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 .
DETAILED ACTION
This action is responsive to amendment filed March 31, 2026.
Status of Claims
Applicant amended the claims. Claims 1-12,14-21 remain pending.
Response to Arguments
Applicant’s arguments, filed 1/27/26, have been fully considered but are not persuasive.
On pages 9-14 of remarks, Applicant argues that the calculations/operations mentioned in claim 1 “include inherently computer implemented techniques that are not practically performed in the human mind” (pg 10), and “the claim requires performing adversarial processing on service samples to generate second service samples, which is a computer-implemented operation involving perturbation of data” (pg 11).
In reply, Applicant is reminded that the claims are given their broadest reasonable interpretation. Firstly, there is no mention within the claims of what those “computer implemented techniques” are which cannot be performed in the mind. There is no discussion of what types of computer components and/or network configurations are required to implement the claim, and there is no discussion of what type of component interactions are required to implement the claim. Secondly, the claim does not actually perform adversarial processing. Rather the claim mentions that a forecast value is based off of a second sample, and then passingly mentions that the sample is obtained from an adversarial process. The claim doesn’t actually perform the adversarial processing, rather it is worded such that the claim only performs obtaining a forecast value based on the second sample. Accordingly, the 101 rejection is maintained.
On pages 15-16 of remarks, Applicant argues that Kar and Cormode do not teach the claims, specifically claim 1.
In reply, Applicant is reminded that the claims are given their broadest reasonable interpretation. In this case, the claims do not provide sufficient details and functionality to make them clearly and functionally different from the teachings of Kar and Cormode. Specifically Kar provides analogous teachings in the same technical field as the invention, ie evaluating robustness of a model. Cormode was only relied upon to show that calculating quantiles and calculating values based on the quantiles is old and well known in the art of making estimates and assessments of a monitored environment (ie. model).
Applicant has failed to explain how Kar’s robustness analysis is fundamentally different than the claims robustness analysis, and how Kars reliance upon accuracy as a label dependent metric is used for determining the robustness. The claim fails to mention what kind of threshold or label is functionally integrated into the claim, or whether one kind is preferred over another. Furthermore, it is noted that the amended limitation of “wherein the robustness score is determined without dependence upon…”, is a negative limitation which does not impart any structure or technical requirements on the claim. The claim implies that it doesn’t depend upon a certain unnamed type of threshold and label. Therefore it remains that there can still be other types of thresholds and labels that are depended upon.
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 1-12,14-21 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the judicial exception of an “abstract idea”, as outlined in the 2019 Revised Patent Subject Matter Eligibility Guidance. Under broadest reasonable interpretation, the terms of the claims are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art.
The claimed invention in general, and Claim 1 as a representative example, is deemed abstract because it relates to evaluating robustness of a service/model via a calculated analysis of data (see the instant specification: at least Abstract, Background and Summary).
According to Step 2A, Prong One of the eligibility analysis, the instant claims recite a judicial exception. See MPEP 2106.04 Claim 1, as the representative example, comprises functional limitations which are deemed to be abstract because they do not go beyond a broad type of data collection and data analysis, where:
The first functional limitation recites “… obtaining a forecasting result …”. At its face-value and based on broadest reasonable interpretation according to the specification, this is mere data collection in the form of collecting data that is representative of different values and information. This is a mental processes, since a person such as a manager or administrator can perform these data collecting functions in their mind (or on paper) using their own abilities of observation, memory and evaluation. Accordingly the limitation is abstract since it encompasses a mental process (and/or organizing human activity);
The second functional limitations are “calculating a first quantiles…” and “calculating a second quantiles…”. This is a basic type of data analysis or computation. In this case, the “calculating” may be practically performed in the human mind (or on paper) by simply observing and performing a mathematical analysis on data values. The limitation is abstract since it encompasses a mental process (and/or organizing human activity);
The final functional limitations are “determining respective forecasting errors…” and “determining a robustness score…”. As mentioned above, this is a type of data analysis or computation since values and conclusions can be determined in a person’s mind or on paper, and can be based on the collected or observed information. Accordingly drawing conclusions from collected data is deemed abstract since it encompasses a mental process (and/or organizing human activity).
It has been shown that the claim recites an abstract idea which is a judicial exception. According to Step 2A, Prong Two of the eligibility analysis, this judicial exception is not integrated into a practical application that would make it patent eligible. The recitation of additional claim elements such as “forecasting model” and “adversarial processing”, does not impose any meaningful limits on practicing the abstract idea. These elements are ancillary and inconsequential to a practical application. “Official Notice” is taken that the additional elements are recited at a high level of generality such that they amount to no more than mere generic type processing that apply the judicial exception. See MPEP 2106.05 (a) through (h).
Finally, according to Step 2B of the eligibility analysis, where the claims are taken as a whole, the additional elements are seen as extra-solution activity that do not add an inventive concept to the claims, and are insufficient to amount to significantly more than the judicial exception. Essentially, the claim limitations are neither a technical improvement of a computer or network itself, nor are they a transformative technological process of a computer, network, or other element, and are thus seen to fall within the “Mental Process” and/or “Organizing Human Activity” categories of abstract ideas. Therefore, the claims are not patent eligible.
Claims 14,15 are slight variations of claim 1 and thus rejected based upon the same rationale given above for claim 1.
