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 .
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 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.
DETAILED ACTION
Remarks
This Final action is in response to communications filed on 03/31/2026 claim(s) 1-3, 7-10, 14-17 and 20 are amended per Applicant's request. Therefore, claims 1-20 are presently pending in the application and have been considered as follows.
The examiner has removed rejections related to 35 USC 112(b) and the previously held claim objection as the amendments have overcome the issues previously presented.
Response to Arguments
Applicant's arguments filed 03/31/2026 have been fully considered but they are not persuasive.
-The applicants’ remarks on 11-14 with respect to:
“Applicant respectfully traverses the Office's assertions and asserts that the pending claims fully comply with the requirements of 35 U.S.C. § 101. First, the pending claims do not recite a judicial exception that includes an abstract idea.”
“These features are not well-understood, routine, conventional activity in the field, and are expressly tied to the recited application and programming technology. Moreover these features, particularly the identification and rectification of performance degraded programs to maintain operational stability, have no meaning in the abstract, in the absence of the recited technology, for application anomaly detection. In light of the foregoing, Applicant respectfully asserts that the pending claims are not directed to an abstract idea. In contrast with the alleged abstract idea identified by the Office, the idea underlying the claims is inherently technology centric and non-abstract.”
“The idea of application anomaly detection involving identification and rectification of performance degraded programs to maintain operational stability is unlike any concept found to be abstract by a court. For example, unlike the claims at issue in Alice in which a computer was merely used as a tool to implement an abstract idea that could be performed without the use of computers, the fundamental idea underlying the present claims (i.e., application anomaly detection), is tied to computer technology and cannot be performed by a human mentally or using a pen and paper.”
Have been carefully considered but are non-persuasive;
The examiner notes that while the specification states “The system allows for identification and rectification of performance degraded programs ahead of time to maintain operational stability in production environment” MPEP 2106.04(d)(1) states “...if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification.” While evaluating claims 1, 8 and 15 the examiner notes that there is not active rectification step in the claims and that the claims merely recite extracting, storing, filtering, monitoring, identifying, applying a machine learning model, determining degraded programs, generating a report and creating a feedback channel. The claimed steps amount to data collection, mental processes/mathematical concepts and presenting data which are abstract ideas. As such the argument is considered unpersuasive.
-The applicants’ remarks on 14-15 with respect to:
“Second, even if the pending claims recite a judicial exception, they are not "directed to" a judicial exception as the pending claims, as a whole, integrate the purported judicial exception into a practical application and are thus eligible.”
“To the extent that the Office continues to argue that the claimed invention is "directed to" a judicial exception, Applicant, nevertheless, asserts that the pending claims contain an inventive concept. Indeed, the claimed invention includes meaningful recitations beyond generally linking the alleged mental process to a particular technological environment, such as a computer. Rather, the pending claims recite a particular way of implementing application anomaly detection involving identification and rectification of performance degraded programs to maintain operational stability.”
“Applicant respectfully submits that the judicial exception is integrated into a practical application in light of Example 40 of the Subject Matter Eligibility Examples: Abstract Ideas published by the USPTO in January 2019. Example 40 - Adaptive Monitoring of Network Traffic Data recites a method claim for adaptive monitoring of traffic data through a network appliance connected between computing devices in a network.”
“As such, the pending claims integrate the alleged judicial exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, even more so than the claims in Example 40 deemed eligible by the Patent Office, such that the claim is more than a drafting effort designed to monopolize the judicial exception.”
Have been carefully considered but are non-persuasive;
The examiner respectfully disagrees and notes that example 40 was deemed eligible because it actively limits collection of additional Netflow protocol data to when the initially collected data reflects an abnormal condition, which avoids excess traffic volume on the network and hindrance of network performance. The collected data can then be used to analyze the cause of the abnormal condition. This provides a specific improvement over prior systems, resulting in improved network monitoring. The Background for this example states “...NetFlow records are very large, the continual generation and export of NetFlow records in such a setup substantially increases the traffic volume on the network, which hinders network performance. Moreover, continual analysis of the network is not always necessary when the network is performing under normal conditions. Applicant’s invention addresses this issue by varying the amount of network data collected based on monitored events in the network. That is, the system will only collect NetFlow protocol data and export a NetFlow record when abnormal network conditions are detected.... This network data, for example, could include network delay, packet loss, or jitter. Periodically, the network data is compared to a predefined quality threshold. If this network data is greater than the predefined quality threshold, an abnormal condition is detected. When an abnormal condition is present, the system begins collecting NetFlow protocol data, which can later be used for analyzing the abnormal condition. During this time, the network appliance continues to monitor the network conditions (i.e., comparing collected network data to the predetermined quality threshold) and when the abnormal condition no longer exists, NetFlow protocol data is no longer collected.” This improvement is reflected in the claim of example 40. In regards to the current application, the amended claim only generates a report and create a feedback channel for training in response to the abstract steps that lead to the determination of degraded programs. Generating a report is insignificant extra-solution activity (e.g. presentation of data) and updating a machine learning model mathematically does not physically alter the computer or networks operational stability it only improves the accuracy of the abstract detection which does not by itself integrate the abstract idea into a practical application. As such the argument is considered non-persuasive.
