DETAILED ACTION
Status of Claims
Claim(s) 16-30 are pending and are examined herein.
Claim(s) 16-30 are rejected under 35 U.S.C. §§§ 112, 101 and 103.
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 .
Information Disclosure Statement
The information disclosure statement IDS(s) submitted on November 06, 2023 is in compliance with the provisions of 37 CFR 1.97 and have been considered by the examiner.
Priority
Acknowledgment is made of the applicant’s claim for foreign priority under 35 U.S.C. 119 to the foreign applications identified in the Application.
Claim Objections
Claim(s) 18 and 21 are objected to for following issue:
Claim 18 recites “The method of claim 16, wherein an individual output model comprises the two or more sub-models, and the two or more sub-models comprise a sensor model and a stack model for a semiconductor sensor operation.” Claim 18, which depends from parent claim 16, recites “the two or more sub-models” without sufficient antecedent basis for the “two or more sub-models” in claim 16. The claim should either recite “two or more sub-models” or should be depending from dependent claim 17, which recites for a clear antecedent basis of the claimed “the two or more sub-models.”
Claim 21 recites “The method of claim 16, wherein the quantity of input models is different than the quantity of output models.” Claim 21, as currently drafted, depends from parent claim 16 and refers to “the quantity” without a clear antecedent basis for “a quantity.” The claim should be depending on dependent claim 20 for proper antecedent basis for the claimed quantity.
Appropriate correction is required.
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.
Claim(s) 16-30 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, for pre-AIA the applicant regards as the invention.
Regarding Claim 16, the claim recites limitations that renders the scope of the claimed invention indefinite for the following reasons:
The claim recites the limitation “processing, with one or more input models of a modular autoencoder model, one or more inputs to a first level of dimensionality suitable for combination with other inputs;” lines 2-3. This limitation recites the phrase “suitable for combination with other inputs;” without clearly establishing an objective boundaries for the claimed term “suitable.” The term “suitable” is a subjective term of degree and the claim does not provide any objective standard for determining what dimensionality qualifies as being “suitable.” The claim does not specify what type of combination is required, nor does it define what makes the dimensionality appropriate for such combination. The specification does not provide a standard for determining what constitute as suitable. As a result, a person ordinary skill in the art cannot determine the metes and bounds of this limitation.
The claim further recites the limitation “expanding, with the common model, the low dimensional data in the latent space into one or more expanded versions of the one or more inputs, the one or more expanded versions of the one or more inputs having increased dimensionality compared to the low dimensional data in the latent space, the one or more expanded versions of the one or more inputs suitable for generating one or more different outputs;” lines 8-12. This limitation recites “the one or more inputs suitable for generating one or more different outputs” without a clear standard for what represents a suitable input for generating one or more different outputs. This is a subjective term without objective boundaries. The claim does not specify any objective criteria for when expanded versions are suitable versus not suitable. The claim and the specification does define structural requirements or performance metric/threshold for the claimed term. Accordingly, one of ordinary skill in the art would not be reasonably apprised by the scope of the claim.
For at least the above reasons, claim 1 does not particularly point out and distinctly claim the invention and it therefore indefinite under 35 USC § 112(b).
Regarding Claims 27, 29, and 30, the claims recite substantially similar limitations as those of claim 1 and are rejected for similar reasons and rationale.
Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim(s) 16 and 19 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim(s) 19 and 26-27 of copending application No. 18/270,074. Although the claims at issue are not identical, they are not patentably distinct from each other. Corresponding claims/features and the rejection based on anticipation/obviousness analysis is described below.
Claims of the Present Application Filed on 06/26/2023
Claims of Copending Application # 18/270,074 Filed on 06/28/2023
16. (New) A method for parameter estimation, the method comprising:
processing, with one or more input models of a modular autoencoder model, one or more inputs to a first level of dimensionality suitable for combination with other inputs;
combining, with a common model of the modular autoencoder model, the processed inputs and reducing a dimensionality of the combined processed inputs to generate low dimensional data in a latent space, the low dimensional data in the latent space having a second level of resulting reduced dimensionality that is less than the first level;
expanding, with the common model, the low dimensional data in the latent space into one or more expanded versions of the one or more inputs, the one or more expanded versions of the one or more inputs having increased dimensionality compared to the low dimensional data in the latent space, the one or more expanded versions of the one or more inputs suitable for generating one or more different outputs;
using, with one or more output models of the modular autoencoder model, the one or more expanded versions of the one or more inputs to generate the one or more different outputs, the one or more different outputs being approximations of the one or more inputs, the one or more different outputs having the same or increased dimensionality compared to the expanded versions of the one or more inputs; and
estimating, with a prediction model of the modular autoencoder model, one or more parameters based on the low dimensional data in the latent space and/or the one or more outputs.
19. (New) The method of claim 16, wherein the one or more input models, the common model, and the one or more output models are separate from each other and correspond to process physics differences in different parts of a manufacturing process and/or a sensing operation such that each of the one or more input models, the common model, and/or the one or more output models are trained together and/or separately, but individually configured based on the process physics for a corresponding part of the manufacturing process and/or sensing operation, apart from other models in the modular autoencoder model.
Examiner’s Note: According to claim 22 and paragraph [0051] of the present application, the term “common model” is defined as encoder-decoder architecture.
19. (New) A method for estimating, with a modular autoencoder model having an extended range of applicability, parameters of interest for optical metrology operations by enforcing known properties of inputs to the modular autoencoder model in a decoder of the modular autoencoder model, the method comprising:
causing an encoder of the modular autoencoder model to encode an input to generate a low dimensional representation of the input in a latent space; and
causing the decoder of the modular autoencoder model to generate an output corresponding to the input by decoding the low dimensional representation, wherein the decoder is configured to enforce, during decoding, a known property of the encoded input to generate the output, wherein the known property is associated with a known physical relationship between the low dimensional representation in the latent space and the output, and wherein a parameter of interest is estimated based on the output and/or the low dimensional representation of the input in the latent space.
26. (New) The method of claim 19, the method further comprising:
processing, with an input model of the modular autoencoder model, the input to a first level of dimensionality suitable for combination with other inputs, and providing the processed input to the encoder;
receiving, with an output model of the modular autoencoder model, an expanded version of the input from the decoder and generating an approximation of the input based on the expanded version; and
estimating, with a prediction model of the modular autoencoder model, the parameter of interest based on the low dimensional representation of the input in the latent space and/or the output.
27. (New) The method of claim 26, wherein the input model, the encoder/ decoder, and the output model are separate from each other and correspond to process physics differences in different parts of a manufacturing process and/or a sensing operation such that each of the input model, the encoder/ decoder, and/or the output model can be trained together but individually configured based on the process physics for a corresponding part of the manufacturing process and/or sensing operation, apart from other models in the modular autoencoder model.
With respect to instant claim 16, As shown above, claim 26 of the copending application (which incorporate the features of parent claim 19) recites limitations that anticipate the limitations of the instant claim 16. Specifically, all the limitations recited in the present claim 16 are either explicitly disclosed in the corresponding claims 19 and 26 or inherent in the context of the autoencoder architecture (e.g., dimensionality relationships). While the claims of the copending application do not explicitly recite dimensionality reduction and increased dimensionality, these features are inherent in the autoencoder architecture, as encoding into the latent space reduces dimensionality and decoding produces expanded outputs. Different in terminology or explicit recitation of inherent properties do not make the claim patentably distinct.