Dependent claims are rejected based upon the same rationale given for the base claims which they depend from. Furthermore, the dependent claims fail to include additional elements that would be deemed sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-12,14-21 are rejected under 35 U.S.C. 103 as being unpatentable over Kar et al (US Publication 20210287050) in view of Cormode et al (US Publication 20070136285).
In reference to claim 1, Kar teaches a method for evaluating robustness of a service forecasting model, comprising:
for any first service object in a plurality of service objects, obtaining a forecasting result of the service forecasting model for a service label of a first service object, wherein the forecasting result comprises a first forecasting value obtained through forecasting based on a first service sample corresponding to the first service object and a second forecasting value obtained through forecasting based on a corresponding second service sample, and the second service sample is a sample obtained by performing adversarial processing on the first service sample; (see at least ¶s 25,28,29,38, which teaches testing model output comprising values obtained from a first data sample corresponding to a first object under test, and from a modified data sample which is obtained from performing a perturbation/adversarial processing procedure through a perturbation generation model)
calculating first quantiles respectively corresponding to the plurality of service objects based on a first forecasting value of each service object and a first set comprising each first forecasting value; calculating second quantiles respectively corresponding to the plurality of service objects based on a second forecasting value of each service object and the first set; (see at least ¶s 55,57,58, which teaches calculating first and second values corresponding to the first and modified data samples)
determining respective forecasting errors of service labels of the plurality of service objects based on the first quantiles and the second quantiles that respectively correspond to the plurality of service objects; and determining a robustness score of the service forecasting model against an adversarial attack based on the respective forecasting errors of the service labels of the plurality of service objects, (see at least ¶s 30,59, which teaches determining the respective probabilities of the calculated values, and determining how well trained/robust the model is based on the comparison)
wherein the robustness score is determined without dependence upon a threshold or a sample label of the first service sample to perform a service forecasting model procedure across different service scenarios in a same manner (see at least ¶s 30,57-59, which teaches generating a robustness score depending on other criteria).
Kar fails to explicitly teach calculating quantiles, and forecasting errors based on the quantiles. However, Cormode teaches determining quantiles and rankings based on prediction/forecasting models (see Cormode, at least Abstract & Background). And further discloses a quantile tracking system that determines error tolerances for respective calculated quantiles (see Cormode, at least ¶s 24-26,36). It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the display interface of Kar based on the teachings of Cormode for the purpose of utilizing optimization techniques for approximating the quality of objects and models.
In reference to claim 2, this is taught by Cormode, see at least ¶s 48,51, which teaches ranking values based on the prediction model. One of ordinary skill in the art would be motivated to modify Kar based on the teachings of Cormode in accordance to the rationale given for claim 1.
In reference to claim 3, this is taught by Cormode, see at least ¶s 53,54, which teaches utilizing a same quantile for the determination. One of ordinary skill in the art would be motivated to modify Kar based on the teachings of Cormode in accordance to the rationale given for claim 1.
In reference to claim 4, this is taught by Cormode, see at least ¶s 48-51, which teaches calculating the quantiles according to the ranking values. One of ordinary skill in the art would be motivated to modify Kar based on the teachings of Cormode in accordance to the rationale given for claim 1.
In reference to claim 5, this is taught by Cormode, see at least ¶s 20,25-27, which teaches determining quantile error tolerance and approximating it with respect to quantile summaries. One of ordinary skill in the art would be motivated to modify Kar based on the teachings of Cormode in accordance to the rationale given for claim 1.
In reference to claim 6, this is taught by Cormode, see at least ¶ 36, which teaches error determination based on a difference of quantile values. One of ordinary skill in the art would be motivated to modify Kar based on the teachings of Cormode in accordance to the rationale given for claim 1.
In reference to claim 7, this is taught by Kar, see at least ¶s 30,53, which teaches a robustness result based on calculated average values.
In reference to claim 8, this is taught by Kar, see at least ¶s 38,54, which teaches a plurality of multiple alternate/modified objects that are used for generating probabilities related to the model.
In reference to claim 9, this is taught by Cormode, see at least ¶s 47,48,51, which teaches determining ranking numbers for each respective object. One of ordinary skill in the art would be motivated to modify Kar based on the teachings of Cormode in accordance to the rationale given for claim 1.
In reference to claim 10, this is taught by Cormode, see at least ¶s 47,48,51, which teaches determining ranking numbers for each respective object. One of ordinary skill in the art would be motivated to modify Kar based on the teachings of Cormode in accordance to the rationale given for claim 1.
In reference to claim 11, this is taught by Kar, see at least ¶s 28-30, which teaches different classification and probability values.
In reference to claim 12, this is taught by Kar, see at least ¶s 19-21, which teaches image recognition and sample data is a perturbed/adversarial image.
Claims 14-21 correspond to claims 1-12 and are slight variations thereof. Therefore claims 14-21 are rejected based upon the same rationale as given above.
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.
For any subsequent response that contains new/amended claims, Applicant is required to cite its corresponding support in the specification. (See MPEP chapter 2163.03 section (I.) and chapter 2163.04 section (I.) and chapter 2163.06) Applicant may not introduce any new matter to the claims or to the specification.
In formulating a response/amendment, Applicant is encouraged to take into consideration the prior art made of record but not relied upon, as it is considered pertinent to applicant's disclosure. See attached Form 892.
Contact & Status
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAMY M OSMAN whose telephone number is (571)272-4008. The examiner can normally be reached Mon-Fri, 9AM-5PM.
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/Ramy M Osman/
Primary Examiner, Art Unit 2457
May 14, 2026
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