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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mental processes – concepts performed in the human mind.
Regarding claim 1, the claim is directed to mental processes.
The limitations ‘receiving, upon occurrence of a first recovery event associated with a corresponding one of a plurality of components in a first system, a first set of corresponding recovery event data, the corresponding one of the plurality of components in the first system being device or hardware component that is part of the first system, the first recovery event being associated with an error that is corrected as a result of executing at least a portion of a recovery sequence, the first system being a computing system; retrieving a set of first corresponding performance metrics associated with the corresponding one of the plurality of components; providing the first set of corresponding recovery event data and the first set of corresponding performance metrics to a first time sequence machine learning model’ are mental processes – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion. The specification states in paragraph 0042 - Additionally, it should be understood that in the embodiments disclosed herein, one or more of the steps can be performed manually.
Step 2A: Prong two
This judicial exception is not integrated into a practical application because the additional elements ‘the first time sequence machine learning model configured to analyze the first set of corresponding recovery event data and the first set of corresponding performance metrics to generate a first likelihood of failure metric for the corresponding one of the plurality of components in the first system’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)). The machine learning model is described at a high-level of generality.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements ‘in response to the first likelihood of failure metric exceeding a first threshold, automatically generating a first control signal, the first control signal causing a change of state of another one of the plurality of components, so as to mitigate at least one impact of a possible failure of the corresponding one of the plurality of components’ are directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)).
Regarding claim 2, the limitation ‘wherein the automatic action is configured to trigger at least one of logical and physical isolation of the corresponding one of the plurality of components’ is directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)).
Regarding claim 3, the limitation ‘wherein the first time sequence machine learning model is trained using failure data associated with one or more other components having one or more characteristics in common with the corresponding one of the plurality of components of the first system’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f))..
Regarding claim 4, the limitation ‘wherein the first time sequence machine learning model is tuned based on at least one of the first set of corresponding recovery event data and the first likelihood of failure metric’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Regarding claim 5, the limitation ‘further comprising: continually tuning the first time sequence machine learning model based on at least one of the first set of recovery event data and the first likelihood of failure metric and a second recovery event information and one or more second likelihood of failure metrics, wherein the second recovery event information and the one or more second likelihood of failure metrics are generated in and communicated by a second system that is in operable communication with the first system’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Regarding claim 6, the limitation ‘further comprising at least one of setting a value and adjusting a value of the first threshold based on at least one of pre-failure event data and failure event data of the first system’ is a mental process – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion.
Regarding claim 7, the limitation ‘further comprising at least one of setting a value and adjusting a value of the first threshold based on at least one of pre-failure event data and failure event data of a second system in operable communication with the first system’ is a mental process – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion.
Regarding claim 8, the limitation ‘wherein the first set of corresponding recovery event data results from execution of a recovery flow having a plurality of steps and wherein the first set of corresponding recovery data comprises information relating to depth of recovery completed, the depth of recovery corresponding to progress through the plurality of steps’ is directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), indicating the type of data.
Regarding claim 9, the limitation ‘further comprising: storing the first likelihood of failure metric in a database, along with one or more corresponding conditions, or events associated with the first likelihood of failure metric – is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, MPEP 2106.05(d) iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; providing a simulation system configured to simulate the first system - is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)); configuring the simulation system to simulate the one or more corresponding conditions or events associated with the first likelihood of failure metric - is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)) and able to be performed by a human using a computer as a tool per paragraph 0042; exercising a predetermined recovery flow in the simulation system, wherein the predetermined recovery flow is configured to perform at least one action responsive to mitigate an issue simulated in the simulation system - is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)) and able to be performed by a human using a computer as a tool per paragraph 0042; evaluating the predetermined recovery flow based on how well it mitigates the issue - is a mental process – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion; and adjusting the predetermined recovery flow, based on results of exercising it in the simulation system, to improve an ability of the predetermined recovery flow to mitigate the issue - is a mental process – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion’ .