With respect to instant claim 19, As shown above, claim 27 of the copending application (which incorporate features of parent claims 26 and 19) anticipate the limitations recited in claim 19 of present application. The broader recitation of “the common model” is defined by the specification as encoder-decoder architecture. These is no specific feature that would make claim 19 patentably distinct from the corresponding claims of the copending application 19, 26, and 27.
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.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult MPEP 2106 for more details of the analysis.
Under Step 1 analysis,
Claims 16-26 recite a method (representing a process);
Claims 27-28 and 30 recite a non-transitory computer readable medium (representing an article of manufacture); and
Claim 29 recites a system (representing a machine).
Therefore, each set of the claims falls into one of the four statutory categories (i.e., process, machine, article of manufacture, or composition of matter).
Claims
16-30 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, and hence is not patent-eligible subject matter.
Regarding Claim 16,
Step 2A Prong 1: The claim recites an abstract idea enumerated in the 2019 PEG.
processing, with one or more input models of a modular autoencoder model, one or more inputs to a first level of dimensionality suitable for combination with other inputs; (This step falls under the mental process and/or mathematical concepts. Examiner’s note: the “processing” step, as drafted, and under its broadest reasonable interpretation (BRI), covers concepts that can be practically performed in the human mind with the aid of pen and paper. See MPEP § 2106.04(a)(2)(I) & (III). Under broadest reasonable interpretation (BRI) in light of the specification paragraph [0239], this step defines a data preprocessing operation which involves filtering/transforming/converting input data into a model friendly format. This encompasses the mathematical calculation and mental process that can be performed by an individual with the aid of pen and paper. It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." See MPEP 2106.04(a)(2)(I). With respect to the recitation of “with one or more input models of a modular autoencoder model” that is nothing other than merely using computer component to perform the abstract idea.)
combining, with a common model of the modular autoencoder model, the processed inputs and reducing a dimensionality of the combined processed inputs to generate low dimensional data in a latent space, the low dimensional data in the latent space having a second level of resulting reduced dimensionality that is less than the first level; (This falls under the Mental Processes and/or Mathematical Concept. The “combining” step, as drafted, and under their broadest reasonable interpretation, cover concepts that would fall under the mental process and mathematical concept. Examiner note: this step involves mathematically calculating to combine or merge numbers into a compressed format or mathematically generating a reduced representation of the transformed values/data. The claim does not define the technical implementation of the combing step and merely uses the common model at a high level of generality to perform this step. The common model is broadly interpreted as a mathematical model/equation used to perform the claimed step. See MPEP § 2106.04(a)(2)(I) & (III).)
expanding, with the common model, the low dimensional data in the latent space into one or more expanded versions of the one or more inputs, the one or more expanded versions of the one or more inputs having increased dimensionality compared to the low dimensional data in the latent space, the one or more expanded versions of the one or more inputs suitable for generating one or more different outputs; (The “expanding” step, as drafted, and under their broadest reasonable interpretation, cover concepts that would fall under the mental process and mathematical concept. Examiner note: this step defines the reverse operation of the combining step, which represents a mathematical transformation of the compressed version into high dimensional. The generating of one or more different outputs also defines a mathematical calculation of computing approximations. The claim does not define the technical implementation of the expanding operation in the context of modular autoencoder and merely recites the common model at a high level of generality as tool to perform this step. The common model is broadly interpreted as a mathematical model/equation used to perform the claimed step. Accordingly, this step falls under the mathematical concepts and mental processes. See MPEP § 2106.04(a)(2)(I) & (III).)
using, with one or more output models of the modular autoencoder model, the one or more expanded versions of the one or more inputs to generate the one or more different outputs, the one or more different outputs being approximations of the one or more inputs, the one or more different outputs having the same or increased dimensionality compared to the expanded versions of the one or more inputs; (This is part of the abstract idea identified above (i.e., Mental Processes and/or Mathematical Concept). Examiner note: it merely describes generating approximations from transformed data, which is a mathematical operation that can be performed manually with pen and paper. The claim does not recite any technical implementation of the output generation beyond dimensionality relationships, which themselves fall within mathematical concepts. Additionally, the high-level recitation of one or more models, merely invokes a generic tool to perform the abstract idea. See MPEP § 2106.04(a)(2)(I) & (III).)
estimating, with a prediction model of the modular autoencoder model, one or more parameters based on the low dimensional data in the latent space and/or the one or more outputs. (The “estimating” step, as drafted and under its broadest reasonable interpretation, covers concepts that falls under the mathematical concept and mental processes. The parameter estimation using previously determined data falls within a mathematical operation and decision making process that can be practically performed in the human mind with the aid of pen and paper. See MPEP § 2106.04(a)(2)(I) & (III).)
Step 2A Prong 2: Under this prong, we evaluate whether the claim recites additional elements that integrate the abstract idea into a practical application by considering the claim as a whole. The judicial exception is not integrated into a practical application.
Additional Elements Analysis:
The claim recite the additional elements such as:
“one or more input models of a modular autoencoder model”, “a common model of the modular autoencoder model”, “one or more output models of the modular autoencoder model”, and “a prediction model of the modular autoencoder model.” These elements amount to no more than merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). In other words, the claim invokes computer and/or other machinery in its ordinary capacity merely as a tool to perform the abstract idea.
Step 2B: Under this prong, the claim must include additional elements that amount to significantly more than the judicial exception. These elements must not be well-understood, routine, or conventional in the relevant field. When viewed individually and as an ordered combination, the claim does not include any such additional elements that are sufficient to amount to significantly more (i.e., inventive concept).
Additional Elements Analysis:
As explained above, the claimed additional elements merely represents generic computer component (i.e., conventional models) configured to perform the abstract ideas. As described in MPEP § 2106.05(f), additional elements that invoke computers or other machinery merely as a tool to perform an existing process will generally not amount to significantly more than a judicial exception.
Therefore, claim 1 does not recite patent-eligible subject matter.
Regarding Claim 17,
Step 2A Prong 1: Claim 17, which incorporates the rejection of claim 16, doesn’t recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
wherein individual input models and/or output models comprise two or more sub-models, (This limitations is part of the additional elements of merely uses computer instructions to perform the abstract idea on a computer. See MPEP § 2106.05(f).) and
the two or more sub-models associated with different portions of a sensing operation and/or a manufacturing process. (This limitation amounts to linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h). The claim limitation merely defines the intended use or filed of use of the claimed method. This does not meaningfully limit the abstract idea because it merely linked the use of the abstract idea to a particular technological environment. See MPEP § 2106.05(e).)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above, the additional element of using one sub-models amounts to invoking computer component as a tool to perform an existing process and/or amounts to generally linking the use of a judicial exception to a particular technological environment or field of use. It is noted that a claim directed to a judicial exception cannot be made eligible simply by limiting the exception to a particular technological use. See MPEP § 2106.05(h).
Therefore, claim 17 is ineligible.
