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 .
Response to Amendment
Applicant' s amendment and response filed 5/4/2026 has been entered and made record. This application contains 11 pending claims.
Claims 1-2, 9, and 11 have been amended.
Response to Arguments
Applicant’s arguments filed 5/4/2026 regarding claims rejections under 35 U.S.C. 101 in claim 1-11 have been fully considered but they are not persuasive.
The applicant argues on pages 6-7 of the remark filed on 5/4/2026 that “… Applicant disagrees with the rejections. … However, the elements of claim 1 are integrally related (i.e., linked together), such that any alleged abstract ideas are integrated into a practical application under Step 2A, Prong Two. … Applicant submits that the independent claims are directed to patent-eligible subject matter for at least the reasons set forth below.”
The Examiner respectfully disagrees applicant’s argument. The steps of “determining second test outputs from the respective machine learning model corresponding to the last model and based on the test inputs of the models”, and
“determining a discrepancy, wherein the discrepancy characterizes a difference between a distribution of the first test outputs determined from the last model and a distribution of the second test outputs determined by the respective machine learning model corresponding to the last model” are mathematical concept, therefore, they are considered to be an abstract idea. The step of “verifying and/or validating whether the technical system fulfills the criterion, wherein the verifying and/or validating is characterized by maximizing a probability of a distribution of measurement outputs of a last component of the technical system to not fulfill the criterion with respect to a distribution of measurement outputs and under a constraint stipulating that a discrepancy of the distribution of measurement outputs and the distribution of first test outputs may not exceed the discrepancy determined” is a combination of a mathematical concept and a mental process, therefore, it is considered to be an abstract idea. Thus, the claims are directed to an abstract idea.
The applicant argues on pages 7-9 of the remark filed that “… Indeed, the amended claims integrate any alleged abstract ideas into a practical application of verifying and/or validating a technical system (e.g., autonomous vehicle, robot, etc.). …
Therefore, the specific combination of claim 1 integrates any alleged abstract ideas into a practical application of verifying and/or validating a technical system. Accordingly, claim 1 recites additional elements that amount to a practical application of any alleged abstract ideas under Step 2A, Prong Two of the 2-step inquiry. Independent claim 11 recites similar language as claim 1 and therefore also recites additional elements that amount to a practical application of any alleged abstract ideas under Step 2A, Prong two of the 2-step inquiry.”
The Examiner respectfully disagrees applicant’s argument. Practical application can be demonstrated by additional elements that are sufficient to integrate the judicial exception into a practical application. The additional elements “obtaining models for a plurality of components included in the technical system, wherein a connection between the obtained models characterizes which component of the plurality of components passes which signal to which other component of the plurality of components”; “obtaining a plurality of validation measurements, wherein each validation measurement includes a measurement input and a measurement output, wherein the measurement output is obtained from a component of the technical system for the measurement input when the measurement input is provided to the component”; “for each respective component of the plurality of components, training a respective machine learning model to predict measurement outputs of the respective component based on inputs of the respective component, wherein at least parts of the validation measurements are used as a training dataset and wherein the respective machine learning model corresponds to the model obtained for the respective component”; “obtaining first test outputs from a last model of the models based on test inputs, wherein the first test outputs are obtained by propagating the test inputs through the connection of the models”; and “wherein the second test outputs are obtained by propagating the test inputs through a connection of the respective machine learning models, wherein the connection of the respective machine learning models is according to the connection of the models the respective machine learning models correspond to” are not sufficient to integrate the abstract idea into a practical application because they only add insignificant extra-solution activities to the judicial exception. Therefore, the current claims do not recite additional elements that are indicative of integration of an abstract idea into a practical application.
