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
Claim(s) 1-14 and 16-35 has/have been examined.Claim(s) 1-14 and 16-35 have been rejected.
Response to Arguments
The arguments submitted October 10, 2025 have been fully considered but are not persuasive.
Regarding the 101 rejection, Applicant argues that derivation of rules and remediations by a AIOps cannot reasonably be said to be capable of performance in a human mind. The examiner respectfully disagrees. While the AIOps platform is not a mental construct, a human mind with the aid of pen and paper is capable of performing identifying and evaluating rules and identifying associated remediations. Use of the AIOps is evaluated separately as an additional element but its claimed use does not integrate the judicial exception or amount to significantly more than the judicial exception for the reasons described in the rejection below.
Applicant argues that the use of rules and remediations improve the functioning of the data storage system itself. The examiner respectfully disagrees. Claim 1 recites identifying availability of a remediation. The claim determines a remediation but does not implement any specific result that would improve the system. Other claims recite a remediation action (e.g. claim 8) but the non-specificity of the language means that the action may include displaying remediation information, which is not considered a step that concretely improves the functioning of a system.
Arguments regarding the 103 rejection are moot in view of the new grounds of rejection. The examiner notes that claim 15 has not been listed and is considered cancelled. Previous claim listings had claim 15 recite limitations that substantially mirrored claim 8. Claim 8 is rejected below and claim 15, if it were to reappear with its previous limitations, would likely be rejected on the same grounds.
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 1-14, 16-24, 26-29 and 31-34 are rejected under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Below is an evaluation using the 2019 Revised Patent Subject Matter Eligibility Guidance.
Regarding claim 1, Step 1 is satisfied because a series of instructions form a process.
At step 2a prong 1, an abstract idea is recited: steps of the claim could be performed as a mental process. These steps include receiving a notification, determining a deviation, identifying a set of one or more rules, and evaluating the set of one or more rules to identify availability of a remediation based on community wisdom including telemetry data collected from a plurality of data storage system.
At step 2a prong 2, the claim recites additional elements but these elements do not integrate the judicial exception into a practical application. The claim recites a machine readable medium, one or more processors, and an artificial intelligence for information technology operations (AIOps) platform. These elements do not integrate the judicial exception into a practical application because they only apply the mental process to a generic computer system. Note that the AIOps platform uses elaborate language in its labeling, but it is not clear that this connotes any particular limitations. While the AIOps platform may have specific data stored, the storage of this data is akin to specific programming instructions stored for a generic computer processor. The details of the data stored does not serve to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
At step 2b, the claim recites additional elements but these elements do not amount to significantly more than the judicial exception. The claim recites a machine readable medium, one or more processors, and an artificial intelligence for information technology operations (AIOps) platform. The additional elements also do not amount to significantly more than the judicial exception because they are conventional computing devices which are only generally linked to the abstract idea without meaningfully limiting the mental process.
Regarding claims 2-6, 9, 20, 23 and 24, these claims recite additional limitations of the mental process but their inclusion does not push the mental process beyond what can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper. See MPEP § 2106.04(a)(2)(III). The claims do not recite additional elements which must be evaluated in step 2a prong 2 or step 2b.
Regarding claims 7 and 8, these claims recite causing a remediation action to be executed. While this comes close to implementing an improvement which may serve to integrate the judicial exception into a practical application (similar to Enfish), the claimed remediation is not limited to implementing an resolution but also includes displaying determined steps for remediation (paragraphs 61, 77 and 78 of the specification), which could also be considered a remediation.
Regarding claims 10, 11, 13 and 14, these claims recite limitations found in claims 1, 2, 6 and 7, respectively, and are respectively rejected on the same grounds as claims 1, 2, 6 and 7.
Regarding claim 12, this claim recites use of a machine learning model. This limitation does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception because the machine learning model is a generic component and like a computer, may be used for performing what would otherwise be a mental process. In this case the implementation of the machine learning model is generic and only generally links the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).Regarding claims 16, 18 and 19, these claims recite limitations found in claims 1, 3 and 6, respectively, and are respectively rejected on the same grounds as claims 1, 3 and 6.
