DETAILED ACTION
The following is a Non-Final Office action in response to communications received 1/22/26. Claim(s) 2-4, 6, 14, and 18 has(have) been canceled. Claim(s) 1, 13, and 17 has(have) been amended. Therefore, claim(s) 1, 5, 7-13, 15-17, and 19-26 is(are) pending and addressed below.
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, 7-13, 15-17, and 19-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim(s) recite(s) the limitation(s) of “predict, by the sequential machine learning model architecture respectively executing a plurality of machine learning models, a future operational state of the at least one device, at least one impact of the future operational state, and at least one trend associated with the at least one impact, wherein the predictions are based at least in part on the received data, wherein a portion of outputs of the plurality of machine learning models are coupled to a portion of inputs of the plurality of machine learning models” and “generate, by the sequential machine learning model architecture, a remediation plan for the at least one device based at least in part on the predictions” in claims 1 and 13 and
“predicting, by the sequential machine learning model architecture respectively executing a plurality of machine learning models, a future operational state of the at least one device, at least one impact of the future operational state, and at least one trend associated with the at least one impact, wherein the predictions are based at least in part on the received data, wherein the predictions are based at least in part on the received data, wherein a portion of outputs of the plurality of machine learning models are coupled to a portion of inputs of the plurality of machine learning models” and “generating, by the sequential machine learning model architecture, a remediation plan for the at least one device based at least in part on the predictions” in claim 17.
This/These limitation(s), as drafted, is(are) a process (processes) that, under its (their) broadest reasonable interpretation, cover(s) performance of the limitation(s) in the mind but for the recitation of generic computer components. That is, other than reciting “at least one processing device”, “a processor”, “a memory”, and “one or more device components” in claim 1, “a non-transitory processor-readable storage medium”, “at least one processing device” and “one or more device components” in claim 13, and “at least one device”, “one or more device components”, “at least one processing device”, “a processor”, and “a memory” in claim 17, nothing in the claim elements precludes the steps from practically being performed in the mind. The mere nominal recitation of generic processing components does not take the claim limitation(s) out of the mental processes grouping.
The examiner notes that “predict, by the sequential machine learning model architecture respectively executing a plurality of machine learning models, a future operational state of the at least one device, at least one impact of the future operational state, and at least one trend associated with the at least one impact, wherein the predictions are based at least in part on the received data, wherein a portion of outputs of the plurality of machine learning models are coupled to a portion of inputs of the plurality of machine learning models” involves subjective choices as to the factors, weights, and basis used in determining each of the predicted operational state, impact, and trends and the correspondence between the operation data of the devices and components and their future state, impact, and trends and includes the concepts of evaluation, judgment, and opinion and
“generate, by the sequential machine learning model architecture, a remediation plan for the at least one device based at least in part on the predictions” involves subjective choices as to the factors and basis used to create a remediation plan and the judgment and/or opinions of how to the create a plan that corresponds to subjectively predicted states, impacts, and trends and includes the concepts of observation, evaluation, judgment, and opinion in claims 1 and 13 and
“predicting, by the sequential machine learning model architecture respectively executing a plurality of machine learning models, a future operational state of the at least one device, at least one impact of the future operational state, and at least one trend associated with the at least one impact, wherein the predictions are based at least in part on the received data, wherein the predictions are based at least in part on the received data, wherein a portion of outputs of the plurality of machine learning models are coupled to a portion of inputs of the plurality of machine learning models” involves subjective choices as to the factors, weights, and basis used in determining each of the predicted operational state, impact, and trends and the correspondence between the operation data of the devices and components and their future state, impact, and trends and includes the concepts of evaluation, judgment, and opinion and
“generating, by the sequential machine learning model architecture, a remediation plan for the at least one device based at least in part on the predictions” involves subjective choices as to the factors and basis used to create a remediation plan and the judgment and/or opinions of how to the create a plan that corresponds to subjectively predicted states, impacts, and trends and includes the concepts of observation, evaluation, judgment, and opinion in claim 17. Thus, the claim(s) recite(s) a mental process, concepts that may be performed in the human mind, in this case being observation, evaluation, judgment, and opinion.
This judicial exception is not integrated into a practical application because the additional elements recited including “receive, by the sequential machine learning model architecture, data corresponding to operation of at least one device and one or more device components”, “wherein the data corresponding to the operation of the at least one device and the one or more device components comprises at least one of central processing unit utilization, memory consumption, drive usage, device model, drive model, memory model and memory size”, “wherein the program code when executed by the at least one processing device further causes the at least one processing device to train the plurality of machine learning models with at least a portion of the data corresponding to the operation of the at least one device and the one or more device components”, “wherein, in predicting the future operational state of the at least one device, the at least one processing device is configured to use a stochastic machine learning model to predict respective probabilities of one or more future operational states of the at least one device based on a most recent known operational state of the at least one device”, and “wherein the at least one impact of the future operational state comprises at least one of degraded performance, data loss and an increase in crash frequency” in claims 1 and 13 and
“receiving, by the sequential machine learning model architecture, data corresponding to operation of at least one device and one or more device components”, “wherein the data corresponding to the operation of the at least one device and the one or more device components comprises at least one of central processing unit utilization, memory consumption, drive usage, device model, drive model, memory model and memory size”, “wherein the method further comprises training the plurality of machine learning models with at least a portion of the data corresponding to the operation of the at least one device and the one or more device components”, “wherein the predicting of the future operational state of the at least one device executes a stochastic machine learning model to predict respective probabilities of one or more future operational states of the at least one device based on a most recent known operational state of the at least one device”, “wherein the at least one impact of the future operational state comprises at least one of degraded performance, data loss and an increase in crash frequency”, and “wherein the method is performed by at least one processing device comprising a processor coupled to a memory configured to operate as the sequential machine learning model architecture” in claim 17 are recited at a high level of generality, i.e., as generic processor performing a generic computer function. Generic processor limitations are no more than mere instructions to apply the exception using a generic computer component.
