Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Claims 1-20 are presented for examination in this application, 18/479,866, filed 10/03/2023.
The Examiner cites particular sections in the references as applied to the claims below for the convenience of the applicant(s). Although the specified citations are representative of the teaching in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant(s) fully consider the references in their entirety as potentially teaching all or part of the claimed, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Drawings
The drawings submitted on 10/03/2023 have been considered and accepted.
Information Disclosure Statement
Acknowledgement is made of the information disclosure statement filed 10/03/2023. All patents and non-patent literature have been considered.
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 as being unpatentable because the claimed invention in these claims is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1 – Is the claim directed to a process, machine, manufacture, or a composition of matter?
Yes, the claim is directed to a method.
Step 2 – Prong 1 – Does the claim recite an abstract idea, law of nature, or a natural phenomenon?
Yes, the claim recites at least one abstract idea:
identifying, based on selection criteria, incidents in a lookback window from a current time — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind or by a human using pen and paper (see MPEP 2106.4(a)(2) III. C.).
identifying a current state based on the incidents — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind or by a human using pen and paper (see MPEP 2106.4(a)(2) III. C.).
identifying, using a machine-learning (ML) model and based on the current state a subset of objects of interest that are likely to occur in a prediction window — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind or by a human using pen and paper (see MPEP 2106.4(a)(2) III. C.).
wherein each training lookback window is used to identify incidents occurring in each training lookback window — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind or by a human using pen and paper (see MPEP 2106.4(a)(2) III. C.).
wherein each training prediction window is used to identify which of the objects of interest occurred in the each training prediction window — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind or by a human using pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2 – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
identifying, using a machine-learning (ML) model and based on the current state, a subset of objects of interest that are likely to occur in a prediction window — this limitation is directed to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 210605(f)(2)).
wherein each training datum of the training data comprises a training lookback window and a training prediction window — this limitation is directed to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 210605(f)(2)).
transmitting or displaying a notification indicating the subset of the objects of interest — this limitation is directed to mere data outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as an insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activities in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “transmitting or displaying a notification indicating the subset of the objects of interest” limitation was found to be an insignificant extra-solution activity in claim 1. This limitation is recited at a high level of generality and amounts to transmitting data over a network, which is a well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.).
Independent claims 10 and 17 are analogous claims and therefore the same rejection and rationale apply to them. In addition, claim 17 recites additional elements analyzed under step 2A prong 2 and Step 2B.
Claim 17 recites “one or more non-transitory computer readable media storing instructions operable to cause one or more processors to perform operations comprising”. The non-transitory computer readable media amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 210605(f)(2)). The processors are considered generic computer components as well. Further under step 2B, including instructions corresponds to storing information in memory, of which being a well-understood, routine, and conventional activity (see MPEP 2106.05(d) II. (iv.)).
Dependent claim(s) 2-9, 11-16, and 18-20 when analyzed as a whole are held to be patent ineligible under 35 U.S.C 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The claims are reciting further embellishment of the judicial exception.
Claim 2: recites identifying incident templates associated with incidents and determining respective counts of distinct incident templates which is equivalent to observing and evaluating (mental process). Claims 11 and 18 are analogous.
Claim 3: recites identifying services that trigger incidents and determining respective counts of the distinct services which is equivalent to observing and evaluating (mental process). Claims 12 and 19 are analogous
Claim 4: recites a range of time that is used for observation which is equivalent to observing and evaluating (mental process). Claims 13 are analogous.
Claim 5: recites a range of time that is used for observation which is equivalent to observing and evaluating (mental process).
Claim 6: recites receiving data related to information technology, identifying services for the data, and generating incidents from the events. The receiving and generating data limitations amount to mere data gathering and outputting, considered a pre-solution activity (data gathering) which is an insignificant extra-solution activity (see 2106.05(g) (3)). This limitation is recited at a high level of generality and amounts to transmitting data over a network, which is a well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.). It further details identifying data which amounts to observing and evaluating (mental process). Additionally, it recites data specific to that of being in information technology which amounts to field of use (see MPEP 2106.05(h)). Claim 14 is analogous.
Claim 7: recites receiving binary values from the ML model which amounts to amount to mere data gathering and outputting, considered a pre-solution activity (data gathering) which is an insignificant extra-solution activity (see 2106.05(g) (3)). This limitation is recited at a high level of generality and amounts to transmitting data over a network, which is a well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.). Claims 15 and 20 are analogous.
