DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
2. 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 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.
In view of the new 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register Vol. 84, No. 4, January 7, 2019), the Examiner has considered the claims and has determined that under step 1, claims 16-18 are to an article of manufacture, claims 19-28 are to a process, claim 29 is to a machine, and claim 30 is to an article of manufacture. Next under the new step 2A prong 1 analysis, the claims are considered to determine if they recite an abstract idea (judicial exception) under the following groupings: (a) mathematical concepts, (b) certain methods of organizing human activity, or (c) mental processes. The independent claims contain at least the following bolded limitations (see representative independent claims) that fall into the grouping of mathematical concepts and/or mental processes:
16. A non-transitory computer readable medium having instructions thereon, the instructions configured to cause a computer to execute a modular autoencoder model for estimating parameters of interest from a combination of available channels of measurement data from an optical metrology platform by estimating retrievable quantities of information content using a subset of a plurality of input models based on the available channels, the instructions causing operations comprising:
causing the plurality of input models to compress a plurality of inputs based on the available channels such that the plurality of inputs are suitable for combination with each other; and
causing a common model to combine the compressed inputs and generate low dimensional data in a latent space based on the combined compressed inputs, wherein the low dimensional data estimates the retrievable quantities, and wherein the low dimensional data in the latent space is configured to be used by one or more additional models to generate approximations of the plurality of inputs and/or estimate a parameter based on the low dimensional data.
19. A method for estimating parameters of interest from a combination of available channels of measurement data from an optical metrology platform by estimating retrievable quantities of information content using a subset of a plurality of input models of a modular autoencoder model based on the available channels, the method comprising: causing the plurality of input models to compress a plurality of inputs based on the available channels such that the plurality of inputs are suitable for combination with each other; and
causing a common model of the modular autoencoder model to combine the compressed inputs and generate low dimensional data in a latent space based on the combined compressed inputs, wherein the low dimensional data estimates the retrievable quantities, and wherein the low dimensional data in the latent space is configured to be used by one or more additional models to generate approximations of the plurality of inputs and/or estimate a parameter based on the low dimensional data.
29. A system for estimating parameters of interest from a combination of available channels of measurement data from an optical metrology platform by estimating retrievable quantities of information content using a subset of a plurality of input models of a modular autoencoder model based on the available channels, the system comprising: the plurality of input models, the plurality of input models configured to compress a plurality of inputs based on the available channels such that the plurality of inputs are suitable for combination with each other; and
a common model of the modular autoencoder model configured to combine the compressed inputs and generate low dimensional data in a latent space based on the combined compressed inputs,
wherein the low dimensional data estimates the retrievable quantities, and
wherein the low dimensional data in the latent space is configured to be used by one or more additional models to generate approximations of the plurality of inputs and/or estimate a parameter based on the low dimensional data.
30. 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, the instructions causing operations comprising:
causing a plurality of input models to compress a plurality of inputs such that the plurality of inputs are suitable for combination with each other; and
causing a common model to combine the compressed inputs and generate low dimensional data in a latent space based on the combined compressed inputs, the low dimensional data in the latent space configured to be used by one or more additional models to generate approximations of the one or more inputs and/or predict the parameter based on the low dimensional data,
wherein the common model is configured to combine the compressed inputs and generate the low dimensional data regardless of which ones of the plurality of inputs are combined by the common model.