The examiner notes that while executing a remediation plan could possibly improve the functioning of a computer, it is not “a particular solution” to a specific problem (An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome, see MPEP 2106.05(a), The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it", see MPEP 2106.05(f)), but instead a generic solution to any general problem. In this case the generic “remediation plan” could apply to any issue and is not particular way to solve any specific problem or “impact”. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the additional elements fail to improve the functionality of the computer itself.
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 when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology or effects a transformation or reduction of a particular article to a different state or thing. Their collective functions merely provide conventional computer implementation. Furthermore, the applicant’s own specification details the generic nature of the computing components, which also precludes them from presenting anything significantly more (p 19-20, fig. 8).
Claim(s) 5, 7-12, 15, 16, and 19-26 do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim(s) 5, 7, 8, 15, 19, 23, and 25 simply detail types of machine learning model and do(es) not provide a practical application and also do(es) not provide significantly more in that the computer system is not improved or even affected.
Claim(s) 9, 24, and 26 simply converts time-series data and trains a machine learning model with the converted data and do(es) not provide a practical application and also do(es) not provide significantly more in that the computer system is not improved or even affected.
Claim(s) 10, 16, and 20 involve a mental process in the subjective choice as to what metric value corresponds to a reduction in an impact and do(es) not provide a practical application and also do(es) not provide significantly more in that the computer system is not improved or even affected.
Claim(s) 11 and 21 simply detail a basis of the remediation plan and do(es) not provide a practical application and also do(es) not provide significantly more in that the computer system is not improved or even affected.
Claim(s) 12 and 22 simply generates a visualization of a network topology and do(es) not provide a practical application and also do(es) not provide significantly more in that the computer system is not improved or even affected.
Claim(s) 1, 5, 7-13, 15-17, and 19-26 is(are) therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Response to Arguments
Applicant's arguments filed 5/26/26 have been fully considered but they are not persuasive.
In response to applicant's argument (see p. 8 of remarks) that the "claims do not recite an abstract idea", the examiner respectfully disagrees.
The examiner notes that “predict, by the sequential machine learning model architecture respectively executing a plurality of machine learning models, a future operational state of the at least one device, at least one impact of the future operational state, and at least one trend associated with the at least one impact, wherein the predictions are based at least in part on the received data, wherein a portion of outputs of the plurality of machine learning models are coupled to a portion of inputs of the plurality of machine learning models” involves subjective choices as to the factors, weights, and basis used in determining each of the predicted operational state, impact, and trends and the correspondence between the operation data of the devices and components and their future state, impact, and trends and includes the concepts of evaluation, judgment, and opinion and
“generate, by the sequential machine learning model architecture, a remediation plan for the at least one device based at least in part on the predictions” involves subjective choices as to the factors and basis used to create a remediation plan and the judgment and/or opinions of how to the create a plan that corresponds to subjectively predicted states, impacts, and trends and includes the concepts of observation, evaluation, judgment, and opinion.
In response to applicant's argument (see p. 8 of remarks) that the claims clearly integrate the abstract ideas into a practical application, the examiner respectfully disagrees.
The examiner notes that applicant does not seem to indicate what is the "improvement" in computer technology. The examiner notes that amended claim 1, receives data, predicts an operational state, impact, and trend, and generates a remediation plan. However, after performing these steps the computer is in the same state as before the steps are performed. The remediation plan is generated but is not performed and as such no resolution is implemented and even if the remediation plan was performed it would be insufficient because it does not provide a "particular solution".
The examiner notes that while executing a remediation plan could possibly improve the functioning of a computer, it is not "a particular solution" to a specific problem (An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome, see MPEP 2106.05(a), The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it", see MPEP 2106.05(f)), but instead a generic solution to any general problem. In this case the generic "remediation plan" could apply to any issue and is not particular way to solve any specific problem or generic "impact". Accordingly, these additional elements do not integrate the abstract idea into a practical application or provide significantly more than the exception, because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the additional elements fail to improve the functionality of the computer itself.
Conclusion
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 JOSHUA P LOTTICH whose telephone number is (571)270-3738. The examiner can normally be reached Mon - Fri, 9:00am - 5:30pm.
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/JOSHUA P LOTTICH/ Primary Examiner, Art Unit 2113
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