-The applicants’ remarks on 15-16 with respect to:
“Joglekar relates to, in general, evaluating metrics (e.g., quality of service metrics) corresponding to a monitored computer, detecting metric anomalies, and issuing alerts. (See Joglekar at Abstract). Ruikar generally discloses of facilitate anomaly detection within a networking system, involving receiving a plurality of performance metric messages at a database system, extracting a plurality of anomaly detection messages included in the performance metric messages, storing the plurality of anomaly detection messages in an in- memory database. (See Ruikar at Abstract). Joglekar, singularly or in combination with Ruikar, fails to teach or suggest the features recited by independent Claims 1, 8, and 15, as amended.”
“Specifically, Joglekar, singularly or in combination with Ruikar, fails to teach or suggest the following recitations of independent Claims 1, 8, and 15: (1) identifying changes in application workload performance and apply the anomaly detection artificial intelligence machine learning model, wherein identifying the changes in the application workload performance further comprises deriving performance metric data variances based on prior relational database workload executions data comprising CPU, volume, response time, get- pages and averages associated with a preceding time period. Joglekar, singularly or in combination with Ruikar and Jonsen, further fails to teach or suggest the features of (2) determining degraded programs based on identifying the changes in the application workload performance, and (3) generating a report of application workload performance metrics for the degraded programs comprising a workload pattern associated with each degraded program, wherein generating the report comprises removing false-positives and false-negatives in flagged programs, as recited by independent Claims 1, 8, and 15.”
“In light of the above, because the cited references fail to disclose, each and every claimed feature of the independent Claims 1, 8, and 15, Applicant respectfully submits that the independent Claims 1, 8, and 15, as well as the claims that depend respectively therefrom, are patentable over the cited references. Accordingly, Applicant respectfully requests the Office to withdraw the rejections under sections 101 and 103 and thereafter issue a notice of allowance.”
Have been carefully considered but are non-persuasive;
The examiner respectfully disagrees and notes in regards to “identifying changes in application workload performance and apply the anomaly detection artificial intelligence machine learning model, wherein identifying the changes in the application workload performance further comprises deriving performance metric data variances based on prior relational database workload executions data comprising CPU, volume, response time, get- pages and averages associated with a preceding time period” Joglekar explicitly teaches determining metric thresholds and deviations based on a set of metric value averages and metric value standard deviations derived from a predetermined number of sets of metrics corresponding to preceding time intervals. See abstract “These metrics are transmitted to a monitoring server that dynamically determines metric thresholds corresponding to normal metrics and anomalous metrics.”, and the following definitions Para. 0030 “A “time interval” may include a period of time. For example, a time interval may include a period of time with a defined start time and a defined end time, such as “12:00 P.M. to 1:00 P.M.” A time interval may correspond to an event or a measurement, such as the time interval during which a set of data was collected.” Para. 0031 “A “metric” may include something that can be measured. Metrics may be used to quantitatively assess something, such as a process or event. A metric may have a corresponding “metric value.” For example, the metric “latency” may have a corresponding metric value of 100 ms. Metrics may be included in a “set of metrics,” which may include a collection of one or more metrics. A set of metrics may include, for example, a latency metric and a CPU usage metric. A set of metrics may correspond to a monitored computer or a monitored service.” Para. 0034 “A “metric threshold” may include a threshold used to evaluate a metric value. A metric value can be compared to a metric threshold in order to determine if the metric value is greater than or less than the metric threshold. A metric threshold comparison can be used to conditionally trigger some action” Para. 0058 “FIGS. 6 and 7 in order to describe metric collecting procedures, and dynamic metric threshold calculation and metric classification respectively.” Furthermore, Joglekar teaches these metrics are collected and correspond to computers and/or services that are monitored and periodically transferring sets of metrics and associated metric values to a monitoring server, see para. 0062-0066. Joglekar identifies performance metrics including latency (e.g. response time), CPU cycles (e.g. CPU), number of page faults (e.g. get-pages), number of I/O read/write calls (e.g. Volume), number of system calls, uptime memory capacity, processing speed and changes in these metrics. See para. 0063-0064. Joglekar further teaches that these metric values are stored in a metric database by time interval, retrieving prior metric values from the metric database, determining metric thresholds based on previously collected metrics and calculating a metric deviation value corresponding to a difference between a current metric value and a corresponding threshold, see para 0066-0071. Lastly Joglekar discloses applying a machine learning model to the metric values to produce anomaly scores, see para. 0071-0072. Ruikar further teaches a database system environment that receives performance metric messages, filter anomaly detection data and use metrics that include latency, resource utilization percentages and number of active database connections, see para. 0027-0030. Thus the combination teaches or suggest the application workload performance analysis as broadly claimed.
