 10:
Step 2A Prong 1: claim 10, which incorporates the rejection of claim 1, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
The anomaly detecting is trained using the reconstructed version of the time-series dataset and the anomaly preserving version of the time-series dataset. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 10 is ineligible.
With respect to claim 11:
The claim recites similar limitations as corresponding to claim 1. Therefore, the same subject matter analysis that was utilized for claim 1, as described above, is equally applicable to claim 11. Therefore, claim 11 is ineligible.
With respect to claim 12:
The claim recites similar limitations as corresponding to claim 2. Therefore, the same subject matter analysis that was utilized for claim 2, as described above, is equally applicable to claim 12. Therefore, claim 12 is ineligible.
With respect to claim 13:
The claim recites similar limitations as corresponding to claim 3. Therefore, the same subject matter analysis that was utilized for claim 3, as described above, is equally applicable to claim 13. Therefore, claim 13 is ineligible.
With respect to claim 14:
The claim recites similar limitations as corresponding to claim 4. Therefore, the same subject matter analysis that was utilized for claim 4, as described above, is equally applicable to claim 14. Therefore, claim 14 is ineligible.
With respect to claim 15:
The claim recites similar limitations as corresponding to claim 5. Therefore, the same subject matter analysis that was utilized for claim 5, as described above, is equally applicable to claim 15. Therefore, claim 15 is ineligible.
With respect to claim 16:
The claim recites similar limitations as corresponding to claim 6. Therefore, the same subject matter analysis that was utilized for claim 6, as described above, is equally applicable to claim 16. Therefore, claim 16 is ineligible.
With respect to claim 18:
The claim recites similar limitations as corresponding to claim 8. Therefore, the same subject matter analysis that was utilized for claim 8, as described above, is equally applicable to claim 18. Therefore, claim 18 is ineligible.
With respect to claim 20:
The claim recites similar limitations as corresponding to claim 1. Therefore, the same subject matter analysis that was utilized for claim 1, as described above, is equally applicable to claim 20. Therefore, claim 20 is ineligible.
With respect to claim 21:
Step 2A Prong 1: claim 21, which incorporates the rejection of claim 1, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
responsive to determining the one or more anomalies, reconfiguring a network to account for the one or more anomalies. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
Therefore, claim 21 is ineligible.
With respect to claim 22:
Step 2A Prong 1: claim 22, which incorporates the rejection of claim 21, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
reconfiguring the network to account for the one or more anomalies comprises revising the anomaly detecting machine learning model. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
Therefore, claim 22 is ineligible.
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.
Claims 1-7, 10-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Horry (US 2024/0362463 A1) in view of Ahmed (NPL: “Semantic and Anomaly Preserving Sampling Strategy for Large-Scale Time Series Data”) and Kim (US 2024/0202325 A1).
Regarding claim 1, Horry teaches:
A method comprising: ([0008] “We describe a method for training an autoencoder to classify behavior of an engineering asset based on real-time data, wherein the autoencoder comprises an encoder and a decoder, the method of training comprising:”)
generating, via a reconstruction machine learning model, a reconstructed version of the time- series dataset based on the anomaly preserving version of the time-series dataset, wherein the reconstructed version of the time-series dataset is a smaller dataset than the time-series dataset that oversamples under-represented values in the time-series dataset. ([0020] “The method may further comprise running the training data through the over-arching autoencoder to obtain reconstructed data.”)
Horry does not teach:
obtaining a distribution of values of time-series dataset;
based on the distribution of the values, sampling the time-series dataset to generate an anomaly preserving version of the time-series dataset; and
determining, using an anomaly detecting machine learning model, one or more anomalies in a further time-series dataset, wherein the anomaly detecting machine learning model was trained using the reconstructed version of the time-series dataset.