Regarding claim 10, the limitation ‘further comprising: aggregating at least one of recovery event data and performance metrics from the plurality of components into a set of aggregated field data; and tuning the first time sequence machine learning model based at least in part on the aggregated field data’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)) and able to be performed by a human using a computer as a tool per paragraph 0042.
Regarding claim 11, with the exception of the limitations ‘a processor; and a non-volatile memory in operable communication with the processor and storing computer program code that when executed on the processor causes the processor to execute a process operable to perform operations of’, the claim is directed to mental processes.
The limitations ‘receiving, upon occurrence of a first recovery event associated with a corresponding one of a plurality of components in a first system, a first set of corresponding recovery event data, the corresponding one of the plurality of components in the first system being device or hardware component that is part of the first system, the first recovery event being associated with an error that is corrected as a result of executing at least a portion of a recovery sequence, the first system being a computing system; retrieving a set of first corresponding performance metrics associated with the corresponding one of the plurality of components; providing the first set of corresponding recovery event data and the first set of corresponding performance metrics to a first time sequence machine learning model; if the first likelihood of failure metric exceeds a first threshold’ are mental processes – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion. The specification states in paragraph 0042 - Additionally, it should be understood that in the embodiments disclosed herein, one or more of the steps can be performed manually.
Step 2A: Prong two
This judicial exception is not integrated into a practical application because the additional elements ‘a processor; and a non-volatile memory in operable communication with the processor and storing computer program code that when executed on the processor causes the processor to execute a process operable to perform operations of; the first time sequence machine learning model configured to analyze the first set of corresponding recovery event data and the first set of corresponding performance metrics to generate a first likelihood of failure metric for the corresponding one of the plurality of components in the first system’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)). The machine learning model is described at a high-level of generality.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements ‘in response to the first likelihood of failure metric exceeding a first threshold, automatically generating a first control signal, the first control signal causing a change of state of another one of the plurality of components, so as to mitigate at least one impact of a possible failure of the corresponding one of the plurality of components’ are directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)).
Regarding claim 12, the limitation ‘wherein the automatic action is configured to trigger at least one of logical and physical isolation of the corresponding one of the plurality of components’ is directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)).
Regarding claim 13, the limitation ‘comprising at least one of setting a value and adjusting a value of the first threshold based on at least one of pre-failure event data and failure event data of the first system’ is a mental process – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion.
Regarding claim 14, the limitation ‘comprising at least one of setting a value and adjusting a value of the first threshold based on at least one of pre-failure event data and failure event data of a second system in operable communication with the first system’ is a mental process – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion.
Regarding claim 15, the limitation ‘aggregating at least one of recovery event data and performance metrics from the plurality of components into a set of aggregated field data; and tuning the first time sequence machine learning model based at least in part on the aggregated field data’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)) and able to be performed by a human using a computer as a tool per paragraph 0042.
Regarding claim 16, the limitation ‘wherein the first set of corresponding recovery event data results from execution of a recovery flow having a plurality of steps and wherein the first set of corresponding recovery data comprises information relating to depth of recovery completed, the depth of recovery corresponding to progress through the plurality of steps’ is directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), indicating the type of data.
Regarding claim 17, with the exception of the limitations ‘A computer program product including a non-transitory computer readable storage medium having computer program code encoded thereon that when executed on a processor of a computer causes the computer to operate a failure prediction system’, the claim is directed to mental processes.
The limitations ‘receiving, upon occurrence of a first recovery event associated with a corresponding one of a plurality of components in a first system, a first set of corresponding recovery event data, the corresponding one of the plurality of components in the first system being device or hardware component that is part of the first system, the first recovery event being associated with an error that is corrected as a result of executing at least a portion of a recovery sequence, the first system being a computing system; retrieving a set of first corresponding performance metrics associated with the corresponding one of the plurality of components; providing the first set of corresponding recovery event data and the first set of corresponding performance metrics to a first time sequence machine learning model; if the first likelihood of failure metric exceeds a first threshold’ are mental processes – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion. The specification states in paragraph 0042 - Additionally, it should be understood that in the embodiments disclosed herein, one or more of the steps can be performed manually.