Secondly regarding “determining degraded programs based on identifying the changes in the application workload performance”, Joglekar discloses determining whether monitored services are anomalous based on metric thresholds, metric deviation values and machine learning anomaly scores as indicated above and with reference to para 0071, 0072, 0148-0152. Joglekar also teaches that the monitored services includes databases management services and other software based services, see para. 0008, 0024, 0081 and 0082. Rukiar establishes that various terms like “code”, “software code”, “application”, “software application”, “program”, “software program”, “package”, “software code”, “code”, and “software package” may be used interchangeably, see para. 0026. Thus, the combination shows determining a monitored software service, application, program has anomalous performance reads on the limitation related to determining a degraded program.
Lastly, regarding “generating a report of application workload performance metrics for the degraded programs comprising a workload pattern associated with each degraded program, wherein generating the report comprises removing false-positives and false-negatives in flagged programs”, Joglekar teaches that metric anomaly patterns comprises temporally organized sequences of metric sets and associated metric values where at least one metric set is anomalous, see para. 0073-0074, 0109-114 and 0152-0156. Furthermore, Joglekar teaches visualization of metrics, metric anomalies and metric anomaly patterns for review by security or operations teams, see para. 0076 and 0118-0121. Ruikar also teaches transmitting metrics data, scoring data and incident alerts to monitoring logic for display on a dashboard user interface, see para. 0028 and 0036-0038. Finally, Joglekar teaches that its metric-threshold approach increases true positives and true negative classification rates and correspondingly decreases false positive and false negative classification rates and that improve thresholds improve alert reliability because high false-positive and false-negative rates limit conventional alert systems, see para. 0015-0018.
As such, the applicant arguments are considered non-persuasive for the reasons listed above.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claims recites a system, computer program product comprising a non-transitory computer-readable medium and method. These are directed to a machine, a manufacture and a series of steps or acts, and falls within one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Claim 1, 8 and 15 are directed to an abstract idea because the following claim limitations recite an abstract idea:
A system, manufacture and method comprising :
Filter the performance metric data using predefined threshold for an anomaly detection artificial intelligence machine learning model; (mental process/mathematical concept: a human-being using a mathematical equation or decision model to predict an output);
monitor application workload across a relational database of an entity for degraded program performance; (mental process: a human-being observing a system for degraded performance.)
deriving performance metric data variances based on prior relational database workload executions data comprising CPU, volume, response time, get-pages and averages associated with a preceding time period; (mathematical concept: a human-being calculating mathematical variances and averages.)
determine degraded programs based on identifying the changes in the application workload performance; (mental process: a human-being judging or determining degradation of a system based on observed changes.)
generate a report of application workload performance metrics for the degraded programs comprising a workload pattern associated with each degraded program, wherein generating the report comprises removing false-positives and false-negatives in flagged programs; (mental process/mathematical concept: a human-being organizing compiled data into a report while not including false positives within said report);
create feedback channel for training the anomaly detection artificial intelligence machine learning model using the performance metric data. (mathematical concept: a human-being adjusting a mathematical model based on previous data.)
