DETAILED ACTION
Status of Claims
This communication is a non-final action on the merits in response to the amendments and arguments filed on March 13, 2026. Claims 1, 11, 14, 16, and 21 were amended. Claims 1-19 and 21 are currently pending and have been examined.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on March 13, 2026 has been entered.
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 1-19 and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-10 and 21 are directed to a machine. Claims 11-15 are directed to a process. Claims 16-19 are directed to an article of manufacture. As such, each claim is directed to a statutory category of invention.
Step 2A Prong 1
The examiner has identified independent Claim 1 as the claim that represents the claimed invention for analysis and is similar to independent Claims 11 and 16.
Independent Claim 1 recites the following abstract ideas: “service level agreement (SLA) management and breach detection, a workflow management to monitor a plurality of data processing jobs and to write, for each data processing job in the plurality of data processing jobs that completes successfully, information that indicates a success state for a respective data processing job identifya set of data processing jobs included among the plurality of data processing jobs to be tracked for SLA compliance; triggera step function for each data processing job in the set of data processing jobs to be tracked for the SLA compliance, wherein the step function includes a wait time that is based on a time when an SLA associated with the set of data processing jobs is to be satisfied; determinewhen the wait time of the step function has elapsed, whether the information that indicates the success state for the respective data processing job is available ; indicates one or more of SLA compliance information or SLA breach information associated with each data processing job in the set of data processing jobs to be tracked for the SLA compliance based on whether the information that indicates the success state for the respective data processing job is present ; adjust one or more values of one or more weights based on the SLA compliance information or the SLA breach information matching a predicted SLA status; sendone or more messages for one or more data processing jobs associated with an SLA breach, wherein the one or more data processing jobs associated with the SLA breach are in the set of data processing jobs, and wherein the one or more data processing jobs associated with the SLA breach are identified based on the information that indicates the success state for the respective data processing job not being present ; and extendone or more SLA compliance times for the one or more data processing jobs associated with the SLA breach based on a recommendation to extend the one or more SLA compliance times , wherein the one or more SLA compliance times provide one or more deadlines for completion of the one or more data processing jobs associated with the SLA breach
The limitations, as drafted, are a process that, under its broadest reasonable interpretation, relates to legal interactions including agreements in the form of contracts (i.e., service level agreement (SLA) management and breach detection, a workflow management to monitor a plurality of data processing jobs and to write, for each data processing job in the plurality of data processing jobs that completes successfully, information that indicates a success state for a respective data processing job; identify a set of data processing jobs included among the plurality of data processing jobs to be tracked for SLA compliance; trigger a step function for each data processing job in the set of data processing jobs to be tracked for the SLA compliance, wherein the step function includes a wait time that is based on a time when an SLA associated with the set of data processing jobs is to be satisfied; determine when the wait time of the step function has elapsed, whether the information that indicates the success state for the respective data processing job is available; indicates one or more of SLA compliance information or SLA breach information associated with each data processing job in the set of data processing jobs to be tracked for the SLA compliance based on whether the information that indicates the success state for the respective data processing job is present; send one or more messages for one or more data processing jobs associated with an SLA breach, wherein the one or more data processing jobs associated with the SLA breach are in the set of data processing jobs, and wherein the one or more data processing jobs associated with the SLA breach are identified based on the information that indicates the success state for the respective data processing job not being present; and extend one or more SLA compliance times for the one or more data processing jobs associated with the SLA breach based on a recommendation to extend the one or more SLA compliance times, wherein the one or more SLA compliance times provide one or more deadlines for completion of the one or more data processing jobs associated with the SLA breach), but for the recitation of generic computer components (i.e., a system comprising one or more memories, one or more processors communicatively coupled to the one or more memories, system that operates in a serverless environment, a designated storage location, a serverless compute lambda function that is automatically triggered at a scheduled time, the serverless compute lambda function is configured to allocate computing resources to the serverless compute lambda function at the scheduled time, render a user interface, adjust weights associated with a machine learning model, the machine learning model is re-trained using the recommendation to extend the one or more SLA compliance times as a re-training input). If a claim limitation, under its broadest reasonable interpretation, relates to legal interactions including agreements in the form of contracts, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)). In particular, the claim recites the additional elements of a system comprising one or more memories, one or more processors communicatively coupled to the one or more memories, system that operates in a serverless environment, a designated storage location, a serverless compute lambda function that is automatically triggered at a scheduled time, the serverless compute lambda function is configured to allocate computing resources