However, Ahmed does teach:
obtaining a distribution of values of time-series data; (Section 2. Problem Formulation subsection Data reduction problem “Given a time series dataset D = {(t1,y1), (t2,y2),...(tn,yn)},where ith data point (ti,yi) is a tuple comprising of time ti and the corresponding value of interest yi at that time, the goal of data reduction is to reduce it to a smaller subset S such that the downstream data analyses applied to a time series visualization of S are: (i) cheaper to apply because the size of S is smaller compared to the size of D, and (ii) produce observations that preserve the semantics as if the analyses were applied to D.” and Section 2.2 Semantics and Anomaly Preservation subsection Anomaly Preservation “So, we introduce a composite metric to measure the anomaly preservation capability of a data reduction technique.” The applicant specification describes distribution of values of time-series data as applying a metric to the time-series data to understand the data’s relation to the other values.)
based on the distribution of the values, sampling the time-series data to generate an anomaly preserving version of the time-series data; and (Section 3. PASS Methodology “PASS (Preserving Anomaly and Semantics Sampling) is a specialized data reduction and sampling strategy that reduces data for the visualization of large-scale time series data as a line chart. Given a dataset D, we first split the dataset into a number of windows having similar properties in terms of trend and angular orientation. Figure 2 represents an example line chart of a dataset. This dataset can be split into 7–8 windows depending on angular orientation. This strategy ensures that minimum, maximum, and important anomalous behaviors are not lost from trend while reducing the amount of data.”).
Horry and Ahmed are considered analogous art to the claimed invention because they are in the same field of endeavor being handling anomalous data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the anomaly detection system of Horry with the anomaly preserving sampling of Ahmed. One would want to do this to reduce the size of the data (Ahmed Conclusion).
Kim teaches:
determining, using an anomaly detecting machine learning model, one or more anomalies in a further time-series dataset, wherein the anomaly detecting machine learning model was trained using the reconstructed version of the time-series dataset. ([0016] “Accordingly, one of the embodiments discloses an anomaly detector that comprises at least one processor, and memory having instructions stored thereon that form modules of the anomaly detector, where the at least one processor is configured to execute the instructions of the modules of the anomaly detector. The modules comprise an input interface configured to accept input data, a first neural network having an autoencoder architecture that comprises an encoder trained to encode the input data and a decoder trained to decode the encoded input data to reconstruct the input data. The modules further comprise a loss estimator configured to compare a plurality of parts of the input data with corresponding plurality of parts of the reconstructed input data to determine a sequence of losses for different components of a reconstruction error. The modules further comprise a second neural network trained in a supervised manner to classify the sequence of losses to detect an anomaly to produce a result of anomaly detection including one or a combination of a type of the anomaly and a severity of the anomaly. The anomaly detector further comprises an output interface configured to render a result of the anomaly detection.” The second neural network that is used to detect anomalies is trained using the reconstruction data created by the first neural network).
Horry, Ahmed and Kim are considered analogous art to the claimed invention because they are in the same field of endeavor being handling anomalous data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the data reconstruction of Horry with the anomaly preserving sampling of Ahmed and the anomaly detector of Kim. One would want to do this to reduce the size of the data before using Kim’s anomaly detector model.
Regarding claim 2, Horry in view of Ahmed and Kim teaches claim 1 as outlined above. Horry further teaches:
storing the anomaly preserving version of the time-series dataset. ([0027] “The training data may be stored on a database which may be separate to the system which implements the prediction.”)
Regarding claim 3, Horry in view of Ahmed and Kim teaches claim 1 as outlined above. Horry further teaches:
analyzing a property of the time-series dataset including by using the reconstructed version of the time-series dataset. ([0019] “Generating the plurality of data sets may comprise applying a clustering analysis to the obtained encodings and/or applying a KDtree algorithm to the obtained encodings. The KDtree algorithm and the cluster analysis may be applied simultaneously. Applying a clustering analysis may comprise fitting multiple clustering algorithms over the obtained encodings; selecting the best clustering algorithm and obtaining a plurality of clusters by fitting the selected clustering algorithm to the training data; whereby the generated plurality of data sets comprise the plurality of clusters. Applying a KDtree algorithm may comprise fitting the KDtree algorithm on the obtained encodings; selecting an under-represented data set generated by the fitting step; and finding multiple data sets which are most similar to the selected data set; whereby the generated plurality of data sets comprise the selected data set and the multiple data sets.”)
