DETAILED ACTION
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 .
Election/Restrictions
Applicant’s election with traverse of Group I, claims 1-8 and 17-20 in the reply filed on 3/4/2026 is acknowledged, although no grounds for the traversal were provided. This is not found persuasive for at least the following:
As stated in the Examiner's Restriction Requirement, mailed 2/25/2026, restriction for examination purposes is proper because all the inventions listed are distinct and there would be a serious search and/or examination burden for one or more of the following reasons: the inventions have acquired separate status in the art, require different field of search, and the prior art would not be applicable to all groups.
Examiner appreciates that there exist some similarities between the claim groups; however, as the claims are currently presented, the claim groups present a significant search burden since each invention contains divergent subject matter. Furthermore, the divergent subject matter disclosed in the different claim groups would require a different field of search in order to encompass the varying limitations from each of the groups. For example, claims 1-8 and 17-20 require an identifying a failed drive and generating a recommendation based on the service history of the failed drive and claims 17-20 require identifying a drive to be installed and generating a recommendation based on the service history of the existing drives.
Therefore, claims 1-8 and 17-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim.
The requirement is deemed proper and is therefore made FINAL.
Claims 1-8 and 17-20 are elected.
Claims 9-16 are withdrawn.
Claims 1-8 and 17-20 are pending and rejected.
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 1-8 and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
Claims 1-8 are directed to a device, which is a machine. Claims 17-20 are directed to a method, which is a process. Therefore, claims 1-8 and 17-20 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Representative claim 1 sets forth the following limitations which recite the abstract idea of providing product recommendations:
in response to an indication that identifies a failed drive representing a drive, of a storage array, that is to be replaced, receiving service history data comprising workload characteristics for the failed drive;
based on the service history data for the failed drive, determining a service classification that indicates a prominent workload characteristic, of the workload characteristics, that is specific to the failed drive; and
as a function of the prominent workload characteristic, generating recommendation data representative of a recommendation that identifies a replacement drive, from among a group of different types of replacement drives, that is to replace the failed drive in the storage array.
The recited limitations above set forth steps to provide product recommendations. These limitations amount to certain methods of organizing human activity, including commercial or legal interactions (e.g. advertising, marketing or sales activities or behaviors).
Such concepts have been identified by the courts as abstract ideas (see: MPEP 2106).
Step 2A (Prong 2):
Examiner notes that representative claim 1 recites additional elements such as a processor, memory, etc. However, even with additional features such as these, the claims fail to integrate the recited judicial exception into a practical application of the exception. The claims merely include instruction to implement an abstract idea on a computer, or to merely use a computer as a tool to perform an abstract idea, while the additional elements do no more than generally link the use of a judicial exception to a particular field of technological environment or field of use.
Furthermore, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement a judicial exception with a particular machine, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In view of the above, under Step 2A (Prong 2), claim 1 does not integrate the recited exception into a practical application (see again: MPEP 2106).
Step 2B:
When taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a processor or computer to perform the steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Certain additional elements also recite well-understood, routine, and conventional activity (See MPEP 2106.05(d)).
Even if considered as an ordered combination, any additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, claim 1 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Dependent claims 2-8 recite further complexity to the judicial exception (abstract idea) of claim 1, such as by further defining the steps for providing product recommendations. Thus, each of claims 2-8 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Therefore, dependent claims 2-8 do not add “significantly more” to the abstract idea. The dependent claims recite additional functions that describe the abstract idea and only generally link the abstract idea to a particularly technological environment, and applied on a generic computer. Further, the additional limitations fail to provide an improvement to the functioning of the computer, another technology, or a technical field.
Even when viewed as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2A/2B for at least similar rationale as discussed above regarding claim 1.
The analysis above applies to all statutory categories of invention. Regarding independent claim 17 (method), the claim recites substantially similar limitations as set forth in claim 1. As such, claim 17 and its dependents 18-20 are rejected for at least similar rationale as discussed above.
Claim Rejections - 35 USC § 103
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 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 of this title, 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 1-8 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gao et al. (U.S. Pre-Grant Publication No. 2021/0149777 A1) (“Gao”), in view of Barker, Jr. et al. (U.S. Pre-Grant Publication No. 2022/0083370 A1) (“Barker”).
