DETAILED ACTION
The following is a Final Office action in response to communications received 12/17/25. Claims 1, 6, 8, 11, 16, 18, and 20 have been amended. Therefore, claims 1-20 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 Objections
Claims 11-19 are objected to because of the following informalities:
In claim 11, The examiner suggests that “generate, using an alert classification network machine learning model” should read “generating, using an alert classification network machine learning model”, in order to correspond the other steps of claim 11.
Appropriate correction is required.
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.
Claim(s) 1-20 is(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) the limitation(s) of “generate, using an alert classification neural network machine learning model comprising a set of neural network layers that process the one or more alert attribute data fields in accordance with trained parameters, deep-learning-based alert priority score for the software alert data object”, “generate, based on the one or more alert attribute data fields and using one or more alert priority score adjustment models, one or more alert priority score adjustment scores for the software alert data object”, “generate an alert priority score for the software alert data object based on the deep-learning-based alert priority score and the one or more alert priority score adjustment scores”, and “generate an alert signature for the software alert data object based on the alert priority score, wherein the alert signature describes a predicted likelihood that the software alert data object is related to at least one software incident data object” in claims 1 and 20, and
“generate, using an alert classification neural network machine learning model comprising a set of neural network layers that process the one or more alert attribute data fields in accordance with trained parameters, deep-learning-based alert priority score for the software alert data object”, “generating, based on the one or more alert attribute data fields and using one or more alert priority score adjustment models, one or more alert priority score adjustment scores for the software alert data object”, “generating an alert priority score for the software alert data object based on the deep- learning-based alert priority score and the one or more alert priority score adjustment scores”, and “generating an alert signature for the software alert data object based on the alert priority score, wherein the alert signature describes a predicted likelihood that the software alert data object is related to at least one software incident data object” in claim 11. 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 processor” and “at least one non-transitory memory” in claim 1 and “at least one non-transitory computer-readable storage medium” in claim 20, 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 “generate, using an alert classification neural network machine learning model comprising a set of neural network layers that process the one or more alert attribute data fields in accordance with trained parameters, deep-learning-based alert priority score for the software alert data object” involves subjective choices as to which factors, criteria, and weights combine into a priority score and the number, types, and levels of priority scores and includes the concepts of evaluation, judgment, and opinion, “generate, based on the one or more alert attribute data fields and using one or more alert priority score adjustment models, one or more alert priority score adjustment scores for the software alert data object” involves subjective choices as to which factors, criteria, and weights combine into a priority score and the number, types, and levels of priority scores and includes the concepts of evaluation, judgment, and opinion, “generate an alert priority score for the software alert data object based on the deep-learning-based alert priority score and the one or more alert priority score adjustment scores” involves subjective choices as to which factors, criteria, and weights combine into a priority score and the number, types, and levels of priority scores and includes the concepts of evaluation, judgment, and opinion, and “generate an alert signature for the software alert data object based on the alert priority score, wherein the alert signature describes a predicted likelihood that the software alert data object is related to at least one software incident data object” involves subjective choices as to the factors, criteria, and weights used to generate the alert signature and the method and factors used to predict a likelihood and includes the concepts of observation, evaluation, judgment, and opinion in claims 1 and 20, and
“generate, using an alert classification neural network machine learning model comprising a set of neural network layers that process the one or more alert attribute data fields in accordance with trained parameters, deep-learning-based alert priority score for the software alert data object” involves subjective choices as to which factors, criteria, and weights combine into a priority score and the number, types, and levels of priority scores and includes the concepts of evaluation, judgment, and opinion, “generating, based on the one or more alert attribute data fields and using one or more alert priority score adjustment models, one or more alert priority score adjustment scores for the software alert data object” involves subjective choices as to which factors, criteria, and weights combine into a priority score and the number, types, and levels of priority scores and includes the concepts of evaluation, judgment, and opinion, “generating an alert priority score for the software alert data object based on the deep- learning-based alert priority score and the one or more alert priority score adjustment scores” involves subjective choices as to which factors, criteria, and weights combine into a priority score and the number, types, and levels of priority scores and includes the concepts of evaluation, judgment, and opinion, and “generating an alert signature for the software alert data object based on the alert priority score, wherein the alert signature describes a predicted likelihood that the software alert data object is related to at least one software incident data object” involves subjective choices as to the factors, criteria, and weights used to generate the alert signature and the method and factors used to predict a likelihood and includes the concepts of observation, evaluation, judgment, and opinion in claim 11. 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 “identify a software alert data object for the software application framework, wherein the software alert data object is associated with one or more alert attribute data fields”, “generate a prediction output user interface configured to receive one or more user feedback data objects associated with the alert signature”, and “retrain the one or more alert priority score adjustment models using the received one or more user feedback data objects” in claims 1 and 20, and
“identifying a software alert data object for the software application framework, wherein the software alert data object is associated with one or more alert attribute data fields”, “performing one or more automated system maintenance operations for the software application framework in accordance with the alert signature”, “generating a prediction output user interface configured to receive one or more user feedback data objects associated with the alert signature”, and “retraining the one or more alert priority score adjustment models using the received one or more user feedback data objects” in claim 11 are recited at a high level of generality, i.e., as generic processor performing a generic computer function. This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component.
The examiner notes that in order to improve the functioning of a computer, a particular solution to a specific problem is required (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)), instead of a generic solution to any general problem or incident. However, neither a generic solution nor a particular solution are present in the claims as amended. 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 ([0075-0090], fig. 3, 4).
Claim(s) 2-10 and 12-19 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.
Claims 2 and 12 identify features of and generate a priority adjustment score which also involves subjective choices 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.
Claims 3 and 13 lists a type of feature 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.
Claims 4, 5, 14, and 15 generates more priority scores involving subjective choices 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.
Claims 6, 7, 16, and 17 generate an alert using subjective choices 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.
Claims 8, 9, 10, 18, and 19 generate a priority score based on subjective choices 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.
Claims 1-20 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 12/17/25 have been fully considered but they are not persuasive.
In response to applicant’s argument (see p. 11 of remarks) that the office action did not consider and is inconsistent with the viewpoints expressed by the Appeals Review Panel, the examiner respectfully disagrees.
The examiner notes that the appeals review panel in: Ex Parte Desjardines discusses determining whether the additional elements “are directed to an improvement to computer functionality versus being directed to an abstract idea” and the limitation adjusting values to optimize an AI system to “optimize performance” of the model while “protecting the performance” of another model.
In the instant application, the examiner notes that none of the additional limitations: “identify a software alert data object for the software application framework, wherein the software alert data object is associated with one or more alert attribute data fields and a deep- learning-based alert priority score”, “generate a prediction output user interface configured to receive one or more user feedback data objects associated with the alert signature”, and “retrain the one or more alert priority score adjustment models using the received one or more user feedback data objects” involve an improvement in the functioning of a computer.
The examiner notes that in order to improve the functioning of a computer, a particular solution to a specific problem is required (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)), instead of a generic solution to any general problem or incident. However, neither a generic solution nor a particular solution are present in the claims as amended. 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.
Specifically, generating a subjective “prediction” based on subjective “priority scores” for a subjectively determined “alert” and then retraining alert priority score adjustment models does not improve the computer itself or provide a particular solution. No incident has been provided with a particular solution, instead a subjective model based on subjective information creates a subjective prediction. However, the prediction is not used to implement a solution to an alert (incident).
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bryce Bonzo can be reached at 5712723655. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOSHUA P LOTTICH/ Primary Examiner, Art Unit 2113