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.). The limitations to "execute a modular autoencoder model for estimating parameters of interest…by estimating retrievable quantities of information content using a subset of a plurality of input models based on the available channels" describe in words the executing of a mathematically based autoencoder model (see published specification paragraph [0217] describing the associated formula) and subset of a plurality of input models to estimate parameters of interest as an output. The limitations of "causing the plurality of input models to compress a plurality of inputs based on the available channels such that the plurality of inputs are suitable for combination with each other" amount to a mental process to combine or merge a number of models to reduce the number of inputs, or amount to describing in words a mathematically-based process to reduce the number of variables/data. The limitations of "causing a common model to combine the compressed inputs and generate low dimensional data in a latent space based on the combined compressed inputs, wherein the low dimensional data estimates the retrievable quantities, and wherein the low dimensional data in the latent space is configured to be used by one or more additional models to generate approximations of the plurality of inputs and/or estimate a parameter based on the low dimensional data" amount to describing in words the application of a mathematical common model to combine (add) the compressed input data to generate low dimensional data as output, and further using the low dimensional data by one or more additional mathematical models to solve for approximations of the plurality of inputs and/or an output parameter. The limitation of "wherein the common model is configured to combine the compressed inputs and generate the low dimensional data regardless of which ones of the plurality of inputs are combined by the combined model" amount to describing in words the mathematical combination of compressed input data without regard to which ones of the plurality of inputs are combined. Thus, at least based on the bolded limitations in the independent claims, it is clear that a series of abstract idea mathematical calculations or mental process-based data evaluations are occurring to apply models, compress (reduce variables or dimensionality of) data, and combine inputs for estimating additional data.
Next in step 2A prong 2, the independent claims are analyzed to determine whether there are additional elements or combination of elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception such that it is more than a drafting effort designed to monopolize the exception, in order to integrate the judicial exception into a practical application. These limitations have been identified and underlined above, and are not indicative of integration into a practical application because: (1) the limitations of "from a combination of available channels of measurement data from an optical metrology platform" amount to adding insignificant extra-solution data gathering activity to the judicial exception (see MPEP 2106.05(g)); and (2) the limitations of "the non-transitory computer readable medium having instructions thereon, the instructions configured to cause a computer to execute…" or "the instructions causing operations" or "the system" amount to mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Next in step 2B, the independent claims are considered to determine if they recite additional elements that amount to an inventive concept (“significantly more”) than the recited judicial exception. The recitation of a combination of available channels of measurement data from an optical metrology platform do not add something significantly more because such limitations amount to adding insignificant extra-solution data gathering activity to the judicial exception (see MPEP 2106.05(g)), and do not describe any gathering of data in an unconventional way. The recitation of the non-transitory computer readable medium having instructions, the computer, the instructions causing operations, and the system do not add something significantly more because they amount to mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Dependent claims 17-18 and 20-28 contain additional limitations that fall under the abstract idea grouping of a mental process or mathematical concepts, as they describe further "data" based manipulations and data comparisons in the training of the autoencoder model, iterative variations of data, and further generation of estimations and approximations.
3. An invention is not rendered ineligible for patent simply because it involves an abstract concept. Applications of such concepts "to a new and useful end" remain eligible for patent protection (see Alice Corp., 134 S. Ct. at 2354 (quoting Benson, 409 U.S. at 67)). However, "a claim for a new abstract idea is still an abstract idea" (see Synopsys v. Mentor Graphics Corp. _F.3d_, 120 U.S.P.Q. 2d1473 (Fed. Cir. 2016)). There needs to be additional elements or combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception or render the claim as a whole to be significantly more than the exception itself in order to demonstrate “integration into a practical application” or an “inventive concept.” For instance, particular physical arrangements for actively obtaining the sensor data, or further physical applications using the calculated approximations of the plurality of inputs and/or estimated parameter to drive a change in operation, transformation, or repair/maintenance of a technology component or technical process could provide integration into a practical application to demonstrate an improvement to the technology or technical field.
Allowable Subject Matter
4. Claims 16-30 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter: In regards to claim 16, the closest prior art, Sha et al. (US Pat. Pub. 2020/0151538, hereinafter "Sha") at least teaches a non-transitory computer readable medium having instructions thereon, the instructions configured to cause a computer (Sha paragraph [0006] teaches a computer readable storage medium having program instructions that are executable by a processing circuit (computer)) to execute a modular autoencoder model for estimating parameters of interest from a combination of available channels of measurement data from an optical metrology platform (Sha paragraph [0043] and [0061]-[0062] teach executing a modular autoencoder model to implement a deep neural network having at least three types of layers to carry out automatic feature extraction (for estimating feature parameters of interest) from a combination of input layer neurons (available channels) of measured aerial images from an aerial image generation system (optical metrology platform)) by estimating retrievable quantities of information content using a subset of a plurality of input models based on the available channels (Sha paragraphs [0060]-[0062] teach estimating feature vectors as retrievable quantities of information content using the autoencoder's subset of generated "unsupervised" learning models based on the available input layer neurons (available channels)), the instructions causing operations comprising:
causing the plurality of input models to compress a plurality of inputs based on the available channels such that the plurality of inputs are suitable for combination with each other (Sha paragraphs [0061]-[0063] teach causing the plurality of unsupervised learning models to compress the plurality of input data based on the available input layer neurons into codings which are combinable with each other).