Claim 8: recites receiving likelihood values from the ML model which amounts to amount to mere data gathering and outputting, considered a pre-solution activity (data gathering) which is an insignificant extra-solution activity (see 2106.05(g) (3)). This limitation is recited at a high level of generality and amounts to transmitting data over a network, which is a well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.).
Claim 9: recites retraining an ML model which amounts to this limitation is directed to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 210605(f)(2)).
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.
Claims 1, 3, 5-10, 12, 14, 15, 17, 19, and 20 are rejected under 35 U.S.C 103 as being unpatentable over Zhao et al. (“Real-Time Incident Prediction for Online Service Systems” hereinafter, Zhao) in view of Mehta et al. (US20200184355A1 hereinafter, Mehta).
Regarding claim 1:
Zhao teaches a method, comprising: identifying, based on selection criteria, incidents in a lookback window from a current time (see pg. 317 section 2.3: “For the current time t, we get the observation window [t −w,t]. Then, we use alert data within the observation window to predict whether an incident will occur within the prediction window [t + tl,t + tl + tp].”);
identifying a current state based on the incidents (see pg. 317 section 2.3: “For the current time t, we get the observation window [t −w,t].”);
identifying, using a machine-learning (ML) model and based on the current state, a subset of objects of interest that are likely to occur in a prediction window (see pg. 324 section 5.1.2: “Although eWarn is designed for incident prediction, it can also assist engineers for incident diagnosis. Even though interpretable results provided by eWarn may not directly pinpoint the root cause (e.g. hardware errors, misconfigurations, or software bugs) of an incident, they can provide useful clues to narrow down the search space of diagnosis. When eWarn gives a positive result, the interpretable analysis component will tell engineers which feature makes the largest contribution to the predicted result. The most important feature may be closely related to the root cause of the incident.”),
wherein the ML model is a k-nearest neighbors model that is trained based on training data obtained from historical data (see pg. 320 section 3.4 : “Here eWarn adopts Local Interpretable Model-agnostic Explanations(LIME)[38] to explain a prediction result by providing relative feature contributions for a single sample to the prediction result. More specifically, LIME is developed to identify an interpretable model that is locally faithful for each individual prediction. It learns a locally weighted linear model on this neighborhood data to explain each of classes in an interpretable way.”),
wherein each training datum of the training data comprises a training lookback window and a training prediction window (see pg. 321 section 4.1: “We recorded the time spent on the offline training stage, which learns a classifier for observation window classification, and the time spent on the online prediction stage, which provides a prediction result for the current observation window in real time.”),
wherein each training lookback window is used to identify incidents occurring in the each training lookback window (see pg. 318 section 3: “According to § 2.3, for a system, we collect a set of time windows (i.e., observation windows) with historical alert data and their labels as training data.”), and
wherein each training prediction window is used to identify which of the objects of interest occurred in the each training prediction window (see pg. 317 section 2.3: “Motivated by existing related works [37, 40, 50], we formulate the problem of incident prediction as time window classification. We use Figure 1 to illustrate the problem formulation. To ensure the real-time online prediction, we adopt the strategy of moving time window with size w and a fixed step ∆t. For the current time t, we get the observation window [t −w,t]. Then, we use alert data within the observation window to predict whether an incident will occur within the prediction window [t + tl,t + tl + tp].”).
Zhao does not explicitly teach transmitting or displaying a notification indicating the subset of the objects of interest.
Mehta, however, analogously teaches transmitting or displaying a notification indicating the subset of the objects of interest (see para [0069]: “The incident prediction engine 360 then outputs the log data and incident prediction scores to a real-time incident monitoring dashboard 370, which may be displayed on a user terminal 112. In this manner, an application owner or system administrator can monitor the log data and incident prediction scores in real time or near real time.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Zhao and Mehta before him or her, to modify the method of claim 1 to include attributes of transmitting or displaying a notification indicating the subset of the objects of interest in order for an administrator to monitor incident prediction scores to aid in incident prediction (see para [0069]: “In this manner, an application owner or system administrator can monitor the log data and incident prediction scores in real time or near real time.”).
Regarding claim 10:
Claim 10 recites analogous limitations to claim 1, with the exception that claim 10 recites a system comprising one or more memories and one or more processors.
Zhao does not explicitly teach a system comprising one or more memories and one or more processors.
Mehta, however, analogously teaches a system comprising one or more memories and one or more processors (see para [0008]: “According to one example, the invention relates to an incident prediction server including a processor, a memory and a network interface.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Zhao and Mehta before him or her, to modify the system of claim 10 to include attributes of a system comprising one or more memories and one or more processors in order to perform the embodiments within a computing system (see para [0001]: “The present invention relates generally to the prediction of computer system incidents, and more specifically to a system and method for predicting incidents such as system outages by using text analysis of logs and historical incident data.”).
