DETAILED ACTION
The instant application having Application No. 18/226,947 filed on 7/27/2023 is presented for examination.
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 .
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Drawings
The applicant’s drawings submitted are acceptable for examination purposes.
Authorization for Internet Communications
The examiner encourages Applicant to submit an authorization to communicate with the examiner via the Internet by making the following statement (from MPEP 502.03):
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.”
Please note that the above statement can only be submitted via Central Fax, Regular postal mail, or EFS Web.
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 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Tennenbaum (US 2007/0288935) in view of Bhide (US 2017/0339024).
As per claim 1, Tennenbaum discloses an adjusted group execution framework comprising:
an instruction loader that loads and parses an instruction file, the instruction file comprises actions and operations that describe running tasks of a monolithic application on a cluster of nodes (Paragraph 28 “Communications can occur between any two or more cluster node modules (for example, between a cluster node module 204a and another cluster node module 204c) and not just between "adjacent" kernels. Each of the cluster node modules 204a-e is in communication with respective kernel modules 206a-e. Thus, the cluster node module 204a communicates with the kernel module 206a. MPI calls and advanced cluster commands are used to parallelize program code received from an optional user interface module 208 and distribute tasks among the kernel modules 206a-e. The cluster node modules 204a-e provide communications among kernel modules 206a-e while the tasks are executing. Results of evaluations performed by kernel modules 206a-e are communicated back to the first cluster node module 204a via the cluster node modules 204a-e, which communicates them to the user interface module 208.”);
an executor that distributes and runs the tasks of the application on the nodes of the cluster in accordance with the actions and operations of the instruction file (Paragraphs 76-87 shows how a MathLink program can be adapted to run on a computing cluster.).
Tennenbaum does not expressly disclose but Bhide discloses a machine learning diagnostic engine that collects key performance indicators (“KPI”) of the application to train a machine learning model that determines a performance state of the application based on runtime metric values of the KPIs (Paragraph 101 “Implementations of the present disclosure are described for monitoring a service at a granular level. For example, one or more aspects of a service can be monitored using one or more key performance indicators for the service. A performance indicator or key performance indicator (KPI) is a type of performance measurement. For example, users may wish to monitor the CPU (central processing unit) usage of a web hosting service, the memory usage of the web hosting service, and the request response time for the web hosting service. In one implementation, a separate KPI can be created for each of these aspects of the service that indicates how the corresponding aspect is performing.”).
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to modify the framework of Tenenbaum to include the teachings of Bhide because it provides for improving operational reliability and performance management by tracking the metrics of each part of the computing cluster. In this way, the combination benefits from the increased reliability of the system.
As per claim 2, Tennenbaum further discloses further comprises a state machine that maintains a record of states and status of operations and actions executed by the executor in a database using a standard query language (Paragraphs 75 and 129).
As per claim 3, Tennenbaum further discloses wherein the executor runs the tasks of the application sequential at nodes of the cluster and in parallel across the nodes in response to the operations of the instruction file (Paragraphs 76-87).
As per claim 4, Tennenbaum further discloses wherein the executor runs the tasks of the application in parallel in each node of the cluster and sequentially across the nodes in response to the operations of the instruction file (Paragraphs 76-87).
As per claim 5, Tennenbaum does not expressly disclose but Bhide discloses wherein the machine learning diagnostic engine is a component of an agent worker that checks performance status of the actions and operations performed by the executor (Paragraph 101).
As per claim 6, Tennenbaum does not expressly disclose but Bhide discloses for each KPI of an action or operation of the tasks of the application, the machine learning diagnostic engine performs operations comprising:
partitions a historical time period into subintervals (Paragraph 321);
averages metric values of the KPI in each subinterval to obtain an average metric value for each subinterval (Paragraph 102);
normalizes the average metric values of each subinterval to obtain a normalized metric value for each subinterval (Paragraph 99);
determines two or more clusters of the normalized metric values, each cluster corresponding to a different performance state of the application (Paragraph 101);
collects and normalizes runtime metric values of the application to obtain normalized metric values (Paragraph 99); and
determines the performance state of the application based on which cluster contains the largest number of normalized metric values to the runtime normalized metric values (Paragraph 97).
As per claims 7-12, they are apparatus claims having similar limitations as cited in claims 1-6 and are rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Behrendt (US 20120331342) discloses a scalable and fault tolerant finite state machine engine, for example, for use in an automated incident management system, logs or records data in persistent storage at different points or levels during various internal processing of an event associated with an information technology element, and action taken associated with the event, by executing a finite state machine instance that encodes policies for handling incidents on such types of information technology elements. In the event that the finite state machine engine is shutdown during processing, the finite state machine engine is able to pick up from where it left off when it was shutdown, for each abnormally terminated finite state machine instance, by using the data logged in the persistent storage and determining a point of processing from where it should continue its execution.
Keohane (US 20090157793) discloses executing a monolithic application program successfully on a grid computing system are provided. Before the program is executed on the grid computing system, the program is executed on a computer on which the program has previously been successfully executed. During its execution, the program is monitored to collect its runtime information. The runtime information is provided to the grid computing system. With this information the grid computing system is able to successfully execute the program.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMOTHY A MUDRICK whose telephone number is (571)270-3374. The examiner can normally be reached 9am-5pm Central Time.
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, Pierre Vital can be reached at (571)272-4215. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TIMOTHY A MUDRICK/Primary Examiner, Art Unit 2198 12/06/2025