This action is in response to the amendments filed on 05/27/2026, in which claims 1-20 stand rejected.
Response to Arguments
Applicant's arguments filed 05/27/2026 have been fully considered but they are not persuasive.
Harutyunyan does not compare real-time performance indicators associated with a particular processing server to anomaly patterns that include sets of previously recorded performance indicators which previously caused performance bottlenecks in relation to a software application processed at the particular processing server.
Response:
Examiner respectfully disagrees. Harutyunyan expressly teaches application-specific KPIs, including response time, error rates, resource contention, latency and throughput and detecting application performance problems by comparing KPI values with thresholds that may be dynamically adjusted based on application behavior over time ([0056], [0102], [0103]). Thus, Harutyunyan teaches detecting application-specific performance problems based on performance indicators, and the claimed use of previously observed performance information would have been an obvious implementation thereof. Accordingly, the rejection is maintained.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Harutyunyan et al. (US 2024/0028442, referred herein after Harutyunyan).
As per claim 1, 8, 15, Harutyunyan discloses a system comprising:
a memory that stores an artificial Intelligence (AI) model (Fig. 19, memory 1908, [0106]); and
a processor communicatively coupled to the memory (Fig. 19, CPU 1902-1905, [0106]); and configured to:
obtain information relating to a plurality of real time performance indicators that indicate real time performance of a processing server deployed at a data center (Fig. 20, step 2002, [0109], run-time KPI values of the KPI are monitored/obtained) wherein the processing server runs a software application ([0037], server computers may host applications);
input the information relating to the real time performance indicators to the AI model, wherein: ([0080], [0090], [0094], “The analytics engine 312 uses machine learning as described below to train a model that contains specific rules relating events recorded in log messages to KPI values of the KPI recorded in a historical time period.”);
the AI model is trained based on a plurality of anomaly patterns associated with the data center, to determine that a performance bottleneck has occurred in relation to one of the plurality of data center equipment; and ([0080], [0094], [0096], [0097], rules are used by the analytics engine 312 to determine which events recorded in run-time log messages identify a probable root cause (ie bottleneck) of a performance problem revealed by the KPI (ie AI model));
each anomaly pattern:
is associated with a particular performance bottleneck previously detected in relation to the software application processed by the processing server; and ([0082], [0083], An outlier KPI value is an indication of abnormal behavior of an object (ie bottleneck));
comprises a set of historical performance indicators recorded in relation to the processing server that caused the particular performance bottleneck associated with the software application (specification defines “a set of historical performance indicators recorded in a pre-selected time period”, Fig. 10A, KPI values (ie performance indicator) associated with an object in a data center stored at historical time period, [0082], abnormal behavior of object is determined by analytic engine that determines which KPI values in the historical time period are outliers. An outlier KPI value is an indication of abnormal behavior of an object (ie bottleneck));
execute a machine-learning algorithm associated with the AI model to:
compare the plurality of real time performance indicators associated with the processing server to sets of historical performance indicators associated with respective anomaly patterns that indicate respective previously recorded performance bottlenecks in relation to the software application processed at the processing server (Fig. 20, step 2003, application-specific KPIs, including response time, error rates, resource contention, latency and throughput and detecting application performance problems by comparing KPI values with thresholds that may be dynamically adjusted based on application behavior over time, [0056], [0102], [0103], Fig. 10B, [0083], threshold 1014 that separates outliers (object with abnormal behaviors, ie bottle neck);
determine a first pattern of at least a portion of the one or more real time performance indicators recorded for the processing server that matches with or closely matches with a first set of historical performance indicators associated with a first anomaly pattern indicating a previously recorded first performance bottleneck in relation to the software application processed at the processing server ([0080], [0094], [0096], rules are used by the analytics engine 312 to determine which events recorded in run-time log messages identify a probable root cause of a performance problem revealed by the KPI);
determine that the first performance bottleneck has occurred in relation to the processing server; and ([0080], [0094], [0096], rules are used by the analytics engine 312 to determine which events recorded in run-time log messages identify a probable root cause of a performance problem revealed by the KPI, determining first probable root cause of a performance problem is interpreted as determining the first performance bottleneck);
in response to determining the first performance bottleneck in relation to the first processing server, implement one or more remediation processes to resolve the first performance bottleneck associated with the processing server, ([0039], The operations manager 132 may be implemented in a VM to collect and processes the object information as described below to detect performance problems and generate recommendations to correct the performance problems, [0040], This example architecture includes a user interface 302 that provides graphical user interfaces for data center management, system administrators, and application owners to receive alerts, view metrics, log messages, and KPIs, and execute recommended remedial measures to correct performance problems);
implement one or more remediation processes to resolve the first performance bottleneck associated with the first data center equipment processing server, wherein resolving the first performance bottleneck associated with the processing server improves performance of the processing server ([0040], [0107], execute recommended remedial measures to correct performance problems).
