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 .
This is in response to Application filed 04/02/26. Claims 1 – 20 has been amended and is pending.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1 – 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Pardeshi et al. US 20210086089 A1 in view of Spryn et al. US 11481616 B2.
Regarding claims 1, 19 and 20, Paradeshi discloses a method of automatically determining configuration information pertaining to a computing workload, the method comprising:
at a machine learning engine:
interfacing with a workload deployed upon a network to determine file structures of the workload [0344];
comparing the determined file structures of the workload with pre-defined representations of file structures stored in a classification database [0107, see determining data, path determinations, classifications, databases and utilizing the neural engine/machine learning engine]; and identifying configuration information pertaining to the workload based on the comparing [0101, shows identifying also see 0147, for supporting Workloads].
Paradeshi doesn’t expressly disclose determining file structures of a workload deployed upon a network by interfacing with the workload, wherein the interfacing includes the machine learning engine performing a multi-stage discovery process, where functionality of each stage is determined by the machine learning engine analyzing data about workload identified from the multi-stage discovery process.
However, Pryn in an analogous art and similar configuration discloses wherein,
“…Once the training phase 813 has completed and the second machine learning model 108 has been adequately trained, then the method 800 enters the deployment phase 819. During the deployment phase, the second machine learning model 108 may be used to provide recommendations for the migration of databases 102 to a cloud computing system….The first machine learning model 104 may be used to generate 823 a compressed representation 112 of characteristics of the database 102 under the workload 118…” as well as showing analyzing a database using a script to show characteristics of the database under a workload [14:5 – 65].
As noted characteristics of the database under the workload is interfaced with machine learning language in multiple phases i.e. training and deployment, and is used to analyze the data and provide recommendations.
Therefore, it would have been obvious to one of ordinary skill in the art before the filing of the invention to combine Paradeshi and Pryn because it would enable analyzing and performing recommendations on the workload utilizing machine learning.
Regarding claim 2, the method of Claim 1 wherein the workload includes at least one of a framework, an operating system, and a software application [0344, shows numerous inferences of workload regarding software application].
Regarding claim 3, the method of Claim 1 wherein the workload includes hardware, elements of the hardware including at least one of: one or more processors, one or more memory devices, one or more storage devices, and one or more network adapters, the method further comprising:
determining a status of a resource pertaining to the hardware by taking a pre- defined number of measurement samples at a node of the hardware, and comparing a function of the measurement samples with a pre-defined threshold value [0344, see pre-empting existing workloads as well as 0315 for threshold values].
Regarding claim 4, the method of Claim 1 wherein the configuration information is at least one of an identifier of a framework or library associated with the workload and at least one of a language, a version, and a name of a framework, operating system, or application deployed upon the workload [0344, shows procession workloads and workloads being software and/or an application].
Regarding claim 5, the method of Claim 1 wherein the configuration information includes type details of a virtualization environment deployed upon the workload, wherein the type details include at least one of a designation as serverless, a designation as a container, and a designation as a virtual machine [0146, see virtual machines].
Regarding claim 6, the method of Claim 1 further comprising:
configuring the machine learning engine to modify representations of file structures stored within, or store additional representations of file structures within, the classification database according to an update of a framework, operating system, or application, or creation of a new framework, operating system, or application [0152, see self modifying].
Regarding claim 7, The method of Claim 1 wherein the identifying includes evaluating a result of the comparing with an accuracy threshold [0116, see evaluating predictive quality and machine learning].
Regarding claim 8, the method of Claim 1 further comprising:
automatically determining a protection action based on the identified configuration information, and issuing an indication of a recommendation of the determined protection action to a controller associated with the workload [0392, see ECC].
Regarding claim 9, the method of Claim 8 further comprising: automatically selecting the recommendation from a recommendation database [0052, recommendation].
Regarding claim 10, the method of Claim 8 wherein the recommendation is selected from a recommendation database by an end-user [0052].
Regarding claim 11, the method of Claim 8 further comprising, prior to issuing the indication of the recommendation, augmenting a recommendation database in response to an input from an end-user defining the recommendation [0052].
Regarding claim 12, the method of Claim 1 further comprising:
deploying software instrumentation upon the workload, the software instrumentation configured to determine real-time performance characteristics of the workload [0104, see data to be analyzed by trained neural network].
Regarding claim 13, the method of Claim 12 wherein the software instrumentation is further configured to indicate a condition of overload perceived at the workload [0097, see fatigue threshold determination].
Regarding claim 14, the method of Claim 1 wherein the identified configuration information includes an indication of a vulnerability associated with the workload, wherein the vulnerability is identified based on an examination of process memory, the indication of the vulnerability further providing a quantification of security risk computed based on the examination of process memory [0127, see dropout probability].
Regarding claim 15, the method of Claim 1 wherein the identified configuration information includes an indication of at least one file that is to be touched by a given process during a lifetime of the given process running upon the workload, the method further comprising:
constraining execution of the given process to prevent the given process from loading files other than the at least one file that is to be touched by the given process, thereby increasing trust in the given process [0115, see constraints].
Regarding claim 16, the method of Claim 1 wherein the workload includes a plurality of workloads [0147, see support one or more workloads].
Regarding claim 17, the method of Claim 16 wherein a framework, an operating system, or an application is distributed or duplicated amongst the plurality of workloads [0344].
Regarding 18, the method of Claim 16 further comprising constructing a topological representation of the plurality of workloads based on identified configuration information corresponding to respective workloads of the plurality thereof [0344].
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Correspondence Information
6. Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chuck Kendall whose telephone number is 571-272-3698. The examiner can normally be reached on 10:00 am - 6:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hyung Sough can be reached on 571-272-6799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Chuck O Kendall/
Primary Examiner, Art Unit 2192
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