Regarding claim 17, this claim recites a fleet of storage systems. This limitation does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception because the storage systems are generic components put to a generic use that does not impact the performance of the judicial exception. The limitation serves to only generally link the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Regarding claims 21 and 22, these claims recite a primary node and backup node. This limitation does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception because the limitation limits the data gathering, instead of limiting the implementation of the judicial exception, and contributes only nominally or insignificantly to the execution of the claimed process.
Regarding claims 28, 29, 33 and 34, these claims recite limitations found in claims 23 and 24 and are rejected on the same grounds as claims 23 and 24.
Regarding claims 26, 27, 31 and 32, these claims recite limitations found in claims 21 and 22 and are rejected on the same grounds as claims 21 and 22.
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 (i.e., changing from AIA to pre-AIA ) 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, 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, 2, 4-14, 16, 17, 19, 20, 22-24, 27-29 and 32-34 are rejected under 35 U.S.C. 103 as being unpatentable over Davlos (PG-PUB 2014/0310222) in view of Dreste (US Patent 5,388,252).
Regarding claim 1, Davlos discloses a non-transitory machine readable medium storing instructions, which when executed by one or more processors cause an auto-heal service to:
after receiving a notification regarding a rule-evaluation trigger event, determine existence of a deviation from a best practice (paragraphs 8-11, technical problems associated with a user device are identified) by a data storage system (paragraph 135, example problems diagnosed are related to an iMac device, which is a data storage device) by:
identifying a set of one or more rules associated with the rule-evaluation trigger event, wherein the set of one or more rules define one or more conditions that are indicative of a root cause of the deviation (paragraph 45, diagnostic decision rules and rule tree hierarchies are generated; the diagnostic engine applies algorithms to create inferences about how to solve a problem; the system creates scripts for applying diagnostic tests to diagnose the problem); and
evaluating the set of one or more rules with respect to one or more of historical data and a current state of the data storage system (paragraph 45, scripts apply diagnostic tests to identify problems); and based on the set of one or more rules, identify availability of a remediation associated with the deviation that addresses or mitigates the deviation (paragraph 45, proposed fix for an identified problem is provided to the user) wherein the set of one or more rules and a plurality of remediations of which the remediation is a part are derived by an artificial intelligence for information technology operations (AIOps) platform (paragraph 8, diagnostic scripts are generated by machine learning system) based on community wisdom including telemetry data (Figure 5, fan speed metrics and read) collected by the AIOps platform from a plurality of data storage systems (paragraph 48 and 53, new cases for data gathered through use of the system are added to the diagnostic database) of the vendor (paragraph 92, the devices diagnosed are other AppleCare devices).
Davlos does not expressly disclose the machine readable medium wherein the artificial intelligence platform is of a vendor of the data storage system.
Dreste teaches a remote diagnostic system in which a manufacturer provides ongoing support and remote diagnosis for a system (column 2 lines 42-48).Prior to the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to modify the device diagnosis system disclosed by Davlos, such that remote diagnostic functionality is performed by a remote device supplier, as taught by Dreste. This modification would have been obvious because technical experts may be available at the remote location of the supplier/manufacturer (column 2 lines 42-44 and column 3 lines 37-44) and, as would be clear to one of ordinary skill in the art, a user who has purchased a device may not have a similar level of expertise.
Regarding claims 2, 4-9, 11, 12, 17, 22-24, Davlos in view of Dreste discloses:
2. (Original) The non-transitory machine readable medium of claim 1, wherein the auto-heal service is operable remotely from the data storage system (Davlos Figure 1 shows the unit under test being remotely connected to the diagnostic AIDE server system).
4. (Original) The non-transitory machine readable medium of claim 2, wherein the instructions further cause the auto-heal service to:
after receiving a second notification regarding a second rule-evaluation trigger event, determine existence of a second deviation from a second best practice by a second data storage system by:
identifying a second set of one or more rules associated with the second rule-evaluation trigger event, wherein the second set of one or more rules define one or more conditions that are indicative of a root cause of the second deviation; and evaluating the second set of one or more rules with respect to one or more of historical data and a current state of the second data storage system; and based on the second set of one or more rules, determine availability of a second remediation associated with the second deviation that addresses or mitigates the second deviation (paragraph 13, testing of multiple devices is performed; since the system is capable of diagnosing multiple issues (Figure 5), it would be clear to one of ordinary skill in the art that the multiple devices diagnosed have different issues and are diagnosed via different diagnostic rules)
5. (Original) The non-transitory machine readable medium of claim 1, wherein the auto-heal service is operable within the data storage system (Davlos paragraph 10, the system can send diagnostic scripts to the user’s device for executing remedial actions on the device).