Claims 1, 8 and 15 recites the following additional elements:
Wherein the system is for “application anomaly detection, wherein the system is structured for identification and rectification of performance degraded programs to maintain operational stability,”
A processing device;
wherein the computer program product is structured for identification and rectification of performance degraded programs to maintain operational stability, the computer program product comprising a non-transitory computer-readable medium comprising code causing an apparatus;
extract at predetermined intervals, from a plurality of data extraction locations, performance metric data from a plurality of applications each associated with one or more programs;
store the performance metric data in a performance metric data repository.
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
The claims fails to achieve a technical solution to a technical problem. Thus the claim fail to provide an improvement to the function of a computer or to a technology itself. The claim culminate with generating a report for application workload performance metrics for degraded programs and creating a feedback channel for training. See MPEP 2106.04(d)(1) and 2106.05(a). The additional elements are recited at a high level of generality and amount to merely using computers as a tool to implement the abstract idea. Thus the additional elements are considered mere instruction to apply the abstract idea See MPEP 2106.05(f). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).Therefore, the examiner must find that the claims fail to integrate the abstract idea into a practical application.
Step 2B:
This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
One way to determine integration into a practical application is when the claimed invention improves the functioning of a computer or improves another technology or technical field. To evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP 2106.04(d)(1) and 2106.05(a).
Likewise to step 2A prong 2, the claims fails to achieve a technical solution to a technical problem. Thus the claim fail to provide an improvement to the function of a computer or to a technology itself. The claim culminate with generating a report for application workload performance metrics for degraded programs and creating a feedback channel for training. See MPEP 2106.04(d)(1) and 2106.05(a). The additional elements are recited at a high level of generality and amount to merely using computers as a tool to implement the abstract idea. Thus the additional elements are considered mere instruction to apply the abstract idea See MPEP 2106.05(f). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).Therefore, the examiner must find that the claims fail to amount to significantly more than the abstract idea itself, even when the additional elements are considered alone and in combination with the abstract idea. (Step 2B: NO).
Therefore, the claims are directed to an abstract idea without significantly more and are unpatentable.
Claim 3, 10 and 17
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Claim 3, 10 and 17 are directed to an abstract idea because the following claim limitations recite an abstract idea:
training of the anomaly detection artificial intelligence machine learning model...using derived performance metric data with variances more than predetermined percentage set intervals to eliminate the false-positives or the false-negatives.; (mathematical concept: a human-being adjusting a mathematical model based on numerical variance percentages.)
Claims 3, 10 and 17 recites the following additional elements:
using a training performance metric data repository to retrieve sample performance metric data at regular intervals.
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
The claims fails to achieve a technical solution to a technical problem. Thus the claim fail to provide an improvement to the function of a computer or to a technology itself. The claim culminate with generating a report for application workload performance metrics for degraded programs and creating a feedback channel for training. See MPEP 2106.04(d)(1) and 2106.05(a). The additional elements is merely a data gathering step necessary to obtain the input to perform the abstract idea and is considered to be insignificant extra-solution activity. See MPEP 2106.05(g). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).Therefore, the examiner must find that the claims fail to integrate the abstract idea into a practical application.
Step 2B:
This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
One way to determine integration into a practical application is when the claimed invention improves the functioning of a computer or improves another technology or technical field. To evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP 2106.04(d)(1) and 2106.05(a).
Likewise to step 2A prong 2, the claims fails to achieve a technical solution to a technical problem. Thus the claim fail to provide an improvement to the function of a computer or to a technology itself. The claim culminate with generating a report for application workload performance metrics for degraded programs and creating a feedback channel for training. See MPEP 2106.04(d)(1) and 2106.05(a). The additional elements is merely a data gathering step necessary to obtain the input to perform the abstract idea and is considered to be insignificant extra-solution activity. See MPEP 2106.05(g). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).Therefore, the examiner must find that the claims fail to amount to significantly more than the abstract idea itself, even when the additional elements are considered alone and in combination with the abstract idea. (Step 2B: NO).
Therefore, the claims are directed to an abstract idea without significantly more and are unpatentable.
Claim 5, 12 and 19
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Claim 5, 12 and 19 are directed to an abstract idea because the following claim limitations recite an abstract idea:
the same abstract idea of the base claim.
Claims 5, 12 and 19 recites the following additional elements:
extracting and storing performance metric data in the performance metric data repository further comprises continual extraction of management facility data, resource analysis optimization data, and dynamic cache pool data from a relational database management system.
