DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is in response to claims filed 07/13/2024.
Claims 9 and 18 are canceled.
Claims 1-4, 6-8, 11-14, 16, and 20 are amended.
Claims 1-8, 10-17 and 19-20 are pending.
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 1-8, 10-17 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, is directed to that judicial exception, an abstract idea, as it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated the claims under the framework provided in the 2019 Patent Eligibility Guidance published in the Federal Register 01/07/2019 and has provided such analysis below.
Step 1:
Claims 1-8 and 10 are directed to methods and fall within the statutory category of processes; Claims 11-17 and 19 are directed to a system and falls within the statutory category of machines. Claim 20 is directed to an article of manufacture of computer readable storage medium. Therefore, “Are the claims to a process, machine, manufacture or composition of matter?” Yes.
In order to evaluate the Step 2A inquiry “Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?” we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
Claims 1, 11 and 20: The limitations of “determine/determining for each of the plurality of IT processes, regularized binary time-series data based on said measured start-times and said related end-times”, “where the regularized binary time-series data are derived from at least an execution delay in the plurality of IT processes;”, “build/building a plurality of vectors, wherein each component of each of vector of the plurality of vectors represents data of a respective one of the regularized binary time-series data of the plurality of IT processes during a given second time interval” and “allocate/allocating each of the plurality of IT processes with respect to available IT resources based on said determined weights”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, but for the recitation of generic computing components being used as a tool to perform the functionality, record start and end times and create a vector with components using a pen and paper. Further, but for the recitation of generic computing components being used as a tool to perform the functionality, a person can use execution delay as part of the determination of time-series data. Lastly, but for the recitation of generic computing components being used as a tool to perform the functionality, a person can mentally evaluate weights to make a determination of which resource to send a task to.
Therefore, yes, Claims 1, 11 and 20 recite judicial exceptions.
The claims have been identified to recite judicial exceptions, Step 2A Prong 2 will evaluate whether the claims are directed to the judicial exception.
Step 2A Prong 2:
Claims 1, 19, and 20: The judicial exceptions are not integrated into practical applications. In particular, the claims recite the following additional elements – “measuring periodically start-times and related end-times of each of the plurality of IT processes during a first time interval”, reciting insignificant extra-solution data gathering activity, MPEP § 2106.05(g). Further, “training a machine-learning system to build a machine-learning model using the plurality of vectors as training data to determine weights for edges between nodes of the machine-learning system; and using said determined weights of said edges between said nodes as indicators for dependencies between the plurality of IT processes”, is a recitation of generic computing components and functions merely being used as a tool to apply the abstract idea (see MPEP § 2106.05(f)).
Therefore, “Do the claims recite additional elements that integrate the judicial exception into a practical application? No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
After having evaluating the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that the Claims 1, 11 and 20 not only recite a judicial exception but that the claims are directed to a judicial exception as a judicial exception has not been integrated into a practical application.
Step 2B:
Claim 1, 11 and 20: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components merely applying the abstract idea, and which do not amount to significantly more than the abstract idea. Further, the insignificant extra-solution activity is Well-Understood, Routine, and Conventional. “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. ii. Performing repetitive calculations” … “iv. Storing and retrieving information in memory”. See MPEP § 2106.05(d)(II).
Therefore, “Do the claims recite additional elements that amount to significantly more than the judicial exception? No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception.
Having concluded analysis within the provided framework, Claims 1, 11 and 20 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding Claims 2 and 12: “visualizing/visualize said dependencies between the plurality of IT processes using said determined weights of said edges”, reciting insignificant extra-solution data display, MPEP § 2106.05(g). With regard to integration into practical application and whether additional elements amount to significantly more, Claims 2 and 12 fail both prongs of Step 2A, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more, performing a well understood, routine, and conventional task of data gathering. “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. iv. Presenting offers and gathering statistics”. See MPEP § 2106.05(d)(II). Therefore, Claims 2 and 12 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding Claims 3 and 13: “determining/determine representative execution times for each of the plurality of IT processes”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can mentally take note of when a task has begun. With regard to integration into practical application and whether additional elements amount to significantly more, Claims 3 and 13 fail both prongs of Step 2A, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, Claims 3 and 13 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding Claims 4 and 14: “wherein the regularized binary time-series data are regularized in respect to a predetermined time unit” and “use/using a binary time-series schema based of said representative execution times”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can mentally regularize time series data known time unit and convert a series of timestamps to binary. With regard to integration into practical application and whether additional elements amount to significantly more, Claims 4 and 14 fail both prongs of Step 2A, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, Claims 4 and 14 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding Claims 5 and 15: “determining said start-times and said related end-times regularly in predefined time intervals” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can incrementally think about the start and end times of processes. With regard to integration into practical application and whether additional elements amount to significantly more, Claims 5 and 15 fail both prongs of Step 2A, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more, performing a well understood, routine, and conventional task of data gathering. Therefore, Claims 5 and 15 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding Claims 6 and 16: “representative execution times are expected execution times or average execution times for each of the plurality of IT processes during said first time interval”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can mentally average the times it took to complete multiple processes. With regard to integration into practical application and whether additional elements amount to significantly more, Claims 6 and 16 fail both prongs of Step 2A, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, Claims 6 and 16 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding Claims 7, 8 and 17: “ML/machine learning system is a fully connected neural network with two layers of nodes” and “each node represents one of the plurality of IT processes”, merely recitations of generic computing components and functions merely being used as a tool to apply the abstract idea (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. With regard to integration into practical application and whether additional elements amount to significantly more, Claims 7 and 17 fail both prongs of Step 2A, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, Claims 7 and 17 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding Claims 10 and 19: “use/using first vectors of said plurality of vectors as input for said machine-learning system; and using second vectors of said plurality of vectors as ground truth, wherein said second vectors are pairwise directly subsequent to respective first vectors” , merely recitations of generic computing components and functions merely being used as a tool to apply the abstract idea (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. With regard to integration into practical application and whether additional elements amount to significantly more, Claims 10 and 19 fail both prongs of Step 2A, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, Claims 10 and 19 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
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 1-8, 10-17 and 19-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Agarwal et al. (US 20080216098 A1) (hereinafter Agrawal), in view of Osborn et al.(US 20230072423 A1)(hereinafter Osborn), in further view Ben Simhon et al. (US 20160210556 A1) (hereinafter Ben), and even further in view of Pal et al. (US 20200050607 A1) (hereinafter Pal).
Regarding Claim 1, Agarwal teaches:
A computer-implemented method for controlling a plurality of information technology (IT) processes comprising:
“have general applicability to the run-time determination of dependencies between logical components or processes in a computing system or network”, (Agarwal: ¶049), “data processing environment may include, for example, application programs, servlets and Enterprise Java Beans (EJBs) running within a Web application server, processes serving Uniform Resource Identifiers (URIs), processes executing Structured Query Language (SQL) requests”, (Agarwal: ¶026), “computer program product comprising program code instructions for controlling the performance of operations within a data processing environment in which the program code runs”, (Agarwal: ¶033).
measuring periodically start-times and related end-times of each of the plurality of IT processes during a first time interval;
“an access log with URIs identifying accessed Web resources, and maintains log records including start and end times for processes running in the managed system 10”, (Agarwal: ¶055), “Each agent periodically queries the configuration information and metrics for a particular subsystem or component of the managed system 10”, (Agarwal: ¶050), “The correlation identifier determines an activity period for each process or component, using events indicating completion of request processing to compute theoretical start and end times”, (Agarwal: ¶057), “Each event comprises an SQL query string, a time stamp that is defined to be the end time of the SQL request, and the start time of the query”, (Agarwal: ¶082).
determining, for each of the plurality of IT processes, regularized binary time-series data based on said measured start-times and said related end-times, during a second time interval; … building a plurality of vectors, wherein each component of each vector of the plurality of vectors represents data of a respective one of the regularized binary time-series data of the plurality of IT processes during a given second time interval;
“A PMI client, which is enabled at time 0, polls the Web Application Server at a regular interval of 3 time units”, (Agarwal: ¶091), “According to Ensel, a time series of objects' activities may then be fed into a Neural Network to judge whether the objects appear to be relate”, (Agarwal: ¶012), “To compute the activity period of an event, the time stamp of the event and the execution time of the component request represented by the event are used”, (Agarwal: ¶084), “The covering interval or `activity period` of the request is then [TS3-PI-X3, TS3] or [7, 12]…”, (Agarwal: ¶093).
and using said determined weights of said edges between said nodes as indicators for dependencies between the plurality of IT processes.
