DETAILED ACTION
The following is a Non-Final Office action in response to communications received 5/22/26. Claims 1, 11, 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 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
“generating, 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” and “perform one or more automated system maintenance operations for the software application framework in accordance with the alert signature” 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” and “performing one or more automated system maintenance operations for the software application framework in accordance with the alert signature” 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.
This judicial exception is not integrated into a practical application because the additional elements recited including “perform one or more automated system maintenance operations for the software application framework in accordance with the alert signature” in claims 1 and 20 and “performing one or more automated system maintenance operations for the software application framework in accordance with the alert signature” in claim 11 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 “performing one or more automated maintenance operations for the software application framework in accordance with the alert signature” could potentially 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 and all possible problems. The examiner notes that “alert signature” and “automated system maintenance operations” are a generic problem and solution and is equivalent to “apply it”, applying a generic “maintenance operation” or solution to any and all “alert signatures” or problems. 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 5/22/26 have been fully considered but they are not persuasive.
In response to applicant’s argument (see p. 10-11 of remarks) that the claims are not directed to an abstract idea, the examiner respectfully disagrees.
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 for substantially similar claim 11.
The examiner notes that the abstract ideas are the choices used in the generation of the subjective scores, models, and alert signature. The examiner notes that “a claim that requires a computer may still recite a mental process” and “if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process” (see MPEP 2106.04(a)(2)(III)(C)) and the use of a neural network machine learning model would constitute using a computer as a tool to perform the mental process.
In response to applicant’s argument (see p. 11 of remarks) that the claims as amended are integrated into a practical application, the examiner respectfully disagrees.
This judicial exception is not integrated into a practical application because the additional elements recited including “perform one or more automated system maintenance operations for the software application framework in accordance with the alert signature” in claims 1 and 20 and “performing one or more automated system maintenance operations for the software application framework in accordance with the alert signature” in claim 11 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 “performing one or more automated maintenance operations for the software application framework in accordance with the alert signature” could potentially 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 and all possible problems. The examiner notes that “alert signature” and “automated system maintenance operations” are a generic problem and solution and is equivalent to “apply it”, applying a generic “maintenance operation” or solution to any and all “alert signatures” or problems. 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.
Conclusion
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
Prosecution Insights