5. However, claim 16 contains allowable subject matter because the closest prior art, Sha et al. (US Pat. Pub. 2020/0151538) fails to anticipate or render obvious causing a common model to combine the compressed inputs and generate low dimensional data in a latent space based on the combined compressed inputs, wherein the low dimensional data estimates the retrievable quantities, and wherein the low dimensional data in the latent space is configured to be used by one or more additional models to generate approximations of the plurality of inputs and/or estimate a parameter based on the low dimensional data, in combination with the rest of the claim limitations as claimed and defined by the Applicant. Similarly, claim 19 contains allowable subject matter because the closest prior art, Sha et al. (US Pat. Pub. 2020/0151538) fails to anticipate or render obvious causing a common model of the modular autoencoder model to combine the compressed inputs and generate low dimensional data in a latent space based on the combined compressed inputs, wherein the low dimensional data estimates the retrievable quantities, and wherein the low dimensional data in the latent space is configured to be used by one or more additional models to generate approximations of the plurality of inputs and/or estimate a parameter based on the low dimensional data, in combination with the rest of the claim limitations as claimed and defined by the Applicant.
Similarly, claim 29 contains allowable subject matter because the closest prior art, Sha et al. (US Pat. Pub. 2020/0151538) fails to anticipate or render obvious a common model of the modular autoencoder model configured to combine the compressed inputs and generate low dimensional data in a latent space based on the combined compressed inputs, wherein the low dimensional data estimates the retrievable quantities, and wherein the low dimensional data in the latent space is configured to be used by one or more additional models to generate approximations of the plurality of inputs and/or estimate a parameter based on the low dimensional data, in combination with the rest of the claim limitations as claimed and defined by the Applicant.
Similarly, claim 30 contains allowable subject matter because the closest prior art, Sha et al. (US Pat. Pub. 2020/0151538) fails to anticipate or render obvious causing a common model to combine the compressed inputs and generate low dimensional data in a latent space based on the combined compressed inputs, the low dimensional data in the latent space configured to be used by one or more additional models to generate approximations of the one or more inputs and/or predict the parameter based on the low dimensional data, wherein the common model is configured to combine the compressed inputs and generate the low dimensional data regardless of which ones of the plurality of inputs are combined by the common model, in combination with the rest of the claim limitations as claimed and defined by the Applicant.6. Dependent claims 17-18 depend from claim 16 and contain allowable subject matter for at least the same reasons as given for claim 16. Dependent claims 20-28 depend from claim 19 and contain allowable subject matter for at least the same reasons as given for claim 19.
Pertinent Art
7. Applicants are directed to consider additional pertinent prior art included on the Notice of References Cited (PTOL 892) attached herewith. The Examiner has pointed out particular references contained in the prior art of record within the body of this action for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply. Applicant, in preparing the response, should consider fully the entire reference as potentially teaching all or part of the claimed invention, as well as the context of the of the passage as taught by the prior art or disclosed by the Examiner. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
A. Dehaene et al. (US Pat. Pub. 2022/0318623) discloses Transformation of Data Samples to Normal Data.
B. Albrecht et al. (US Pat. Pub. 2022/0309292) discloses Growing Labels from Semi-Supervised Learning.
C. Kolouri et al. (US Pat. Pub. 2019/0294149) discloses System and Method for Estimating Uncertainty of the Decision Made By a Supervised Machine Learner.
D. Yoon et al. (US Pat. Pub. 2022/0198280) discloses Novelty Detection Using Deep Learning Neural Network.
Conclusion
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL D LEE whose telephone number is (571)270-1598. The examiner can normally be reached on M to F, 9:30 am to 6 pm.
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/PAUL D LEE/Primary Examiner, Art Unit 2857 12/13/2025