DETAILED ACTION
This action is in response to claims filed 10 July 2023 for application 18349571 filed 10 July 2023. Currently claims 1-10 are pending.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 6-7 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the architecture of a neural network can be interpreted in light of the specification as software per se.
Claims 1-7 and 10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In step 1, claim 1 recites the statutory category of a method. Claim 6 does not recite a statutory category, however, the full analysis will be performed.
In step 2a prong 1, claims 1 and 6 recite, in part, determining model uncertainty and determining the mean value of a neural network having an encoder and decoder. The limitations of determining are processes that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “computer implemented” in the context of the claims, the limitations encompass determining statistical information of a model of a neural network in the mind or with aid. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
In step 2a prong 2, this judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of “computer implemented”. The computer components in the claim are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)). 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. Please see MPEP §2106.04.(a)(2).III.C.
In step 2b, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, either alone or in combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “computer implemented” to perform the steps of the claims amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claims 2-5, 7 and 10 recite further limitations of formulae for variance, mean value, a probability of output, extracting latent variables, using an encoder or aggregator module to determine model uncertainty, and using the method for ascertaining deviations of the system. These limitations amount to the same abstract idea identified above. None of these limitations introduce new additional elements that would amount to a practical application or significantly more.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-10 is/are rejected under 35 U.S.C. 102(A)(1) as being anticipated by Volpp et al. (BAYESIAN CONTEXT AGGREGATION FOR NEURAL PROCESSES)(published January 2021).
Regarding claim 1, Volpp discloses: A computer-implemented method for estimating uncertainties using a neural network including a neural process, in a model, the model modeling a technical system and/or a system behavior of the technical system, the method comprising the following steps:
determining a model uncertainty as a variance (σz 2) of a Gaussian distribution and as a mean value (μz) of the Gaussian distribution using latent variables (z) from a set of contexts (Dc) (
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p4 §3); and
determining a mean value (μy) of an output of the model as a function of an input location (x) using a neural decoder network based on the Gaussian distribution, the latent variables (z) being weights of the neural decoder network (
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p4 §3).
Regarding claim 2, Volpp discloses: The method as recited in claim 1, wherein the variance (σz 2) of the Gaussian distribution, where (σz 2=σz 2(Dc), is calculated using the latent variables (z) from a set of contexts (Dc) of observations, wherein p(z|Dc)=N(z|μz(Dc), σz 2(Dc)) (
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p4 §3).
Regarding claim 3, Volpp discloses: The method as recited in claim 1, wherein the mean value (μz) of the Gaussian distribution, where μz=μz(Dc), is calculated using the latent variables (z) from the set of contexts (Dc) of observations, wherein p(z|Dc)=N(z|μz(Dc),σz 2(Dc)) (
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p4 §3).
Regarding claim 4, Volpp discloses: The method as recited in claim 1, wherein the neural decoder network parameterizes the output of the model, wherein a probability p(y|x,z)=N(y|μy,σn 2) (
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p4 §3).
Regarding claim 5, Volpp discloses: The method as recited in claim 1, wherein the latent variables (z) are extracted from the variance (σz 2) of the Gaussian distribution and from the mean value (μz) of the Gaussian distribution of the output of the model (
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p4 §3).
Regarding claim 6, Volpp discloses: An architecture of a neural network including a neural process, the neural network configured to estimate uncertainties in a model, the neural network configured to:
determine a model uncertainty as a variance (σz 2) of a Gaussian distribution and as a mean value (μz) of the Gaussian distribution using latent variables (z) from a set of contexts (Dc) (
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p4 §3); and
determine a mean value (μy) of an output of the model as a function of an input location (x) using a neural decoder network based on the Gaussian distribution, the latent variables (z) being weights of the neural decoder network (
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p4 §3);
wherein the model models a technical system and/or a system behavior of the technical system (p8 §Experiments), the neural network including at least one neural decoder network, the latent variables (z) being the weights of the neural decoder network (
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p4 §3).
Regarding claim 7, Volpp discloses: The architecture as recited in claim 6, wherein the neural network includes at least one neural encoder network and/or at least one aggregator module, and the neural encoder network and/or the aggregator module is configured to determine the model uncertainty as a variance (σz 2) of the Gaussian distribution and the mean value (μz) of the Gaussian distribution using the latent variables (z) from the set of contexts (Dc) (
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p4 §3).
Regarding claim 8, Volpp discloses: A training method for parameterizing a neural network, the neural network, the neural network being configured to estimate uncertainties in a model, the neural network configured to:
determine a model uncertainty as a variance (σz 2) of a Gaussian distribution and as a mean value (μz) of the Gaussian distribution using latent variables (z) from a set of contexts (Dc) (
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p4 §3), and
determine a mean value (μy) of an output of the model as a function of an input location (x) using a neural decoder network based on the Gaussian distribution, the latent variables (z) being weights of the neural decoder network (
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p4 §3),
wherein the model models a technical system and/or a system behavior of the technical system (p8 §Experiments), and the neural network includes at least one neural decoder network, the latent variables (z) being the weights of the neural decoder network, and wherein the neural network includes at least one neural encoder network and/or at least one aggregator module, the neural encoder network and/or the aggregator module being configured to determine the model uncertainty as the variance (σz 2) of the Gaussian distribution and the mean value (μz) of the Gaussian distribution using the latent variables (z) from the set of contexts (Dc), (
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p4 §3) and wherein the method comprises the following:
training of weights for the neural encoder network and/or the aggregator module, wherein the latent variables (z) are the weights of the neural decoder network (
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p4 §3).
Regarding claim 9, Volpp discloses: The training method as recited in claim 8, wherein the method is a multi-task training method (“We frame probabilistic regression as a multi-task learning problem.” P3 §3 ¶2, see also §3.The Multi-Task CLV Model).
Regarding claim 10, Volpp discloses: The method as recited in claim 1, wherein the method is used for ascertaining an inadmissible deviation of a system behavior of the technical system from a standard value range (
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note: assigning little weight to a task in a high ambiguity area is interpreted as an inadmissible deviation from a standard value range).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC NILSSON whose telephone number is (571)272-5246. The examiner can normally be reached M-F: 7-3.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James Trujillo can be reached at (571)-272-3677. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ERIC NILSSON/ Primary Examiner, Art Unit 2151