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
The claims fails to achieve a technical solution to a technical problem. Thus the claim fail to provide an improvement to the function of a computer or to a technology itself. The claim culminate with generating a report for application workload performance metrics for degraded programs and creating a feedback channel for training. See MPEP 2106.04(d)(1) and 2106.05(a). The additional elements is merely a data gathering step necessary to obtain the input to perform the abstract idea and is considered to be insignificant extra-solution activity. See MPEP 2106.05(g). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).Therefore, the examiner must find that the claims fail to integrate the abstract idea into a practical application.
Step 2B:
This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
One way to determine integration into a practical application is when the claimed invention improves the functioning of a computer or improves another technology or technical field. To evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP 2106.04(d)(1) and 2106.05(a).
Likewise to step 2A prong 2, the claims fails to achieve a technical solution to a technical problem. Thus the claim fail to provide an improvement to the function of a computer or to a technology itself. The claim culminate with generating a report for application workload performance metrics for degraded programs and creating a feedback channel for training. See MPEP 2106.04(d)(1) and 2106.05(a). The additional elements is merely a data gathering step necessary to obtain the input to perform the abstract idea and is considered to be insignificant extra-solution activity. See MPEP 2106.05(g). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).Therefore, the examiner must find that the claims fail to amount to significantly more than the abstract idea itself, even when the additional elements are considered alone and in combination with the abstract idea. (Step 2B: NO).
Therefore, the claims are directed to an abstract idea without significantly more and are unpatentable.
Claim 6 and 13
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Claim 6 and 13 are directed to an abstract idea because the following claim limitations recite an abstract idea:
generating a report of application workload performance metrics for degraded programs further comprises presenting a summary and average of performance metrics of workloads.; (mental process: the human-being compiling and presenting a numerical summary for review.).
Claims 6 and 13 recites the following additional elements:
for database administrator review.
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
The claims fails to achieve a technical solution to a technical problem. Thus the claim fail to provide an improvement to the function of a computer or to a technology itself. The claim culminate with generating a report for application workload performance metrics for degraded programs and creating a feedback channel for training. See MPEP 2106.04(d)(1) and 2106.05(a). The additional elements is merely an output step necessary to output a result of performing the abstract idea and is considered to be post-solution data outputting activity. See MPEP 2106.05(g). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).Therefore, the examiner must find that the claims fail to integrate the abstract idea into a practical application.
Step 2B:
This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
One way to determine integration into a practical application is when the claimed invention improves the functioning of a computer or improves another technology or technical field. To evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP 2106.04(d)(1) and 2106.05(a).
Likewise to step 2A prong 2, the claims fails to achieve a technical solution to a technical problem. Thus the claim fail to provide an improvement to the function of a computer or to a technology itself. The claim culminate with generating a report for application workload performance metrics for degraded programs and creating a feedback channel for training. See MPEP 2106.04(d)(1) and 2106.05(a). The additional elements is merely an output step necessary to output a result of performing the abstract idea and is considered to be post-solution data outputting activity. See MPEP 2106.05(g). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).Therefore, the examiner must find that the claims fail to amount to significantly more than the abstract idea itself, even when the additional elements are considered alone and in combination with the abstract idea. (Step 2B: NO).
Therefore, the claims are directed to an abstract idea without significantly more and are unpatentable.
Claim 7, 14 and 20
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Claim 7, 14 and 20 are directed to an abstract idea because the following claim limitations recite an abstract idea:
the same abstract idea of the base claim.
Claims 7, 14 and 20 recites the following additional elements:
wherein the application anomaly detection is performed within a relational database management system.
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
The claims fails to achieve a technical solution to a technical problem. Thus the claim fail to provide an improvement to the function of a computer or to a technology itself. The claim culminate with generating a report for application workload performance metrics for degraded programs and creating a feedback channel for training. See MPEP 2106.04(d)(1) and 2106.05(a). The additional elements merely describes a field of use, performing anomaly detection within a relational database management system. See MPEP 2106.05(h). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).Therefore, the examiner must find that the claims fail to integrate the abstract idea into a practical application.
Step 2B:
This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
One way to determine integration into a practical application is when the claimed invention improves the functioning of a computer or improves another technology or technical field. To evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP 2106.04(d)(1) and 2106.05(a).