The applicant argues on pages 9-11 of the remark filed that “… As the claims of the present application also provide a technical solution to a technological problem, Applicant respectfully submits that the claims add "significantly more" to any alleged abstract ideas under Step 2B of the eligibility analysis. … By acknowledging that the prior art of record does not teach or suggest the above-identified claim limitations, either alone or in combination, the Office effectively concedes that the claim recites limitations that are not well-understood, routine, or conventional in the relevant art. The Office's statements are therefore internally inconsistent. Indeed, the Office has not shown or provided evidence that the claimed limitations are well- understood, routine, or conventional in the art, and has in fact expressly indicated that the prior art does not teach or suggest features recited in the claims. Thus, the claims recite additional elements that amount to "significantly more" than any alleged judicial exception, at least because the limitations as claimed are not well-understood, routine, or conventional in the art. … Therefore, for at least the reasons above, the claimed invention provides a technical solution to a technological problem and recites additional elements that amount to "significantly more" than any alleged judicial exception, at least because the limitations as claimed are not well-understood, routine, or conventional in the art.”
The Examiner respectfully disagrees applicant’s argument. Significantly more can be demonstrated by additional elements that are not well-understood and conventional that integrate the abstract idea into a practical application. However, the claims do not recite them. The limitations of “obtaining models for a plurality of components included in the technical system, wherein a connection between the obtained models characterizes which component of the plurality of components passes which signal to which other component of the plurality of components”; “obtaining a plurality of validation measurements, wherein each validation measurement includes a measurement input and a measurement output, wherein the measurement output is obtained from a component of the technical system for the measurement input when the measurement input is provided to the component”; “for each respective component of the plurality of components, training a respective machine learning model to predict measurement outputs of the respective component based on inputs of the respective component, wherein at least parts of the validation measurements are used as a training dataset and wherein the respective machine learning model corresponds to the model obtained for the respective component”; “obtaining first test outputs from a last model of the models based on test inputs, wherein the first test outputs are obtained by propagating the test inputs through the connection of the models”; and “wherein the second test outputs are obtained by propagating the test inputs through a connection of the respective machine learning models, wherein the connection of the respective machine learning models is according to the connection of the models the respective machine learning models correspond to” are routine in validating or verifying a technical system, a computer program, and a machine-readable storage medium; and are well-understood and conventional. Therefore, the claim 1 does not contain additional elements that are not well-understood and conventional that integrate the abstract idea into a practical application.
Moreover, the additional elements “obtaining a plurality of validation measurements, wherein each validation measurement includes a measurement input and a measurement output, wherein the measurement output is obtained from a component of the technical system for the measurement input when the measurement input is provided to the component” is taught by “Ueyama US 20210089005” and “Sadilek US 20240093464, as shown in the below rejection; and the additional element ”obtaining first test outputs from a last model of the models based on test inputs, wherein the first test outputs are obtained by propagating the test inputs through the connection of the models” is taught by “Maher US 20230059313”, and “Gonzalez US 20220396024”, as shown in the below rejections.
Claim 11 recites subject matter that are similar to that of claim 1, and therefore, the claims are also patent ineligible.
Dependent claims 2-10 provide additional features/steps which are considered part of an expanded abstract idea of the independent claims, and do not integrate the abstract ideas into a practical application. Therefore, claims 2-10 are also patent ineligible.
Hence, the Examiner submits that the rejections of Claims 1-11 are proper.
