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 .
The instant office action having application number 19/272/698, filed on July 17, 2025, has claims 1-20 pending in this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/24/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 to 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1. Statutory Category
Claims 1 and 9 are directed to a system and method and claim 17 is directed to non-transitory computer-readable medium. Thus, the claims nominally fall within statutory categories.
Step 2A, Prong One- Abstract idea
The claims recites “compare, by a machine learning model, characteristics associated with an existing set of managed flash storage devices of the system to characteristics associated with a new set of managed flash storage devices; and determine whether the new set of managed flash storage devices is qualified for the system based on comparing the characteristics.” This is an evaluation/classification process, i.e., collecting/using information, comparing information, and making the determination. The recited machine learning model merely performs the comparison/decision-making function and does not change the character of the claim from abstract data-analysis and classification concept.
Step 2A, Prong Two-No practical integration.
The additional elements, including a memory, processing device, storage system, managed flash storage devices, firmware, telemetry data and cloud services provider, are used as generic computer storage system components to gather, transmit, analyze and output information. The claims do not recite a particular improvement to the internal operation of the computer, a new flash memory structure, a specific ML architecture, a specific training technique, or a concrete firmware modification that improves operation of flash storage devices. Instead, the claims use generic processing to determine whether a new set of devices is qualified.
Step 2B-No inventive concept.
Considering the claim elements individually and as an ordered combination, the claims add only well-understood, routine, and conventional computer components and functions: storing data, processing data, using a model to compare data, and outputting a qualification decision. The claims do not require unconventional hardware, an improved storage controller architecture, or a specific technical rule that improves flash memory operation. Therefore, the claims do not include significantly more than the abstract idea.
Accordingly claims 1-20 are ineligible under 35 USC 101.
Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of claim 1. The claim recites the additional limitations of “wherein the existing set of managed flash storage devices and the new set of managed flash storage devices offload management responsibilities to one or more storage system controllers”, which is further elaborating on the abstract idea, and therefore it does not amount to significantly more. Same rationale applies to claims 10 and 18.
Claim 3 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of claim 1. The claim recites the additional limitations of “provide the one or more characteristics of the existing set of managed flash storage devices and the one or more characteristics of the new set of managed flash storage devices to a cloud services provider”, which is further elaborating on the abstract idea, and therefore it does not amount to significantly more. Same rationale applies to claims 11 and 19.
Claim 4 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of claim 1. The claim recites the additional limitations of “wherein the data comprises telemetry data associated with the existing set of managed flash storage devices”, which is further elaborating on the abstract idea, and therefore it does not amount to significantly more.
Claim 5 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 5 recites the same abstract idea of claim 1. The claim recites the additional limitations of “determine whether to accept or reject the new set of managed flash storage devices based on a type of change from the existing set of managed flash storage devices”, which is further elaborating on the abstract idea, and therefore it does not amount to significantly more. Same rationale applies to claim 13.
Claim 6 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of claim 1. The claim recites the additional limitations of “determine whether support for the new set of managed flash storage devices exists within firmware of the storage system based on performance parameters received from the machine learning model”, which is further elaborating on the abstract idea, and therefore it does not amount to significantly more. Same rationale applies to claims 14 and 20.
Claim 7 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 7 recites the same abstract idea of claim 1. The claim recites the additional limitations of “wherein the performance parameters comprise a minimum latency, a number of input/output (I/O) operations to be performed, and a data retention time”, which is further elaborating on the abstract idea, and therefore it does not amount to significantly more. Same rationale applies to claim 15.
Claim 8 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 8 recites the same abstract idea of claim 1. The claim recites the additional limitations of “wherein the characteristics comprise at least one of one of error rates, data retention times, modes of failure, read disturb counts, number of program/erase cycles, temperature, powered-on/powered-off times, or latencies”, which is further elaborating on the abstract idea, and therefore it does not amount to significantly more. Same rationale applies to claim 16.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-17 of U.S. Patent No. 11474986. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-22 under examination are obvious, respectively, by claims 1-17 of the reference Patent. Every limitations in the instant application under examination claims are recited in the conflicting reference patent claims, and the differences or additional limitations between the claims are highlighted below by underlining and bolding all limitations.
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the independent claim 1 of the instant application to receive, from the machine learning model, the one or more deterministic characteristics associated with the storage media; generate a data structure comprising the one or more deterministic characteristics for use in a telemetry process to qualify types of storage media; compare one or more deterministic characteristics associated with data received by a new type of storage media with the one or more deterministic characteristics of the storage media; and determine, by the machine learning model, whether the new type of storage media includes a first type of change or a second type of change based on the comparison of the one or more deterministic characteristics.