Likewise to step 2A prong 2, the claims fails to achieve a technical solution to a technical problem. Thus the claim fail to provide an improvement to the function of a computer or to a technology itself. The claim culminate with generating a report for application workload performance metrics for degraded programs and creating a feedback channel for training. See MPEP 2106.04(d)(1) and 2106.05(a). The additional elements merely describes a field of use, performing anomaly detection within a relational database management system. See MPEP 2106.05(h). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).Therefore, the examiner must find that the claims fail to amount to significantly more than the abstract idea itself, even when the additional elements are considered alone and in combination with the abstract idea. (Step 2B: NO).
Therefore, the claims are directed to an abstract idea without significantly more and are unpatentable.
Claims 2, 4, 9, 11, 16 and 18
Regarding claims 2, 4, 9, 11, 16 and 18 the following claim limitations recites an abstract idea
comparing the performance metric data of application workloads for a current time period against an average of a previous time period, wherein the current time period is shorter than the previous time period, wherein the current time period is selected from a previous day, a previous week, and a previous month, wherein the previous time period is selected from the previous week, the previous month, and a previous quarter. (mathematical concept: the human being calculating and comparing numerical values across time periods .)
performance metric data further comprises system management facility data, resource analysis optimization data, and dynamic cache pool data from a relational database management system. (mental process: the human being reading and classifying specific types of data.)
Claims 2, 4, 9, 11, 16 and 18 recites the additional elements:
none
Step 2A, Prong 2 and Step 2B
Claims 2, 4, 9, 11, 16 and 18 fail to recite any new additional elements relative to base claims 1, 8 and 15. Thus, the analysis and findings for step 2A, prong 2 and step 2B incorporates the analysis and findings of claims 1, 8 and 15 however, the analysis and findings includes consideration of claims 1, 8 and 15 as a whole. Therefore, claims 2, 4, 9, 11, 16 and 18 are directed to an abstract idea without significantly more and is unpatentable
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20210374027 to Joglekar et al. (hereinafter “Joglekar .”) in view of US 20200242240 to Ruikar et al. (hereinafter “Ruikar”)
Claim 1
Joglekar teaches a system for application anomaly detection [e.g. Joglekar; Abstract, Para. 0060 – Joglekar discloses a database management system for detecting anomalies], the system comprising:
a processing device; [e.g. Joglekar; Fig. 3 Element 302]
a non-transitory storage device[e.g. Joglekar; Fig. 3 Element 306] containing instructions when executed by the processing device, causes the processing device to:
extract at predetermined intervals, from a plurality of data extraction locations, performance metric data from a plurality of applications each associated with one or more programs; [e.g. Joglekar; Abstract, Para. 0062, 0066 – Joglekar discloses periodically collecting (e.g. extracting) metric values from monitored services 104 and 106 (e.g. data extraction locations of a plurality of applications)]
store performance metric data in a performance metric data repository; [e.g. Joglekar; Abstract, Para. 0062, 0066 – Joglekar discloses storing metric values in a metric database 112 (e.g. performance metric data repository)],
filter the performance metric data using predefined threshold for an anomaly detection artificial intelligence machine learning model; [e.g. Joglekar; Abstract, Para. 0068-0070, 0088-0096, 0138 – Joglekar using lower and upper bound thresholds as filter for anomaly detection]
monitor application workload across a relational database of an entity for degraded application performance; [e.g. Joglekar; Abstract, Para. 0021, 0081, 0087 – Joglekar discloses monitoring for anomalies based on metrics (e.g. degradation in performance) for database management services]
identify changes in application workload performance and apply the anomaly detection artificial intelligence machine learning model; [e.g. Joglekar; Abstract, Para. 0021, 0064, 0108, 0109, 0151 – Joglekar identifies changes and applies the machine learning model.]
wherein identifying the changes in the application workload performance further comprises deriving performance metric data variances based on prior relational database workload executions data comprising CPU, volume, response time, get-pages and averages associated with a preceding time period; [e.g. Joglekar; Abstract, Para. 0021, 0062-0071, 0108, 0109, 0151 – Joglekar discloses deriving a set of metric deviation values (e.g. variances) from current metric values and metric thresholds based on a set of metric averages and asset of metric standard deviations derived from previous data from a previous time interval wherein the data includes latency (e.g. response time), CPU cycles (e.g. CPU), number of page faults (e.g. get-pages), number of I/O read/write calls (e.g. Volume), number of system calls, uptime memory capacity, processing speed and changes in these metric.]
