DETAILED ACTION
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 .
Claims 1-12 have been presented for examination.
Claims 1-12 are rejected.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. EP20177436.1, filed on 05/29/2020.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “design system” in claim 10, which is interpreted to have structure as in instant para [0057] “The design system KS has one or more processors PROC for carrying out the required method steps, and also one or more memories MEM for storing data to be processed”.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12 are rejected under 35 U.S.C. 112(b). The claims are generally narrative and indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors. For example, claim 1 recites a method however the comprising elements are various design components and not positively recited method steps.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1: Claim 1 is directed to a computer-implemented design method (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories.
Step 2A Prong 1: Regarding Prong 1 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A computer-implemented design method for generating structure data sets specifying a technical product, wherein
a) for a multiplicity of design variants of the technical product, in each case a training structure data set specifying the respective design variant and also a training quality value quantifying a predefined design criterion are read in as training data;
b) a Bayesian neural network is trained on the basis of the training data, to determine an associated quality value together with an associated uncertainty indication on the basis of a structure data set;
c) a multiplicity of synthetic structure data sets are generated and fed into the trained Bayesian neural network;
d) a quality value with an associated uncertainty indication is generated for each of the synthetic structure data sets by the trained Bayesian neural network;
e) the generated uncertainty indications are compared with a predefined reliability indication and one of the synthetic structure data sets is selected depending thereon; and
f) the selected structure data set is output for the purpose of producing the technical product.
The examiner submits that the foregoing bolded limitations constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitations in the human mind. For example, “specifying the respective design variant and also a training quality value quantifying a predefined design criterion are read in as training data;
to determine an associated quality value together with an associated uncertainty indication on the basis of a structure data set;
c) a multiplicity of synthetic structure data sets are generated
d) a quality value with an associated uncertainty indication is generated for each of the synthetic structure data sets by the trained Bayesian neural network;
e) the generated uncertainty indications are compared with a predefined reliability indication and one of the synthetic structure data sets is selected depending thereon” in the context of this claim encompasses mentally processing and evaluating data and selection (which can be performed conceptually by a human with pen-and-paper, absent the computer). Furthermore, the use and training of a Bayesian neural network, probabilistic inference, and uncertainty quantification are mathematical relationships/algorithms. These limitations are characteristic of data-only steps and mathematical concepts per the 2019 PEG (abstract idea groupings: mathematical formulas/calculations/data structures; mental processes such as observations/comparisons/selections).
Step 2A Prong 2: Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” The claim does not integrate the abstract ideas into a practical application:
No improvement to the functioning of a computer or another technology is articulated. The claim uses a generic “computer-implemented” Bayesian neural network to process data and make a selection. There is no recited specific computer architecture, specialized hardware, memory management technique, training algorithmic detail that changes how the computer operates, or other technical improvement.
No particular machine is recited beyond a generic computer executing a neural network; the Bayesian neural network is a mathematical model.
No transformation of an article is recited. The step “the selected structure data set is output for the purpose of producing the technical product” is an intended use and amounts to insignificant post-solution activity; the method stops at outputting data and does not actually produce or transform a physical article or control a manufacturing apparatus.
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”).
For the above reason(s), the examiner submits that the above underlined additional limitations do not integrate the above-noted abstract idea into a practical application.
Accordingly, the additional elements are merely instructions to apply the abstract idea on a computer, plus insignificant extra-solution activity (data input/output, comparison/selection), and do not constitute a practical application under the 2019 PEG.
Step 2B: Regarding Step 2B of the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of the recited “computer-implemented” context and the use of a “Bayesian neural network” to generate quality values and uncertainties are, on their face, conventional tools and techniques for data modeling/design optimization absent any claimed unconventional architecture or specific improvement to computer functioning. The steps of receiving training data, training a model, generating synthetic data, scoring the synthetic data, comparing to a threshold, selecting, and outputting are well-understood, routine, and conventional activities in the computational design/ML art. These limitations are recited a high-level of generality and amount to nothing more than applying the exception using generic computer components.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. When considering the elements individually and as an ordered combination, the claims do not include an inventive concept sufficient to transform the abstract idea into a patent-eligible application. The recited hardware (processors, memory, systems) is generic and operates in its ordinary capacity. The steps of training neural networks, generating synthetic data, calculating uncertainty, and optimizing selection are all routine mathematical and computational operations. There is no indication that the claimed invention improves the functioning of the computer or another technology, nor is there any unconventional arrangement of components.
The recited computer hardware is used to gather, process, and output data necessary to perform the calculations of the abstract idea, similar to the following concepts determined by the courts to be insignificant extra-solution activity:
Performing clinical tests to obtain input for an equation, In re Grams, 888 F.2d 835 (Fed. Cir. 1989);
Gathering information using the Internet to verify transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366 (Fed. Cir. 2011);
Testing a system and using the response in a calculation, In re Meyers, 688 F.2d 789 (CCPA 1982);
Consulting and updating an activity log, Ultramercial, 772 F.3d 709 (Fed. Cir. 2014).
As such, the processors, memory, and design/production systems merely perform insignificant extra-solution activity. See MPEP 2106.05(g).