“Containment is used as an indicator of a likely dependency relationship, and a weighting is computed for each dependency relationship based on the consistency of containment”, (Agarwal: Abstract), “Weightings are calculated at run-time for the identified dependency relationships represented by edges”, (Agarwal: ¶099), “Weightings may be interpreted in various ways, but in this specific implementation weightings are a quantitative measure of the extent to which A depends on B”, (Agarwal: ¶100).
Further regarding Claim 1, Agarwal fails to teach:
regularized binary time-series data
However, Osborn teaches: “A binary time series may be labeled with a value of one when an event is detected”, (Osborn: ¶1163), “For example, labels can be created by generating a binary time series with 1 when a specified event occurs and 0 otherwise”, (Osborn: ¶1663), “In some embodiments, varying the regularization weight” … “In some embodiments, the shift operator, δ, may be used to push the time-series backward in time”, (Osborn: ¶920), “as well as their respective time derivatives (e.g. linear or angular velocity or acceleration)”, (Osborn: ¶1417).
building a plurality of vectors, wherein each component of each vector of the plurality of vectors represents data of a respective one of the regularized binary time-series data
However, Osborn teaches: “comprise a time series of K n¬-dimensional vectors {xk|1≤k≤K} at time points t1, t2, . . . , tK during performance of the movements”, (Osborn: ¶469), “For example, the inferential model may use as input a sequence of vectors {xk|1≤k≤K} generated using measurements obtained at time points t1, t2, . . . , tK, where the ith component of vector xj may be a value measured by the ith sensor at time tj”, (Osborn: ¶983), “Once such large vectors are generated, a classifier may be produced based on logistic regression, random forest, or multilayer perceptron, and may be implemented in a gesture classification model”, (Osborn: ¶1177), “The term “feature space” can comprise one or more vectors or data points” … “certain temporal, spatial, and temporospatial characteristics, as well as other characteristics such as frequency, duration, and amplitude, for example”, “according to which the filters weights are W=(L.sup.TC.sup.−1L).sup.−1L.sup.TC.sup.−1, where L is the matrix of spatiotemporal response profiles (i.e. the collection of all h vectors in the MVDR notation below)”, (Osborn 591).
training a machine-learning system to build a machine-learning model using the plurality of vectors as training data to determine weights for edges between nodes of the machine-learning system;
However, Osborn teaches: “The statistical model being trained may take as input a sequence of data sets each of the data sets in the sequence comprising an n-dimensional vector of sensor data”, (Osborn: ¶479), “the time series information obtained at operations 28502 and 28508 may be combined to create training data used for training an inferential model”, (Osborn: ¶979), “training an inference model to determine at least one spatiotemporal waveform and a corresponding weight to be applied to the at least one spatiotemporal waveform”, (Osborn: ¶103), “For example, when the inference model(s) 114 is or includes a neural network, parameters of the neural network (e.g., weights) may be estimated from the training data”, (Osborn: ¶300), “sensor measurements are measured and used to train the inference model” … “the constraints may comprise part of the inference model itself being represented by information (e.g., connection weights between nodes) in the model”, (Osborn: ¶904).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine regularized binary time-series data; building a plurality of vectors, wherein each component of each of said vectors of said plurality of vectors represents data of a respective one of said time-series data; training a machine-learning system to build a machine-learning model using said plurality of vectors as training data to determine weights for edges between nodes of said machine-learning system; of Osborn with the methods and systems of Agarwal resulting in a machine learning system that’s been trained on vectors containing binary time series data. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of “advantageous in cases where multiple different control actions may occur within a threshold amount of time”, (Osborn: ¶296).
Further regarding Claim 1, Agarwal in view of Osborn fails to teach:
where the regularized binary time-series data are derived from at least an execution delay in the plurality of IT processes
However, Ben teaches: “Metrics may also be defined at a system level, such as number of transactions per second in a database or time delay in returning results for a query” … “system may measure and/or collect thousands, millions, or even billions of time-series metrics (that is, the metrics are measured over time)”, (Ben: ¶4), “the binary representation may remain a time series and include a one for any time interval where the metric changed and a zero where the metric did not change” … “the binary assignment may be performed on a periodic timeframe, not necessarily aligned to the samples of the metric, with a 1 indicating that the metric changed during that period and a 0 indicating that the metric did not change”, (Ben: ¶128), “At 1312 control determines a binary representation for the next alternative metric and returns to 1308. At 1316 control applies a binary vectors grouping algorithm”, (Ben: ¶130), “LDA receives a number of groups N and an aligned binary representation of each metric…”, (Ben: ¶131).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine where the regularized binary time-series data are derived from at least an execution delay in the plurality of IT processes of Ben with the methods and systems of Agarwal in view of Osborn resulting in a system able to record time series data from an execution delay. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of “Monitoring metrics allows for problems to be quickly identified and resolved, hopefully before affecting business results, such as losing users, missing revenue, decreasing productivity, etc.”, (Ben: ¶4), “The anomaly system may allow anomalies to be more quickly identified and corrected. In some instances, the anomaly can be remedied before any downtime is suffered”, (Ben: ¶41).
Lastly regarding Claim 1, Agarwal in view of Osborn and Ben fail to teach:
allocating each of the plurality of IT processes with respect to available IT resources based on said determined weights
However, Pal teaches: “Based on the execution objectives, the query characteristics or metadata, and/or available computing resources (e.g., available worker nodes 3306 or available processor cores at the worker nodes 3306) the query coordinator 3304 may optimize the scheduling or assignment of the query. For instance, suppose the execution objective is to reduce bandwidth usage”, (Pal: ¶1057), “For example, the system 16 can reduce the likelihood that there will be insufficient execution resources to execute a query, improve utilization of execution resources of the system 16 (e.g., increase the usage time of the compute resources)”, (Pal: ¶1244), “In certain cases, the “additional amount” of execution resources can be determined based on a weighting factor. The weighting factor can correspond to the number, ratio, or amount of additional execution resources to be allocated to collate or further process data from the first set of execution resources..”, (Pal: ¶1252), “to determine a query-resource allocation for an indexer search, the system 16 can use the total number of execution resources to be used to obtain the set of data and a weighting factor”, (Pal: ¶1253), “the system 16 can allocate execution resources for an indexer portion based on the number of indexers 206 to be used to execute the query and a weighting factor”, (Pal: ¶1269)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine allocating each of the plurality of IT processes with respect to available IT resources based on said determined weights of Pal with the methods and systems of Agarwal in view of Osborn and Ben resulting in a machine learning system that can allocate process to available resources to reduce system usage. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of “increase the number of queries being executed over a period of time (e.g., increase throughput of query executions) and decrease the wait time to execute queries”, (Pal: ¶1244).
Regarding Claim 2, Agarwal teaches:
visualizing said dependencies between the plurality of IT processes using said determined weights of said edges.
“The dependency results may be used by a number of different systems management applications such as visualization” … “attach higher significance to apparent dependencies” … “In particular, a higher weightings can be applied to dependency results” … “results discovered during high load conditions may be filtered out”, (Agarwal: ¶177), “Containment is used as an indicator of a likely dependency relationship, and a weighting is computed for each dependency relationship based on the consistency of containment”, (Agarwal: Abstract), “Weightings are calculated at run-time for the identified dependency relationships represented by edges”, (Agarwal: ¶099), “Weightings may be interpreted in various ways, but in this specific implementation weightings are a quantitative measure of the extent to which A depends on B”, (Agarwal: ¶100).