determine degraded programs based on identifying the changes in the application workload performance; [e.g. Joglekar; Abstract, Para. 0021, 0064, 0108, 0109, 0151 – Joglekar discloses determining the current metrics are anomalous (e.g. degraded programs) if they fall outside corresponding metric thresholds (e.g. identifying the changes in the application workload performance).]
generate a report of application workload performance metrics for degraded programs comprising a workload pattern associated with each degraded program, wherein generating the report comprises removing false-positives and false-negatives in flagged programs; [e.g. Joglekar; Abstract, Para. 0110, 0115, 0117, 0157, 0158 – Joglekar discloses generating an alert (e.g. report) that describes triggered metrics corresponding to a metric anomaly pattern comprising a sequence of metric over time (e.g. workload pattern) and that dynamic thresholding process results in decreases in false positive and false negative classification rates (e.g. removing false positives and false negatives in flagged programs.) .]
and create feedback channel for training the anomaly detection artificial intelligence machine learning model using the performance metric data. [e.g. Joglekar; Abstract, Para. 0066, 0125-0127 – Joglekar discloses using the outputs of the machine learning to train (e.g. feedback channel) ]
While Joglekar teaches the system of claim 1 and Joglekar teaches that the metrics and monitoring is for database management services and further discloses using relational database, for the sake of brevity and compact prosecution the examiner points to Ruikar who discloses explicitly using metrics associated with relational databases for anomaly detection:
extract and store performance metric data in a performance metric data repository; [e.g. Ruikar; Abstract, Para. 0012, 0013, 0015, 0025-0038 – discloses extracting and storing metrics in an in-memory database]
monitor application workload across a relational database of an entity for degraded application performance [e.g. Ruikar; Abstract, Para. 0012, 0013, 0015, 0025-0038 – discloses monitoring and detection of anomalies using metrics of relational databases, wherein the metrics relate to latency, resource utilization percentage, number of active connections in the database or the link]
identify changes in application workload performance and apply the anomaly detection artificial intelligence machine learning model[e.g. Ruikar; Abstract, Para. 0012, 0013, 0015, 0025-0038 – discloses identifying and applying anomaly detection based on machine leering]
generate a report of application workload performance metrics for degraded programs[e.g. Ruikar; Abstract, Para. 0012, 0013, 0015, 0025-0038 – discloses reporting data and results to a dashboard]
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to include the explicit recitation that a relational database as it would have been obvious that the database management service could include relational databases as Ruikar explicitly makes clear that relational databases experience anomalies occurring that may affect system performance as indicated in paragraph 0002 of Ruikar as section 2141 III of the MPEP provides examples of rationales that may support a conclusion of obviousness include: a simple of one known element (e.g. relational databases) for another to obtain predictable results (e.g. predicting anomalies).
Claim 2:
Joglekar teaches the system of claim 1, wherein filtering the performance metric data using predefined threshold for an anomaly detection artificial intelligence machine learning model, further comprises comparing the performance metric data of application workloads for a current time period against an average of a previous time period, wherein the current time period is shorter than the previous time period, wherein the current time period is selected from a previous day, a previous week, and a previous month, wherein the previous time period is selected from the previous week, the previous month, and a previous quarter. [e.g. Joglekar; Abstract, Para. 0063, 0068-0070, 0088-0096, 0124, 0125, 0131-0141, 0145-0147 – Joglekar discloses current metric and current metric values associated with a current time interval (e.g. performance metric data of application workloads for a current time period), retrieving prior sets of metrics and associated metric values corresponding to predetermined time periods or time intervals (e.g. previous time period), determining metric averages from the prior sets of metric and associated metric values (e.g. average of a previous time period), determining thresholds based on the metric average (e.g. predefined threshold) and determining whether each current metric value is within a corresponding metric threshold (e.g. comparing the performance metric data of application workloads for a current time period against an average of a previous time period). Joglekar further discloses current metric corresponding to a current interval and threshold derived from multiple prior metric sets and intervals and provides examples of 15 second and 1 minute intervals (e.g. current time period is shorter than the previous time period) and selecting sets of metrics corresponding to particular time intervals such as last hour, day , week, (e.g. selecting current and previous comparison time periods such as a day, week, month, quarter.) Joglekar further that any appropriate method of selecting the plurality of sets of metrics and their associated metric values can be employed (e.g. design choice and obvious to choose any time period)]
While Joglekar teaches the system of claim 2, and assuming arguendo that it is believed that Joglekar does not expressly recite selection of time windows (e.g. days, weeks, months, quarters, etc.) it would be an obvious design choice of Joglekar as Joglekar does not limit this selection and expressly states “any appropriate method of selecting the plurality of sets of metrics and their associated metric values can be employed.”