Regarding claim 1, Gao teaches a device, comprising:
at least one processor (para [0066]); and
at least one memory that stores executable instructions that, when executed by the at least one processor (para [0066]), facilitate performance of operations, comprising:
in response to an indication that identifies a failed drive representing a drive, of a storage array, that is to be replaced (para [0022], systems in which two or more storage devices are employed and there is at least one storage device that is available as a spare storage device to replace a drive that has failed or is predicted to fail.);
based on the service history data for the failed drive, determining a service classification that indicates a prominent workload characteristic, of the workload characteristics, that is specific to the failed drive (para [0025], method determines and uses ranking parameters that are determined to be the most unique, i.e., show the greatest variance from one drive to the next; para [0026], spare drive ranking system that is based on a set of ranking parameters that are used to determine how the spare drive will perform once that spare drive is installed is part of the storage array); and
as a function of the prominent workload characteristic, generating recommendation data representative of a recommendation that identifies a replacement drive, from among a group of different types of replacement drives, that is to replace the failed drive in the storage array (para [0024], method uses a spare drive ranking system that determines a “best” drive in the spare pool through drive internal parameters, drive vintage performance, system failure rate and system performance, etc; para [0026], method determines the best quality drive based on a set of ranking parameters that includes temperature as one of the ranking parameters).
Although Gao teaches the above device, Gao does not explicitly teach receiving service history data comprising workload characteristics for the failed drive.
In a similar field of endeavor, Barker teaches receiving service history data comprising workload characteristics for the failed drive (Fig. 4; para [0152], load model (412) that predicts performance load on the storage system (408) based on characteristics of workloads (420, 422, 424) executing on the storage system (408) in dependence upon data (404) collected from a plurality of storage systems (402, 406) may be carried out, for example, through the use of machine learning techniques).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the noted limitations as taught by Barker in the device of Gao, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Namely, an improved device to effectively generate a particular load model specific to a particular combination of hardware, software, configuration settings, or other attributes of a particular storage system configuration (See Barker: para [0152]).
Regarding claim 2, Gao and Barker teach the above device of claim 1. Gao also teaches wherein the prominent workload characteristic is at least one first member of a group of different types of input/output (IO) transactions served by the failed drive during historical operation within the storage array, or at least one second member of a group of different environmental conditions under which the failed drive historically operated within the storage array (para [0024]; para [0026], method determines the best quality drive based on a set of ranking parameters that includes temperature as one of the ranking parameters).
Regarding claim 3, Gao and Barker teach the above device of claim 2. Gao also teaches wherein the group of different types of IO transactions comprises at least one of: a read IO transaction, a write IO transaction, a sequential read/write IO transaction, a random read/write IO transaction, a read-modify-write IO transaction, a transactional read/write IO transaction, a bulk read/write IO transaction, a compressed read/write IO transaction, an encrypted read/write IO transaction, or an IO transaction of a specific IO size, wherein the specific IO size is at least one of 8 kilobytes, 64 kilobytes, or 128 kilobytes (para [0024], such metrics may include drive type, vintage performance, overall system failure rate, sector reassign count in the drive, sector reallocation count in the drive, drive firmware level, drive temperature, drive power on hours, and a time since last background media scan).
Regarding claim 4, Gao and Barker teach the above device of claim 2. Gao also teaches wherein the group of different environmental conditions comprises at least one of: a temperature measure, an electromagnetic radiation measure, a humidity measure, a seismic measure, a geographical location, or a power on/off frequency (para [0024]; para [0026], method determines the best quality drive based on a set of ranking parameters that includes temperature as one of the ranking parameters).
Regarding claim 5, Gao and Barker teach the above device of claim 1. Barker also teaches wherein the operations further comprise, in response to receiving telemetry data from a group of drives that operate in a group of storage arrays, updating, based on the telemetry data, the service history data (para [0152], machine learning algorithms may be fed with information describing various performance characteristics of various storage systems (as extracted from the telemetry data) and information describing various workloads that are executing on various storage systems (as extracted from the telemetry data) to identify correlations between the amount of performance load that was placed on a particular storage system given the characteristics of the workloads that were executing on the particular storage system at the same point in time).
Regarding claim 6, Gao and Barker teach the above device of claim 5. Barker also teaches wherein the operations further comprise determining the workload characteristics based on an analysis of the service history data (para [0152], machine learning algorithms may be fed with information describing various performance characteristics of various storage systems).
