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. DETAILED ACTION This communication is in response to the application filed 0 3 October 2023. Claims 1-20 are pending. The rejections are as stated below. 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 non-statutory subject matter. In particular, claims are directed to a judicial exception (abstract idea) without significantly more. Claim 1 (exemplary) recites a series of steps for performing workload optimization (see abstract and ¶ 0025 , of Applicant’s specification). The claim is directed to a process, which is a statutory category of invention. The claim is then analyzed to determine whether it is directed to a judicial exception. Independent method claims 1, recites the limitations of r eceiving a storage management indication to process one or more workloads; and applying, based on the indication, at least one configuration. These limitations, as drafted, are processes that, under its broadest reasonable interpretation covers steps directed to organizing human activity, namely a fundamental economic practice of performing work optimization . Under the Guidance, certain methods of organizing human activity, including a fundamental economic practice, represent an abstract idea. See Guidance, 84 Fed. Reg. at 52. In addition to raising the abstract recitation of “‘organizing human activity,’ the claims recite “mental processes”. Claim 1, includes steps that reasonably can be performed by a human (pen and paper). For example, a human can perform the steps recited above in claim 1, by evaluating the received information and giving an opinion or make a decision. Under the Guidance, “mental processes—concepts performed in the human mind (including an observation, evaluation, judgment, opinion)” also constitute an abstract idea. See Guidance, 84 Fed. Reg. at 52. Accordingly, the above recitations, and the claim as a whole, recite an abstract idea involving mental processes. Therefore, it is clear that exemplary independent claim 1 recites limitations, under the Revised Guidance, fall under the category of abstract ideas related to “certain methods of organizing human activity” and/or “mental processes” 2019 Revised Guidance, 84 Fed. Reg. at 52 . See MPEP § 2106.04(a)(2). Accordingly, independent claim 1 recites an abstract idea. Next, the claim is analyzed to determine if it is integrated into a practical application. The recited judicial exception may be integrated into a practical application by identifying whether there are any additional elements recited in the claim beyond the judicial exception and evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application. The claim recites additional limitation of a storage system and an application (claim 1) and a computer processor and a computer memory (claim 1 3 ) to perform the steps. The processor in the steps is recited at a high level of generality, i.e., as a generic computer performing a generic computer function of processing data (see Applicant’s specification ¶ 004 0 ). This generic computer limitations are no more than mere instructions to apply the exception using generic computer component. Also, these limitations are an attempt to limit the abstract idea to a particular technological environment. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(h). The claim is directed to the abstract idea. Next, the claim is analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract ideas (whether claim provides inventive concept). As discussed above, the recitation of the claimed limitations amounts to mere instructions to implement the abstract idea on a server (using the computer as a tool to implement the abstract idea). Taking the additional elements individually and in combination, the processor at each step of the process performs purely generic computer functions. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. The same analysis applies here, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at or provide an inventive concept. See MPEP 2106.05(h). Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea itself. Therefore, the claim does not amount to significantly more than the recited abstract idea. Therefore, the claim is not patent eligible. The analysis above applies to the statutory category of invention of claims 1 , 13 and 1 8 . Furthermore, dependent claims 2-1 2, 14-17, 19 and 20 do not add limitations that meaningfully limit the abstract idea. The dependent claims do not impart patent eligibility to the abstract idea of the independent claims. Therefore, none of the dependent claims alone or as an ordered combination add limitations that qualify as integrating the abstract idea into a practical application. Lastly, dependent claim 12 includ e s the additional element of “a machine learning model”, However, the additional element does not integrate the abstract idea into a practical application and is not sufficient to amount to significantly more than the judicial exception because the additional element is simply steps to train a model performed by a generic machine learning model . The claim merely amounts to the application or instructions to apply the abstract idea on a processor, and is considered to amount to nothing more than requiring a generic processor to merely carry out the abstract idea itself. Accordingly, claims 1-20 are rejected as ineligible for patenting under 35 U.S.C. 101 based upon the same analysis. The instant claims are rejected under 35 USC 101 in view of The Decision in Alice Corporation Ply. Ltd. v. CLS Bank International, et al. in a unanimous decision, the Supreme Court held that the patent claims in Alice Corporation Pty. Ltd. v. CLS Bank International, el al. ("Alice Corp. ") are not patent-eligible under 35 U.S.C. § 101. 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. Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Narayanam et al ( US 20200241788 A1 ), hereinafter “ Narayanam ”. Regarding claims 1 , 13 and 1 8 , Narayanam discloses a n apparatus and a corresponding method of application-driven storage workload optimization (abstract), comprising: a computer processor (figure 4 , element 1 6 and ¶ 003 9 ); and a computer memory operatively coupled to the computer processor, the computer memory having disposed within it computer program instructions that, when executed by the computer processor, cause the apparatus to carry out the steps of (figure 4 , element 28 and ¶ 003 9 ) comprising: receiving, by a storage system, a storage management indication provided by an application, wherein the storage system processes one or more workloads for the application ( abstract and ¶ ¶ 00 02-0005 ); and adjusting /applying , based on the storage management indication, at least one configuration within the storage system ( abstract, figure 1 and ¶¶ 00 02 -00 04, 0020, 0024 and 0032 ). Regarding claims 2 and 1 4 , Narayanam discloses the storage management indication includes one or more quality of service requirements for the one or more workloads processed by the storage system, wherein the one or more quality of service requirements include a latency requirement, a throughput requirement, a bandwidth requirement, or a retention requirement ( abstract and ¶ ¶ 0002-0004, 00 21 -00 23 and 0029 ). Regarding claims 3 and 1 5 , Narayanam discloses the storage management indication includes a workload priority value for a workload that the storage system will process for the application (¶¶ 00 29-0031 ). Regarding claims 4 and 1 6 , Narayanam discloses the workload priority value specifies whether the storage system is to prioritize processing of a first workload over a second workload, or a scheduling of the first workload or the second workload, or a storage tier for a dataset associated with the first workload or the second workload ( abstract and ¶¶ 0 030-0036 ). Regarding claims 5 and 1 7 , Narayanam discloses the storage management indication includes a storage tier or storage type specified for data associated with the one or more workloads ( abstract and ¶¶ 0002-0005, 0 0 27 -003 2 ). Regarding claim 6, Narayanam discloses the storage management indication includes one or more attributes of a dataset associated with a workload that the storage system processes for the application, wherein the one or more attributes include a size of the dataset, a type of the dataset, a language associated with the dataset, an I/O pattern associated with the dataset, or a structural attribute of the dataset (¶ ¶ 0 028-0036 ). Regarding claim 7, Narayanam discloses the step of receiving the storage management indication as part of metadata of an I/O operation sent by the application ( abstract , figure 2 and ¶¶ 0002-0005, 0032 -0035 ). Regarding claim 8 , Narayanam discloses the step of receiving the storage management indication as a characteristic of a dataset separately from an I/O operation sent by the application ( abstract and ¶¶ 0002-0005, 0032-0035 ). Regarding claim s 9 and 19 , Narayanam discloses the step of analyzing one or more components of a request for workload processing by the application; and identifying, from the one or more components, the storage management indication ( abstract and ¶¶ 0002-0005, 0032-0035 ). Regarding claim 10, Narayanam discloses the step of changing a workload priority value for a particular workload that is being processed by the storage system (¶¶ 00 32-0038 ). Regarding claim s 1 1 and 20 , Narayanam discloses the step of changing a configuration of a storage component or storage device of the storage system (¶¶ 003 2 -0038 ). Regarding claim 1 2 , Narayanam discloses training a machine learning model to determine an updated configuration for the storage system based on received storage management indications, the training comprising: obtaining training data sets, each training data set of historical data comprising: one or more storage management indications received from one or more applications; one or more configurations implemented on the storage system based on the one or more storage management indications; training the machine learning model based on the training data sets; and applying the machine learning model to determine the updated configuration to be implemented on the storage system (¶¶ 0005, 0020-0026 and 0035 ). Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Martin et al. (US 10261717 B1) discloses “ Techniques are described for performing data storage optimization. A first I/O workload for a first data portion of a first snapshot of a first logical device is tracked. First processing is performed by a data storage optimizer to determine a set of one or more data movement optimizations. The first processing uses the first I/O workload for the first snapshot. The set of one or more data movement optimizations include a first data movement that is any of a promotion to move data included in the first data portion from a first storage tier to a higher performance storage tier and a demotion to move data included in the first data portion from the first storage tier to a lower performance storage tier. The first data movement is performed . ” Fisk (US 20040025162 A1) discloses “ The invention relates to methods and associated systems for managing application workloads and data storage resources. Techniques are disclosed for determining the I/O capacity of a data storage resource for a given workload and allocating resources according to administrator requirements. The invention may be implemented as a transparent layer between the application and the data storage resource, for example, in the file system. ” Kozlovsky et al. (US 10042572 B1) discloses “… techniques for use in providing an optimal configuration for a data storage system. In one example, a method comprises the following steps. A request is received from a user to assist in the configuration of the data storage system. The request is received remote from the data storage system and includes values of application workload parameters. Optimal configuration parameters are determined for the data storage system in response to receiving the request. The optimal configuration parameters are determined based on the application workload parameters and one of a model and application best practices. Additionally, the optimal configuration parameters are provided to a target system such that the target system is able to establish the optimal configuration for the data storage system. ” Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hani Kazimi whose telephone number is (571) 272-6745. The examiner can normally be reached Monday-Friday from 8:30 AM to 5:00 PM. 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, Abhishek Vyas can be reached on (571) 270-1836. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov . Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. Respectfully Submitted /HANI M KAZIMI/ Primary Examiner, Art Unit 3691