6. (Original) The non-transitory machine readable medium of claim 1, wherein the one or more rules are derived at least in part based on telemetry data received by a vendor of the data storage system from data storage systems of the vendor that are of a same or similar class and type as the data storage system (Davlos paragraphs 8 and 45, device properties derived from serial number are used as input to the diagnostic engine; Davlos paragraph 135, the reasoning engine selects test cases that are most relevant to a model of an iMac; it would be clear to one of ordinary skill in the art that the system thus collects device data from other instances of the model of iMac).
7. (Original) The non-transitory machine readable medium of claim 1, wherein the instructions further cause the auto-heal service to:
cause an administrative user of the data storage system to be notified of the deviation and the remediation via a graphical user interface associated with the data storage system (Davlos paragraph 163, a graphical user interface can display suggestions for addressing an identified problem); and
after receiving an indication the remediation is authorized by the administrative user, cause one or more remediation actions to be executed by the data storage system that implement the remediation (Davlos paragraph 165, a user chooses to run diagnostic tests suggested by the system; the diagnostic tests are executed and a video about a recommended fix is shown).
8. (Original) The non-transitory machine readable medium of claim 1, wherein the instructions further cause the auto-heal service to automatically cause one or more remediation actions to be executed by the data storage system that implement the remediation (Davlos paragraph 10, diagnostic scripts can be sent to the user’s device for executing remedial actions on the device).
9. (Original) The non-transitory machine readable medium of claim 1, wherein the rule-evaluation trigger event comprises an event that is scheduled on a periodic basis, an event management system event, or an event representing an on-demand rule-evaluation (Davlos paragraph 164, diagnostic tests are performed based on a user confirming a suggested fix action).
11. (Original) The method of claim 10, wherein the method is operable external to the data storage system (abstract of Dreste, a remote service computer operates from a remote location).
12. (Original) The method of claim 10, wherein said evaluating the set of one or more rules involves an inference made by a machine-learning model (Davlos paragraph 45, the diagnostic engine applies inferences about how to solve a problem).
17. (Original) The storage system of claim 16, wherein the auto-heal service is operable remotely from the storage system (abstract of Dreste, a remote service computer operates from a remote location) and is associated with a fleet of related storage systems including the storage system (Davlos paragraph 135, the reasoning engine selects test cases that are most relevant to a model of an iMac; the tests are therefore associated with other instances of the model).
22. (New) The non-transitory machine readable medium of claim 21, wherein the primary node is operable to collect and report telemetry data relating to the cluster to the AIOps platform (Davlos paragraph 48, the diagnostic platform is updated over time; paragraph 129, new solutions are uploaded to the AIDE for further analysis).
23. (New) The non-transitory machine readable medium of claim 1, wherein a given rule of the set of one or more rules includes the one or more defined conditions, which involve the current state or a historical state of the data storage system and when true are indicative of the existence of the deviation (Davlos Figure 5 shows an example decision tree, conditions of the test fan indicate the issue and resulting remedy).
24. (New) The non-transitory machine readable medium of claim 1, wherein a given rule of the set of one or more rules comprises code, within a file, which when executed performs evaluation of the one or more conditions defined by the given rule (Davlos paragraph 10, the system can send diagnostic scripts to the user device; and paragraph 8, scripts to diagnosing a problem may be packaged and sent to the device).
Regarding claim 10, this claim recites limitations found in claim 1 and is rejected on the same grounds as claim 1.
Regarding claims 13 and 14, these claims recite limitations found in claims 6 and 7, respectively, and are respectively rejected on the same grounds as claims 6 and 7.
Regarding claim 16, this claim recites limitations found in claim 1 and is rejected on the same grounds as claim 1.
Regarding claims 19 and 20, these claims recite limitations found in claims 6 and 7, respectively, and are respectively rejected on the same grounds as claims 6 and 7.
Regarding claims 27-29 and 32-34, these sets of claims each recite limitations found in claims 22-24, respectively, and are respectively rejected on the same grounds as claims 22-24.
Claims 3 and 18 are rejected under 35 U.S.C. 103 as being unpatentabe over Davlos in view of Dreste and Tarlano (PG-PUB 2019/0102244).