Claim 3:
Joglekar teaches the system of claim 1, further comprising training of the anomaly detection artificial intelligence machine learning model using a training performance metric data repository to retrieve sample performance metrics data at regular intervals using derived performance metrics data with variances more than predetermined percentage set intervals to eliminate the false-positives or the false-negatives. [e.g. Joglekar; Abstract, Para. 0015-0016, 0068-0070, 0088-0096, 0131-0139, 0148 – Joglekar uses past data labeled with anomaly score that are derive from metric deviation values (e.g. variances) using deviation multipliers (e.g. predetermined percentage set intervals) to train the machine learning model that results in a decrease in false positive and false negative classification rates.]
Claim 4:
Joglekar and Ruikar teaches the system of claim 1, wherein performance metric data further comprises system management facility data, resource analysis optimization data, and dynamic cache pool data from a relational database management system. [e.g. Joglekar; Abstract, Para. 0063 – Joglekar discloses metrics including open file descriptors (integers that can be used to identify an opened file), process scheduling type (user mode or system mode), number of page faults (both minor and major), number of I/O read write calls, number of system calls, uptime, system temperature, system memory capacity, CPU cycles, system processing speed, number of heartbeats, a change in the number of minor faults, a change in the number of major faults, a change in the uptime, a change in the number of heartbeats, a change in the number of CPU cycles, etc.] [e.g. Ruikar; Abstract, Para. 0012, 0013, 0015, 0025-0038 – discloses monitoring and detection of anomalies using metrics of relational databases, wherein the metrics relate to latency, resource utilization percentage, number of active connections in the database or the link]
Claim 5:
Joglekar and Ruikar teaches the system of claim 1, wherein extracting and storing performance metric data in the performance metric data repository further comprises continual extraction [e.g. Joglekar; Abstract, Para. 0062 “periodically collect metrics and associated metric values”] of management facility data, resource analysis optimization data, and dynamic cache pool data from a relational database management system. [e.g. Joglekar; Abstract, Para. 0063 – Joglekar discloses metrics including open file descriptors (integers that can be used to identify an opened file), process scheduling type (user mode or system mode), number of page faults (both minor and major), number of I/O read write calls, number of system calls, uptime, system temperature, system memory capacity, CPU cycles, system processing speed, number of heartbeats, a change in the number of minor faults, a change in the number of major faults, a change in the uptime, a change in the number of heartbeats, a change in the number of CPU cycles, etc.] [e.g. Ruikar; Abstract, Para. 0012, 0013, 0015, 0025-0038 – discloses monitoring and detection of anomalies using metrics of relational databases, wherein the metrics relate to latency, resource utilization percentage, number of active connections in the database or the link]
Claim 6:
Joglekar teaches the system of claim 1, wherein generating a report of application workload performance metrics for degraded programs further comprises presenting a summary and average of performance metrics of workloads for database administrator review. [e.g. Joglekar; Abstract, Fig. 4 Para. 0118-0121, 0136]
Claim 7:
Joglekar and Ruikar teaches the system of claim 1, wherein the application anomaly detection is performed within a relational database management system. [e.g. Joglekar; Abstract, Fig. 4 Para. 0008, 0081 – database management service] [e.g. Ruikar; Abstract, Para. 0012, 0013, 0015, 0025-0038 – discloses monitoring and detection of anomalies using metrics of relational databases, wherein the metrics relate to latency, resource utilization percentage, number of active connections in the database or the link]
Regarding claims 8-20 they are method claims essentially corresponding to the above recitations, and they are rejected, at least, for the same reasons.
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 CHRISTOPHER C HARRIS whose telephone number is (571)270-7841. The examiner can normally be reached Monday through Friday between 8:00 AM to 4:00 PM CST.
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/CHRISTOPHER C HARRIS/Primary Examiner, Art Unit 2432
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