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 Arguments
Applicant’s arguments with respect to the unity of invention restriction (Remarks p. 2), filed 6/1/2026, are persuasive. The election requirement has been reformatted.
Applicant argues, “the listed groupings of claims do not correspond to distinct species. Rather, many of the listed groupings are directed to different, compatible aspects of the same disclosed training approach…” Remarks p. 2. This is moot now in view of the lack of unity among the claims, because the test for lack of unity is whether the grouped embodiments are linked by a common special technical feature that contributes over the prior art.
That being said, the election with traverse to species 2 (claims 32, 34-48, 60) and 61 is noted.
Allowable Subject Matter
Claims 34-38 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: Claim 34 is recites “first noise signal is … based on an expected noise value of the plurality of training time series, wherein the expected noise value characterizes an average intensity of noise of the training time series.” Adversarial Examples in Deep Learning for Multivariate Time Series Regression by Mode et al sec. III teaches adding noise to a time series, but it doesn’t make noise based on an expected noise value based on the average intensity of noise in the training time series.
Election/Restrictions
REQUIREMENT FOR UNITY OF INVENTION
As provided in 37 CFR 1.475(a), a national stage application shall relate to one invention only or to a group of inventions so linked as to form a single general inventive concept (“requirement of unity of invention”). Where a group of inventions is claimed in a national stage application, the requirement of unity of invention shall be fulfilled only when there is a technical relationship among those inventions involving one or more of the same or corresponding special technical features. The expression “special technical features” shall mean those technical features that define a contribution which each of the claimed inventions, considered as a whole, makes over the prior art.
The determination whether a group of inventions is so linked as to form a single general inventive concept shall be made without regard to whether the inventions are claimed in separate claims or as alternatives within a single claim. See 37 CFR 1.475(e).
When Claims Are Directed to Multiple Categories of Inventions:
As provided in 37 CFR 1.475 (b), a national stage application containing claims to different categories of invention will be considered to have unity of invention if the claims are drawn only to one of the following combinations of categories:
(1) A product and a process specially adapted for the manufacture of said product; or
(2) A product and a process of use of said product; or
(3) A product, a process specially adapted for the manufacture of the said product, and a use of the said product; or
(4) A process and an apparatus or means specifically designed for carrying out the said process; or
(5) A product, a process specially adapted for the manufacture of the said product, and an apparatus or means specifically designed for carrying out the said process.
Otherwise, unity of invention might not be present. See 37 CFR 1.475 (c).
This application contains claims directed to more than one species of the generic invention. These species are deemed to lack unity of invention because they are not so linked as to form a single general inventive concept under PCT Rule 13.1.
The species are as follows:
Claim 33 first noise calculated from enlarging distance between output signal;
Claims 34-38 expected and denoising with a covariance matrix;
Claims 34 and 39-41 adverse perturbation is limited based on expected noise value;
Claims 34, 39, 42-44 perturbation based on random number or a predefined value;
Claims 34, 39, 42 and 45-57 perturbation based on formula in claim 45;
Claims 34, 39, 42 and 48 projected perturbation based on formula in claim 48;
Claims 49-50 training on sensor data;
Claim 51 second operating state;
Claims 52-53 signal characteristics and an internal combustion engine;
Claim 54 production machine;
And Claims 55-59 different types of neural networks.
Applicant is required, in reply to this action, to elect a single species to which the claims shall be restricted if no generic claim is finally held to be allowable. The reply must also identify the claims readable on the elected species, including any claims subsequently added. An argument that a claim is allowable or that all claims are generic is considered non-responsive unless accompanied by an election.
Upon the allowance of a generic claim, applicant will be entitled to consideration of claims to additional species which are written in dependent form or otherwise require all the limitations of an allowed generic claim. Currently, the following claim(s) are generic: 32, 60 and 61.
The groups of inventions listed above do not relate to a single general inventive concept under PCT Rule 13.1 because, under PCT Rule 13.2, they lack the same or corresponding special technical features for the following reasons:
Groups 1-10 lack unity of invention because even though the inventions of these groups require the technical feature of “training the machine learning system, including: a. ascertaining a first training time series of input signals from a plurality of training time series and a desired training output signal which corresponds to the first training time series, the desired training output signal characterizing a desired classification and/or a desired regression result of the first training time series; b. ascertaining a worst possible training time series, the worst possible training time series characterizing an overlap of the first training time series with an ascertained first noise signal; c. ascertaining a training output signal based on the worst possible training time series using the machine learning system; and d. adapting at least one parameter of the machine learning system according to a gradient of a loss value, wherein the loss value characterizes a deviation of the desired output signal from the ascertained training output signal.”, this technical feature is not a special technical feature as it does not make a contribution over the prior art in view of Adversarial Examples in Deep Learning for Multivariate Time Series Regression by Mode et al and Benchmarking adversarial attacks and defenses for time-series data by Siddiqui et al.