Regarding Claim 18,
Step 2A Prong 1: Claim 18, which incorporates the rejection of claim 16, doesn’t recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
wherein an individual output model comprises the two or more sub-models, (This limitations is part of the additional elements of merely uses computer instructions to perform the abstract idea on a computer. See MPEP § 2106.05(f).) and
the two or more sub-models comprise a sensor model and a stack model for a semiconductor sensor operation. (This limitation amounts to linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h). The claim limitation merely defines the intended use or filed of use of the claimed method. This does not meaningfully limit the abstract idea because it merely linked the use of the abstract idea to a particular technological environment. See MPEP § 2106.05(e).)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above, the additional element of using one sub-models amounts to invoking computer component as a tool to perform an existing process and/or amounts to generally linking the use of a judicial exception to a particular technological environment or field of use. It is noted that a claim directed to a judicial exception cannot be made eligible simply by limiting the exception to a particular technological use. See MPEP § 2106.05(h).
Therefore, claim 18 is ineligible.
Regarding Claim 19,
Step 2A Prong 1: Claim 19, which incorporates the rejection of claim 16, doesn’t recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
wherein the one or more input models, the common model, and the one or more output models are separate from each other and correspond to process physics differences in different parts of a manufacturing process and/or a sensing operation such that each of the one or more input models, the common model, and/or the one or more output models are trained together and/or separately, but individually configured based on the process physics for a corresponding part of the manufacturing process and/or sensing operation, apart from other models in the modular autoencoder model. (This limitations is part of the additional elements that merely using computer components to perform the aforementioned abstract idea and/or generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP § 2106.05 (f) & (h).)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As outlined above, the additional elements amounts to invoking computer components as a tool to perform an existing process and/or amounts to generally linking the use of a judicial exception to a particular technological environment or field of use. These high-level recitations are not sufficient to integrate the judicial exception into a practical application or amount to significantly more. Additionally, these elements cannot provide an inventive concepts.
Therefore, claim 19 is ineligible.
Regarding Claim 20,
Step 2A Prong 1: Claim 20, which incorporates the rejection of claim 16, recites further limitation such as:
determining a quantity of the one or more input models, and/or a quantity of the one or more output models, based on process physics differences in different parts of a manufacturing process and/or a sensing operation. (This is an abstract idea of a mental process. The “determining” step recites a process that can be performed in the human mind. The concept of determining the number of models based on process physics differences in different parts of a manufacturing process and/or a sensing operation can be determined manually in the human mind. The quantity determination of models is recited at a high level of generality, and the claim does not define the technical implementation the determination. See MPEP § 2106.04(a)(2)(III).)
Step 2A Prong 2: The claim does not recite additional element that integrates the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, claim 20 is ineligible.
Regarding Claim 21,
Step 2A Prong 1: Claim 21, which incorporates the rejection of claim 20, recites further limitation such as:
wherein the quantity of input models is different than the quantity of output models. (That is part of the abstract idea recited in claim 20 of determining the quantity of models for different manufacturing process and/or a sensing operation.)
Step 2A Prong 2: The claim does not recite additional element that integrates the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, claim 21 is ineligible.
Regarding Claim 22,
Step 2A Prong 1: Claim 22, which incorporates the rejection of claim 16, recites further limitation such as:
processing the one or more inputs to the first level of dimensionality, and reducing the dimensionality of the combined processed inputs comprises encoding; and expanding the low dimensional data in the latent space into the one or more expanded versions of the one or more inputs comprises decoding. (That is part of the abstract idea identified in claim 16. Dependent claim 22 merely specifies the mathematical operations (i.e., transforming inputs to first level dimensionality, reducing the dimensionality the processed inputs, and expanding the low dimensional data) is performed by encoder-decoder architecture. This high level recitation does not define technical implementation of the autoencoder. For example, the claim does not link this process to technical training of the autoencoder. This remains high level mathematical operation and merely invokes computer as a tool to perform them. See MPEP § 2106.04(a)(2)(I).)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
The recitation of “the common model comprises encoder-decoder architecture and/or variational encoder-decoder architecture;” conventional machine learning architecture that is recited at a high level of generality. This amounts invoking computers or other machinery in their ordinary capacity merely as a tool to perform an existing process. See MPEP § 2106.05(f).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above in step 2A, prong Two, the additional element of using encoder-decoder architecture to perform the abstract idea amounts to no more than invoking generic computer components. Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection or amount to inventive concepts. See MPEP § 2106.05(d).
Therefore, claim 22 is ineligible.
Regarding Claim 23,
Step 2A Prong 1: Claim 23, which incorporates the rejection of claim 16, doesn’t recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
training the modular autoencoder model by comparing the one or more different outputs to corresponding inputs, and adjusting a parameterization of the one or more input models, the common model, and/or the one or more output models to reduce or minimize a difference between an output and a corresponding input. (These limitations merely describes generic machine learning training that not sufficient to integrate the abstract idea into a practical application. In other words, the claim recites computers or other machinery in its ordinary capacity merely as a tool to perform an existing process. See MPEP § 2106.05 (f). These generic training steps are standard and conventional in the context of machine learning. As evidence by Bogo et. al., (Pub. No: US 20200310370A1), which state that autoencoders are “well known in the field of machine learning” and describes conventional training by encoding an input, reconstructing the input, computing a difference between the input and reconstructed output, and updating neural network weights using standard, well-known backpropagation algorithms to minimize the reconstruction error. See Bogo [0018] and [0054]. Accordingly, the generic recitation of training the autoencoder merely defines a generic computer functions and is not sufficient to integrate the aforementioned abstract idea to a practical application.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As outlined above, the additional training limitations represents well-understood, routine, and conventional machine learning process. Accordingly, these generic training recitations cannot provide an inventive concepts.
Therefore, claim 23 is ineligible.
Regarding Claim 26,
Step 2A Prong 1: Claim 26, which incorporates the rejection of claim 16, recites further limitation such as:
generating, with one or more auxiliary models of the modular autoencoder model, labels for at least some of the low dimensional data in the latent space, the labels configured to be used by the prediction model for estimations. (This falls under the abstract idea of mental process and/or mathematical concepts. The limitation of generating labels for low dimensional data in the latent space (i.e., vector representation) amounts to applying a mathematical function to numerical vector data to produce a classification or output value. Such mathematical evaluation of data can be performed in the human mind or with pen and paper. The recitation of auxiliary models merely invokes generic computer implementation of this mathematical operations.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
The recitations of “one or more auxiliary models of the modular autoencoder model” and “prediction model” amount to no more than reciting generic computer components to perform the abstract idea on a computer. This generic recitation cannot provide an inventive concept. See MPEP § 2106.05(f).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above in step 2A, prong Two, merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection or amount to inventive concepts. See MPEP § 2106.05(d).
Therefore, claim 26 is ineligible.
Regarding Claim 27,
The claim recites similar limitations as corresponding claim 16. Therefore, the same analysis (subject matter eligibility analysis) that was utilized for claim 16, as described above, is equally applicable to claim 27. The only difference is that claim 16 is drawn to a method, and claim 27 is drawn to a non- computer readable medium. The recitation of “a non-transitory computer readable medium having instructions thereon, the instructions configured to cause a computer to execute a modular autoencoder model for parameter estimation...” merely defines computer component and instructions to implement a judicial exception, and hence the claimed additional elements listed above are merely generic elements and the implementation of the elements merely amount to no more than instruction to apply the abstract idea using a generic computer component. Therefore, the additional elements do not integrate the judicial exception into a practical application or amount to significantly more. See MPEP 2106.05(f).