Dependent Claims
Dependent claims 2-12 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims 2-9 are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Specifically, claims 2-6 recite particular types of generative processes (such as variational autoencoders or generative adversarial networks), training approaches, or data inputs, which are mathematical techniques and models routinely used in the field of machine learning and data generation. Claims 7-9 recite specifying uncertainty indication by variance, standard deviation, or probability distribution, or handling multiple criteria, which are also mathematical concepts and standard data processing steps. These limitations do not add significantly more than the abstract idea itself, nor do they amount to an inventive concept or practical application beyond the judicial exception.
Therefore, dependent claims 2-12 are not patent eligible under the same rationale as provided for in the rejection of independent claim 1.
Therefore, claim(s) 1-12 is/are ineligible under 35 U.S.C. §101.
In addition, claims 11-12 are 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 claims could be considered signal per se. The claims recite a computer-readable medium that is not limited to non-transitory tangible media. The broadest reasonable interpretation of a claim drawn to a computer readable medium covers transitory propagating signals per se. The computer readable medium recited in claim 11-12 encompasses a transitory, propagating signal, which is not a process, machine, manufacture, or composition of matter. The claims "cover material not found in any of the four statutory categories [and thus] falls outside the plainly expressed scope of § 101 ." Id. at 1354. Although the specification gives examples of a computer readable medium, it does not definitively state that it is non-transitory. The Examiner respectfully recommends amending the claims to recite “non-transitory computer-readable medium”.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-12 are rejected under 35 U.S.C. 103 as being obvious over Dweik et al. (USPGPUB US 20180260501 A1, hereinafter “Dweik”) in view of Li, Gong, and Jing Shi. "Applications of Bayesian methods in wind energy conversion systems." Renewable Energy 43 (2012): 1-8. (Hereinafter “Li”)
Regarding claim 1, Dweik teaches a computer-implemented design method for generating structure data sets specifying a technical product (Dweik, Fig. 1, [0028], [0031]), wherein
a) for a multiplicity of design variants of the technical product, in each case a training structure data set specifying the respective design variant and also a training quality value quantifying a predefined design criterion are read in as training data; (Dweik, at least [0033]-[0035], “Control volumes can also simulate run conditions for the preconfigured elements, the preconfigured components and/or a system associated with the 3D model. The preconfigured elements and/or the preconfigured components can be included in the library of data elements 114…. the modeling component 104 can employ 3D computer-aided design (CAD) data to automatically create computational domains and/or control volumes (e.g., chambers/elements/components) for the 3D model that can be employed (e.g., by the machine learning component 106) to generate predictions for simulated machine conditions for a device associated with the 3D model…The one or more characteristics determined by the machine learning component 106 can include, for example, one or more fluid characteristics associated with the one or more 3D models generated by the modeling component 104, one or more thermal characteristics associated with the one or more 3D models generated by the modeling component 104, one or more combustion characteristics associated with the one or more 3D models generated by the modeling component 104, one or more electrical characteristics associated with the one or more 3D models generated by the modeling component 104 and/or one or more other characteristics associated with the one or more 3D models generated by the modeling component 104.”)
b) a Bayesian neural network is trained on the basis of the training data, to determine an associated quality value together with an associated uncertainty indication on the basis of a structure data set; ([0037] “The machine learning component 106 (e.g., one or more machine learning processes performed by the machine learning component 106) can employ, for example, a support vector machine (SVM) classifier to learn and/or generate inferences with respect to the one or more 3D models generated by the modeling component 104. Additionally or alternatively, the machine learning component 106 (e.g., one or more machine learning processes performed by the machine learning component 106) can employ other classification techniques associated with Bayesian networks, decision trees and/or probabilistic classification models. Classifiers employed by the machine learning component 106 (e.g., one or more machine learning processes performed by the machine learning component 106) can be explicitly trained (e.g., via a generic training data) as well as implicitly trained (e.g., via receiving extrinsic information). For example, with respect to SVM's that are well understood, SVM's are configured via a learning or training phase within a classifier constructor and feature selection module. A classifier is a function that maps an input attribute vector, x=(x1, x2, x3, x4, xn), to a confidence that the input belongs to a class—that is, f(x)=confidence(class).”; [0038])
c) a multiplicity of synthetic structure data sets are generated and fed into the trained Bayesian neural network; ([0031], [0035], [0037] “the machine learning component 106 can perform a probabilistic based utility analysis that weighs costs and benefits related to the one or more 3D models generated by the modeling component 104. The machine learning component 106 (e.g., one or more machine learning processes performed by the machine learning component 106) can also employ an automatic classification system and/or an automatic classification process to facilitate learning and/or generating inferences with respect to the one or more 3D models generated by the modeling component 104”)
d) a quality value with an associated uncertainty indication is generated for each of the synthetic structure data sets by the trained Bayesian neural network; ([0037] “ In an aspect, the machine learning component 106 can also employ measured data and/or streamed data to set boundary conditions for one or more machine learning processes. For example, the machine learning component 106 can also employ measured data and/or streamed data to set boundary conditions for supply chambers and sink chambers and/or to establish driving forces for simulated physics phenomena (e.g., fluid dynamics, thermal dynamics, combustion dynamics, angular momentum, etc.).”)