Regarding claim 7, Gao and Barker teach the above device of claim 6. Barker also teaches wherein the analysis comprises using a machine learning model trained on the service history data to identify the workload characteristics in response to a deep learning multi-class and multi-label time-series transformer model (para [0152], machine learning algorithms may be fed with information describing various performance characteristics of various storage systems (as extracted from the telemetry data) and information describing various workloads that are executing on various storage systems (as extracted from the telemetry data) to identify correlations between the amount of performance load that was placed on a particular storage system given the characteristics of the workloads that were executing on the particular storage system at the same point in time).
Regarding claim 8, Gao and Barker teach the above device of claim 1. Barker also teaches wherein the operations further comprise:
receiving customer preference data indicative of a preference for the replacement drive (para [0141], customer preferences and other useful information); and
weighting the recommendation based on the preference (para [0151], calculated according to some formula that takes as inputs the weighted or unweighted combination of such factors).
Regarding claim 17, Gao teaches a method, comprising:
receiving, by a device comprising at least one processor, an indication that identifies a failed drive representing a drive, of a storage array, that is to be replaced (para [0022], systems in which two or more storage devices are employed and there is at least one storage device that is available as a spare storage device to replace a drive that has failed or is predicted to fail.);
based on the service history data for the failed drive, determining, by the device, a service classification that indicates a prominent workload characteristic, of the workload characteristics, that is specific to the failed drive (para [0025], method determines and uses ranking parameters that are determined to be the most unique, i.e., show the greatest variance from one drive to the next; para [0026], spare drive ranking system that is based on a set of ranking parameters that are used to determine how the spare drive will perform once that spare drive is installed is part of the storage array); and
as a function of the prominent workload characteristic, generating, by the device, a recommendation that identifies a replacement drive, from among a group of different types of replacement drives, that is to replace the failed drive in the storage array (para [0024], method uses a spare drive ranking system that determines a “best” drive in the spare pool through drive internal parameters, drive vintage performance, system failure rate and system performance, etc; para [0026], method determines the best quality drive based on a set of ranking parameters that includes temperature as one of the ranking parameters).
Although Gao teaches the above device, Gao does not explicitly teach in response to the indication, retrieving, by the device, service history data comprising workload characteristics for the failed drive.
In a similar field of endeavor, Barker teaches in response to the indication, retrieving, by the device, service history data comprising workload characteristics for the failed drive (Fig. 4; para [0152], load model (412) that predicts performance load on the storage system (408) based on characteristics of workloads (420, 422, 424) executing on the storage system (408) in dependence upon data (404) collected from a plurality of storage systems (402, 406) may be carried out, for example, through the use of machine learning techniques).
The combination would have been obvious for at least the rationale set forth above.
Regarding claim 18, Gao and Barker teach the above method of claim 17. Barker also teaches further comprising, in response to receiving telemetry data from a group of drives that operate in a group of storage arrays, updating, by the device, the service history data based on the telemetry data (para [0152], machine learning algorithms may be fed with information describing various performance characteristics of various storage systems (as extracted from the telemetry data) and information describing various workloads that are executing on various storage systems (as extracted from the telemetry data) to identify correlations between the amount of performance load that was placed on a particular storage system given the characteristics of the workloads that were executing on the particular storage system at the same point in time).
Regarding claim 19, Gao and Barker teach the above method of claim 17. Barker also teaches further comprising determining, by the device, the workload characteristics based on an analysis of the service history data (para [0152], machine learning algorithms may be fed with information describing various performance characteristics of various storage systems).
Regarding claim 20, Gao and Barker teach the above method of claim 17. Barker also teaches further comprising, in response to receipt of customer preference data indicative of a preference for the replacement drive, weighting, by the device, the recommendation based on the preference (para [0152], machine learning algorithms may be fed with information describing various performance characteristics of various storage systems (as extracted from the telemetry data) and information describing various workloads that are executing on various storage systems (as extracted from the telemetry data) to identify correlations between the amount of performance load that was placed on a particular storage system given the characteristics of the workloads that were executing on the particular storage system at the same point in time).
Conclusion
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/ANAND LOHARIKAR/Primary Examiner, Art Unit 3689