to the serverless compute lambda function at the scheduled time, render a user interface, adjust weights associated with a machine learning model, the machine learning model is re-trained using the recommendation to extend the one or more SLA compliance times as a re-training input (in addition to the non-transitory CRM of Claim 16). The computer hardware is recited at a high level of generality (i.e., generic serverless / cloud platform environment, generic computers receiving, processing, and transmitting information, generic storage location storing information, generic interface displaying information, generic lambda function allocating resources to run, and generic recitation of a machine learning model outputting information and being retrained with the output) such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application, since they do not involve improvements to the functioning of a computer or to any other technology or technical field (MPEP 2106.05(a)), they do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), they do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and they do not apply or use the abstract idea in some other meaningful way beyond generally linking its use to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e)). Therefore, the claim is directed to an abstract idea without a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. The additional elements of using computer hardware (a system comprising one or more memories, one or more processors communicatively coupled to the one or more memories, system that operates in a serverless environment, a designated storage location, a serverless compute lambda function that is automatically triggered at a scheduled time, the serverless compute lambda function is configured to allocate computing resources to the serverless compute lambda function at the scheduled time, render a user interface, adjust weights associated with a machine learning model, the machine learning model is re-trained using the recommendation to extend the one or more SLA compliance times as a re-training input (in addition to the non-transitory CRM of Claim 16)) amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Therefore, the claim is not patent-eligible.
Dependent claim 21 recites utilizing a “feedback loop to optimize one or more operational parameters of the machine learning model,” which is recited as a generic ML training technique used to implement the abstract idea, and does not integrate the abstract idea into a practical application, nor is it sufficient to amount to significantly more than the abstract idea when considered both individually and as an ordered combination.
Dependent claims 2-10, 12-15, and 17-19 do not include any additional elements beyond those identified above. They further define the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above. As such, they do not integrate the abstract idea into a practical application, nor are they sufficient to amount to significantly more than the abstract idea when considered both individually and as an ordered combination.
Therefore, dependent claims 2-10, 12-15, 17-19, and 21 are directed to an abstract idea, and do not include additional elements that integrate the abstract idea into a practical application, or that are sufficient to amount to significantly more than the abstract idea. Thus, the aforementioned claims are not patent-eligible.
Allowable Subject Matter
Claims 1-19 and 21 would be allowable if rewritten or amended to overcome the rejection under 35 U.S.C. 101 set forth in this Office action.
Maskall, in combination with the other references relied upon, teaches configuring a workflow management system to monitor a plurality of data processing jobs and to write, for each data processing job in the plurality of data processing jobs that completes successfully, information that indicates a success state for a respective data processing job to a designated storage location; identify, by the one or more processors and at a scheduled time, a set of data processing jobs included among the plurality of data processing jobs to be tracked for SLA compliance; trigger, by the one or more processors, a step function for each data processing job in the set of data processing jobs to be tracked for the SLA compliance, wherein the step function includes a wait time that is based on a time when an SLA associated with the set of data processing jobs is to be satisfied; determine, by the one or more processors, when the wait time of the step function has elapsed, whether the information that indicates the success state for the respective data processing job is available in the designated storage location; render, by the one or more processors, a user interface that indicates one or more of SLA compliance information or SLA breach information associated with each data processing job in the set of data processing jobs to be tracked for the SLA compliance based on whether the information that indicates the success state for the respective data processing job is present in the designated storage location; send, by the one or more processors, one or more messages for one or more data processing jobs associated with an SLA breach, wherein the one or more data processing jobs associated with the SLA breach are in the set of data processing jobs, and wherein the one or more data processing jobs associated with the SLA breach are identified based on the information that indicates the success state for the respective data processing job not being present in the designated storage location; and extend, by the one or more processors, one or more SLA compliance times for the one or more data processing jobs associated with the SLA breach based on a recommendation to extend the one or more SLA compliance times provided by a machine learning model, wherein the one or more SLA compliance times provide one or more deadlines for completion of the one or more data processing jobs associated with the SLA breach, and wherein the machine learning model is re-trained using the recommendation to extend the one or more SLA compliance times as a re-training input. However, the combination of references does not teach that the SLA management system operates in a serverless environment, or using a serverless compute lambda function that’s automatically triggered at a scheduled time, wherein the serverless compute lambda function is configured to allocate computing resources to the serverless compute lambda function at the scheduled time.