Dweik does appear to expressly teach:
e) the generated uncertainty indications are compared with a predefined reliability indication and one of the synthetic structure data sets is selected depending thereon; and
f) the selected structure data set is output for the purpose of producing the technical product.
However Li teaches
e) the generated uncertainty indications are compared with a predefined reliability indication and one of the synthetic structure data sets is selected depending thereon; (Li, 2.4. Bayesian model selection and averaging section and 3.1. Wind resource assessment and estimation, 4th paragraph) and
f) the selected structure data set is output for the purpose of producing the technical product. (Li, 3.4. Reliability evaluation of wind turbine components section)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to modify Dweik’s system which includes a machine learning component with Li’s Bayesian neural network model to provide a natural way to handle missing data, allow combination of data with domain knowledge, facilitate learning about causal relationships between variables, provide a method for avoiding the overfitting of data, predict with good accuracy even with rather small sample sizes, and can be easily combined with decision analytic tools. (See Li, 1. Introduction section, 3rd paragraph)
Regarding claim 2, Dweik in combination with Li teaches the method as claimed in claim 1, Dweik further teaches wherein the synthetic structure data sets are generated by a trainable generative process. (Dweik, [0037])
Regarding claim 3, Dweik in combination with Li teaches the method as claimed in claim 2, Dweik further teaches wherein the generative process is carried out by a variational autoencoder (Dweik, [0038] “a set of stacked auto-encoder computations”) and/or by generative adversarial networks.
Regarding claim 4, Dweik in combination with Li teaches the method as claimed in claim 2, Li further teaches wherein the generative process is trained on the basis of the training structure data sets, to reproduce training structure data sets on the basis of random data fed in, in that a multiplicity of random data are generated and fed into the trained generative process, and in that the synthetic structure data sets are generated by the trained generative process on the basis of the fed-in multiplicity of generated random data. (Li, 2.2. Hierarchical modeling and Bayesian network section)
Regarding claim 5, Dweik in combination with Li teaches the method as claimed in claim 2, Dweik further teaches wherein further structure data sets are fed into a trained generative process, and in that the synthetic structure data sets are generated by the trained generative process depending on the further structure data sets fed in. (Dweik, [0074]-[0076])
Regarding claim 6, Dweik in combination with Li teaches the method as claimed in claim 2, Li further teaches wherein the generative process is trained, on the basis of the training structure data sets, to reproduce training structure data sets on the basis of random data fed in, in that a multiplicity of data values are generated and fed into the trained generative process, in that for a data value respectively fed in, a synthetic structure data set is generated by the trained generative process, and an associated quality value with an associated uncertainty indication is generated by the trained Bayesian neural network on the basis of the synthetic structure data set, in that in the context of an optimization method an optimized data value is ascertained in such a way that an uncertainty quantified by the respective uncertainty indication is reduced and/or a design criterion quantified by the respective quality value is optimized, and in that the synthetic structure data set generated for the optimized data value is output as selected structure data set. (Li, 2.3. Bayesian neural network section)
Regarding claim 7, Dweik in combination with Li teaches the method as claimed in claim 1, Dweik further teaches wherein a respective uncertainty indication is specified by a variance, a standard deviation, a probability distribution, a distribution type and/or a progression indication. (Dweik, [0037])
Regarding claim 8, Dweik in combination with Li teaches the method as claimed in claim 1, Dweik further teaches wherein the uncertainty indication generated for the selected structure data set is output in a manner assigned to the selected structure data set. (Dweik, [0069])
Regarding claim 9, Dweik in combination with Li teaches the method as claimed in claim 1, Li further teaches wherein a plurality of design criteria are predefined, in that the Bayesian neural network is trained to determine criterion-specific uncertainly indications for criterion-specific quality values, in that a plurality of criterion-specific uncertainty indications are generated for each of the synthetic structure data sets by the trained Bayesian neural network, and in that one of the synthetic structure data sets is selected depending on the generated criterion-specific uncertainly indications. (Li, 2.3. Bayesian neural network section)
Regarding claim 10, Dweik in combination with Li a design system for generating structure data sets specifying a technical product, configured for carrying out a method as claimed in claim 1. (Dweik, Fig. 4, element 102, 112, 110)
Regarding claim 11, Dweik in combination with Li a computer program product, comprising a computer readable hardware storage device having computer readable program code stored therein, the program code executable by a processor of a computer system to implement a method configured for carrying out a method as claimed in claim 1. (Dweik, [0030])
Regarding claim 12, Dweik in combination with Li a computer-readable storage medium comprising a computer program product as claimed in claim 11. (Dweik, [0030])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
INAMDAR et al. (US 20220036282 A1) teaches a system and a method for validating a candidate recommendation model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANISS CHAD whose telephone number is (571)270-3832 or email: aniss.chad@uspto.gov. The examiner can normally be reached M-F 8:00-4:00pm EST.
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, James Trammell can be reached at 571-272-6712. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ANISS CHAD/
Supervisory Patent Examiner
Art Unit 3662