Therefore, claim 27 is ineligible.
Regarding Claim 28,
The claim recites similar limitations as corresponding claim 26. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 26, as described above, is equally applicable to claim 28.
Therefore, claim 28 is ineligible.
Regarding Claim 29,
The claim recites similar limitations as corresponding claim 16. Therefore, the same analysis (subject matter eligibility analysis) that was utilized for claim 16, as described above, is equally applicable to claim 29. The only difference is that claim 16 is drawn to a method, and claim 29 is drawn to a system.
Therefore, claim 29 is ineligible.
Regarding Claim 30,
The claim recites similar limitations as corresponding claim 16. Therefore, the same analysis (subject matter eligibility analysis) that was utilized for claim 16, as described above, is equally applicable to claim 30. The only difference is that claim 16 is drawn to a method, and claim 30 is drawn to a non- computer readable medium. The recitation of “a non-transitory computer readable medium having instructions thereon, the instructions configured to cause a computer to execute a modular autoencoder model for parameter estimation...” merely defines computer component and instructions to implement a judicial exception, and hence the claimed additional elements listed above are merely generic elements and the implementation of the elements merely amount to no more than instruction to apply the abstract idea using a generic computer component. Therefore, the additional elements do not integrate the judicial exception into a practical application or amount to significantly more. See MPEP 2106.05(f).
Therefore, claim 30 is ineligible.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 16, 22-23, 27, and 29-30 are rejected under 35 U.S.C. 103 as being unpatentable over Shazeer et al., (Pub. No.: US 20200364405 A1) in view of Rothberg et al., (Pub. No.: US 20190347523 A1).
Regarding Claim 16,
Shazeer discloses the following:
A method for parameter estimation, the method comprising: (Shazeer, [0038] “This specification describes a multi model neural network architecture including a single deep learning model that can simultaneously learn different machine learning tasks from different machine learning domains.” [0063] “The multi task multi modal machine learning model 100 can be trained to perform different machine learning tasks from different machine learning domains or modalities using training data.”)
processing, with one or more input models of a modular autoencoder model, one or more inputs to a first level of dimensionality suitable for combination with other inputs; (Shazeer, [0043]-[0045] “The multi task multi modal machine learning model 100 includes multiple input modality neural networks 102 a-102 c, ... Data inputs, e.g., data input 110, received by the multi task multi modal machine learning model 100 are provided to the multiple input modality neural networks 102 a-102 c and processed by an input modality neural network corresponding to the modality (domain) of the data input. ... The input modality neural networks 102 a-102 c are configured to process received data inputs and to generate as output mapped data inputs from a unified representation space, e.g., mapped data 112. ... Each input modality neural network of the multiple input modality networks 102 a-c is configured to map received machine learning model data inputs of one of multiple machine learning domains or modalities to mapped data inputs of a unified representation space. That is, each input modality neural network is specific to a respective modality (and not necessarily a respective machine learning task) and defines transformations between the modality and the unified representation. For example, input modality neural network 102 a may be configured to map received machine learning model data inputs of a first modality, e.g., data inputs 110, to mapped data inputs of the unified representation space. Mapped data inputs of the unified representation space can vary in size.”) [Examiner’s Note: The input modality neural network 102a-c would read on the claimed input models.]
combining, with a common model of the modular autoencoder model, the processed inputs and reducing a dimensionality of the combined processed inputs to generate low dimensional data in a latent space, the low dimensional data in the latent space having a second level of resulting reduced dimensionality that is less than the first level; (Shazeer, [0049]-[0051] “The encoder neural network 104 is a neural network that is configured to process mapped data inputs from the unified representation space, e.g., mapped data input 112, to generate respective encoder data outputs in the unified representation space, e.g., encoder data output 114. Encoder data outputs are in the unified representation space. ... the encoder neural network 104 and decoder neural network 106 may include (i) one or more convolutional neural network layers, e.g., a stack of multiple convolutional layers with various types of connections between the layers, (ii) one or more attention neural network layers configured to perform respective attention mechanisms, (iii) one or more sparsely gated neural network layers.”) [Examiner’s Note: a shared encoder neural network receives the mapped outputs form all input modality networks to generate encoded unified representation. The autoencoder model consist of the encoder-decoder architecture reads on the common model.]
expanding, with the common model, the low dimensional data in the latent space into one or more expanded versions of the one or more inputs, the one or more expanded versions of the one or more inputs having increased dimensionality compared to the low dimensional data in the latent space, the one or more expanded versions of the one or more inputs suitable for generating one or more different outputs; (Shazeer, [0005] “a decoder neural network that is configured to process encoder data outputs to generate respective decoder data outputs from the unified representation space;” [0049]-[0051] “The decoder neural network 106 is a neural network, e.g., an autoregressive neural network, that is configured to process encoder data outputs from the unified representation space, e.g., encoder data output 114, to generate respective decoder data outputs from an output space, e.g., decoder data output 116. ... the encoder neural network 104 and decoder neural network 106 may include (i) one or more convolutional neural network layers, e.g., a stack of multiple convolutional layers with various types of connections between the layers, (ii) one or more attention neural network layers configured to perform respective attention mechanisms, (iii) one or more sparsely gated neural network layers.” Further see [0080].) [Examiner’s Note: The autoencoder model consist of the encoder-decoder architecture reads on the common model. The decoder network takes the encoded data to generate/reconstructed outputs. The encoded representation reads on the low dimensional latent space in a latent space and the decoder data outputs reads on the expanded version of the encoded data.]
using, with one or more output models of the modular autoencoder model, the one or more expanded versions of the one or more inputs to generate the one or more different outputs, ..., the one or more different outputs having the same or increased dimensionality compared to the expanded versions of the one or more inputs; (Shazeer, [0005] “a plurality of multiple output modality neural networks, wherein each output modality neural network corresponds to a different modality and is configured to map decoder data outputs from the unified representation space that correspond to received data inputs of the corresponding modality to data outputs of the corresponding modality.” [0047]-[0048] “each output modality neural network of the multiple output modality networks 108 a-c is configured to map data outputs of the unified representation space received from the decoder neural network, e.g., decoder data output 116, to mapped data outputs of one of the multiple modalities. That is, each output modality neural network is specific to a respective modality and defines transformations between the unified representation and the modality. For example, output modality neural network 108 c may be configured to map decoder data output 116 to mapped data outputs of a second modality, e.g., data output 118.” Further See [0081].) [Examiner’s Note: The output modality networks receive decoder outputs and amp them to approximated outputs. These outputs correspond to the “one or more different outputs” generated by the “output models.” The output modality network process the expanded/decoded signal from the decoder and produce outputs at the modality level.] and
estimating, ..., one or more parameters based on the low dimensional data in the latent space and/or the one or more outputs. (Shazeer, [0003] “Neural networks are machine learning models that employ one or more layers of nonlinear units to predict an output for a received input. ... Each layer of the network generates an output from a received input in accordance with current values of a respective set of parameters. Neural networks may be trained on machine learning tasks using training data to determine trained values of the layer parameters and may be used to perform machine learning tasks on neural network inputs.”)