Regarding Claim 3, Agarwal teaches:
determining representative execution times for each of the plurality of IT processes.
“such as invocation and average execution time counters, for accounting and performance tuning purposes”, (Agarwal: ¶019), “the events are used to calculate an activity period which contains the period of execution of the component”, (Agarwal: ¶020), “To compute the activity period of an event, the time stamp of the event and the execution time of the component request represented by the event are used”, (Agarwal: ¶084), “If the current event corresponds to the N+1.sup.th request to the servlet, then X.sub.N+1 (the execution time of the request)”, (Agarwal: ¶085), “time value T.sub.1 is actually the execution time of the servlet request X.sub.1”, (Agarwal: ¶086).
Regarding Claim 4, Agarwal teaches:
wherein said determining for each of the plurality IT processes the regularized binary time-series data comprises: using a binary time-series schema based of said representative execution times.
“such as invocation and average execution time counters, for accounting and performance tuning purposes”, (Agarwal: ¶019), “The counter T.sub.N records for servlet A the average execution time of a request to the servlet”, (Agarwal: ¶092), “The run-time activity data may include the number of requests made to a monitored component, the average response time of a monitored component so far”, (Agarwal: ¶173), “The Web application server also includes counters and provides access to a system clock enabling monitoring of access counts and average response times of servlets and EJBs”, (Agarwal: ¶055).
Further regarding Claim 4, Agarwal in view of Ben and Pal fails to teach:
wherein the regularized binary time-series data are regularized in respect to a predetermined time unit,
the regularized binary time-series
However, Osborn teaches: “A binary time series may be labeled with a value of one when an event is detected”, (Osborn: ¶1163), “For example, labels can be created by generating a binary time series with 1 when a specified event occurs and 0 otherwise”, (Osborn: ¶1663), “In some embodiments, varying the regularization weight” … “the shift operator, δ, may be used to push the time-series backward in time”, (Osborn: ¶920), “as well as their respective time derivatives (e.g. linear or angular velocity or acceleration)”, (Osborn: ¶1417), “be time-series data (e.g., data recorded over a period of time), including” … “and/or a sequence of measurement values with a known sampling time interval and a known start time)”, (Osborn: ¶958), “In some examples, spike event information may be detected within 5 seconds, within 1 second, within 500 ms, within 100 ms, or within 10 ms of the occurrence of the electrical event”, (Osborn: ¶1111).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine wherein said regularized binary time-series data are regularized in respect to a predetermined time unit, regularized binary time-series of Osborn with the methods and systems of Agarwal in view of Ben and Pal resulting in a machine learning system regularize binary time series data with regards to a predetermined time unit. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of “advantageous in cases where multiple different control actions may occur within a threshold amount of time”, (Osborn: ¶296).
Regarding Claim 5, Agarwal teaches:
determining said start-times and said related end-times regularly in predefined time intervals.
“and maintains log records including start and end times for processes running in the managed system 10”, (Agarwal: ¶055), “polls the Web Application Server at a regular interval of PI milliseconds”, (Agarwal: ¶076), “polls the Web Application Server at a regular interval of 3 time units”, (Agarwal: ¶091), “Each event comprises an SQL query string, a time stamp that is defined to be the end time of the SQL request, and the start time of the query”, (Agarwal: ¶082).
Regarding Claim 6, Agarwal teaches:
representative execution times are expected execution times or average execution times for each of the plurality of IT processes during said first time interval.
“such as invocation and average execution time counters, for accounting and performance tuning purposes”, (Agarwal: ¶019), “The counter T.sub.N records for servlet A the average execution time of a request to the servlet”, (Agarwal: ¶092), “The run-time activity data may include the number of requests made to a monitored component, the average response time of a monitored component so far”, (Agarwal: ¶173), “The Web application server also includes counters and provides access to a system clock enabling monitoring of access counts and average response times of servlets and EJBs”, (Agarwal: ¶055).
Regarding Claim 7, Agarwal in view of Ben and Pal fails to teach:
machine learning (ML) system is a fully connected neural network with two layers of nodes.
However, Osborn teaches: “a statistical model (e.g., a neural network) trained using any suitable number of layers and any suitable number of nodes in each layer”, (Osborn: ¶1839), “neural networks with fully connected (e.g., dense) layers, Long Short-Term Memory (LSTM) layers, convolutional layers, Temporal Convolutional Layers (TCL)”, (Osborn: ¶986).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine ML system is a fully connected neural network with two layers of nodes of Osborn with the methods and systems of Agarwal in view of Ben and Pal resulting in a machine learning system that has two connected layers of nodes. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of “advantageous in cases where multiple different control actions may occur within a threshold amount of time and it is not important to distinguish an order in which these control actions occur (e.g., a user may activate two patterns of neural activity within the threshold amount of time)”, (Osborn: ¶296).
Regarding Claim 8, Agarwal teaches:
each node represents one of the plurality of IT processes.
“Attributes are maintained for each node to capture its runtime status” … “each resource or component is represented as a node and a dependency between nodes is represented as a directed edge or link”, (Agarwal: ¶099), “It is assumed that the node state can be examined and a determination made at run-time of whether the node has a problem, and the assumption is valid for response-time related problems”, (Agarwal: ¶136), “determining run-time dependencies between logical components of a data processing environment”, (Agarwal: Abstract).
Regarding Claim 10, Agarwal in view of Ben and Pal fails to teach:
using first vectors of said plurality of vectors as input for said machine-learning system;
However, Osborn teaches: “The statistical model being trained may take as input a sequence of data sets each of the data sets in the sequence comprising an n-dimensional vector of sensor data”, (Osborn: ¶479), “comprise a time series of K n¬-dimensional vectors {xk|1≤k≤K} at time points t1, t2, . . . , tK during performance of the movements”, (Osborn: ¶469), “For example, the inferential model may use as input a sequence of vectors {xk|1≤k≤K} generated using measurements obtained at time points t1, t2, . . . , tK, where the ith component of vector xj may be a value measured by the ith sensor at time tj”, (Osborn: ¶983), “Once such large vectors are generated, a classifier may be produced based on logistic regression, random forest, or multilayer perceptron, and may be implemented in a gesture classification model”, (Osborn: ¶1177), “The term “feature space” can comprise one or more vectors or data points” … “certain temporal, spatial, and temporospatial characteristics, as well as other characteristics such as frequency, duration, and amplitude, for example”, “according to which the filters weights are W=(L.sup.TC.sup.−1L).sup.−1L.sup.TC.sup.−1, where L is the matrix of spatiotemporal response profiles (i.e. the collection of all h vectors in the MVDR notation below)”, (Osborn 591).
and using second vectors of said plurality of vectors as ground truth,
However, Osborn teaches: “After defining vectors for all of the detected spike events, a similarity metric may be used to identify vectors having values that cluster together, and thus are likely to represent spike events generated,” (Osborn:¶588), “output is similar to the ground truth spike times indicating that the accuracy of the automatic spike detection is high”, (Osborn: ¶595), “the constraints may be learned by the statistical model through training based on ground truth data on the position and exerted forces of the hand and wrist in the context of recorded sensor data”, (Osborn: ¶446), “vector xj is a value measured by the ith sensor at time tj and/or derived from the value measured by the ith sensor at time tj”, (Osborn: ¶479), “ground truth data (e.g., label time series data) may be obtained by multiple sensors”, (Osborn: ¶976).
wherein said second vectors are pairwise directly subsequent to respective first vectors.