Regarding claim 4, Horry in view of Ahmed and Kim teaches claim 1 as outlined above. Ahmed further teaches:
the anomaly preserving version of the time-series dataset is a smaller size than the time-series dataset. (Section 3. PASS Methodology “This dataset can be split into 7–8 windows depending on angular orientation. This strategy ensures that minimum, maximum, and important anomalous behaviors are not lost from trend while reducing the amount of data.”).
Regarding claim 5, Horry in view of Ahmed and Kim teaches claim 1 as outlined above. Horry further teaches:
generating the reconstructed version of the time-series dataset includes providing the anomaly preserving version of the time-series dataset to the reconstruction machine learning model. ([0020] “The method may further comprise running the training data through the over-arching autoencoder to obtain reconstructed data.”)
Regarding claim 6, Horry in view of Ahmed and Kim teaches claim 1 as outlined above. Horry further teaches:
the reconstruction machine learning model is trained using the time-series data and the anomaly preserving version of the time-series dataset. ([0008] “We describe a method for training an autoencoder to classify behavior of an engineering asset based on real-time data, wherein the autoencoder comprises an encoder and a decoder, the method of training comprising: obtaining training data and test data comprising multiple data records for at least one engineering asset which corresponds to the engineering asset whose behavior is to be classified, … generating a plurality of data sets from the obtained encodings, wherein the generated plurality of data sets include under-represented data sets;”)
Regarding claim 7, Horry in view of Ahmed and Kim teaches claim 1 as outlined above. Horry further teaches:
at least one of the reconstruction machine learning model or the anomaly detecting machine learning model is a multi-variate machine learning model. ([0017] “The autoencoder may be a long short-term memory (LSTM) autoencoder. The encoder may receive an input X comprising a plurality of points x.sup.(i) each of which may be an m-dimensional vector at time instance t.sub.i. The input is a multivariate time-series and each time step is a fixed time-window length cut from the time-series.” The autoencoder takes input of multivariate data).
Regarding claim 10, Horry in view of Ahmed and Kim teaches claim 1 as outlined above. Horry further teaches:
the anomaly detecting machine learning model is trained using the reconstructed version of the time-series dataset and the anomaly preserving version of the time-series dataset. ([0022] “Once the autoencoder is trained, it may be used to classify behavior and provide real-time anomaly detection and failure prediction”).
Regarding claim 11, Horry teaches:
A system comprising: one or more processors; and a memory coupled to the one or more processors, wherein the memory is configured to provide the one or more processors with instructions which when executed cause the one or more processors: ([0052] “Some of the internal detail of the display system 10 is shown in FIG. 1. There are standard components of whichever hardware solution is deployed, including for example a display 20, a processor 30, memory 40 and an interface 42 for connecting with the sensors 50, 52, 54, 56, 58.”)
generate, via a reconstruction machine learning model, a reconstructed version of the time- series dataset based on the anomaly preserving version of the time-series dataset wherein the reconstructed version of the time-series dataset is a smaller dataset than the time-series dataset that oversamples under-represented values in the time-series dataset; and. ([0020] “The method may further comprise running the training data through the over-arching autoencoder to obtain reconstructed data.”)
Horry does not teach:
obtaining a distribution of values of time-series dataset;
based on the distribution of the values, sampling the time-series dataset to generate an anomaly preserving version of the time-series dataset; and
determining, using an anomaly detecting machine learning model, one or more anomalies in a further time-series dataset, wherein the anomaly detecting machine learning model was trained using the reconstructed version of the time-series dataset.