While Shazeer describes the Multi-task multi-modal machine learning system that includes multiple input models, shared autoencoder, and output models. Shazeer does not clear define the dimensionality relationships of the processed data that are obvious properties in the context of encoder/decoder processing architecture. Shazeer does not appear to explicitly teach:
estimating, with a prediction model of the modular autoencoder model, one or more parameters based on the low dimensional data in the latent space and/or the one or more outputs.
However, Shazeer in view of Rothberg teaches the following:
combining, with a common model of the modular autoencoder model, the processed inputs and reducing a dimensionality of the combined processed inputs to generate low dimensional data in a latent space, the low dimensional data in the latent space having a second level of resulting reduced dimensionality that is less than the first level; (Rothberg, [0067)] “encoder 104 may be configured to receive input and output a latent representation (which may have a lower dimensionality than the dimensionality of the input data) ...” [0105] “The output of the first encoder (e.g., feature representation 206), the joint modality representation (e.g., knowledge base 230), and the first modality embedding (e.g., one of modality embeddings 232), may be used to generate input (e.g., feature representation 208) to a first decoder ...” Further see [0092].)
expanding, with the common model, the low dimensional data in the latent space into one or more expanded versions of the one or more inputs, the one or more expanded versions of the one or more inputs having increased dimensionality compared to the low dimensional data in the latent space, the one or more expanded versions of the one or more inputs suitable for generating one or more different outputs; (Rothberg, [0067] “encoder 104 may be configured to receive input and output a latent representation (which may have a lower dimensionality than the dimensionality of the input data) and the first decoder may be configured to reconstruct the input data from the latent representation. In some embodiments, the encoder and decoder may be part of an auto-encoder.” [0105] “The output of the first encoder (e.g., feature representation 206), the joint modality representation (e.g., knowledge base 230), and the first modality embedding (e.g., one of modality embeddings 232), may be used to generate input (e.g., feature representation 208) to a first decoder for the first modality (e.g., decoder 210). In turn, the output of the decoder 210 may be compared with the input provided to the first encoder ...”)
using, with one or more output models of the modular autoencoder model, the one or more expanded versions of the one or more inputs to generate the one or more different outputs, the one or more different outputs being approximations of the one or more inputs, the one or more different outputs having the same or increased dimensionality compared to the expanded versions of the one or more inputs; (Rothberg, [0068] “Accordingly, in some embodiments, during training, output of the statistical model 100 is compared to the input and the parameter values of the memory 105 are updated iteratively, based on a measure of distance between the input and the output, using stochastic gradient descent (with gradients calculated using backpropagation when the encoder and decoder are neural networks) or any other suitable training algorithm.” [0104]-[0105] “generating output using the respective decoders, comparing the input with the generated output, and updating the parameters values of the joint modality representation and/or the modality embeddings based on the difference between the input and output. ... In turn, the output of the decoder 210 may be compared with the input provided to the first encoder ...”) [Examiner’s Note: the decoder outputs are reconstructions/approximations of the original inputs, during training stage is used to minimize difference between input and output (reconstruction loss). This explicit reconstruction combination with the network outputs disclosed by Shazeer reads on the claimed “outputs being approximations of the one or more inputs.”] and
estimating, with a prediction model of the modular autoencoder model, one or more parameters based on the low dimensional data in the latent space and/or the one or more outputs. (Rothberg, [0082]-[0085] “As shown in FIG. 2B, the multi-modal statistical model 250 includes predictor 252 for prediction task 256 and task embeddings 254. ... These weighted feature representations may then be aggregated (e.g., as a weighted sum or product) via operation 260 to generate input for the predictor 252.” [0090] “the multi-modal statistical model 250 of FIG. 2B comprises encoder 204, encoder 214, knowledge base 230, modality embeddings 232, predictor 252, and task embeddings 254 and parameters of the components 230, 232, 252, and 254 may be estimated as part of process 300.” [0108] “act 308 may comprise estimating parameter values for one or more components of the multi-modal statistical model using a supervised learning technique. ... the parameters of a predictor (e.g., predictor 252 in the example of FIG. 2B) may be estimated at act 308. Additionally, in some embodiments, parameters of one or more task embeddings (e.g., one or more of task embeddings 254) may be estimated at act 308. ... the parameter values estimated as part of act 306 may be estimated using supervised learning based on the labeled training data accessed at act 304.”) [Examiner’s Note: Rothberg teaches a predictor component that is separate from the encoder-decoder, it takes as input latent/feature representations form the encoded inputs to estimate parameters for prediction tasks.]
Accordingly, at the effective filing date, it would have been prima facie obvious to one ordinarily skilled in the art of machine learning to modify the combination of Shazeer and Rothberg to incorporate the techniques for performing a prediction task using a multi-modal statistical model as taught by Rothberg. One would have been motivated to make such a combination in order to enable generating multi-modal statistical models for any suitable number of data modalities, but also improves the computer technology used to train and deploy such machine learning systems (Rothberg [0051]).
Regarding Claim 22, Shazeer in view of Rothberg teaches the elements of claim 16 as outlined above, and further teaches:
Wherein:
the common model comprises encoder-decoder architecture and/or variational encoder-decoder architecture; (Shazeer, [0043] “The multi task multi modal machine learning model 100 includes multiple input modality neural networks 102 a-102 c, an encoder neural network 104, a decoder neural network 106, and multiple output modality neural networks 108 a-108 c.”)
processing the one or more inputs to the first level of dimensionality, and reducing the dimensionality of the combined processed inputs comprises encoding; (Shazeer, [0045] “Each input modality neural network of the multiple input modality networks 102 a-c is configured to map received machine learning model data inputs of one of multiple machine learning domains or modalities to mapped data inputs of a unified representation space.” [0049] “The encoder neural network 104 is a neural network that is configured to process mapped data inputs from the unified representation space, e.g., mapped data input 112, to generate respective encoder data outputs in the unified representation space, e.g., encoder data output 114. Encoder data outputs are in the unified representation space.”) and reducing the dimensionality of the combined processed inputs comprises encoding; (Rothberg, [0127]-[0128] “In some embodiments, the input data may be converted or otherwise pre-processed into a representation suitable for providing to the encoder for the first modality. For example, categorical data may be one-hot encoded prior to being provided to the encoder for the first modality. As another example, image data may be resized prior to being provided to the encoder for the first modality. ... Next, process 400 proceeds to act 406, where the input data is provided as input to the first encoder, which generates a first feature vector as output. FIG. 2B, input 202 for modality “A”, is provided as input to the encoder 204 for modality “A”, and the encoder 204 produces a first feature vector (e.g., feature representation 206 as output).” [0092] “The first encoder may be configured to receive, as input, data having the first modality and output a latent representation (which may have a lower dimensionality than the dimensionality of the input data) and the first decoder may be configured to reconstruct the input data from the latent representation.”) and
expanding the low dimensional data in the latent space into the one or more expanded versions of the one or more inputs comprises decoding. (Shazeer, [0050] “The decoder neural network 106 is a neural network, e.g., an autoregressive neural network, that is configured to process encoder data outputs from the unified representation space, e.g., encoder data output 114, to generate respective decoder data outputs from an output space, e.g., decoder data output 116.”)