Regarding claim 3, Davlos in view of Dreste discloses the non-transitory machine readable medium of claim 2. Davlos in view of Dreste does not expressly disclose the medium wherein the notification is received by the auto-heal service via a publisher-subscriber messaging queue system implemented by the data storage system.
Tarlano teaches a remotely implemented service that receives observation data and determines a predictive failure trend (Figure 5). Clients who have subscribed are asynchronously notified of failure predictions (paragraph 31). Pushable events include I/O events, network events, device driver and hardware events (paragraph 41).
Prior to the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to modify the remote device diagnostics system disclosed by Davlos in view of Dreste such that device failures are predicted and failure predictions are subscribed to, as taught by Tarlano. This modification would have been obvious because the model of subscribing to predicted failure events allows reactive software applications to observe the predictions and handle future failures beyond present time error and exception handling (Tarlano paragraph 46).
Regarding claim 18, this claim recites limitations found in claim 3 and is rejected on the same grounds as claim 3.
Claims 21, 26 and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Davlos in view of Dreste and Gold (PG-PUB 2004/0059735).
Regarding claim 21, Davlos in view of Dreste discloses the non-transitory machine readable medium of claim 5, wherein the data storage system includes a plurality of nodes organized as a cluster (Figure 1b shows the use of multiple AIDE Servers behind a load balancer).
Davlos in view of Dreste does not expressly disclose the medium wherein the auto-heal service is implemented on a primary node of the plurality of nodes, and wherein a second node of the plurality of nodes serves as a backup node for the auto-heal service should the primary node experience a failover event.
Gold teaches a distributed computing environment in which a duplicate application is accessed upon failure to access a first application (paragraph 3). The duplicate application being accessed on a backup application server (Figure 1).Prior to the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to modify the device diagnostics system disclosed by Davlos in view of Dreste such that a secondary duplicate diagnostic application is accessible on a backup server, as taught by Gold. This modification would have been obvious because switching to a backup server is a known recovery procedure when communication failure occurs with a first server (Gold paragraph 2).Regarding claims 26 and 31, these claims recite limitations found in claim 21 and are rejected on the same grounds as claim 21.
Claims 25, 30 and 35 are rejected under 35 U.S.C. 103 as being unpatentable over Davlos in view of Dreste and Nulty (PG-PUB 2014/0130111).
Regarding claim 25, Davlos in view of Dreste discloses the non-transitory machine readable medium of claim 5, wherein the set of one or more rules is in a form of a machine-learning model (paragraph 8, diagnostic scripts are generated by machine learning system).
Davlos in view of Dreste does not expressly disclose the medium wherein the set of one or more rules is delivered to the data storage system.
Nulty teaches a diagnostic system in which an agent may be installed in a customer device (paragraph 32) (and thus delivered to the customer device).Prior to the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to modify the device diagnostics system disclosed by Davlos in view of Dreste such that a diagnostic agent is delivered to the customer device, as taught by Nulty. This modification would have been obvious because some problems may be particularly difficult for a customer to diagnose on their own (paragraph 28) or to diagnose remotely by a service provider (paragraph 31) and a diagnostic agent may be useful to detect, diagnose or repair service affecting conditions (Nulty paragraph 10).
Regarding claims 30 and 35, these claims recite limitations found in claim 25 and are rejected on the same grounds as claim 25.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Dickgiesser teaches use of a trained machine learning model used to service degradations, the MLM being trained on historic telemetry data and being used to predict future service degradations. Bates-Maricle teaches a machine learning chatbot which receives a prompt with embedded information and generates an error diagnosis.
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 extension fee 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 date of this final action.
This action is a final rejection and closes the prosecution of this application. Applicant’s reply under 37 CFR 1.113 to this action is limited to an appeal to the Patent Trial and Appeal Board, an amendment complying with the requirements set forth below, or a request for continued examination (RCE) to reopen prosecution where permitted. Please note that the Office also offers initiatives that are available to applicants after the close of prosecution. See https://www.uspto.gov/patents/initiatives/uspto-patent-applications-iniatives-timeline for more information.
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Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH SCHELL whose telephone number is (571) 272-8186. The examiner can normally be reached on Monday through Friday 9AM-5:00PM (Pacific Time).
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JS/JOSEPH O SCHELL/Primary Examiner, Art Unit 2114
Prosecution Insights