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 1-3, 5-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
At step 1, if no statutory category rejection was given above, then the claims have been determined to have a statutory category.
At step 2a, prong one, referring to claim 1, as emphasized, there is disclosed a method, performed by a generic computer, of clustering records using a machine-learning based model (which identifies, tags, and clusters in “real-time” using a threshold), creating a problem record and populating it, linking the clustered records to the problem record, providing a notification of the problem record, receiving a resolution for the problem record, updating the machine-learning based model, and using that model to identify code to change. Claim 1 recited, “A method for identifying and handling incidents using a machine-learning based model, the method comprising, performing by one or more processors, operations including: clustering a sub-group of incident records from among a plurality of incident records using the machine-learning based model based on a rolling time window and a number of incident records in the sub-group of incident records, wherein the machine-learning based model is configured to cluster the incident records based on identifying incidents, tagging changes in a computer system corresponding to the incidents, and clustering the incident records in real-time based on a threshold, wherein each of the incident records is a record of an outage of the computer system; creating a problem record based on the clustered incident records; populating the problem record with information related to the clustered incident records; linking the clustered incident records to the problem record; providing a notification that the problem record has been created; receiving a resolution for the problem record; updating, based on the resolution, the machine-learning based model to learn an association between extracted features of the resolution and extracted features of the clustered incident records; and using the updated machine-learning based model to determine identify a portion of computer code associated with an incoming incident record that when changed may remedy the outage to the computer system, thereby improving the operation of the computer system, wherein the portion of computer code includes at least one of a branch, file, class, or module.”
The limitations of clustering, creating, populating, linking, notifying, receiving, updating, and identifying code, as crafted, are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of additional elements that do not integrate the judicial exception into a practical application. That is, nothing in the claims as noted precludes the step from practically being performed in the mind, possibly with the aid pen and paper. For example, these steps perform steps of observation, evaluation, judgment, or opinion.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of additional elements that do not integrate the judicial exception into a practical application, then it falls within the "Mental Processes" grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
At step 2a, prong two, this judicial exception is not integrated into a practical application. In particular the claim additionally recites a generic computer (including the generic computer that is the “machine” of “machine learning”, “real-time”, processor, and memory).
Even when viewed in combination, the additional elements in this claim do no more than automate the mental processes a person may use to perform, using the computer components as a tool.
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. The claim is directed to an abstract idea.
At step 2b, claim 1 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 a generic computer 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.
With respect to the generic computer, the courts have found limitations directed to generic computers, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP2106.05(d), for example TLI Communications, Flook, Alice Corp, and Versata.
Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible. The claim merely implements the abstract idea using one additional element.
Further referring to claims 2-3, 5-10, the claims further describe data and data analysis.
Referring to claims 11-20, see rejection of claims 1-3, 5-10 above.
Response to Arguments
Applicant's arguments filed 23 January 2026 have been fully considered but they are not persuasive.
Regarding Applicant’s argument (page 11) that “as amended”, “clustering … using the machine-learning model… clustering in real-time…” cannot be practically performed in the human mind, Applicant should note the delineation of abstract and additional elements above. “Machine learning” presumably uses a generic computer (assuming this describes a step of learning performed by a machine, rather than merely an algorithm that may be used by machines to perform a process of learning). “Real-time” presumably requires a timing deadline, the specifics of which are unclaimed. To the extent that it implies a certain speed of processing, this merely describes a generic property of a generic processor. A human can group data, which is a step of observation, evaluation, judgment, or opinion. Regarding “huge volumes”, this is not claimed.
Regarding Applicant’s argument (page 12) that training a machine-learning model to learn associations is inherently a computational operation, as above, to the extent that machine learning involves a machine, this is the generic computer that implements the learning. Humans can learn an association, which is a step of observation, evaluation, judgment, or opinion, and humans can also learn new things.
Regarding Applicant’s argument (page 12) that the claims find similarity to example 47, it is unclear how the claims are similar except for the use of “real-time”. Applicant should note that the use of “real-time” in example 47 was in the context of packet analysis and action, i.e., detection, dropping packets, and blocking traffic. In the instant claims, the unspecific deadline of real-time is only used in clustering, a step of observation, evaluation, judgment, or opinion.
Regarding Applicant’s argument (page 13) that the claims improve computer system operation, specifically the problem of outages, and that the solution is also computer-specific. Examiner first notes that, at a high level, what is being claimed is simply debugging. Humans write programs for computers, humans debug the programs. Computers implementing that code have their operations improved through the execution of less buggy code, but the acts of programming and debugging are themselves observation, evaluation, judgment, or opinion. Here, more specifically regarding the claimed invention, a generic machine is made to perform steps of analysis (observation, evaluation, judgment, or opinion) to aid in the debugging of code. Yes, debugged code improves the operation of a computer, but the debugging itself is abstract and regarding technology, not itself an improvement of technology.
Regarding Applicant’s argument (page 13-14) again citing example 47, see above. “Identifying” is a step of observation, evaluation, judgment, or opinion, not a practical application, and it further does not integrate into practical application.
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 GABRIEL L CHU whose telephone number is (571)272-3656. The examiner can normally be reached weekdays 8 am to 5 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ashish Thomas can be reached at (571)272-0631. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GABRIEL CHU/Primary Examiner, Art Unit 2114
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