However, Osborn teaches: “such that all sensor data provided as input to the statistical model corresponds to time series data at the same time resolution”, (Osborn: ¶477), “The statistical model being trained may take as input a sequence of data sets each of the data sets in the sequence comprising an n-dimensional vector of sensor data”, (Osborn: ¶479), “Combining the time series information obtained at operations 28502 and 28508 to create training data”, (Osborn: ¶982), “generating one or more training datasets by time-shifting at least a portion of the neuromuscular activity data over the first time series relative to the second time series, to associate the neuromuscular activity data with at least a portion of the ground truth data”, (Osborn: ¶1009). Examiner notes: vectors arranged in temporal order and are at the same time resolution as opposed to being ordered loosely and being indexed in a uniform temporal progression.
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine using first vectors of said plurality of vectors as input for said machine-learning system; and using second vectors of said plurality of vectors as ground truth, wherein said second vectors are pairwise directly subsequent to respective first vectors of Osborn with the methods and systems of Agarwal in view of Ben and Pal resulting in a machine learning system that can access ground truth vectors as well as understand change overtime for multiple vector sets. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of “time shifting the” … “activity signals to substantially align with a timing of the corresponding movement (Osborn: ¶931).
Regarding Claim 11, Agarwal teaches:
A control system for controlling a plurality of information technology (IT) processes comprising one or more processors and a memory operatively coupled to said one or more processors,
“FIG. 2. IBM Corporation's WebSphere Application Server v4.0 software (Web application server 100) and DB2 v7.1 software (database server 110) are installed on a first computer (2 GHz processor, 1 GB memory)”, (Agarwal: ¶151), “have general applicability to the run-time determination of dependencies between logical components or processes in a computing system or network”, (Agarwal: ¶049), “data processing environment may include, for example, application programs, servlets and Enterprise Java Beans (EJBs) running within a Web application server, processes serving Uniform Resource Identifiers (URIs), processes executing Structured Query Language (SQL) requests”, (Agarwal: ¶026), “computer program product comprising program code instructions for controlling the performance of operations within a data processing environment in which the program code runs”, (Agarwal: ¶033).
wherein said memory stores program code portions which, when executed by said one or more processors, enable said one or more processors to:
“a computer program product comprising program code instructions for controlling the performance of operations within a data processing environment in which the program code runs”, (Agarwal: ¶033), “FIG. 2. IBM Corporation's WebSphere Application Server v4.0 software (Web application server 100) and DB2 v7.1 software (database server 110) are installed on a first computer (2 GHz processor, 1 GB memory)”, (Agarwal: ¶151).
measure periodically start-times and related end-times of each of the plurality of IT processes;
“an access log with URIs identifying accessed Web resources, and maintains log records including start and end times for processes running in the managed system 10”, (Agarwal: ¶055), “Each agent periodically queries the configuration information and metrics for a particular subsystem or component of the managed system 10”, (Agarwal: ¶050), “The correlation identifier determines an activity period for each process or component, using events indicating completion of request processing to compute theoretical start and end times”, (Agarwal: ¶057), “Each event comprises an SQL query string, a time stamp that is defined to be the end time of the SQL request, and the start time of the query”, (Agarwal: ¶082).
determine, for each of the plurality of IT processes , regularized binary time-series data based on said measured start-times and said related end-times; … build a plurality of vectors, wherein each component of each vector of the plurality of vectors represents data of a respective one of the regularized binary time-series data of the plurality of IT processes;
“A PMI client, which is enabled at time 0, polls the Web Application Server at a regular interval of 3 time units”, (Agarwal: ¶091), “According to Ensel, a time series of objects' activities may then be fed into a Neural Network to judge whether the objects appear to be relate”, (Agarwal: ¶012), “To compute the activity period of an event, the time stamp of the event and the execution time of the component request represented by the event are used”, (Agarwal: ¶084), “The covering interval or `activity period` of the request is then [TS3-PI-X3, TS3] or [7, 12]…”, (Agarwal: ¶093).
and use said determined weights of said edges between said nodes as indicators for dependencies between the plurality of IT processes.
“Containment is used as an indicator of a likely dependency relationship, and a weighting is computed for each dependency relationship based on the consistency of containment”, (Agarwal: Abstract), “Weightings are calculated at run-time for the identified dependency relationships represented by edges”, (Agarwal: ¶099), “Weightings may be interpreted in various ways, but in this specific implementation weightings are a quantitative measure of the extent to which A depends on B”, (Agarwal: ¶100).
Further regarding Claim 11, Agarwal fails to teach:
regularized binary time-series data
However, Osborn teaches: “A binary time series may be labeled with a value of one when an event is detected”, (Osborn: ¶1163), “For example, labels can be created by generating a binary time series with 1 when a specified event occurs and 0 otherwise”, (Osborn: ¶1663), “In some embodiments, varying the regularization weight” … “In some embodiments, the shift operator, δ, may be used to push the time-series backward in time”, (Osborn: ¶920), “as well as their respective time derivatives (e.g. linear or angular velocity or acceleration)”, (Osborn: ¶1417).
build a plurality of vectors, wherein each component of each vector of the plurality of vectors represents data of a respective one of the regularized binary time-series data of the plurality of IT processes;
However, Osborn teaches: “comprise a time series of K n¬-dimensional vectors {xk|1≤k≤K} at time points t1, t2, . . . , tK during performance of the movements”, (Osborn: ¶469), “For example, the inferential model may use as input a sequence of vectors {xk|1≤k≤K} generated using measurements obtained at time points t1, t2, . . . , tK, where the ith component of vector xj may be a value measured by the ith sensor at time tj”, (Osborn: ¶983), “Once such large vectors are generated, a classifier may be produced based on logistic regression, random forest, or multilayer perceptron, and may be implemented in a gesture classification model”, (Osborn: ¶1177), “The term “feature space” can comprise one or more vectors or data points” … “certain temporal, spatial, and temporospatial characteristics, as well as other characteristics such as frequency, duration, and amplitude, for example”, “according to which the filters weights are W=(L.sup.TC.sup.−1L).sup.−1L.sup.TC.sup.−1, where L is the matrix of spatiotemporal response profiles (i.e. the collection of all h vectors in the MVDR notation below)”, (Osborn 591).
train a machine-learning system to build a machine-learning model using the plurality of vectors as training data to determine weights for edges between nodes of the machine-learning system;
However, Osborn teaches: “The statistical model being trained may take as input a sequence of data sets each of the data sets in the sequence comprising an n-dimensional vector of sensor data”, (Osborn: ¶479), “the time series information obtained at operations 28502 and 28508 may be combined to create training data used for training an inferential model”, (Osborn: ¶979), “training an inference model to determine at least one spatiotemporal waveform and a corresponding weight to be applied to the at least one spatiotemporal waveform”, (Osborn: ¶103), “For example, when the inference model(s) 114 is or includes a neural network, parameters of the neural network (e.g., weights) may be estimated from the training data”, (Osborn: ¶300), “sensor measurements are measured and used to train the inference model” … “the constraints may comprise part of the inference model itself being represented by information (e.g., connection weights between nodes) in the model”, (Osborn: ¶904).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine regularized binary time-series data; building a plurality of vectors, wherein each component of each of said vectors of said plurality of vectors represents data of a respective one of said time-series data; training a machine-learning system to build a machine-learning model using said plurality of vectors as training data to determine weights for edges between nodes of said machine-learning system; of Osborn with the methods and systems of Agarwal resulting in a machine learning system that’s been trained on vectors containing binary time series data. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of “advantageous in cases where multiple different control actions may occur within a threshold amount of time”, (Osborn: ¶296).
