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 .
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 1-5, 7-8, 10-14 and 16-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites, performing phased based predictions, obtaining a sampling dataset, detecting device failure, generating failure base, training the model and deploying the updated model. These limitations, under their broadest reasonable interpretation recite an abstract idea. Specifically collecting and analyzing data related to storage device data and forming opinions and outputs from the analysis. If a claim limitation, under its broadest reasonable interpretation this covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Metal Processes” grouping of abstract ideas. Applicant’s limitation of detecting, the by one or more processors, device failure for the group of storage devices based on the sampling dataset would fall within the mathematical concepts grouping. Applicant further discloses this in ¶0013 (unsupervised algorithms can be employed to facilitate the detection). Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into practical application. In particular, the claim recites - obtaining, by the one or more processors, a sampling dataset by selecting a group of storage devices from the respective sampling scopes with the corresponding sampling ratio. The collecting step is recited at a high level of generality (i.e., as a general means of gathering network traffic data for use in the comparison step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The processor in all steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Deploying the updated model is an insignificant extra solution activity (MPEP 2106.05(g)). Initiating a corrective action is a insignificant post solution activity. The corrective action limitation merely applies the result of the predictive analysis and constitutes an insignificant post solution active.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform both the predicting, obtaining detecting and generating steps amounts 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. The claim is not patent eligible.
Claims 2-5, 7-8, and 20-23, additional elements are generic computer components used to implement the abstract idea. They fail to improve the technology or require any specialized machines or components. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)). Claims 2-5 and 7-9 recite additional, specific mathematical algorithms and mental process. These claims do not recite any additional limitations that recite any specific technological improvement to the storage device or computer functionality. These are also considered abstract ideas following the same analysis as claim 1.
Claims 10-14, 16 and 17 are the system embodiments of claims 1-9. Other than a shift in Step 1 to a system, as opposed to a method, these claims follow the same analysis and conclusion of ineligibility.
Claims 18-20 are the computer program product embodiments of claims 1-9. Other than a shift in Step 1 to an apparatus, as opposed to a method, these claims follow the same analysis and conclusion of ineligibility.
Response to Arguments
Applicant's arguments filed 12/10/2025 have been fully considered but they are not persuasive. Applicant states:
Applicant respectfully submits that the claimed subject matter inextricably requires physical object such as "one or more processors" to apply anomaly prediction model on real-time monitoring data of storage devices, determining sampling scopes and sampling ratios using phase-based predictions based on the real-time monitoring, such that a group of storage devices is selected (subset of storage devices) using the sampling ratios for failure detection, and thus retraining/updating the anomaly prediction model (machine learning model; See at [0042] of the as-filed specification), which are not possible to be performed by a human mind. Further, the re-trained machine learning model is applied to detect a new device failure and thus, initiating a replacement or repair of the device that has the detected new device failure, which is also not possible by a human mind. Therefore, the claimed features are tied to a machine to perform recursive training of machine learning models and initiation of a replacement/repair of defective storage device and thus, do not represent "mental process." Accordingly, Applicant's claims are not similar to the alleged abstract idea. Therefore, the claimed features are inextricably tied to a machine and do not represent "mental process" as alleged by the Office Action.
Examiner respectfully disagrees. The rejection does not rely solely on the mental process grouping. The claim also recites a mathematical concept, including application of anomaly and failure prediction models and determination of sampling rations based on monitored data. The claimed processor and storage device does not integrate the mathematical concept into practical application, because the processor and storage are recited at a high level of generality and are used to perform well know functions. The newly amended claim now recites limitations initiating a repair or replacement of the storage device, this is recited at a high level of generality. The claim does not specify how the repair or replacement is technically performed or how the storage system operation is modified. The corrective action is applied as a result of the predictive analysis after the judicial exception has been performed and results in an insignificant post solution activity that does not integrate the exception into practical application.
Applicant states:
Thus, the claimed features of amended claim 1 provide technological
improvements in the field of "failure detection of massive storage devices." Accordingly, Applicant has shown teachings in the specification that describe a practical implementation and how functionality, efficiency, and accuracy of "processor/system that implements failure detection of massive storage devices" is improved by using machine learning models for phase-based dynamic predictions and retraining/updating the models to enhance15
the efficiency and further improve prediction/detection accuracy. Further, these re-trained models are used to detect a new device failure and thus, initiating a replacement or repair operation of a storage device that has the detected new device failure. Therefore, Applicant has established a clear nexus between the claim language and the practical implementation of the alleged judicial exception, and improvements to the technology.
Therefore, claim 1 integrates any alleged abstract idea into a practical application and satisfies Step 2A, Prong Two.
Examiner respectfully disagrees. Although the specification describes improved predication accuracy and detection resulting from passed based predication and retraining of ML models, the claims do not recite a specific improvement to the operation of the storage device or computer system itself. The claimed processors apply, train and update prediction models to improve the results of the analysis. The claimed improvements are directly related to the mathematical analysis itself rather than from a claimed technological improvement in the functioning of the computer storage system. The claimed limitation of initiating a repair or replacement after failure detection does not claim a technological improvement, the claim does not recite a particular improvement because the claim does not recite a particular mechanism for performing the repair/replacement or modifying operation of the storage system. The claimed limitation do not integrate the judicial exception into practical application.
Applicant states:
Step 2B concerns analyzing the claims for additional elements and if the additional elements raise the claims as a whole to be directed towards "significantly more" than the identified abstract idea, thereby rendering them patent eligible. However, because amended independent claim 1 is patent eligible as concluded at Step 2A Prong One/Two, the patent-eligibility analysis does not proceed to Step 2B.
Further, the Examiner has acknowledged that the cited prior art fails to disclose or suggest the claimed combination of limitations in the past office actions and further no prior-art rejection is listed for the claims in the instant office action. This further supports that claim 1 embodies an inventive concept under Step 2B.
Therefore, amended independent claim 1 is patent-eligible as concluded at Step 2A Prong One/Two and Step 2B.
Examiner respectfully disagrees. The argument that the lack of a prior art rejection demonstrates an inventive concept under step 2B is not persuasive. A prior art rejection under 102/103 is a separate issue to whether a claim recites significantly more than a judicial exception under 101. Step 2B requires consideration to whether additional element amount to significantly more than the judicial exception.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Prior art Elyasi (US 11,669,754) discloses a system and method for optimizing the dataset used for retraining a storage device failure prediction model. A storage failure prediction model can be used to predict whether a SSD may fail in the future. Training storage device failure prediction models may use a relatively large amount of data and may involve frequent retraining, for example, due to dynamically-changing parameters. In some embodiments, various methods may be used to enhance the dataset used for training the storage device failure prediction model. In other embodiments, the amount of training data can be reduced by using a window-based weighted data sampling scheme to select a smaller amount of data according to the recency of the data. In some embodiments, data that can be used for training the storage device failure prediction model may include instances of storage device failure, anomalies, and outliers (e.g., relevant data points from a relevant dataset). In some embodiments, the anomalies and outliers can be identified using anomaly detection algorithms, rule-based methods, cluster-based methods (column 4, lines 16-48)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES EHNE whose telephone number is (571)272-2471. The examiner can normally be reached 8:00-5:00 M-F.
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/CHARLES EHNE/ Primary Examiner, Art Unit 2113
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