Regarding Claim 23, Shazeer in view of Rothberg teaches the elements of claim 16 as outlined above, and further teaches:
training the modular autoencoder model by comparing the one or more different outputs to corresponding inputs, and adjusting a parameterization of the one or more input models, the common model, and/or the one or more output models to reduce or minimize a difference between an output and a corresponding input. (Shazeer, [0063] “The multi task multi modal machine learning model 100 can be trained to perform different machine learning tasks from different machine learning domains or modalities using training data. ... The training data may be used to adjust the input modality neural networks 102 a-c, encoder neural network 104, decoder neural network 106, and output modality neural networks 108 a-c weights from initial values to trained values, e.g., by processing the training examples and adjusting the neural network weights to minimize a corresponding loss function.” Further see Rothberg [0068] and [0104]-[0105].)
Regarding Claim 27,
The claim recites substantially similar limitations as corresponding claim 16 and is rejected for similar reasons as claim 16 using similar teachings and rationale. Claim 16 is directed to a method, and claim 27 is directed to a non-transitory computer readable medium.
Shazeer in view of Rothberg also discloses methods, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods. A system of one or more computers can be configured to perform particular operations or actions by virtue of software, firmware, hardware, or any combination thereof installed on the system that in operation may cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.
Regarding Claim 29,
The claim recites substantially similar limitations as corresponding claim 16 and is rejected for similar reasons as claim 16 using similar teachings and rationale. Claim 16 is directed to a method, and claim 29 is directed to a system.
Shazeer in view of Rothberg also discloses methods, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods. A system of one or more computers can be configured to perform particular operations or actions by virtue of software, firmware, hardware, or any combination thereof installed on the system that in operation may cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.
Regarding Claim 30,
The claim recites substantially similar limitations as corresponding claim 16 and is rejected for similar reasons as claim 16 using similar teachings and rationale. Claim 16 is directed to a method and claim 30 is directed to a non-transitory computer readable medium. The primary difference is that claim 30 broadly recites a “machine-learning model” rather than a “modular autoencoder model,” and generically labels the components as first, second, third, and fourth models instead of input, common, output, and prediction models. Accordingly, the same teaching and rationale applied to claim 16 are equally applicable to the broader scope of claim 30.
Claim(s) 17, 20, and 21 rejected under 35 U.S.C. 103 as being unpatentable over Shazeer in view of Rothberg as described above, and further in view of et al., Schlake et al., (Pub. No.: US 20210349453 A1).
Regarding Claim 17,
Shazeer in view of Rothberg teaches the elements of claim 16 as outlined above, and further teaches:
wherein individual input models and/or output models comprise two or more sub-models, (Shazeer, [0056] “The input output mixer neural network may include one or more attention neural network layers configured to perform respective attention mechanisms, and one or more convolutional neural network layers. An example input output mixer neural network is illustrated and described in more detail below with reference to FIG. 4.”)
While Shazeer in view of Rothberg teaches the input/output network includes stack of multiple network layers to perform operations, Shazeer in view of Rothberg does not appear to explicitly teach:
the two or more sub-models associated with different portions of a sensing operation and/or a manufacturing process.
However, Schlake, in combination with Shazeer and Rothberg, teaches:
wherein individual input models and/or output models comprise two or more sub-models, the two or more sub-models associated with different portions of a sensing operation and/or a manufacturing process. (Schlake, [0041] “a dynamic predictive model for an industrial plant that comprises a plurality of sub-models, wherein the inputs of the model are distributed across the inputs of the sub-models, the outputs of the model are compiled from the outputs of the sub-models, and at least one output of one sub-model is processed into at least one input of one other sub-model.” [0075] “FIG. 3 illustrates the model 10 generated in this manner. The model 10 consists of two sub-modules 13 a and 13 b that correspond to sub-units 1 a and 1 b. The first sub-module 13 a gets the current values of process variables 41-44 and 48, as well as the set-points 51-54, that pertain to vessel V1 as inputs. Based on these inputs, the sub-model 13 a predicts how the process variables 41-44 and 48 will evolve to future values 41′-44′ and 48′.” [0015] “The plant also has a plurality of sensors. Each such sensor measures at least one process variable of one of the physical process, and/or of the plant as a whole. For example, there may be a sensor measuring the temperature in a vessel, and/or a sensor for the turbidity of a mixture inside the vessel that is a measure of how homogeneous the mixture is.”)
Accordingly, at the effective filing date, it would have been prima facie obvious to one ordinarily skilled in the art to modify the combination of Shazeer, Rothberg, and Schlake to incorporate the method for generating a dynamic model as taught by Schlake. One would have been motivated to make such a combination in order to generate a dynamic model needed for the model predictive control of the overall plant makes the creation of the model much more efficient and much more transparent (Schlake [0026]).
Regarding Claim 20, Shazeer in view of Rothberg teaches the elements of claim 16 as outlined above:
Shazeer in view of Rothberg does not appear to explicitly teach:
determining a quantity of the one or more input models, and/or a quantity of the one or more output models, based on process physics differences in different parts of a manufacturing process and/or a sensing operation.
However, Schlake, in combination with Shazeer and Rothberg, teaches:
determining a quantity of the one or more input models, and/or a quantity of the one or more output models, based on process physics differences in different parts of a manufacturing process and/or a sensing operation. (Schlake, [0018]-[0019] “a division of a representation of the plant into sub-units is obtained. ... If the plant is a modular industrial plant, then the already existing division into the physical process modules that make up the plant may be used.” [0021] “If no division into physical process modules exists, the layout of the plant may be actively searched for suitable sub-units. In other words, the representation of the plant may be actively divided into sub-units. Several strategies for doing this, which may be used individually or in arbitrary combinations, are detailed below.” [0028] “Moreover, the model may be much more easily adapted to any changes in the plant. For example, the trend is going from monolithic plants to modular plants where modules may be added and removed, or brought on-line and off-line, as needed. Whenever a change of this type happens, the overall model for the plant may be adapted in a straight-forward manner by simply adding and removing identical copies of one and the same sub-model in the right place.” Further See [0038].)
The same motivation that was utilized for combining Shazeer, Rothberg, and Schlake as set forth in claim 17 is equally applicable to claim 20.
Regarding Claim 21, the combination of Shazeer, Rothberg, and Schlake teaches the elements of claim 20 as outlined above, and further teaches:
Shazeer further teaches: wherein the quantity of input models is different than the quantity of output models. (Shazeer, [0044] “For convenience, the example multi task multi modal machine learning model 100 is shown as including three input modality networks and three output modality neural networks. However, in some implementations the number of input or output modality neural networks may be less or more, in addition the number of input modality neural networks may not equal the number of output modality neural networks.”)
Claim(s) 18 is rejected under 35 U.S.C. 103 as being unpatentable over Shazeer in view of Rothberg as described above, and further in view of Kim et al., (Pub. No.: US 20140291678 A1).
Regarding Claim 18, the combination of Shazeer in view of Rothberg teaches the elements of claim 16 as outlined above.
While Shazeer in view of Rothberg teaches that an individual output model comprises the two or more sub-models, Shazeer in view of Rothberg does not appear to explicitly teach:
the two or more sub-models comprise a sensor model and a stack model for a semiconductor sensor operation.