Regarding claim 17:
Claim 17 recites analogous limitations to claim 1, with the exception that claim 17 recites one or more non-transitory computer readable media storing instructions operable to cause one or more processors to perform operations.
Zhao does not explicitly teach one or more non-transitory computer readable media storing instructions operable to cause one or more processors to perform operations.
Mehta, however, analogously teaches one or more non-transitory computer readable media storing instructions operable to cause one or more processors to perform operations (see para [0088]: “The modules described above may comprise software stored in the memory (e.g., non-transitory computer readable medium containing program code instructions executed by the processor) for executing the methods described herein.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Zhao and Mehta before him or her, to modify the non-transitory computer readable media of claim 17 to include attributes of having a non-transitory computer readable media in order to contain program code to execute the methods and embodiments of the invention (see para [0088]: “The modules described above may comprise software stored in the memory (e.g., non-transitory computer readable medium containing program code instructions executed by the processor) for executing the methods described herein.”).
Regarding claim 3:
Zhao in view of Mehta teaches the method of claim 1.
Zhao further teaches wherein the objects of interest are services that trigger incidents (see pg. 320 section 3.4: “.In particular, the most important feature may be related to the root cause of the predicted incident, which may assist engineers in incident troubleshooting”), and
wherein identifying the current state based on the incidents comprises: identifying services that triggered the incidents (see pgs. 316-317: “. In this table, the columns represent the occurring time of an alert, the detailed textual description about an alert, the server generating an alert1, the service generating an alert, the severity of an alert (“1” refers to the highest severity), the type of an alert, respectively.”); and
determining respective counts of distinct services in the services (see pg. 318 section 3.1: “In addition to textual features extracted from alert contents, inspired by Table 2, some other attributes are also important for incident prediction and we identify them as the second type of features in eWarn, called statistical features”. … “Alert count, refers to the number of alerts that occur within a time window, including the total number of alerts, the number of alerts with different severities (1 ∼ 3), the number of alerts with different types (e.g., application, database, memory, middleware, network, hardware, etc.).” ),
wherein the current state comprises the distinct services and the respective counts of the distinct services (see pg. 317 section 2.3 : “For the current time t, we get the observation window [t −w,t]. Then, we use alert data within the observation window to predict whether an incident will occur within the prediction window [t + tl,t + tl + tp]. Lead time (tl ) in Figure 1 is the minimum time interval that engineers need to react to an early warning (e.g., master-standby switch). That is, we have to leave enough time (i.e., Lead time) for engineers to handle the early warning after predicting an incident. To complete the prediction task, collecting training data is required.”).
Regarding claims 12 and 19:
Claims 12 and 19 recite analogous limitations to claim 3 and therefore are rejected on the same grounds.
Regarding claim 5:
Zhao in view of Mehta teaches the method of claim 1.
Zhao further teaches wherein the prediction window is within 120 minutes from the current time (see pg. 323 fig. 5 that shows several prediction windows between 0 and 120 minutes).
Regarding claim 6:
Zhao in view of Mehta teaches the method of claim 1.
Zhao further teaches receiving, during the lookback window, events related to information technology components (see pg. 316 section 1: “To overcome these challenges, in this paper we propose eWarn (short for early Warning), an approach utilizing historical data to predicting incidents in real time based on alert data. In particular, we formulate the problem of incident prediction as a binary classification task of observation windows (to be presented in § 2.3), where positive and negative samples refer to whether an incident will occur or not within a particular time horizon based on the alert data within the corresponding observation window respectively..”);
identifying respective services of a plurality of services for processing the events (see pgs. 316-417 section 2.1: “In this table, the columns represent the occurring time of an alert, the detailed textual description about an alert, the server generating an alert1, the service generating an alert, the severity of an alert (“1” refers to the highest severity), the type of an alert, respectively.”); and
generating incidents, by the respective services, from the of the events based on criteria of the events (see pg. 316 section 1: “In this paper, we aim to propose a novel approach to predicting general incidents in real time. Similar to AirAlert, we also utilize lightweight alert data for prediction since alerts are more high level and comprehensive. More specifically, alerts are generated to report anomalies from other monitoring data (e.g., metrics [46], logs [19, 31], and traces [55]), and thus avoid processing massive logs or metrics.”).
