DETAILED ACTION
The following is a Final Office action in response to communications received 8/28/26. Claim(s) 12-14 has(have) been canceled. Claim(s) 1, 2, 6-11, and 15-20 has(have) been amended. Claim(s) 21-23 has(have) been added. Therefore, claim(s) 1-11 and 15-23 is(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 20-23 are objected to because of the following informalities:
In claim 20, “generate a first set of health scores artificial intelligence (AI) models” should read “generate a first set of health scores using one or more artificial intelligence (AI) models”. For the purposes of examination the examiner will presume this is the case.
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) 1-11 and 15-23 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 20 recite(s) the limitation(s) of “generating, by the computing device, a first set of health scores using one or more artificial intelligence (AI) models that perform AI-based multivariate analysis on the unstructured metric data”,
“generating, by the computing device, a second set of health scores using a log data analysis module that performs AI-based multivariate analysis on the unstructured log data”, and
“determining, by the computing device, a similarity score based on the first set of health scores and the second set of health scores by applying a discrete time series matching algorithm having time elasticity that counts a minimum number of operations required to transform the first set of health scores into the second set of health scores or vice-versa” in claim 1,
“generate a first set of health scores using one or more artificial intelligence (AI) models that perform AI-based multivariate analysis on the unstructured metric data”,
“generate a second set of health scores using a log data analysis module that performs AI-based multivariate analysis on the unstructured log data”, and
“determine a similarity score based on the first set of health scores and the second set of health scores by applying a discrete time series matching algorithm having time elasticity that counts a minimum number of operations required to transform the first set of health scores into the second set of health scores or vice-versa” in claim 16, and
“generate a first set of health scores using one or more artificial intelligence (AI) models that perform AI-based multivariate analysis on the unstructured metric data, the one or more AI models comprising at least one selected from a matrix profile model, a seasonal trend decomposition (STD) model, an elliptic envelope model, and an isolation forest model”,
“generate a second set of health scores using a log data analysis module that performs AI-based multivariate on the unstructured log data, the log data analysis module comprising a textscalar, a graph embedding component, a graph attention network, a spatial short Fourier component, and a gated recurrent unit (GRU)”, and
“determine a similarity score based on the first set of health scores and the second set of health scores by applying a discrete time series matching algorithm having time elasticity that counts a minimum number of operations required to transform the first set of health scores into the second set of health scores or vice-versa” in claim 20.
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 “a computing device” in claim 1, “one or mor computer readable storage media” in claim 16, and “a processor”, “a computer readable memory”, “one or more computer readable storage media” 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 “generating, by the computing device, a first set of health scores using one or more artificial intelligence (AI) models that perform AI-based multivariate analysis on the unstructured metric data” involves subjective choices with respect to the type of health score (1-10, 1-100, high and low, good and bad, green, yellow, and red, etc.), the factors and metrics used to create the scores, the correspondence between the factors and metrics and the score, and the algorithm used to generate the health scores and includes the concepts of evaluation, judgment, and opinion,
“generating, by the computing device, a first set of health scores using one or more artificial intelligence (AI) models that perform AI-based multivariate analysis on the unstructured metric data” involves subjective choices with respect to the type of health score (1-10, 1-100, high and low, good and bad, green, yellow, and red, etc.), the factors and metrics used to create the scores, the correspondence between the factors and metrics and the score, and the algorithm used to generate the health scores and includes the concepts of evaluation, judgment, and opinion, and
“determining, by the computing device, a similarity score based on the first set of health scores and the second set of health scores by applying a discrete time series matching algorithm having time elasticity that counts a minimum number of operations required to transform the first set of health scores into the second set of health scores or vice-versa” involves subjective choices with respect to the type of similarity score (1-10, 1-100, high and low, good and bad, green, yellow, and red, etc.), the subjective health scores used to create the similarity score, the correspondence between the health scores and the similarity score, and the algorithm used to generate the health scores and includes the concepts of evaluation, judgment, and opinion in claim 1,
“generate a first set of health scores using one or more artificial intelligence (AI) models that perform AI-based multivariate analysis on the unstructured metric data” involves subjective choices with respect to the type of health score (1-10, 1-100, high and low, good and bad, green, yellow, and red, etc.), the factors and metrics used to create the scores, the correspondence between the factors and metrics and the score, and the algorithm used to generate the health scores and includes the concepts of evaluation, judgment, and opinion,
“generate a second set of health scores using a log data analysis module that performs AI-based multivariate analysis on the unstructured log data” involves subjective choices with respect to the type of health score (1-10, 1-100, high and low, good and bad, green, yellow, and red, etc.), the factors and metrics used to create the scores, the correspondence between the factors and metrics and the score, and the algorithm used to generate the health scores and includes the concepts of evaluation, judgment, and opinion, and
“determine a similarity score based on the first set of health scores and the second set of health scores by applying a discrete time series matching algorithm having time elasticity that counts a minimum number of operations required to transform the first set of health scores into the second set of health scores or vice-versa” involves subjective choices with respect to the type of similarity score (1-10, 1-100, high and low, good and bad, green, yellow, and red, etc.), the subjective health scores used to create the similarity score, the correspondence between the health scores and the similarity score, and the algorithm used to generate the health scores and includes the concepts of evaluation, judgment, and opinion in claim 16, and
“generate a first set of health scores using one or more artificial intelligence (AI) models that perform AI-based multivariate analysis on the unstructured metric data, the one or more AI models comprising at least one selected from a matrix profile model, a seasonal trend decomposition (STD) model, an elliptic envelope model, and an isolation forest model” involves subjective choices with respect to the type of health score (1-10, 1-100, good and bad, green, yellow, and red, etc.), the factors and metrics used to create the scores, the correspondence between the factors and metrics and the score, and the algorithm or models used to generate the health scores and includes the concepts of evaluation, judgment, and opinion,
“generate a second set of health scores using a log data analysis module that performs AI-based multivariate on the unstructured log data, the log data analysis module comprising a textscalar, a graph embedding component, a graph attention network, a spatial short Fourier component, and a gated recurrent unit (GRU)” involves subjective choices with respect to the type of health score (1-10, 1-100, high and low, good and bad, green, yellow, and red, etc.), the factors and metrics used to create the scores, the correspondence between the factors and metrics and the score, and the algorithm used to generate the health scores and includes the concepts of evaluation, judgment, and opinion, and
“determine a similarity score based on the first set of health scores and the second set of health scores by applying a discrete time series matching algorithm having time elasticity that counts a minimum number of operations required to transform the first set of health scores into the second set of health scores or vice-versa” involves subjective choices with respect to the type of similarity score (1-10, 1-100, high and low, good and bad, green, yellow, and red, etc.), the subjective health scores used to create the similarity score, the correspondence between the health scores and the similarity score, and the algorithm used to generate the health scores and includes the concepts of evaluation, judgment, and opinion in claim 20.