Please, see the comparison table below:
Instant Application 19272698
Patent No. 11474986
1. A system comprising: a memory; and a processing device, operatively coupled to the memory, the processing device configured to: compare, by a machine learning model, characteristics associated with an existing set of managed flash storage devices of the system to characteristics associated with a new set of managed flash storage devices; and determine whether the new set of managed flash storage devices is qualified for the system based on comparing the characteristics.
9. A method, comprising: comparing, by a machine learning model executed on a processing device, characteristics associated with an existing set of managed flash storage devices of a storage system to characteristics associated with a new set of managed flash storage devices; and determining whether the new set of managed flash storage devices is qualified for the storage system based on comparing the characteristics.
17. A non-transitory, computer-readable media having instructions thereupon which, when executed by a processor, cause the processor to perform a method comprising: comparing, by a machine learning model executed on a processing device, characteristics associated with an existing set of managed flash storage devices of a storage system to characteristics associated with a new set of managed flash storage devices; and determining whether the new set of managed flash storage devices is qualified for the storage system based on comparing the characteristics.
1. A system comprising: a memory; and a processing device, operatively coupled to the memory, to: provide data associated with storage media of a storage system as an input to a machine learning model executed by the processing device, wherein the machine learning model identifies one or more deterministic characteristics of the storage media from the data; receive, from the machine learning model, the one or more deterministic characteristics associated with the storage media; generate a data structure comprising the one or more deterministic characteristics for use in a telemetry process to qualify types of storage media; compare one or more deterministic characteristics associated with data received by a new type of storage media with the one or more deterministic characteristics of the storage media; and determine, by the machine learning model, whether the new type of storage media includes a first type of change or a second type of change based on the comparison of the one or more deterministic characteristics.
7. A method, comprising: providing data associated with storage media utilized by one or more storage systems as an input to a machine learning model executed by a processing device; receiving, from the machine learning model, one or more deterministic characteristics associated with the storage media, wherein the one or more deterministic characteristics are determined by the machine learning model based on the data associated with the storage media; generating, by the processing device, a data structure comprising the one or more deterministic characteristics for use in a telemetry process to qualify types of storage media; comparing one or more deterministic characteristics associated with data received by a differing type of storage media with the one or more deterministic characteristics of the storage media; and determining, by the machine learning model, whether the differing type of storage media includes a first type of change or a second type of change based on the comparing.
13. A non-transitory computer readable storage medium storing instructions, which when executed, cause a processing device to: provide data associated with storage media utilized by one or more storage systems as an input to a machine learning model executed by the processing device; receive, from the machine learning model, one or more deterministic characteristics associated with the storage media, wherein the one or more deterministic characteristics are determined by the machine learning model based on the data associated with the storage media; generate, by the processing device, a data structure comprising the one or more deterministic characteristics for use in a telemetry process to qualify types of storage media compare one or more deterministic characteristics associated with data received by a differing type of storage media with the one or more deterministic characteristics of the storage media; and determine, by the machine learning model, whether the differing type of storage media includes a first type of change or a second type of change based on the comparing.
"A later patent claim is not patentably distinct from an earlier patent claim if the later claim is obvious over, or anticipated by, the earlier claim. In re Longi, 759 F.2d at 896, 225 USPQ at 651 (affirming a holding of obviousness-type double patenting because the claims at issue were obvious over claims in four prior art patents); In re Berg, 140 F.3d at 1437, 46 USPQ2d at 1233 (Fed. Cir. 1998) (affirming a holding of obviousness-type double patenting where a patent application claim to a genus is anticipated by a patent claim to a species within that genus). " ELI LILLY AND COMPANY v BARR LABORATORIES, INC., United States Court of Appeals for the Federal Circuit, ON PETITION FOR REHEARING EN BANC (DECIDED: May 30, 2001).
The application claim 1 does not contain specific limitations as shown in the patent claim 1; however, according to In re Goodman, the application claim 1 is generic to the species of information covered by claim 1 of the patent. Thus, the generic invention is anticipated by the species of the patented invention.
The application claim 9 does not contain specific limitations as shown in the patent claim 7; however, according to In re Goodman, the application claim 9 is generic to the species of information covered by claim 7 of the patent. Thus, the generic invention is anticipated by the species of the patented invention.