Mode teaches training the machine learning system, including:
a. ascertaining a first training time series of input signals from a plurality of training time series and a desired training output signal which corresponds to the first training time series, the desired training output signal characterizing a desired classification and/or a desired regression result of the first training time series; (Mode sec. III A “Let X be a multivariate time series (MTS). X can be defined as a sequence such that X = [x1,x2,...,xT], T =| X | is the length of X, and xi ∈ RN is a N dimension…D = (x1,F1),(x2,F2),...,(xT,FT) is the dataset of pairs (xi,Fi) where Fi is a label corresponding to xi…. Time series regression task consists of training the model on D…” F is the output signal, X is the input time series.)
b. ascertaining a worst possible training time series, the worst possible training time series characterizing an overlap of the first training time series with an ascertained first noise signal; (Mode sec. III A “X’ denotes the adversarial example, a perturbed version of X such that ˆF= ˆF’ …” The perturbed version of X is the worst possible training time series.)
c. ascertaining a training output signal based on the worst possible training time series using the machine learning system; and (Mode sec. III A “ˆF’” F hat prime is the training output signal based on X prime (the worst possible training time series.)
Mode doesn’t explicitly teach training on the worst possible time series.
However, Siddiqui teaches d. adapting at least one parameter of the machine learning system according to a gradient of a loss value, wherein the loss value characterizes a deviation of the desired output signal from the ascertained training output signal. (Siddiqui sec. 3.2 “The idea is to just train a classifier on the attacked examples rather than the original ones. As the model inherently learns to be robust to these attacks during training… where y indicates the model’s prediction on the input x.” and Siddiqui’s equation below.)
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Siddiqui, Mode and the claims all generate adversarial (noisy) examples. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to train on the adversarial examples so that the trained model could be “robust to these attacks…” Siddiqui sec. 3.2
In Remarks filed 6/1/2026, an election with traverse was made to group 2 (claims 34-38) with traverse. Affirmation of this election must be made by applicant in replying to this Office action. Claims 33 and 39-59 are withdrawn from further consideration by the examiner, 37 CFR 1.142(b), as being drawn to a non-elected invention.
Claim Rejections - 35 USC § 103
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 32, 60 and 61 are rejected under 35 U.S.C. 103 as being unpatentable over Adversarial Examples in Deep Learning for Multivariate Time Series Regression by Mode et al and Benchmarking adversarial attacks and defenses for time-series data by Siddiqui et al.
Mode teaches claims 32, 60 and 61. A method for a computer-implemented machine learning system, the machine learning system being configured to ascertain an output signal based on a time series of input signals of a technical system, the output signal characterizing a classification and/or a regression result of at least one first operating state and/or at least one first operating variable of the technical system, the method comprising the following steps: (Mode sec. III A “Let X be a multivariate time series (MTS). … dataset of pairs (xi,Fi) where Fi is a label corresponding to xi…. Time series regression task consists of training the model on D…”)
training the machine learning system, including:
a. ascertaining a first training time series of input signals from a plurality of training time series and a desired training output signal which corresponds to the first training time series, the desired training output signal characterizing a desired classification and/or a desired regression result of the first training time series; (Mode sec. III A “Let X be a multivariate time series (MTS). X can be defined as a sequence such that X = [x1,x2,...,xT], T =| X | is the length of X, and xi ∈ RN is a N dimension…D = (x1,F1),(x2,F2),...,(xT,FT) is the dataset of pairs (xi,Fi) where Fi is a label corresponding to xi…. Time series regression task consists of training the model on D…” F is the output signal, X is the input time series.)
b. ascertaining a worst possible training time series, the worst possible training time series characterizing an overlap of the first training time series with an ascertained first noise signal; (Mode sec. III A “X’ denotes the adversarial example, a perturbed version of X such that ˆF= ˆF’ …” The perturbed version of X is the worst possible training time series.)
c. ascertaining a training output signal based on the worst possible training time series using the machine learning system; and (Mode sec. III A “ˆF’” F hat prime is the training output signal based on X prime (the worst possible training time series.)
Mode doesn’t explicitly teach training on the worst possible time series.
However, Siddiqui teaches d. adapting at least one parameter of the machine learning system according to a gradient of a loss value, wherein the loss value characterizes a deviation of the desired output signal from the ascertained training output signal. (Siddiqui sec. 3.2 “The idea is to just train a classifier on the attacked examples rather than the original ones. As the model inherently learns to be robust to these attacks during training… where y indicates the model’s prediction on the input x.” and Siddiqui’s equation below.)
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Siddiqui, Mode and the claims all generate adversarial (noisy) examples. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to train on the adversarial examples so that the trained model could be “robust to these attacks…” Siddiqui sec. 3.2
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Austin Hicks whose telephone number is (571)270-3377. The examiner can normally be reached Monday - Thursday 8-4 PST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mariela Reyes can be reached at (571) 270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AUSTIN HICKS/Primary Examiner, Art Unit 2142
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