This judicial exception is not integrated into a practical application because the additional elements recited including “receiving, by a computing device, unstructured metric data and unstructured log data from an external system” in claim 1,
“receive unstructured metric data and unstructured log data from an external system” and “output the first set of health scores and the second set of health scores and corresponding timestamps to a graphical user interface (GUI)” in claim 16, and
“receive unstructured metric and unstructured log data from an external system” and “output the first set of health scores and the second set of health scores and corresponding timestamps to a graphical user interface (GUI), wherein the metric data comprises a central processing unit (CPU) utilization, memory utilization, and network bytes” in claim 20 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 a resolution to “anomalies” could potentially improve the functioning of a computer, it would need to be 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)), instead of a generic solution to any and all possible problems. The examiner notes that a resolution and “anomalies” would be a generic problem and solution and equivalent to “apply it”, applying a generic resolution or solution to any and all “anomalies” 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 ([0050-0058], fig. 1).
Claim(s) 2-11, 17-19, and 21-23 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(s) 2 simply details the source or type of metric data and do(es) not provide a practical application and also do(es) not provide significantly more in that the computer system itself is not improved or even affected.
Claim(s) 3 and 4 simply output a message and do(es) not provide a practical application and also do(es) not provide significantly more in that the computer system itself is not improved or even affected.
Claim(s) 5 simply further details the determination of the similarity score and do(es) not provide a practical application and also do(es) not provide significantly more in that the computer system itself is not improved or even affected.
Claim(s) 6-9 simply detail AI model types used and do(es) not provide a practical application and also do(es) not provide significantly more in that the computer system itself is not improved or even affected.
Claim(s) 10 and 23 involve mental processes in the generation of health and similarity scores and do(es) not provide a practical application and also do(es) not provide significantly more in that the computer system itself is not improved or even affected.
Claim(s) 11 simply outputs health scores and do(es) not provide a practical application and also do(es) not provide significantly more in that the computer system itself is not improved or even affected.
Claim(s) 19 simply details types of AI models and do(es) not provide a practical application and also do(es) not provide significantly more in that the computer system itself is not improved or even affected.
Claim(s) 14 simply further details a model and involve mental processes in the calculation of anomaly scores and do(es) not provide a practical application and also do(es) not provide significantly more in that the computer system itself is not improved or even affected.
Claim(s) 18 and simply further details log data analysis and do(es) not provide a practical application and also do(es) not provide significantly more in that the computer system itself is not improved or even affected.
Claim(s) 17, 21, and 22 simply output messages and details types of metric data and do(es) not provide a practical application and also do(es) not provide significantly more in that the computer system itself is not improved or even affected.
Claim(s) 1-11 and 15-23 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 8/28/26 have been fully considered but they are not persuasive.
In response to applicant’s argument (see p. 17-19 of remarks) that “the steps differentiate the claims from being part of any mental process at least because none of them can be practically performed by a human mind”, the examiner respectfully disagrees.
The examiner agrees that the human mind cannot perform AI-based multivariate analysis or apply a discrete time series matching algorithm. The examiner notes that “A Claim That Requires a Computer May Still Recite a Mental Process” (MPEP 2106.04(a)(2)(III)(C)). The examiner notes that the mental processes involve the subjective choices with respect to the type of health score (1-10, 1-100, high and low, good and bad, green, yellow, and red, etc.), the factors and metrics used to create the scores, the correspondence between the factors and metrics and the score, and the algorithm used to generate the health scores and includes the concepts of evaluation, judgment, and opinion and
subjective choices with respect to the type of similarity score (1-10, 1-100, high and low, good and bad, green, yellow, and red, etc.), the subjective health scores used to create the similarity score, the correspondence between the health scores and the similarity score, and the algorithm used to generate the health scores and includes the concepts of evaluation, judgment, and opinion.
The examiner notes that while AI models and AI-based analysis, and therefore a computer, may be used to perform the steps, there are still mental processes involved in steps themselves.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/JOSHUA P LOTTICH/ Primary Examiner, Art Unit 2113