Regarding claim 14:
Claim 14 recites analogous limitations to claim 6 and therefore is rejected on the same grounds.
Regarding claim 7:
Zhao in view of Mehta teaches the method of claim 1.
Zhao further teaches wherein identifying, using the ML model and based on the current state, the subset of the objects of interest that are likely to occur in the prediction window comprises: receiving from the ML model respective binary values for the objects of interest (see pg. 320 section 3.3: “To better handle imbalance data, eWarn adopts the widely-used SMOTE[13] oversampling strategy to balance the positive samples and negative ones, in which the minority class(positive windows) is over-sampled by creating synthetic examples through finding k-nearest neighbors along the minority class.”),
wherein a first binary value is associated with each of the objects of the subset of the objects of interest and a second binary value is associated with the remaining objects of the objects of interest (see pg. 320 section 3.3: “To better handle imbalance data, eWarn adopts the widely-used SMOTE[13] oversampling strategy to balance the positive samples and negative ones, in which the minority class(positive windows) is over-sampled by creating synthetic examples through finding k-nearest neighbors along the minority class.”).
Regarding claims 15 and 20:
Claims 15 and 20 recite analogous limitations to claim 7 and therefore are rejected on the same grounds.
Regarding claim 8:
Zhao in view of Mehta teaches the method of claim 7.
Zhao further teaches wherein identifying, using the ML model and based on the current state, the subset of the objects of interest that are likely to occur in the prediction window further comprises: receiving, from the ML model, respective likelihood values in association with at least some of the respective binary values (see pg. 320 section 3.3: “To better handle imbalance data, eWarn adopts the widely-used SMOTE[13] oversampling strategy to balance the positive samples and negative ones, in which the minority class(positive windows) is over-sampled by creating synthetic examples through finding k-nearest neighbors along the minority class.” … “With the built classification model, in the running process of the system, eWarn predicts whether an incident will occur within the prediction window in real time based on the current observation window.”).
Regarding claim 9:
Zhao in view of Mehta teaches the method of claim 1.
Zhao further teaches periodically retraining the ML model based on new training data (see pg. 324 section 5.2: “To make eWarn adapt to the dynamic environment and maintain the prediction performance, we build an incremental training pipeline where our model is trained with newly arriving alert data and incidents periodically, so that new patterns can be captured properly in time. Besides, retraining a model on new data from scratch suffers from high costs, but our XGBoost-based approach can be incrementally updated to achieve stably good performance with negligible cost.”).
Claims 2, 4, 11, 13, and 18 are rejected under 35 U.S.C 103 as being unpatentable over Zhao et al. (“Real-Time Incident Prediction for Online Service Systems” hereinafter, Zhao) in view of Mehta et al. (US20200184355A1 hereinafter, Mehta) in further view of Li et al. (“FLAP: An End-to-End Event Log Analysis Platform for System Management” hereinafter, Li).
Regarding claim 2:
Zhao in view of Mehta teaches the method of claim 1.
Zhao does not explicitly teach wherein the objects of interest are incident templates, and wherein identifying the current state based on the incidents comprises: identifying incident templates associated with the incidents, wherein incidents that are semantically similar are associated with a same incident template; and determining respective counts of distinct incident templates in the incident templates, wherein the current state comprises the distinct incident templates and the respective counts of the distinct incident templates.