However, Ahmed does teach:
obtaining a distribution of values of time-series dataset; (Section 2. Problem Formulation subsection Data reduction problem “Given a time series dataset D = {(t1,y1), (t2,y2),...(tn,yn)},where ith data point (ti,yi) is a tuple comprising of time ti and the corresponding value of interest yi at that time, the goal of data reduction is to reduce it to a smaller subset S such that the downstream data analyses applied to a time series visualization of S are: (i) cheaper to apply because the size of S is smaller compared to the size of D, and (ii) produce observations that preserve the semantics as if the analyses were applied to D.” and Section 2.2 Semantics and Anomaly Preservation subsection Anomaly Preservation “So, we introduce a composite metric to measure the anomaly preservation capability of a data reduction technique.” The applicant specification describes distribution of values of time-series data as applying a metric to the time-series data to understand the data’s relation to the other values.)
based on the distribution of the values, sampling the time-series dataset to generate an anomaly preserving version of the time-series dataset; and (Section 3. PASS Methodology “PASS (Preserving Anomaly and Semantics Sampling) is a specialized data reduction and sampling strategy that reduces data for the visualization of large-scale time series data as a line chart. Given a dataset D, we first split the dataset into a number of windows having similar properties in terms of trend and angular orientation. Figure 2 represents an example line chart of a dataset. This dataset can be split into 7–8 windows depending on angular orientation. This strategy ensures that minimum, maximum, and important anomalous behaviors are not lost from trend while reducing the amount of data.”).
Horry and Ahmed are considered analogous art to the claimed invention because they are in the same field of endeavor being handling anomalous data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the anomaly detection system of Horry with the anomaly preserving sampling of Ahmed. One would want to do this to reduce the size of the data (Ahmed Conclusion).
Kim teaches:
determining, using an anomaly detecting machine learning model, one or more anomalies in a further time-series dataset, wherein the anomaly detecting machine learning model was trained using the reconstructed version of the time-series dataset. ([0016] “Accordingly, one of the embodiments discloses an anomaly detector that comprises at least one processor, and memory having instructions stored thereon that form modules of the anomaly detector, where the at least one processor is configured to execute the instructions of the modules of the anomaly detector. The modules comprise an input interface configured to accept input data, a first neural network having an autoencoder architecture that comprises an encoder trained to encode the input data and a decoder trained to decode the encoded input data to reconstruct the input data. The modules further comprise a loss estimator configured to compare a plurality of parts of the input data with corresponding plurality of parts of the reconstructed input data to determine a sequence of losses for different components of a reconstruction error. The modules further comprise a second neural network trained in a supervised manner to classify the sequence of losses to detect an anomaly to produce a result of anomaly detection including one or a combination of a type of the anomaly and a severity of the anomaly. The anomaly detector further comprises an output interface configured to render a result of the anomaly detection.” The second neural network that is used to detect anomalies is trained using the reconstruction data created by the first neural network).
Horry, Ahmed and Kim are considered analogous art to the claimed invention because they are in the same field of endeavor being handling anomalous data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the data reconstruction of Horry with the anomaly preserving sampling of Ahmed and the anomaly detector of Kim. One would want to do this to reduce the size of the data before using Kim’s anomaly detector model.
Regarding claim 12, Horry in view of Ahmed and Kim teaches claim 11 as outlined above. Claim 12 recites similar limitations corresponding to claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Regarding claim 13, Horry in view of Ahmed and Kim teaches claim 11 as outlined above. Claim 13 recites similar limitations corresponding to claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Regarding claim 14, Horry in view of Ahmed and Kim teaches claim 11 as outlined above. Claim 14 recites similar limitations corresponding to claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale.
Regarding claim 15, Horry in view of Ahmed and Kim teaches claim 11 as outlined above. Claim 15 recites similar limitations corresponding to claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Regarding claim 16, Horry in view of Ahmed and Kim teaches claim 11 as outlined above. Claim 16 recites similar limitations corresponding to claim 6 and is rejected for similar reasons as claim 6 using similar teachings and rationale.
Regarding claim 20, Horry teaches:
A computer program product, the computer program product being embodied in a non- transitory computer readable storage medium and comprising computer instructions for ([0028] “According to another aspect of the invention, there is a non-transitory computer-readable medium comprising processor control code which when running on a system causes the system to carry out the method described above.”)
generating, via a reconstruction machine learning model, a reconstructed version of the time- series dataset based on the anomaly preserving version of the time-series dataset wherein the reconstructed version of the time-series dataset is a smaller dataset than the time-series dataset that oversamples under-represented values in the time-series dataset; and ([0020] “The method may further comprise running the training data through the over-arching autoencoder to obtain reconstructed data.”)