As per claim 2, 9, 16, Harutyunyan discloses the system of Claim 1, wherein: the AI model is further trained based on respective remediation processes associated with the plurality of anomaly patterns ([0090], model retrained);
each remediation process associated with a respective anomaly pattern was implemented to resolve a respective previously detected performance bottleneck associated with the respective anomaly pattern; and ([0039], [0040], [0107], remedial measures associated with performance problem to resolve the detected bottleneck( abnormal behavior));
the processor is further configured to execute the machine-learning model to: determine the one or more remediation processes associated with the first anomaly pattern ([0039], [0040], [0107]).
As per claim 3, 10, 17, Harutyunyan discloses the system of Claim 1, wherein the set of historical performance indicators associated with a particular anomaly pattern associated with a particular data center equipment comprise one or more of hardware performance indicators indicating performance of one or more hardware components of the particular data center equipment or software performance indicators indicating performance of one or more software applications hosted by the particular data center equipment ([0081], the object (ie a particular data center equipment) can be an application, component of a distributed application, a VM, a container, computer hardware, such as a host or switch).
As per claim 4, 11, 18, Harutyunyan discloses the system of Claim 1, wherein the one or more remediation processes comprises migrating processing of one or more software applications from the processing server to a second data center equipment of the data center ([0039], Depending on the type of the performance problem, recommendations include reconfiguring a virtual network of a VDC or migrating VMs from one server computer to another, powering down server computers, replacing VMs disabled by physical hardware problems and failures, spinning up cloned VMs on additional server computers to ensure that services provided by the VMs are accessible to increasing demand or when one of the VMs becomes compute or data-access bound.)
As per claim 5, 12, 19, Harutyunyan discloses the system of Claim 1, wherein the one or more remediation processes comprises generating an alert message indicating that the first performance bottleneck has occurred in relation to the processing server (Fig. 20, step 2007, [0109], In block 2007, an alert identifying the violation of the KPI threshold and the log messages in a graphical user interface of an electronic display device).
As per claim 6, 13, 20, Harutyunyan discloses the system of Claim 1, wherein a performance indicator comprises an informational message generated in relation to a data center equipment, an error message generated in relation to the data center equipment, or a measured value of a performance metric associated with the data center equipment ([0048], [0049], A KPI is a metric that can be constructed from other metrics and is used as a indicator of the health of an application executing in the data center, [0054], a KPI that is constructed from other metrics and is used to measure the performance level of a VM, container, or components of a distributed application. )
As per claim 7, Harutyunyan discloses the system of Claim 1, wherein a performance indicator comprises one or more of a central processing unit (CPU) response time, CPU usage, memory usage, network latency packet loss, software application error rate, software application response time, or unresponsive software application ([0004], [0055], KPIs for an application include average response times to client request, error rates, contention time for resources, or a peak response time, [0054], The metrics CPU usage (t.sub.m), Memory usage(t.sub.m), and Network throughput(t.sub.m) of an object are measured at points in time).
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 KAMINI B PATEL whose telephone number is (571)270-3902. The examiner can normally be reached on M-F 8-4:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ashish Thomas can be reached on 571-272-0631. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KAMINI B PATEL/Primary Examiner, Art Unit 2114
Prosecution Insights