Li, however, analogously teaches wherein the objects of interest are incident templates (see pg. 1547 section abstract: “Specifically, in FLAP, state-of-the-art template learning techniques are used to extract useful information from unstructured raw logs; advanced data transformation techniques are proposed and leveraged for event transformation and storage; effective event pattern mining, event summarization, event querying, and failure prediction techniques are designed and integrated for log analytics; and user-friendly interfaces are utilized to present the informative analysis results intuitively and vividly.”), and
wherein identifying the current state based on the incidents comprises: identifying incident templates associated with the incidents (see fig. 7 which shows a time in place that is current based on the observed chunks and the predict chunk. Also see pg. 1550 section 3.2: “To measure the similarity between log messages, a message is transformed into a treeT = {V,E,L,vroot,P}, where V is the set of nodes, E is the set of edges, P is the set of log message segments, L is the mapping function between the set of nodes and the set of log messages segments, i.e. L : V → P, and vroot is the root node.”),
wherein incidents that are semantically similar are associated with a same incident template (see pg. 1550 section 3.2: “Given a set of messages S = {s1,s2,··· ,sn}, the objective of the template learning is to find a representative set of k ≤ n log messages, named S∗, to express the information of S as much as possible, where each element in S∗ represents one type of event, and k is a user-defined parameter. To quantify the goodness of S∗, the event coverage (JC(S∗,S)) of S∗ is used as the objective function and its form is defined as follows:
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In Equation (1), FC(x∗,x) is the similarity function of the log message x∗ and the log message x.”); and
determining respective counts of distinct incident templates in the incident templates (see pg. 1550 section 3.2: “In template learning, the problem of discovering the event types is formulated as follows: Given a set of messages S = {s1,s2,··· ,sn}, the objective of the template learning is to find a representative set of k ≤ n log messages, named S∗, to express the information of S as much as possible, where each element in S∗ represents one type of event, and k is a user-defined parameter. To quantify the goodness of S∗, the event coverage (JC(S∗,S)) of S∗ is used as the objective function”),
wherein the current state comprises the distinct incident templates and the respective counts of the distinct incident templates (see pg. 1550 section 3.2: “Given a set of messages S = {s1,s2,··· ,sn}, the objective of the template learning is to find a representative set of k ≤ n log messages, named S∗, to express the information of S as much as possible, where each element in S∗ represents one type of event, and k is a user-defined parameter”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Zhao, Mehta, and Li before him or her, to modify the method of claim 2 to include attributes of wherein the objects of interest are incident templates, and wherein identifying the current state based on the incidents comprises: identifying incident templates associated with the incidents, wherein incidents that are semantically similar are associated with a same incident template; and determining respective counts of distinct incident templates in the incident templates, wherein the current state comprises the distinct incident templates and the respective counts of the distinct incident templates in order to work with complex raw textual logs (see pg. 1550 section 3.2: “To deal with complex raw textual logs, we apply template learning for automatic event extraction [21]. In general, template learning utilizes the format and structural information of the raw logs in the clustering process to generate events.”).
Regarding claims 11 and 18:
Claims 11 and 18 recite analogous limitations to claim 2 and therefore are rejected on the same grounds.
Regarding claim 4:
Zhao in view of Mehta teaches the method of claim 1.
Zhao does not explicitly teach wherein the lookback window is within a range of 15 to 30 minutes in duration prior to the current time.
Li, however, analogously teaches wherein the lookback window is within a range of 15 to 30 minutes in duration prior to the current time (see pg. 1555: section 6.1: The segmented sequences are then divided into a training set and a validation set. For the weight cost, we set the positive samples 100 times the weight of the negative samples. For every 30 minutes, the events will be aggregated into one event chunk and the features will be extracted. Also, the models will be used to quantify the probability of the failure occurrence for the future 10 minutes.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Zhao, Mehta, and Li before him or her, to modify the method of claim 4 to include attributes of wherein the lookback window is within a range of 15 to 30 minutes in duration prior to the current time in order to aid with predicting system failures in the next hour using historical logs of with sliding windows where each window is in a time range of 10 minutes (see pg. 1555 section 6.1: The task of prediction is to identify whether there is system failures in the next hour, using the event log available for the past 1 hours. To build the predictive model, the historic logs are segmented into multiple event sequences with sliding windows, where each event chunk has a time range of 10 minutes.”).
Regarding claim 13:
Zhao in view of Mehta teaches the system of claim 10.
Zhao further teaches wherein the prediction window is within 120 minutes from the current time (see pg. 323 fig. 5 that shows several prediction windows between 0 and 120 minutes).
Zhao does not explicitly teach wherein the lookback window is within a range of 15 to 30 minutes in duration prior to the current time.
Li, however, teaches wherein the lookback window is within a range of 15 to 30 minutes in duration prior to the current time (see pg. 1555: section 6.1: The segmented sequences are then divided into a training set and a validation set. For the weight cost, we set the positive samples 100 times the weight of the negative samples. For every 30 minutes, the events will be aggregated into one event chunk and the features will be extracted. Also, the models will be used to quantify the probability of the failure occurrence for the future 10 minutes.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Zhao, Mehta, and Li before him or her, to modify the system of claim 13 to include attributes of wherein the lookback window is within a range of 15 to 30 minutes in duration prior to the current time in order to aid with predicting system failures in the next hour using historical logs of with sliding windows where each window is in a time range of 10 minutes (see pg. 1555 section 6.1: The task of prediction is to identify whether there is system failures in the next hour, using the event log available for the past 1 hours. To build the predictive model, the historic logs are segmented into multiple event sequences with sliding windows, where each event chunk has a time range of 10 minutes.”).
Conclusion
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/ANDREW BRACERO/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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