Horry does not teach:
obtaining a distribution of values of time-series dataset;
based on the distribution of the values, sampling the time-series dataset to generate an anomaly preserving version of the time-series dataset; and
determining, using an anomaly detecting machine learning model, one or more anomalies in a further time-series dataset, wherein the anomaly detecting machine learning model was trained using the reconstructed version of the time-series dataset.
However, Ahmed does:
obtaining a distribution of values of time-series dataset; (Section 2. Problem Formulation subsection Data reduction problem “Given a time series dataset D = {(t1,y1), (t2,y2),...(tn,yn)},where ith data point (ti,yi) is a tuple comprising of time ti and the corresponding value of interest yi at that time, the goal of data reduction is to reduce it to a smaller subset S such that the downstream data analyses applied to a time series visualization of S are: (i) cheaper to apply because the size of S is smaller compared to the size of D, and (ii) produce observations that preserve the semantics as if the analyses were applied to D.” and Section 2.2 Semantics and Anomaly Preservation subsection Anomaly Preservation “So, we introduce a composite metric to measure the anomaly preservation capability of a data reduction technique.” The applicant specification describes distribution of values of time-series data as applying a metric to the time-series data to understand the data’s relation to the other values.)
based on the distribution of the values, sampling the time-series dataset to generate an anomaly preserving version of the time-series dataset; and (Section 3. PASS Methodology “PASS (Preserving Anomaly and Semantics Sampling) is a specialized data reduction and sampling strategy that reduces data for the visualization of large-scale time series data as a line chart. Given a dataset D, we first split the dataset into a number of windows having similar properties in terms of trend and angular orientation. Figure 2 represents an example line chart of a dataset. This dataset can be split into 7–8 windows depending on angular orientation. This strategy ensures that minimum, maximum, and important anomalous behaviors are not lost from trend while reducing the amount of data.”).
Horry and Ahmed are considered analogous art to the claimed invention because they are in the same field of endeavor being handling anomalous data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the anomaly detection system of Horry with the anomaly preserving sampling of Ahmed. One would want to do this to reduce the size of the data (Ahmed Conclusion).
Kim teaches:
determining, using an anomaly detecting machine learning model, one or more anomalies in a further time-series dataset, wherein the anomaly detecting machine learning model was trained using the reconstructed version of the time-series dataset. ([0016] “Accordingly, one of the embodiments discloses an anomaly detector that comprises at least one processor, and memory having instructions stored thereon that form modules of the anomaly detector, where the at least one processor is configured to execute the instructions of the modules of the anomaly detector. The modules comprise an input interface configured to accept input data, a first neural network having an autoencoder architecture that comprises an encoder trained to encode the input data and a decoder trained to decode the encoded input data to reconstruct the input data. The modules further comprise a loss estimator configured to compare a plurality of parts of the input data with corresponding plurality of parts of the reconstructed input data to determine a sequence of losses for different components of a reconstruction error. The modules further comprise a second neural network trained in a supervised manner to classify the sequence of losses to detect an anomaly to produce a result of anomaly detection including one or a combination of a type of the anomaly and a severity of the anomaly. The anomaly detector further comprises an output interface configured to render a result of the anomaly detection.” The second neural network that is used to detect anomalies is trained using the reconstruction data created by the first neural network).
Horry, Ahmed and Kim are considered analogous art to the claimed invention because they are in the same field of endeavor being handling anomalous data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the data reconstruction of Horry with the anomaly preserving sampling of Ahmed and the anomaly detector of Kim. One would want to do this to reduce the size of the data before using Kim’s anomaly detector model.
Claims 8-9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Horry
in view of Ahmed, Kim and Crotinger (US 2018/0324199 A1).