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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As to claim 1, the claim recites “A method for verifying and/or validating whether a technical system fulfills a criterion, wherein the technical system emits output signals based on input signals supplied to the technical system, the method comprising the following steps:
a. obtaining models for a plurality of components included in the technical system, wherein a connection between the obtained models characterizes which component of the plurality of components passes which signal to which other component of the plurality of components;
b. obtaining a plurality of validation measurements, wherein each validation measurement includes a measurement input and a measurement output, wherein the measurement output is obtained from a component of the technical system for the measurement input when the measurement input is provided to the component;
c. for each respective component of the plurality of components, training a respective machine learning model to predict measurement outputs of the respective component based on inputs of the respective component, wherein at least parts of the validation measurements are used as a training dataset and wherein the respective machine learning model corresponds to the model obtained for the respective component;
d. obtaining first test outputs from a last model of the models based on test inputs, wherein the first test outputs are obtained by propagating the test inputs through the connection of the models;
e. determining second test outputs from the respective machine learning model corresponding to the last model and based on the test inputs of the models, wherein the second test outputs are obtained by propagating the test inputs through a connection of the respective machine learning models, wherein the connection of the respective machine learning models is according to the connection of the models the respective machine learning models correspond to;
f. determining a discrepancy, wherein the discrepancy characterizes a difference between a distribution of the first test outputs determined from the last model and a distribution of the second test outputs determined by the respective machine learning model corresponding to the last model; and
g. verifying and/or validating whether the technical system fulfills the criterion, wherein the verifying and/or validating is characterized by maximizing a probability of a distribution of measurement outputs of a last component of the technical system to not fulfill the criterion with respect to a distribution of measurement outputs and under a constraint stipulating that a discrepancy of the distribution of measurement outputs and the distribution of first test outputs may not exceed the discrepancy determined in step f.”
Under the Step 1 of the eligibility analysis, we determine whether the claim is directed to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process for claim 1).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the bold type portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental processes (concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions).
In claim 1, the steps of “determining second test outputs from the respective machine learning model corresponding to the last model and based on the test inputs of the models”, and
“determining a discrepancy, wherein the discrepancy characterizes a difference between a distribution of the first test outputs determined from the last model and a distribution of the second test outputs determined by the respective machine learning model corresponding to the last model” are mathematical concept, therefore, they are considered to be an abstract idea.
The step of “verifying and/or validating whether the technical system fulfills the criterion, wherein the verifying and/or validating is characterized by maximizing a probability of a distribution of measurement outputs of a last component of the technical system to not fulfill the criterion with respect to a distribution of measurement outputs and under a constraint stipulating that a discrepancy of the distribution of measurement outputs and the distribution of first test outputs may not exceed the discrepancy determined” is a combination of a mathematical concept and a mental process, therefore, it is considered to be an abstract idea.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The claim comprises the following additional elements:
obtaining models for a plurality of components included in the technical system, wherein a connection between the obtained models characterizes which component of the plurality of components passes which signal to which other component of the plurality of components; obtaining a plurality of validation measurements, wherein each validation measurement includes a measurement input and a measurement output, wherein the measurement output is obtained from a component of the technical system for the measurement input when the measurement input is provided to the component; for each respective component of the plurality of components, training a respective machine learning model to predict measurement outputs of the respective component based on inputs of the respective component, wherein at least parts of the validation measurements are used as a training dataset and wherein the respective machine learning model corresponds to the model obtained for the respective component; obtaining first test outputs from a last model of the models based on test inputs, wherein the first test outputs are obtained by propagating the test inputs through the connection of the models; and wherein the second test outputs are obtained by propagating the test inputs through a connection of the respective machine learning models, wherein the connection of the respective machine learning models is according to the connection of the models the respective machine learning models correspond to”.
The additional elements “obtaining models for a plurality of components included in the technical system, wherein a connection between the obtained models characterizes which component of the plurality of components passes which signal to which other component of the plurality of components”; “obtaining a plurality of validation measurements, wherein each validation measurement includes a measurement input and a measurement output, wherein the measurement output is obtained from a component of the technical system for the measurement input when the measurement input is provided to the component”; “for each respective component of the plurality of components, training a respective machine learning model to predict measurement outputs of the respective component based on inputs of the respective component, wherein at least parts of the validation measurements are used as a training dataset and wherein the respective machine learning model corresponds to the model obtained for the respective component”; “obtaining first test outputs from a last model of the models based on test inputs, wherein the first test outputs are obtained by propagating the test inputs through the connection of the models”; and “wherein the second test outputs are obtained by propagating the test inputs through a connection of the respective machine learning models, wherein the connection of the respective machine learning models is according to the connection of the models the respective machine learning models correspond to” are not sufficient to integrate the abstract idea into a practical application because they only add insignificant extra-solution activities to the judicial exception.