The closest NPL, “A Systematic Review of Service Level Management in the Cloud,” teaches a system for SLA management and breach detection that operates in a serverless environment. However, it does not teach using a serverless compute lambda function that’s automatically triggered at a scheduled time, wherein the serverless compute lambda function is configured to allocate computing resources to the serverless compute lambda function at the scheduled time.
Response to Arguments
Applicant’s Argument Regarding 35 USC 101 Rejection of Claims 1-19 and 21:
Claim 1 recites "adjust, by the one or more processors, one or more values of one or more weights associated with a machine learning model based on the SLA compliance information or the SLA breach information matching a predicted SLA status," and "extend, by the one or more processors, one or more SLA compliance times for the one or more data processing jobs associated with the SLA breach based on a recommendation to extend the one or more SLA compliance times provided by the machine learning model." (emphasis added). These limitations reflect a specific improvement to machine learning technology, namely, improved accuracy of machine learning models for SLA management and breach detection, as described in at least paragraph 32 of the specification. Furthermore, the amended features of claim 1 are nearly identical to the eligible features identified in Ex Parte Desjardins.
As discussed in the USPTO's recent memorandum regarding Ex Parte Desjardins (hereinafter, "Desjardins Memo"), claims that recite improvements to the functioning of a computer or to another technology or technical field—such as adjusting parameters of a machine learning model to optimize system performance—are not directed to a judicial exception under Step 2A, Prong Two. For example, the Desjardins Memo recites: “The second paragraph of MPEP § 2106.05(a), subsection I, is revised to add new examples xiii and xiv to the list of examples that may show an improvement in computer functionality: xiii. An improved way of training a machine learning model that protected the model's knowledge about previous tasks while allowing it to effectively learn new tasks… xiv. Improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams.”
Accordingly, claim 1 sets forth a specific improvement to a machine learning model that directly aligns with example xiv of the Desjardins Memo, which is now included in § 2106.05(a) of the MPEP.
Examiner’s Response: Applicant’s arguments have been fully considered but they are not persuasive.
Claim 1 recites only the idea of a solution and does not recite details of how a solution to a problem is accomplished, and per MPEP 2106.05(f), this does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea. Regarding paragraph [0032], the paragraph does not provide any details of how the claimed invention provides an improvement to the functioning of machine learning technology. It recites the feedback loop for optimizing parameters and accuracy at a high level, and further recites the weights in an exemplary manner. Further, neither the claim nor the amended features are analogous to the claims identified in Ex Parte Desjardins.
The claims of Desjardins were directed to a method—which was the Applicant’s invention itself—used to solve the problem of catastrophic forgetting in the field of machine learning technology. They were not simply using a known technique to improve their machine learning model. Further, the written description provided support for the technical solution to the technical problem. In contrast, the present claims are directed to managing SLA breach and compliance information, and the machine learning model is used as a tool to implement the abstract idea. Further, adjusting values of weights based on information matching (or differing from) a prediction is a generic training technique to error-correct a machine learning model, and the improved accuracy is an expected result of using it.
Conclusion
The prior art made of record and not relied upon, considered pertinent to applicant’s disclosure or directed to the state of art, is listed on the enclosed PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KARMA EL-CHANTI whose telephone number is (571)272-3404. The examiner can normally be reached T-Sa 10am-6pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached at (571)270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KARMA A EL-CHANTI/Examiner, Art Unit 3629 /SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
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