Regarding claim 8, Horry in view of Ahmed and Kim teaches claim 1 as outlined above. Ahmed teaches the anomaly preserving version of the time-series data and Horry teaches the machine learning model. However Horry nor Ahmed teaches:
generating a pairing of the anomaly preserving version of the time-series dataset and the reconstruction machine learning model; generating a mapping for a configuration item type of a plurality of different configuration item types to the pairing of the anomaly preserving version of the time-series dataset and the reconstruction machine learning model; and storing the mapping for the configuration item type.
However Crotinger does:
generating a pairing of the anomaly preserving version of the time-series dataset and the reconstruction machine learning model; generating a mapping for a configuration item type of a plurality of different configuration item types to the pairing of the anomaly preserving version of the time-series dataset and the reconstruction machine learning model; and storing the mapping for the configuration item type. ([0040] “In some embodiments, the databases 108 may include a configuration management database (CMDB) that may store the data, e.g., time-series data, concerning CIs 110 mentioned above along with data related various IT assets that may be present within the network 112.” A configuration management database is what the claim is describing with using anomaly preserving time-series data and machine learning models.)
Horry, Ahmed, Kim and Crotinger are considered analogous art to the claimed invention because they are in the same field of endeavor being anomaly detection. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the anomaly detection system of Horry with the anomaly preserving sampling of Ahmed with the configuration management database of Crotinger. One would want to do this so that the models and data are easily accessible and linked together.
Regarding claim 9, Horry in view of Ahmed, Kim and Crotinger teaches claim 8 as outlined above. Crotinger further teaches:
receiving an identifier of a configuration item of the configuration item type; retrieving the pairing of the anomaly preserving version of the time-series dataset and the reconstruction machine learning model and the mapping for the configuration item type; retrieving the anomaly preserving version of the time-series dataset; and retrieving the reconstruction machine learning model. ([0040] “In some embodiments, the databases 108 may include a configuration management database (CMDB) that may store the data, e.g., time-series data, concerning CIs 110 mentioned above along with data related various IT assets that may be present within the network 112.” A configuration management database is what the claim is describing)
Regarding claim 18, Horry in view of Ahmed and Kim teaches claim 11 as outlined above. Claim 18 recites similar limitations corresponding to claim 8 and is rejected for similar reasons as claim 8 using similar teachings and rationale.
Claims 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Horry in view of Ahmed, Kim and Ouyang (US 2019/0239101 A1).
Regarding claim 21, Horry in view of Ahmed and Kim teaches claim 1. None of them teach the elements of claim 21. However, Ouyang does:
responsive to determining the one or more anomalies, reconfiguring a network to account for the one or more anomalies. ([0042] “Reconfiguration manager 2202 may receive data indicating detected or anticipated anomalies and network performance health scores from the intelligent anomaly detector and network performance health monitor, respectively. Furthermore, based on the received information, reconfiguration manager 2202 may send data (e.g., regarding anomalies and health scores) and/or different commands to node 116, to change device settings at node 116 or its operating parameters to new values.”)
Horry, Ahmed, Kim and Ouyang are considered analogous art to the claimed invention because they are in the same field of endeavor being anomaly detection. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the anomaly detection system of Horry with the anomaly preserving sampling of Ahmed with the network reconfiguration of Ouyang. One would want to do this to keep the network running at optimal health (Ouyang [0001]-[0003]).
Regarding claim 22, Horry in view of Ahmed, Kim and Ouyang teaches claim 21. Ouyang further teaches:
reconfiguring the network to account for the one or more anomalies comprises revising the anomaly detecting machine learning model. ([0042] “Reconfiguration manager 2202 may receive data indicating detected or anticipated anomalies and network performance health scores from the intelligent anomaly detector and network performance health monitor, respectively. Furthermore, based on the received information, reconfiguration manager 2202 may send data (e.g., regarding anomalies and health scores) and/or different commands to node 116, to change device settings at node 116 or its operating parameters to new values.”)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 DANIEL P GRUSZKA whose telephone number is (571)272-5259. The examiner can normally be reached M-F 9:00 AM - 6:00 PM ET.
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/DANIEL GRUSZKA/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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