Further regarding Claim 11, Agarwal in view of Osborn fails to teach:
where the regularized binary time-series data are derived from at least an execution delay in the plurality of IT processes
However, Ben teaches: “Metrics may also be defined at a system level, such as number of transactions per second in a database or time delay in returning results for a query” … “system may measure and/or collect thousands, millions, or even billions of time-series metrics (that is, the metrics are measured over time)”, (Ben: ¶4), “the binary representation may remain a time series and include a one for any time interval where the metric changed and a zero where the metric did not change” … “the binary assignment may be performed on a periodic timeframe, not necessarily aligned to the samples of the metric, with a 1 indicating that the metric changed during that period and a 0 indicating that the metric did not change”, (Ben: ¶128), “At 1312 control determines a binary representation for the next alternative metric and returns to 1308. At 1316 control applies a binary vectors grouping algorithm”, (Ben: ¶130), “LDA receives a number of groups N and an aligned binary representation of each metric…”, (Ben: ¶131).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine where the regularized binary time-series data are derived from at least an execution delay in the plurality of IT processes of Ben with the methods and systems of Agarwal in view of Osborn resulting in a system able to record time series data from an execution delay. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of “Monitoring metrics allows for problems to be quickly identified and resolved, hopefully before affecting business results, such as losing users, missing revenue, decreasing productivity, etc.”, (Ben: ¶4), “The anomaly system may allow anomalies to be more quickly identified and corrected. In some instances, the anomaly can be remedied before any downtime is suffered”, (Ben: ¶41).
Lastly regarding Claim 11, Agarwal in view of Osborn and Ben fail to teach:
allocate each of the plurality of IT processes with respect to available IT resources based on said determined weights
However, Pal teaches: “Based on the execution objectives, the query characteristics or metadata, and/or available computing resources (e.g., available worker nodes 3306 or available processor cores at the worker nodes 3306) the query coordinator 3304 may optimize the scheduling or assignment of the query. For instance, suppose the execution objective is to reduce bandwidth usage”, (Pal: ¶1057), “For example, the system 16 can reduce the likelihood that there will be insufficient execution resources to execute a query, improve utilization of execution resources of the system 16 (e.g., increase the usage time of the compute resources)”, (Pal: ¶1244), “In certain cases, the “additional amount” of execution resources can be determined based on a weighting factor. The weighting factor can correspond to the number, ratio, or amount of additional execution resources to be allocated to collate or further process data from the first set of execution resources..”, (Pal: ¶1252), “to determine a query-resource allocation for an indexer search, the system 16 can use the total number of execution resources to be used to obtain the set of data and a weighting factor”, (Pal: ¶1253), “the system 16 can allocate execution resources for an indexer portion based on the number of indexers 206 to be used to execute the query and a weighting factor”, (Pal: ¶1269)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine allocating each of the plurality of IT processes with respect to available IT resources based on said determined weights of Pal with the methods and systems of Agarwal in view of Osborn and Ben resulting in a machine learning system that can allocate process to available resources to reduce system usage. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of “increase the number of queries being executed over a period of time (e.g., increase throughput of query executions) and decrease the wait time to execute queries”, (Pal: ¶1244).
Claim 12, Agarwal teaches:
visualize said dependencies between the plurality of IT processes using said determined weights of said edges.
“The dependency results may be used by a number of different systems management applications such as visualization” … “attach higher significance to apparent dependencies” … “In particular, a higher weightings can be applied to dependency results” … “results discovered during high load conditions may be filtered out”, (Agarwal: ¶177), “Containment is used as an indicator of a likely dependency relationship, and a weighting is computed for each dependency relationship based on the consistency of containment”, (Agarwal: Abstract), “Weightings are calculated at run-time for the identified dependency relationships represented by edges”, (Agarwal: ¶099), “Weightings may be interpreted in various ways, but in this specific implementation weightings are a quantitative measure of the extent to which A depends on B”, (Agarwal: ¶100).
Regarding Claim 13, Agarwal teaches:
determine representative execution times for each of the plurality of IT processes.
“such as invocation and average execution time counters, for accounting and performance tuning purposes”, (Agarwal: ¶019), “the events are used to calculate an activity period which contains the period of execution of the component”, (Agarwal: ¶020), “To compute the activity period of an event, the time stamp of the event and the execution time of the component request represented by the event are used”, (Agarwal: ¶084), “If the current event corresponds to the N+1.sup.th request to the servlet, then X.sub.N+1 (the execution time of the request)”, (Agarwal: ¶085), “time value T.sub.1 is actually the execution time of the servlet request X.sub.1”, (Agarwal: ¶086).
Regarding Claim 14, Agarwal teaches:
wherein said one or more processors, during said determining for each of the plurality of IT processes the regularized binary time-series data, are also enabled to: use a binary time-series schema based on said representative execution times.
“such as invocation and average execution time counters, for accounting and performance tuning purposes”, (Agarwal: ¶019), “The counter T.sub.N records for servlet A the average execution time of a request to the servlet”, (Agarwal: ¶092), “The run-time activity data may include the number of requests made to a monitored component, the average response time of a monitored component so far”, (Agarwal: ¶173), “The Web application server also includes counters and provides access to a system clock enabling monitoring of access counts and average response times of servlets and EJBs”, (Agarwal: ¶055).
Further regarding Claim 14. Agarwal in view of Ben and Pal fails to teach:
wherein the regularized binary time-series data are regularized in respect to a predetermined time unit,
regularized binary time-series
However, Osborn teaches: “A binary time series may be labeled with a value of one when an event is detected”, (Osborn: ¶1163), “For example, labels can be created by generating a binary time series with 1 when a specified event occurs and 0 otherwise”, (Osborn: ¶1663), “In some embodiments, varying the regularization weight” … “the shift operator, δ, may be used to push the time-series backward in time”, (Osborn: ¶920), “as well as their respective time derivatives (e.g. linear or angular velocity or acceleration)”, (Osborn: ¶1417), “be time-series data (e.g., data recorded over a period of time), including” … “and/or a sequence of measurement values with a known sampling time interval and a known start time)”, (Osborn: ¶958), “In some examples, spike event information may be detected within 5 seconds, within 1 second, within 500 ms, within 100 ms, or within 10 ms of the occurrence of the electrical event”, (Osborn: ¶1111).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine wherein said regularized binary time-series data are regularized in respect to a predetermined time unit, regularized binary time-series of Osborn with the methods and systems of Agarwal in view of Ben and Pal resulting in a machine learning system regularize binary time series data with regards to a predetermined time unit. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of “advantageous in cases where multiple different control actions may occur within a threshold amount of time”, (Osborn: ¶296).
Regarding Claim 15, Agarwal teaches:
determining said start-times and said related end-times regularly in predefined time intervals.
“and maintains log records including start and end times for processes running in the managed system 10”, (Agarwal: ¶055), “polls the Web Application Server at a regular interval of PI milliseconds”, (Agarwal: ¶076), “polls the Web Application Server at a regular interval of 3 time units”, (Agarwal: ¶091), “Each event comprises an SQL query string, a time stamp that is defined to be the end time of the SQL request, and the start time of the query”, (Agarwal: ¶082).
Regarding Claim 16, Agarwal teaches:
representative execution times are expected execution times or average execution times for each of the plurality of IT processes during said first time interval.
“such as invocation and average execution time counters, for accounting and performance tuning purposes”, (Agarwal: ¶019), “The counter T.sub.N records for servlet A the average execution time of a request to the servlet”, (Agarwal: ¶092), “The run-time activity data may include the number of requests made to a monitored component, the average response time of a monitored component so far”, (Agarwal: ¶173), “The Web application server also includes counters and provides access to a system clock enabling monitoring of access counts and average response times of servlets and EJBs”, (Agarwal: ¶055).
Regarding Claim 17, Agarwal in view of Ben and Pal fails to teach:
machine-learning system is a fully connected neural network with two layers of nodes
However, Osborn teaches: “a statistical model (e.g., a neural network) trained using any suitable number of layers and any suitable number of nodes in each layer”, (Osborn: ¶1839), “neural networks with fully connected (e.g., dense) layers, Long Short-Term Memory (LSTM) layers, convolutional layers, Temporal Convolutional Layers (TCL)”, (Osborn: ¶986).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine ML system is a fully connected neural network with two layers of nodes of Osborn with the methods and systems of Agarwal in view of Ben and Pal resulting in a machine learning system that has two connected layers of nodes. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of “advantageous in cases where multiple different control actions may occur within a threshold amount of time and it is not important to distinguish an order in which these control actions occur (e.g., a user may activate two patterns of neural activity within the threshold amount of time)”, (Osborn: ¶296).