The application claim 17 does not contain specific limitations as shown in the patent claim 13; however, according to In re Goodman, the application claim 17 is generic to the species of information covered by claim 13 of the patent. Thus, the generic invention is anticipated by the species of the patented invention.
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 to 20 are rejected under 35 USC 103(a) as being unpatentable over Panabaker et al. (US 7925807 B2) (hereinafter Panabaker) in view of Faigon et al. (US 10270788 B2) (hereinafter Faigon).
As per claims 1, 9 and 17, Panabaker discloses a memory [memory controller 104]; and a processing device, operatively coupled to the memory [host processing component 106], the processing device configured to: compare, by a machine learning model, characteristics associated with an existing set of managed flash storage devices of the system to characteristics associated with a new set of managed flash storage devices [At 702, method 700 can obtain characteristics of a non-volatile memory module, as described herein. At 704, method 700 can compare the obtained characteristics to features of a host application(s)., col. 17, line 6]; and determine whether the new set of managed flash storage devices is qualified for the system based on comparing the characteristics [At 708, method 700 can determine a suitable control module from the control schema, based on the characteristics/features of the host application(s), col. 17, line 26]. Panabaker does not explicitly disclose that the comparison is performed by a machine learning model. On the other hand, Faigon discloses a machine learning model [When the disclosed machine learning based anomaly detection is applied to observe new users or devices, it detects patterns that are seen for the first time, col. 4, line 29]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the comparison and qualification process of Panabaker to utilize the machine learning analysis techniques taught by Faigon in order to automate device qualification, improve predictive accuracy, reduce manual rule generation, and provide adaptive decision making based on historical operational data. Such modification would merely involve the predictable use of known machine learning techniques for their established purpose.
As per claims 2, 10 and 18, Panabaker discloses wherein the existing set of managed flash storage devices and the new set of managed flash storage devices offload management responsibilities to one or more storage system controllers [By offloading processing to the memory controller 104 (e.g., compression, encryption, filtering, sorting, etc.), data processing can be done in parallel between the controller 104 and the host processing device 106, col. 9, line 36].
As per claims 3, 11 and 19, Panabaker discloses provide the one or more characteristics of the existing set of managed flash storage devices and the one or more characteristics of the new set of managed flash storage devices to a cloud services provider [the control/management of the raw memory cells 304 can be optimal for the characteristics of the memory system, col. 12, line 49].
As per claims 4, Faigon discloses wherein the data comprises telemetry data associated with the existing set of managed flash storage devices [the collected content metadata provides details on file exposure, including whether files are private, shared internally, col. 18, line 24].
As per claims 5 and 13, Panabaker discloses determine whether to accept or reject the new set of managed flash storage devices based on a type of change from the existing set of managed flash storage devices [At 806, the identified management algorithms can be compared with requirements/specifications of one or more host applications, col. 17, line 52].
As per claims 6, 14 and 20, Faigon discloses determine whether support for the new set of managed flash storage devices exists within firmware of the storage system based on performance parameters received from the machine learning model [There are two main types of machine learning: Supervised and unsupervised. The former is where the computer learns from a dataset of labeled training data whereas the latter is where the computer makes sense of unlabeled data and finds patterns that are hard to detect otherwise, col. 3, line 54].
As per claims 7 and 15, Faigon discloses wherein the performance parameters comprise a minimum latency, a number of input/output (I/O) operations to be performed, and a data retention time [Sample event 300 is composed of two parts: output or target features and input features, col. 9, line 34].
As per claims 8 and 16, , Panabaker discloses wherein the characteristics comprise at least one of one of error rates, data retention times, modes of failure, read disturb counts, number of program/erase cycles, temperature, powered-on/powered-off times, or latencies [Class and device specific characteristics can include read/write times, erase times, management and/or control algorithms (e.g., wear-leveling algorithm, data interleaving algorithm, block addressing algorithm, read, write and/or erase algorithm, data transformation algorithms, and/or the like), etc. Device specific characteristics can include number of erase cycles implemented for various memory blocks, a particular maximum or minimum read/write/erase time of a memory module, location of bad blocks of cells, an amount (e.g., percentage) or type of data (e.g., identified by meta-data) stored in memory, or the like, col. 7, line 28].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NOOSHA ARJOMANDI whose telephone number is (571)272-9784. The examiner can normally be reached on (571)272-9784.
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June 12, 2026
/NOOSHA ARJOMANDI/Primary Examiner, Art Unit 2166
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