Step 2A: Prong two
This judicial exception is not integrated into a practical application because the additional elements ‘A computer program product including a non-transitory computer readable storage medium having computer program code encoded thereon that when executed on a processor of a computer causes the computer to operate a failure prediction system; the first time sequence machine learning model configured to analyze the first set of corresponding recovery event data and the first set of corresponding performance metrics to generate a first likelihood of failure metric for the corresponding one of the plurality of components in the first system’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)). The machine learning model is described at a high-level of generality.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements ‘in response to the first likelihood of failure metric exceeding a first threshold, automatically generating a first control signal, the first control signal causing a change of state of another one of the plurality of components, so as to mitigate at least one impact of a possible failure of the corresponding one of the plurality of components’ are directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)).
Regarding claim 18, the limitation ‘triggering at least one of logical and physical isolation of the corresponding one of the plurality of components’ is directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)).
Regarding claim 19, the limitation ‘wherein the first set of corresponding recovery event data results from execution of a recovery flow having a plurality of steps and wherein the first set of corresponding recovery data comprises information relating to depth of recovery completed, the depth of recovery corresponding to progress through the plurality of steps’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Regarding claim 20, the limitation ‘aggregating at least one of recovery event data and performance metrics from the plurality of components into a set of aggregated field data; and computer program code for tuning the first time sequence machine learning model based at least in part on the aggregated field data’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)) and able to be performed by a human using a computer as a tool per paragraph 0042.
There is no prior art rejection for claims 1-20 because of the inclusion of the following limitations: ‘providing the first set of corresponding recovery event data and the first set of corresponding performance metrics to a first time sequence machine learning model, the first time sequence machine learning model configured to analyze the first set of corresponding recovery event data and the first set of corresponding performance metrics to generate a first likelihood of failure metric for the corresponding one of the plurality of components in the first system; and initiating, if the first likelihood of failure metric exceeds a first threshold, automatic generation of a first control signal configured to initiate an automatic action within the first system configured to mitigate at least one impact of a possible failure of the corresponding one of the plurality of components.
Response to Arguments
Applicant's arguments and amendments filed 10/28/2025 have been fully considered but they are not persuasive. The 101 rejection still stands. The newly added limitations do not overcome the 101 rejection. The newly added limitations pertaining to the ‘receiving’ limitation is concerned with adding the type of information that is being received. There is no indication of tying the ‘receiving’ limitation to any type of specific hardware. The limitation pertaining to ‘in response to the first likelihood of failure metric exceeding a first threshold, automatically generical a firs control signal’ is adding insignificant extra-solution activity and is not directed to adding what is performing this limitation.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The closest prior art: USPN 20230069498 – paragraph 0018 - At 110, the processor 102 may train a predictive data model using the plurality of images as inputs and the repair events as known outputs to the predictive data model. For example, the processor 102 may identify repair events (e.g., break-fix repairs) based on the repair event data, identify a plurality of failure windows and operational windows associated with the plurality of IT devices based on the identified repair events (e.g., break-fix repairs) and a size of the failure windows, and classify a plurality of sliding windows associated with the plurality of images based on the plurality of failure windows and operational windows. As used herein, a “failure window” (see, the failure window 344 from FIG. 3) includes and/or refers to a defined period of time, which may be referred to as a window of a specific length, before the IT device has a break-fix repair and sometimes including the break-fix repair. A break-fix repair may include a repair to a broken component and/or the IT device. The failure window may be a particular size, which may be set as further described herein. An “operational window” (see, the operational window 342 from FIG. 3) includes and/or refers to a defined period of time or a window of a specific length proceeding the failure window. A “sliding window” (see, the sliding window 346 from FIG. 3) includes and/or refers a defined period of time under analysis, such as a number of days or other period of times of event codes to include in an image. In some examples, the sliding window is less than the length of the failure window.
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Yolanda L Wilson whose telephone number is (571)272-3653. The examiner can normally be reached M-F (7:30 am - 4 pm).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bryce Bonzo can be reached at 571-272-3655. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Yolanda L Wilson/Primary Examiner, Art Unit 2113