However, Kim, in combination with Shazeer and Rothberg, teaches the limitation:
the two or more sub-models comprise a sensor model and a stack model for a semiconductor sensor operation. (Kim, [0041]-[0042] “embodiments of the present invention provide a semiconductor sensor reliability system and method. Specifically, the present invention provides in-situ positioning of a reliability sensor (hereinafter sensors) within each functional block, as well as at critical locations, of a semiconductor system. ... In general, the sensor models a behavior (e.g., aging process) of the location (e.g., functional block) in which it is positioned and comprises a plurality of stages connected as a network and a self-digitizer. ... semiconductor 10 can include one or more wafer/layer 12A-N, each of which has various functional blocks 14.” [0045]-[0046] “sensors 42A-N are configured to model the behavior of the functional block in which they are positioned. ... Sensors 42A-N are positioned at locations within circuit block 40 subject to semiconductor system manufacturing process variation. Sensors 42A-N collect, process, and store sensed data about both predicted and unpredicted semiconductor degradation.”)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art, before the effective date of the claimed invention, having the combination of Shazeer, Rothberg, and Kim to incorporate the method for sensing semiconductor reliability operation as taught by Kim. One would have been motivated to make such a combination in order to provide higher-level reliability across the system hierarchy, as well as a need to identify and isolate the most likely runtime component failure to avoid catastrophic system failure. Doing so would reduce semiconductor system degradation in real-time (Kim [0041]).
Claim(s) 19, 25, 26, and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Shazeer in view of Rothberg as described above, and further in view of et al., Sha et al., (Pub. No.: US 20200151538 A1).
Regarding Claim 19, Shazeer in view of Rothberg teaches the elements of claim 16 as outlined above, and further teaches:
wherein the one or more input models, the common model, and the one or more output models are separate from each other and correspond to process physics differences in different parts of ... operation such that each of the one or more input models, such that each of the one or more input models, the common model, and/or the one or more output models are trained together and/or separately, but individually configured based on the process physics for a corresponding part of the ... operation, apart from other models in the modular autoencoder model. (Shazeer, [0043] “The multi task multi modal machine learning model 100 includes multiple input modality neural networks 102 a-102 c, an encoder neural network 104, a decoder neural network 106, and multiple output modality neural networks 108 a-108 c.” [0027] “ The model can be trained to perform the multiple machine learning tasks jointly, thus simplifying and improving the efficiency of the training process. In addition, by training the model jointly, in some cases less training data may be required to train the model (to achieve the same performance) compared to when separate training processes are performed for separate machine learning tasks.” [0063] “The multi task multi modal machine learning model 100 can be trained to perform different machine learning tasks from different machine learning domains or modalities using training data. The multi task multi modal machine learning model 100 can be trained jointly to perform different machine learning tasks from different machine learning domains, so that the multi task multi modal machine learning model 100 simultaneously learns multiple machine learning tasks from different machine learning domains.”)
While Shazeer in view of Rothberg describes the separate components of the machine learning architecture performing different machine learning tasks from different domains while being jointly trained. Shazeer in view of Rothberg does not define the domains tasks correspond to process physics for a corresponding part of the manufacturing process and/or sensing operation.
However, it would have been obvious in view of Sha. Hereinafter, Sha, in combination with Shazeer and Rothberg, teaches:
individually configured based on the process physics for a corresponding part of the manufacturing process and/or sensing operation, apart from other models in the modular autoencoder model. (Sha, [0026] “As such the technical solutions are rooted in and/or tied to computer technology in order to overcome a problem specifically arising in the realm of computers, specifically manufacturing semiconductors, such as integrated chips.” [00311] “In one or more examples, the synthesis controller 116 uses lithography, such as photolithography, for manufacturing the physical implementation, that is chip or semiconductor 120. The photo-lithography process 220 in semiconductor fabrication consists in duplicating desired mask shapes 210 as best as possible onto a semiconductor wafer 120.” [0047] “Referring to the flowchart of FIG. 5, training the DNN 610 can include accessing multiple chip layouts (511) and generating aerial images 330 for each of the chip layouts (512).” [0061] “FIG. 8 illustrates a flowchart of another method of automatic feature extraction from aerial images for test pattern sampling and pattern coverage inspection according to one or more embodiments of the present invention. In this case, training the neural network (510) includes using the simulated aerial images 330 of existing chip layouts (511, 512) as input for unsupervised training. The method includes generating a set of codings based on the aerial images 330 that are obtained from aerial image generation 410 using an artificial neural network in an unsupervised manner, such as using an autoencoder (613). An autoencoder learns to compress data (“encode”) from the input layer into a short code, and then decompress that code (“decode”) in the output layer so that the output closely matches the original input data.”)
Shazeer, Rothberg, and sha are from the same field of endeavor and their disclosure generally relates to (neural network Learning methods).
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, having the combination of Shazeer, Rothberg, and Sha, to incorporate the aerial image generation system for a physical synthesis system used to synthesize a physical design such as a semiconductor chip as taught by sha. One would have been motivated to make such a combination in order to improve the chip layout early in the fabrication process so that the chip can be reworked to eliminate the defects from the end product (sha [0065]).
Regarding Claim 25, Shazeer in view of Rothberg teaches the elements of claim 16 as outlined above.
the one or more input models and/or the one or more output models comprise dense feed-forward layers, convolutional layers, and/or residual network architecture of the modular autoencoder model; the common model comprises feed forward and/or residual layers; (Shazeer, [0067]-[0068] “The stack of depth wise separable convolutional neural network layers includes two skip- connections 220, 222 between the stack input 210 and the outputs of (i) the second convolutional step 204 and (ii) the fourth convolutional step 208. The stack of depth wise separable convolutional neural network layers also includes two residual connections 214 and 216. ... The example encoder neural network 104 includes a residual connection 306 between the data input 302 and a timing signal 304. After the timing signal 304 has been added to the input 302, the combined input is provided to the convolutional module 308 for processing. The convolutional module 308 includes multiple convolutional neural network layers, e.g., depth wise separable convolutional neural network layers, as described above with reference to FIGS. 1 and 2. The convolutional module 308 generates as output a convolutional output, e.g., convolutional output 322.”) and
the prediction model comprises feed forward and/or residual layers. (Rothberg, [0083]-[0084] “In the embodiment illustrated in FIG. 2B, it is assumed that the encoders 204 and 214, the decoders 210 and 220, the knowledge base 230, and the modality embeddings 232 have been previously trained as shown by the fill pattern having diagonal lines extending downward from left to right, and that the predictor 252 and task embeddings 254, ... the predictor 252 may comprise a linear model (e.g., a linear regression model), a generalized linear model (e.g., logistic regression, probit regression), a neural network or other non-linear regression model, ...”)
Shazeer in view of Rothberg does not appear to explicitly teach:
the one or more parameters are semiconductor manufacturing process parameters;
However, Sha, in combination with Shazeer and Rothberg, teaches:
the one or more parameters are semiconductor manufacturing process parameters (Sha, [0024] “Alternatively, or in addition, aerial image parameters of the electronic circuit can be categorized using predetermined features to be inspected from the aerial images. In one or more examples, manual selection of image parameters for the aerial images are provided for analyzing the pattern coverage. In one or more examples, such aerial image parameters are limited to one-dimensional (1D) cutlines. As can be understood by a person skilled in the art, such existing solutions operate using engineering judgment, where a user provides particular features to inspect in aerial images.” [0026] “Described herein are technical solutions for test pattern sampling and pattern coverage inspection, which are used for semiconductor manufacturing, particularly using photolithography.”)