Regarding Claim 19, Agarwal in view of Ben and Pal fails to teach:
use first vectors of said plurality of vectors as input for said machine-learning system,
However, Osborn teaches: “The statistical model being trained may take as input a sequence of data sets each of the data sets in the sequence comprising an n-dimensional vector of sensor data”, (Osborn: ¶479), “comprise a time series of K n¬-dimensional vectors {xk|1≤k≤K} at time points t1, t2, . . . , tK during performance of the movements”, (Osborn: ¶469), “For example, the inferential model may use as input a sequence of vectors {xk|1≤k≤K} generated using measurements obtained at time points t1, t2, . . . , tK, where the ith component of vector xj may be a value measured by the ith sensor at time tj”, (Osborn: ¶983), “Once such large vectors are generated, a classifier may be produced based on logistic regression, random forest, or multilayer perceptron, and may be implemented in a gesture classification model”, (Osborn: ¶1177), “The term “feature space” can comprise one or more vectors or data points” … “certain temporal, spatial, and temporospatial characteristics, as well as other characteristics such as frequency, duration, and amplitude, for example”, “according to which the filters weights are W=(L.sup.TC.sup.−1L).sup.−1L.sup.TC.sup.−1, where L is the matrix of spatiotemporal response profiles (i.e. the collection of all h vectors in the MVDR notation below)”, (Osborn 591).
and use second vectors of said plurality of vectors as ground truth,
However, Osborn teaches: “After defining vectors for all of the detected spike events, a similarity metric may be used to identify vectors having values that cluster together, and thus are likely to represent spike events generated,” (Osborn:¶588), “output is similar to the ground truth spike times indicating that the accuracy of the automatic spike detection is high”, (Osborn: ¶595), “the constraints may be learned by the statistical model through training based on ground truth data on the position and exerted forces of the hand and wrist in the context of recorded sensor data”, (Osborn: ¶446), “vector xj is a value measured by the ith sensor at time tj and/or derived from the value measured by the ith sensor at time tj”, (Osborn: ¶479), “ground truth data (e.g., label time series data) may be obtained by multiple sensors”, (Osborn: ¶976).
wherein said second vectors are pairwise directly subsequent to respective first vector
However, Osborn teaches: “such that all sensor data provided as input to the statistical model corresponds to time series data at the same time resolution”, (Osborn: ¶477), “The statistical model being trained may take as input a sequence of data sets each of the data sets in the sequence comprising an n-dimensional vector of sensor data”, (Osborn: ¶479), “Combining the time series information obtained at operations 28502 and 28508 to create training data”, (Osborn: ¶982), “generating one or more training datasets by time-shifting at least a portion of the neuromuscular activity data over the first time series relative to the second time series, to associate the neuromuscular activity data with at least a portion of the ground truth data”, (Osborn: ¶1009). Examiner notes: vectors arranged in temporal order and are at the same time resolution as opposed to being ordered loosely and being indexed in a uniform temporal progression.
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine using first vectors of said plurality of vectors as input for said machine-learning system; and using second vectors of said plurality of vectors as ground truth, wherein said second vectors are pairwise directly subsequent to respective first vectors of Osborn with the methods and systems of Agarwal in view of Ben and Pal resulting in a machine learning system that can access ground truth vectors as well as understand change overtime for multiple vector sets. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of “time shifting the” … “activity signals to substantially align with a timing of the corresponding movement (Osborn: ¶931).
Regarding Claim 20, Agarwal teaches:
A computer program product for controlling a plurality of information technology (IT) processes, said computer program product comprising a computer readable storage medium having program instructions embodied therewith, said program instructions being executable by one or more computing systems or controllers to cause said one or more computing systems to:
“FIG. 2. IBM Corporation's WebSphere Application Server v4.0 software (Web application server 100) and DB2 v7.1 software (database server 110) are installed on a first computer (2 GHz processor, 1 GB memory)”, (Agarwal: ¶151), “have general applicability to the run-time determination of dependencies between logical components or processes in a computing system or network”, (Agarwal: ¶049), “data processing environment may include, for example, application programs, servlets and Enterprise Java Beans (EJBs) running within a Web application server, processes serving Uniform Resource Identifiers (URIs), processes executing Structured Query Language (SQL) requests”, (Agarwal: ¶026), “computer program product comprising program code instructions for controlling the performance of operations within a data processing environment in which the program code runs”, (Agarwal: ¶033).
measure periodically start-times and related end-times of each of the plurality of IT processes;
“an access log with URIs identifying accessed Web resources, and maintains log records including start and end times for processes running in the managed system 10”, (Agarwal: ¶055), “Each agent periodically queries the configuration information and metrics for a particular subsystem or component of the managed system 10”, (Agarwal: ¶050), “The correlation identifier determines an activity period for each process or component, using events indicating completion of request processing to compute theoretical start and end times”, (Agarwal: ¶057), “Each event comprises an SQL query string, a time stamp that is defined to be the end time of the SQL request, and the start time of the query”, (Agarwal: ¶082).
determine, for each of the plurality of IT processes, regularized binary time-series data based on said measured start-times and said related end-times; … build a plurality of vectors, wherein each component of each vector of the plurality of vectors represents data of a respective one of the regularized binary time-series data of said plurality of IT processes;
“A PMI client, which is enabled at time 0, polls the Web Application Server at a regular interval of 3 time units”, (Agarwal: ¶091), “According to Ensel, a time series of objects' activities may then be fed into a Neural Network to judge whether the objects appear to be relate”, (Agarwal: ¶012), “To compute the activity period of an event, the time stamp of the event and the execution time of the component request represented by the event are used”, (Agarwal: ¶084), “The covering interval or `activity period` of the request is then [TS3-PI-X3, TS3] or [7, 12]…”, (Agarwal: ¶093).
and use said determined weights of said edges between said nodes as indicators for dependencies between the plurality of IT processes.
“Containment is used as an indicator of a likely dependency relationship, and a weighting is computed for each dependency relationship based on the consistency of containment”, (Agarwal: Abstract), “Weightings are calculated at run-time for the identified dependency relationships represented by edges”, (Agarwal: ¶099), “Weightings may be interpreted in various ways, but in this specific implementation weightings are a quantitative measure of the extent to which A depends on B”, (Agarwal: ¶100).
Further regarding Claim 20, Agarwal fails to teach:
regularized binary time-series data
However, Osborn teaches: “A binary time series may be labeled with a value of one when an event is detected”, (Osborn: ¶1163), “For example, labels can be created by generating a binary time series with 1 when a specified event occurs and 0 otherwise”, (Osborn: ¶1663), “In some embodiments, varying the regularization weight” … “In some embodiments, the shift operator, δ, may be used to push the time-series backward in time”, (Osborn: ¶920), “as well as their respective time derivatives (e.g. linear or angular velocity or acceleration)”, (Osborn: ¶1417).
build a plurality of vectors, wherein each component of each vector of the plurality of vectors represents data of a respective one of the regularized binary time-series data
However, Osborn teaches: “comprise a time series of K n¬-dimensional vectors {xk|1≤k≤K} at time points t1, t2, . . . , tK during performance of the movements”, (Osborn: ¶469), “For example, the inferential model may use as input a sequence of vectors {xk|1≤k≤K} generated using measurements obtained at time points t1, t2, . . . , tK, where the ith component of vector xj may be a value measured by the ith sensor at time tj”, (Osborn: ¶983), “Once such large vectors are generated, a classifier may be produced based on logistic regression, random forest, or multilayer perceptron, and may be implemented in a gesture classification model”, (Osborn: ¶1177), “The term “feature space” can comprise one or more vectors or data points” … “certain temporal, spatial, and temporospatial characteristics, as well as other characteristics such as frequency, duration, and amplitude, for example”, “according to which the filters weights are W=(L.sup.TC.sup.−1L).sup.−1L.sup.TC.sup.−1, where L is the matrix of spatiotemporal response profiles (i.e. the collection of all h vectors in the MVDR notation below)”, (Osborn 591).
