DETAILED ACTION
The following is a Final Office action in response to communications received 3/2/26. Claims 5, 6, 13, 17, 18, 20, and 21 have been canceled. Claims 25-27 have been added. Claims 1, 7, 16, 19, and 22 have been amended. Therefore, claims 1-4, 7-12, 14-16, 19, and 22-27 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) 1-4, 7-12, 14-16, 19, and 22-27 is(are) rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim(s) 1, 16, and 19 recite(s) the limitation(s) of “perform a predictive anomaly detection process to detect anomalous behaviors of applications executing in an information processing system” and “execute an unsupervised machine learning process which analyzes the application-level time-series telemetry data and the hardware-level time-series telemetry data to perform unsupervised detection of anomalous datapoints in the application-level time-series telemetry data and the hardware-level time-series telemetry data to predictively detect anomalous behavior of the application executing in the information processing system in conjunction with performing a first anomaly detection process for the application-level time-series telemetry data and a second anomaly detection process for the hardware-level time-series telemetry data for detecting anomalous behavior in accordance with the execution of the application in the information processing system; wherein the unsupervised machine learning process implements a multi-layer transformer model to perform an attention transformer associative analysis procedure on at least a portion the application-level time-series telemetry data and the hardware-level time-series telemetry data over a given time window to predictively detect the anomalous behavior of the application executing in the information processing system” in claims 1 and 16 and
“perform a predictive anomaly detection process to detect anomalous behaviors of applications executing in an information processing system” and “executing an unsupervised machine learning process which analyzes the application-level time-series telemetry data and the hardware-level time-series telemetry data to perform unsupervised detection of anomalous datapoints in the application-level time-series telemetry data and the hardware-level time-series telemetry data to predictively detect anomalous behavior of the application executing in the information processing system in conjunction with performing a first anomaly detection process for the application-level time-series telemetry data and a second anomaly detection process for the hardware-level time-series telemetry data for detecting anomalous behavior in accordance with the execution of the application in the information processing system; wherein the unsupervised machine learning process implements a multi-layer transformer model to perform an attention transformer associative analysis procedure on at least a portion the application-level time-series telemetry data and the hardware-level time-series telemetry data over a given time window to predictively detect the anomalous behavior of the application executing in the information processing system” in claim 19. 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” in claims 1 and 19 and “a non-transitory processor-readable storage medium” in claim 16, nothing in the claim elements precludes the steps from practically being performed in the mind.
The examiner notes that “perform a predictive anomaly detection process to detect anomalous behaviors of applications executing in an information processing system” and “execute an unsupervised machine learning process which analyzes the application-level time-series telemetry data and the hardware-level time-series telemetry data to perform unsupervised detection of anomalous datapoints in the application-level time-series telemetry data and the hardware-level time-series telemetry data to predictively detect anomalous behavior of the application executing in the information processing system in conjunction with performing a first anomaly detection process for the application-level time-series telemetry data and a second anomaly detection process for the hardware-level time-series telemetry data for detecting anomalous behavior in accordance with the execution of the application in the information processing system; wherein the unsupervised machine learning process implements a multi-layer transformer model to perform an attention transformer associative analysis procedure on at least a portion the application-level time-series telemetry data and the hardware-level time-series telemetry data over a given time window to predictively detect the anomalous behavior of the application executing in the information processing system” involve subjective choices as to which function is used to predict anomalous behavior, the weights and factors chosen to make the prediction, and the degrees or levels of prediction (anomaly, not anomaly, potential anomaly, percentage chance of an anomaly, etc.) and includes the concepts of evaluation, opinion, and judgment in claims 1 and 16 and
“utilizing an unsupervised machine learning model to predictively detect anomalous behavior in at least one of the application-level data and the hardware-level data using a first process for the application-level data and a second process for the hardware-level data in accordance with the execution of the application in the information processing system, based on at least a portion the application-level data and the hardware-level data; wherein the unsupervised machine learning model is further configured to perform an attention transformer associative analysis procedure on at least a portion the application-level data and the hardware-level data over a given time window to predictively detect the anomalous behavior” involve subjective choices as to which function is used to predict anomalous behavior, the weights and factors chosen to make the prediction, and the degrees or levels of prediction (anomaly, not anomaly, potential anomaly, percentage chance of an anomaly, etc.) and includes the concepts of evaluation, opinion, and judgment in claim 19. The mere nominal recitation of generic processing components does not take the claim limitation(s) out of the mental processes grouping. Thus, the claim(s) recite(s) a mental process, concepts that may be performed in the human mind, in this case being evaluation, opinion, and judgment.
This judicial exception is not integrated into a practical application because the additional elements recited including obtaining application-level data, obtaining hardware-level data, analyzing data, and wherein the application being executed is a microservice in the claims are recited at a high level of generality, i.e., as generic processor performing a generic computer function. This generic processor limitations are no more than mere instructions to apply the exception using a generic computer component.
The examiner notes that while “automatically initiating one or more actions … to proactively prevent failure of the microservice on the information processing system due to the detected anomalous behavior” 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)) or a generic solution to any general problem. Instead, there is a generic solution (initiate one or more actions) to a generic problem (anomalous behavior, failure) and as such it is equivalent to “applying” the generic “actions” to the generic “anomalous behavior”, each of which can comprise any action or anomalous behavior. 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, ln. 25 – p. 21, ln. 2, fig. 6).
Claim(s) 2-4. 7-12, 14, 15, and 22-27 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 2 does provide a generic catch all solution, equivalent to “apply it”, but not a particular solution to a specific problem (see MPEP2106.05(a), MPEP2106.05(f)) 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 3 simply further describes the application-level 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 4 simply further describes the hardware-level 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.
Claims 7-9, 22-23, and 25-27 simply further detail the procedure used to predictively detect anomalous behavior 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 10-12 and 24 include a mental process in the form of a subjective “discrepancy score” based on a person’s evaluation, judgment, and opinion 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 14 and 15 simply further detail the type of information processing system 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 1-4, 7-12, 14-16, 19, and 22-27 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 7/6/26 have been fully considered but they are not persuasive.
In response to applicant’s argument (see p. 9-14 of remarks) that the claims cannot reasonably be characterized as covering mental processes, because such claims recite various limitations (e.g. machine learning processes) that cannot be practically performed in the human mind, the examiner respectfully disagrees.
The examiner notes that “claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations” and “claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions” (MPEP 2106.04(a)(2)(III)(A)).
The examiner notes that one limitation comprising a mental process indicates the claim recites a mental process. In this case the subjective thought processes that go into predictively detecting anomalous behavior are mental processes. The examiner also notes that “a claim that requires a computer may still recite a mental process”, “claims can recite a mental process even if they are claimed as being performed on a computer”, 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)).
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.
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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.
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/JOSHUA P LOTTICH/ Primary Examiner, Art Unit 2113
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