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.
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 desired 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 components passes which signal to which other component of the 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 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 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 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, and apparatus for claim 11).
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 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 components passes which signal to which other component of the 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 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 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 components passes which signal to which other component of the 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 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 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 components passes which signal to which other component of the components (Kim, FIG. 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 of the 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 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 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“Madar US 20200278738” teaches “Methods, systems, and apparatus, for handling applications in an ambient computing system. One of the methods includes determining, by a low-power processing component, that particular sensor signals have a particular property. In response, a machine learning engine performs an inference pass over a machine learning model using the sensor signals to generate a model output. If the model output of the machine learning engine matches an application-specific condition, one or more of the other processing components are activated to execute an particular application corresponding to the application-specific condition.”
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/LAL CE MANG/Examiner, Art Unit 2863