train a machine-learning system to build a machine-learning model using the plurality of vectors as training data to determine weights for edges between nodes of the machine- learning system,
However, Osborn teaches: “The statistical model being trained may take as input a sequence of data sets each of the data sets in the sequence comprising an n-dimensional vector of sensor data”, (Osborn: ¶479), “the time series information obtained at operations 28502 and 28508 may be combined to create training data used for training an inferential model”, (Osborn: ¶979), “training an inference model to determine at least one spatiotemporal waveform and a corresponding weight to be applied to the at least one spatiotemporal waveform”, (Osborn: ¶103), “For example, when the inference model(s) 114 is or includes a neural network, parameters of the neural network (e.g., weights) may be estimated from the training data”, (Osborn: ¶300), “sensor measurements are measured and used to train the inference model” … “the constraints may comprise part of the inference model itself being represented by information (e.g., connection weights between nodes) in the model”, (Osborn: ¶904).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine regularized binary time-series data; building a plurality of vectors, wherein each component of each of said vectors of said plurality of vectors represents data of a respective one of said time-series data; training a machine-learning system to build a machine-learning model using said plurality of vectors as training data to determine weights for edges between nodes of said machine-learning system; of Osborn with the methods and systems of Agarwal resulting in a machine learning system that’s been trained on vectors containing binary time series data. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of “advantageous in cases where multiple different control actions may occur within a threshold amount of time”, (Osborn: ¶296).
Further regarding Claim 20, Agarwal in view of Osborn fails to teach:
where the regularized binary time-series data are derived from at least an execution delay in the plurality of IT processes
However, Ben teaches: “Metrics may also be defined at a system level, such as number of transactions per second in a database or time delay in returning results for a query” … “system may measure and/or collect thousands, millions, or even billions of time-series metrics (that is, the metrics are measured over time)”, (Ben: ¶4), “the binary representation may remain a time series and include a one for any time interval where the metric changed and a zero where the metric did not change” … “the binary assignment may be performed on a periodic timeframe, not necessarily aligned to the samples of the metric, with a 1 indicating that the metric changed during that period and a 0 indicating that the metric did not change”, (Ben: ¶128), “At 1312 control determines a binary representation for the next alternative metric and returns to 1308. At 1316 control applies a binary vectors grouping algorithm”, (Ben: ¶130), “LDA receives a number of groups N and an aligned binary representation of each metric…”, (Ben: ¶131).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine where the regularized binary time-series data are derived from at least an execution delay in the plurality of IT processes of Ben with the methods and systems of Agarwal in view of Osborn resulting in a system able to record time series data from an execution delay. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of “Monitoring metrics allows for problems to be quickly identified and resolved, hopefully before affecting business results, such as losing users, missing revenue, decreasing productivity, etc.”, (Ben: ¶4), “The anomaly system may allow anomalies to be more quickly identified and corrected. In some instances, the anomaly can be remedied before any downtime is suffered”, (Ben: ¶41).
Lastly regarding Claim 20, Agarwal in view of Osborn and Ben fail to teach:
allocate each of the plurality of IT processes with respect to available IT resources based on said determined weights
However, Pal teaches: “Based on the execution objectives, the query characteristics or metadata, and/or available computing resources (e.g., available worker nodes 3306 or available processor cores at the worker nodes 3306) the query coordinator 3304 may optimize the scheduling or assignment of the query. For instance, suppose the execution objective is to reduce bandwidth usage”, (Pal: ¶1057), “For example, the system 16 can reduce the likelihood that there will be insufficient execution resources to execute a query, improve utilization of execution resources of the system 16 (e.g., increase the usage time of the compute resources)”, (Pal: ¶1244), “In certain cases, the “additional amount” of execution resources can be determined based on a weighting factor. The weighting factor can correspond to the number, ratio, or amount of additional execution resources to be allocated to collate or further process data from the first set of execution resources..”, (Pal: ¶1252), “to determine a query-resource allocation for an indexer search, the system 16 can use the total number of execution resources to be used to obtain the set of data and a weighting factor”, (Pal: ¶1253), “the system 16 can allocate execution resources for an indexer portion based on the number of indexers 206 to be used to execute the query and a weighting factor”, (Pal: ¶1269)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine allocating each of the plurality of IT processes with respect to available IT resources based on said determined weights of Pal with the methods and systems of Agarwal in view of Osborn and Ben resulting in a machine learning system that can allocate process to available resources to reduce system usage. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of “increase the number of queries being executed over a period of time (e.g., increase throughput of query executions) and decrease the wait time to execute queries”, (Pal: ¶1244).
Response to Arguments
In light of applicants amendments, all Claim objections and rejections under 35 U.S.C. 112(b) have been with withdrawn.
Regarding rejections made under 35 U.S.C. 101:
Applicant argues:
“The Office Action, on pages 4-5, characterizes recitations of claim 1 as an abstract idea. In particular, the Office Action alleges that that The Examiner asserts that " determine/determining for each of said IT processes, regularized binary time-series data " and " build/building a plurality of vectors " can be performed mentally or with pen and paper. Applicant respectfully disagrees.
When properly considered as a whole, independent claim 1 is not directed to an abstract idea, but instead to a computer implemented technical architecture.
The specification describes the technical problem. For example, 11[0002]-[0006] describes that modern IT environments involve complex deployed hardware, middleware, networks, application software, on-premises and cloud computing resources, dynamic virtual machines, dynamic containers, "as-a-service" elements, software-defined components, and edge devices. The specification further explains that no single person can understand the complete enterprise IT landscape and that no single governance repository accurately stores all component dependencies at all times. In particular, the specification, in [0003] states:
" there is no single person who can understand the entire enterprise IT landscape of these components and no single governance repository tool that comprehensively, consistently, and accurately stores all dependencies of the above-mentioned components at all times "
Accordingly, the claimed invention is not directed to a mental activity. Rather, it addresses a computer-specific technical problem: determining and using dependencies between IT processes in complex computing environments.
In view of above problem statements, claim 1 presents a computer-centric improvement. The method does not merely obtain regularized binary time-series data and create vectors. The method requires using said determined weights of said edges between said nodes as indicators for dependencies between said IT processes.
Hence, this is a computer centric technical solution to a technical problem.
Thus, abovementioned features are a computer centric technical solution to a technical problem, not to an abstract mental process, and therefore does not recite a judicial exception under Step 2A, Prong One”
Examiner respectfully disagrees, the limitations “determine/determining for each of the plurality of IT processes, regularized binary time-series data based on said measured start-times and said related end-times”, recites the mental process of regularizing data which can be performed by the human mind, using that regularized data in a computer environment is insufficient for practical application to perform significantly more than the abstract idea. Furthermore, deriving that data from an observation of start and end times can also be performed by the mind by counting from start to end, further reinforcing that this limitation recites a mental process. The limitation “build/building a plurality of vectors, wherein each component of each of vector of the plurality of vectors represents data of a respective one of the regularized binary time-series data of the plurality of IT processes during a given second time interval”, recites the mental process of creating vectors based on the aforementioned observed time series data. The courts have determined that evaluations such as the ones disclosed in the aforementioned limitations are considered “concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions”, MPEP §2106.04(a)(2) III, therefore the Claims recite a judicial exception which brings analysis to Step 2 Prong 2.
Applicant argues:
“Notwithstanding the above remarks, assuming arguendo that claim 1 recites an abstract idea, Applicant respectfully submits that the claim integrates any such alleged abstract idea into a practical application.
The claim language requires allocating each of the plurality IT processes with respect to available IT resources based on said determined weights.
The claim therefore applies an alleged abstract idea to efficiently determine dependencies in the IT process and allocate each of the plurality of IT processes with respect to available IT resources, rather than merely measuring periodically start-times and related end-times of each of the plurality of IT processes.