The same motivation that was utilized for combining Shazeer, Rothberg, and Sha as set forth in claim 19 is equally applicable to claim 25.
Regarding Claim 26,
Shazeer in view of Rothberg teaches the elements of claim 16 as outlined above:
While Shazeer in view of Rothberg teaches the modular autoencoder and parameter estimation/prediction, Shazeer in view of Rothberg does not appear to explicitly recite:
generating, with one or more auxiliary models of the modular autoencoder model, labels for at least some of the low dimensional data in the latent space, the labels configured to be used by the prediction model for estimations.
However, Sha, in combination with Shazeer and Rothberg, teaches the limitation:
generating, with one or more auxiliary models of the modular autoencoder model, labels for at least some of the low dimensional data in the latent space, the labels configured to be used by the prediction model for estimations. (Sha, [0047]- [0050] “Referring to the flowchart of FIG. 5, training the DNN 610 can include accessing multiple chip layouts (511) and generating aerial images 330 for each of the chip layouts (512). [0048] In one or more examples, the aerial images 330 are generated using the aerial image generation 410. The method further includes creating training data by identifying classification labels or types of the aerial images 330 (513). In one or more examples, the aerial images 330 that are obtained by the simulation are labeled with the corresponding types of aerial image. The DNN 610 is trained using the created training data (514). ... In summary, the DNN 610 processes the records that include aerial images 330 and corresponding labels in the training data one at a time, using the weights and functions in the hidden layers 614, then compares the resulting outputs against the desired outputs.)
The same motivation that was utilized for combining Shazeer, Rothberg, and Sha as set forth in claim 19 is equally applicable to claim 26.
Regarding Claim 28,
The claim recites substantially similar limitations as corresponding claim 26 and is rejected for similar reasons as claim 26 using similar teachings and rationale.
Claim(s) 24 is rejected under 35 U.S.C. 103 as being unpatentable over Shazeer in view of Rothberg as described above, and further in view of et al., Sjolund et al., (Pub. No.: US 20190332900 A1).
Regarding Claim 24,
Shazeer in view of Rothberg teaches the elements of claim 16 as outlined above, and further teaches:
wherein the common model comprises an encoder and a decoder, the method further comprising training the modular autoencoder model by: ... providing the decoder signal to the encoder to generate new low dimensional data; comparing the new low dimensional data to the low dimensional data; and adjusting one or more components of the modular autoencoder model based on the comparison to reduce or minimize a difference between the new low dimensional data and the low dimensional data. (Rothberg, [0092] “the first trained statistical model may include an auto-encoder and may comprise a first encoder and a first decoder, each having a respective set of parameters, which may be accessed at act 302. The first encoder may be configured to receive, as input, data having the first modality and output a latent representation (which may have a lower dimensionality than the dimensionality of the input data) and the first decoder may be configured to reconstruct the input data from the latent representation.” [0104]-[0105]) “The iterative learning algorithm may involve providing at least some of the unlabeled training data as input to the encoders of the multi-modal statistical model, generating output using the respective decoders, comparing the input with the generated output, and updating the parameters values of the joint modality representation and/or the modality embeddings based on the difference between the input and output. For example, in some embodiments, training data of a first modality may be provided as input to a first encoder for the first modality (e.g., encoder 204). The output of the first encoder (e.g., feature representation 206), the joint modality representation (e.g., knowledge base 230), and the first modality embedding (e.g., one of modality embeddings 232), may be used to generate input (e.g., feature representation 208) to a first decoder for the first modality (e.g., decoder 210). In turn, the output of the decoder 210 may be compared with the input provided to the first encoder and at least some of the parameter values of the joint modality representation and/or the first modality embedding may be updated based on the difference between the input to the first encoder and the output of the first decoder.”)
As outlined above, while Shazeer in view of Rothberg describes the training process of the components of the autoencoder modular including iteratively updating the models’ parameters during training based on the loss function and using backpropagation. Shazeer in view of Rothberg does not appear to explicitly teach:
applying variation to the low dimensional data in the latent space such that the common model decodes a relatively more continuous latent space to generate a decoder signal;
recursively providing the decoder signal to the encoder to generate new low dimensional data;
However, Sjolund, in combination with Shazeer and Rothberg, teaches the following:
applying variation to the low dimensional data in the latent space such that the common model decodes a relatively more continuous latent space to generate a decoder signal; recursively providing the decoder signal to the encoder to generate new low dimensional data; comparing the new low dimensional data to the low dimensional data; and adjusting one or more components of the modular autoencoder model based on the comparison to reduce or minimize a difference between the new low dimensional data and the low dimensional data. (Sjolund, [0081] “As a result, it may be necessary to enforce some type of regularity (e.g. smoothness) of the latent representation during encoding, which can be done using, for instance, a variational autoencoder. It may also be preferable to have a latent representation which has the same spatial coordinates as the images so that the variables can be interpreted locally. This technique is introduced using Gaussian random fields in various examples below.” [0106] “As an overview of auto-encoders and variational auto-encoders, consider that auto-encoders, work by training two networks simultaneously. The encoder E takes data as input and maps it to a latent variable z, while the decoder D takes the latent variable z and reconstructs the input from it. The networks are trained by minimizing the reconstruction loss, typically taken to be the expected mean squared error...” [0137]-[0138] “This first approach for variational autoencoding is depicted in FIGS. 7 and 8, whereas this second approach for encoding and decoding is depicted in FIGS. 9 and 10. Each of these approaches in the following examples may be usable to learn the latent representation, but with different latent space dimensions and network architecture. FIG. 7 illustrates a data flow diagram of a variational autoencoder, employed in an exemplary encoding process for generating a latent representation of an imaging input. The variational autoencoder is composed of an encoder and a decoder. As shown, the variational autoencoder includes three blocks of convolutional layers in both the encoder (710) and decoder (740) portions of the network. Input data is encoded to a latent vector in the latent space. It is then decoded to reconstruct the input data. As an example, a latent vector is sampled with the mean and standard deviation by reparameterization. As shown, the variational autoencoder involves use of convolution operations (710), and fully connected operations (720A, 720B) that involve sampling (730). The decoding portion of the variational autoencoder further involves transpose convolution operations (740) that can recreate the original data input. FIG. 8 more specifically illustrates these and more detailed data processing operations performed by a neural network of a variational autoencoder, in connection with input convolution processing operations 810, result generation operations 820, and output transpose convolution processing operations 830 (e.g., producing a value useful for reconstruction).”)
Accordingly, it would have been prima facie obvious to one having ordinary skill in the art, before the effective filing date of the claimed invention, having the combination of Shazeer, Rothberg, and Sjolund, to incorporate the method for generating a modality-agnostic image processing model using variational autoencoder models as taught by Sjolund. One would have been motivated to make such a combination in order to improve the performance of the neural network by leveraging information from the unified representation that has been learned on other tasks (Sjolund [0037]).
Conclusion
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/S.A.A./Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121