In conclusion, the above additional elements, considered individually and in combination with the other claims elements do not reflect an improvement to other technology or technical field, do not reflect improvements to the functioning of the computer itself, do not recite a particular machine, do not effect a transformation or reduction of a particular article to a different state or thing, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claim is directed to a judicial exception and require further analysis under the Step 2B.
The above claim, does not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are generically recited and are well-understood/conventional in a relevant art as evidenced by the prior art of record (Step 2B analysis).
For example, obtaining a plurality of validation measurements, wherein each validation measurement includes a measurement input and a measurement output, wherein the measurement output is obtained from a component of the technical system for the measurement input when the measurement input is provided to the component is disclosed by “Ueyama US 20210089005”, [0008], [0009], [0017], [0025], [0033]); and “Sadilek US 20240093464”, [0024], [0025], [0036].
For example, obtaining first test outputs from a last model of the models based on test inputs, wherein the first test outputs are obtained by propagating the test inputs through the connection of the models is disclosed by “Maher US 20230059313”, [0024], [0026], [0055], [0058], [0095]); and “Gonzalez US 20220396024”, [0200], [0235].
The claim, therefore, is not patent eligible.
Independent claim 11 recites subject matter that is similar or analogous to that of claim 1, and therefore, the claim is also patent ineligible.
With regards to the dependent claims, claims 2-10 provide additional features/steps which are considered part of an expanded abstract idea of the independent claims, and do not integrate the abstract ideas into a practical application.
The dependent claims are, therefore, also not patent eligible.
Examiner’s Note
Regarding Claims 1-11, the most pertinent prior arts are “Kim US 20200016759”, “Ueyama US 20210089005”, “Maher US 20230059313”, “Sadilek US20240093464”, “Gonzalez US 20220396024”, “Arov US 20180276375”, and “Rao US 20230078146”.
As to claims 1 and 11, Kim teaches obtaining models for a plurality of components included in the technical system, wherein a connection between the obtained models characterizes which component of the plurality of components passes which signal to which other component of the plurality of components (Kim, Abstract, FIGS. 1-3, [0030]).
Ueyama teaches obtaining a plurality of validation measurements, wherein each validation measurement includes a measurement input and a measurement output, wherein the measurement output is obtained from a component of the technical system for the measurement input when the measurement input is provided to the component (Ueyama, Abstract, [0008], [0009], [0017], [0025], [0033]);
for each respective component, training a respective machine learning model to predict measurement outputs of the respective component based on inputs of the respective component, wherein at least parts of the validation measurements are used as training dataset and wherein the respective machine learning model corresponds to the model obtained for the respective component (Ueyama, [0024], [0026], [0055], [0058], [0095]).
Maher teaches obtaining first test outputs from a last model of the models based on test inputs, wherein the first test outputs are obtained by propagating the test inputs through the connection of the models (Maher, FIG. 3B, [0056], [0101]); and
wherein the second test outputs are obtained by propagating the test inputs through a connection of the respective machine learning models, wherein the connection of the respective machine learning models is according to the connection of the models the respective machine learning models correspond to (Maher, FIG. 3B, [0041], [0048], [0050], [0053], [0056], [0101]).
However, the prior arts of record, alone or in combination, do not fairly teach or suggest “verifying and/or validating whether the technical system fulfills the criterion, wherein the verifying and/or validating is characterized by maximizing a probability of a distribution of measurement outputs of a last component of the technical system to not fulfill the criterion with respect to a distribution of measurement outputs and under a constraint stipulating that a discrepancy of the distribution of measurement outputs and the distribution of first test outputs may not exceed the discrepancy determined” including all limitations as claimed.
Dependent claims 2-10 are also distinguish over the prior art for at least the same reason as claims 1 and 11.
Examiner notes, however, that claims 1-11 are rejected under 35 U.S.C. 101, and therefore, not patent eligible.
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.
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/LAL CE MANG/Examiner, Art Unit 2857
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