Specification, in TT[0085], [0095], clearly mentions:
" [f]urthermore, the IT jobs can also be managed in a way to make use of the knowledge of the inter-process (or inter- IT-job-dependencies) in order to determine a good sequence of scheduled start-times for individual IT jobs. Thereby, the overall load of the underlying hardware system can be averaged, i.e., peak or overload situations can be avoided With the result of the activities 4 or 5 a minimal spanning tree technique is applied to identify the strongest dependences and show them "
Accordingly, in view of the above excerpt, claim 1 applies any alleged abstract idea to a claim 1 applies any alleged abstract idea to efficiently determine dependencies in the IT process, and improves the efficiency of IT process.
Amended Claim 1 requires allocating each of the plurality of IT processes with respect to available IT resources based on said determined weights. As explained in the specification, this process allows the minimization of an overall IT resource usage of the plurality of IT process. See, e.g., [0047].
Therefore, claim 1 integrates the alleged abstract idea into a practical application, and is therefore directed to patent-eligible subject matter under Step 2A, Prong Two”
Examiner respectfully disagrees, as discuss in the prior Office Action, the limitations “allocate/allocating each of the plurality of IT processes with respect to available IT resources based on said determined weights”, recites the mental process of evaluating weights to determine of which resource to use. A person, but for the recitation of generic computing components being used as a tool to perform the functionality, may select a resource based on whichever is most available, using weights does not practically integrate the judicial exception, instead it further supports the judicial exception by essentially perform the task of choosing a lowest or highest value of the weights which can be performed by the human mind. Therefore the limitation of “allocate/allocating each of the plurality of IT processes with respect to available IT resources based on said determined weights” not only recites a judicial exception but the claims are directed to a judicial exception as a judicial exception has not been integrated into a practical application, it is merely a mental plan of which resources a task gets sent to without utilization of those selected resources to perform the planned task. Thus the analysis moves on to Step 2B.
Applicant argues:
“Step 2B concerns analyzing whether the additional elements of the claims raise them as a whole to be directed towards "significantly more" than the identified abstract idea.
However, as independent claim 1 has been found patent eligible under Step 2A Prong One and Two, the patent-eligibility analysis does not proceed to Step 2B.
Independent claims 11 and 20 recite subject matter analogous to independent claim 1, and therefore arguments presented above for claim 1 are equally applicable for independent claims 11 and 20.
Therefore, independent claims 1, 11, and 20 are patent-eligible as concluded at Step 2A Prong One and Two.”
Examiner respectfully disagrees, the Claims have not been found eligible under Step 2A Prongs 1 & 2 for the reasons discussed above, further the additional limitations in the independent claims fail to amount to significantly more than the abstract idea, merely reciting generic computing components merely applying the abstract idea and performing Well-Understood, Routine, and Conventional insignificant extra-solution activity. “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. ii. Performing repetitive calculations” … “iv. Storing and retrieving information in memory”. See MPEP § 2106.05(d)(II). Therefore the Independent Claims do not recite eligible subject matter and thus the 35 U.S.C. 101 rejection is upheld.
Regarding rejections made under 35 U.S.C. 103:
Applicant argues:
“Independent claim 1, as amended, recites, in part: " determining, for each of the IT processes, regularized binary time-series data based on the measured start-times and the related end-times where the regularized binary time-series data are derived from at least an execution delay in the plurality of IT processes " (emphasis added).
The Office Action, on pages 12-13, relies on Agarwal to teach or suggest "determining, for each of said IT processes, regularized binary time-series data based on said measured start-times and said related end-times, during a second time interval" of claim 1.
However, Osborn describes temporal regularization of model outputs using a regularization weight, derivative order, and shift operator to improve temporally smooth output, such as predicted joint angles. Osborn also describes binary time series labels in the context of event detection, where a binary time series is labeled with a value of one when an event is detected and zero when the event is not detected (See, [1163] of Osborn). For example, Osborn describes generating binary labels for events such as detected tap events and using those labels to train a model for event detection (See, ||[1663] of Osborn). Thus, Osborn's binary time series represents the occurrence or non-occurrence of a detected event.
Osborn does not teach or suggest generating binary values from execution delay of IT processes calculated using measured start-times and related end-times. Thus, Osborn's binary event-labeling cannot be equated to the claimed regularized binary time-series.
Further, even if Agrawal and Osborn were combined, the combination would at most suggest monitoring component activity and using binary labels for detected events. Such a combination would still not teach or suggest the claimed feature of determining, for each IT process, regularized binary time-series data based on measured start-times and related end- times, where the binary values are derived from execution delay of the plurality of IT processes.
Thus, Agrawal and Osborn, alone or in combination, fail to teach or suggest the above- mentioned feature of amended claim 1. Other cited reference (Pal) also fails to teach or suggest the above-mentioned feature of amended claim 1
Therefore, the cited references, either alone or in combination, fail to teach or suggest the above-mentioned feature of amended independent claim 1.
Independent claims 11 and 20 recite subject matter analogous to independent claim 1, and therefore arguments presented above for claim 1 are equally applicable for independent claims 11 and 20.”
Examiner respectfully disagrees, in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Firstly Examiner notes that the Office Action does not use Osborn to teach the limitation of “determining, for each of the plurality of IT processes, regularized binary time-series data based on said measured start-times and said related end-times, during a second time interval;”, but instead uses Osborn to teach “regularized binary time-series data”, which it does through: “A binary time series may be labeled with a value of one when an event is detected”, (Osborn: ¶1163), “For example, labels can be created by generating a binary time series with 1 when a specified event occurs and 0 otherwise”, (Osborn: ¶1663), “In some embodiments, varying the regularization weight” … “In some embodiments, the shift operator, δ, may be used to push the time-series backward in time”, (Osborn: ¶920), “as well as their respective time derivatives (e.g. linear or angular velocity or acceleration)”, (Osborn: ¶1417), these citations clearly and explicitly disclose a regularized binary time series data which for the purposes of Osborn sufficiently map to the limitation of “regularized binary time series data”, as stated in the prior Office Action. Osborn was not used to teach “determining, for each of the plurality of IT processes, regularized binary time-series data based on said measured start-times and said related end-times, during a second time interval;”, the prior office action uses Agarwal to teach this limitation through the following disclosure: “an access log with URIs identifying accessed Web resources, and maintains log records including start and end times for processes running in the managed system 10”, (Agarwal: ¶055), “Each agent periodically queries the configuration information and metrics for a particular subsystem or component of the managed system 10”, (Agarwal: ¶050), “The correlation identifier determines an activity period for each process or component, using events indicating completion of request processing to compute theoretical start and end times”, (Agarwal: ¶057), “Each event comprises an SQL query string, a time stamp that is defined to be the end time of the SQL request, and the start time of the query”, (Agarwal: ¶082), “To compute the activity period of an event, the time stamp of the event and the execution time of the component request represented by the event are used”, (Agarwal: ¶084). The citations indicate the collection of start and end times to compute an activity period. Agarwal, through the provided citations teaches the collection of time series data based on start and end times while Osborn teaches the regularization of binary time series data, together they cover the entirety of the limitation of “determining, for each of the plurality of IT processes, regularized binary time-series data based on said measured start-times and said related end-times” thus the Claim does not recite patent eligible subject matter and the 35 U.S.C. 103 rejection is upheld.
Applicant argues:
“Dependent claims 2-8, 10, 12-17, and 19 are also patentable by virtue of their respective dependencies on independent claim 1 and additional recitation recited therein. Claims 9 and 18 are cancelled herein, hence their rejection stands moot. Reconsideration and withdrawal of the rejection of the claims is therefore respectfully requested.”
Applicant’s arguments with respect to claim(s) 2-8, 10, 12-17, and 19 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.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 SHIHAB ALAM whose telephone number is (571)272-8705. The examiner can normally be reached Mon - Fri 7:30am-5pm.
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/S.A./Examiner, Art Unit 2197
/BRADLEY A TEETS/Supervisory Patent Examiner, Art Unit 2197
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