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 statements (IDS) submitted on 10/12/2023 and 12/7/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: a data collection module for collecting data in claim 1, an analyzing component for extracting features in claim 1, a learning component producing mechanistic equations in claim 1, a feature extraction module for extracting features in claim 5, a dimension reduction module for reducing features in claim 5, a regression module for analyzing reduced features in claim 12, a discovery module for producing hidden mechanistic equations in claim 12, a knowledge database module for storing knowledge in claim 17, a developer interface module for developing knowledge in claim 18, a system design module for producing systems in claim 22, a user interface module for receiving inputs in claim 24, and an optimized system module for optimizing systems in claim 25.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim limitations “a data collection module for collecting data in claim 1, an analyzing component for extracting features in claim 1, a learning component producing mechanistic equations in claim 1, a feature extraction module for extracting features in claim 5, a dimension reduction module for reducing features in claim 5, a regression module for analyzing reduced features in claim 12, a discovery module for producing hidden mechanistic equations in claim 12, a knowledge database module for storing knowledge in claim 17, a developer interface module for developing knowledge in claim 18, a system design module for producing systems in claim 22, a user interface module for receiving inputs in claim 24, and an optimized system module for optimizing systems in claim 25” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The claim limitation uses the word "means" or a generic placeholder coupled with functional language, but it is modified by some structure or material that is ambiguous regarding whether that structure or material is sufficient for performing the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 9 and 33 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 9 and 33 recite the limitation “non-dimensional number”. There is insufficient antecedent basis for this limitation in the claim, as a non-dimensional number was not previously disclosed.
Claims 13 and 37 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 13 and 37 recite the limitation "a relationship between the first reduced feature and the second reduced feature is established". There is insufficient antecedent basis for this limitation in the claim, as the first reduced feature and second reduced feature that claims 13 and 37 refer to are in claim 10 and 34, but claims 13 and 37 do not depend on them.
Claims 21 and 45 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 21 and 45 recite the limitation “collected date”. There is insufficient antecedent basis for this limitation in the claim, as a date was not previously disclosed. The examiner believes that the limitation was intended to say “collected data”. Appropriate correction is required.
Claims 24 and 48 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 24 and 48 recites the limitation "new system" and “new design”. There is insufficient antecedent basis for this limitation in the claim.
Claim Objections
Claims 19, 20, 43, and 44 objected to because of the following informalities: the claims recite the limitation “develop interface”. The examiner believes that this was meant to recite “developer interface” as claims 18 and 42 recite a “developer interface”. Appropriate correction is required.
Claim 7 objected to because of the following informalities: “has mechanistic and interpretable nature” is broad. The examiner suggests changing the claim to read: “has a mechanistic and interpretable nature” Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With regard to Claim 1,
Step 2A, Prong 1
This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Claim 1 recites:
A Hierarchical Deep Learning Neural Networks-Artificial Intelligence (HiDeNN-AI) system for data processing, comprising: a data collection module collecting data; an analyzing component extracting at least one feature from the data, and processing the extracted at least one feature to produce at least one reduced feature; and a learning component producing at least one mechanistic equation based on the at least one reduced feature.
The broadest reasonable interpretation of the bolded limitations above are directed to mental processes and mathematical concepts. Extracting at least one feature from data and processing the feature to produce a reduced feature is a mental concept. Producing at least one equation based on at least one reduced feature is a mathematical concept.
Step 2A, Prong 1 (Yes).
Step 2A, Prong 2
This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The additional element is the collecting step.
The collecting step is mere data gathering and is insignificant extra-solution activity. See MPEP 2106.05(g).
Step 2A, Prong 2 (No).
Step 2B
This part of the eligibility analysis evaluates whether the claim as a whole, amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed above:
The collecting step is mere data gathering and is insignificant extra-solution activity. This element amounts to receiving or transmitting data over a network and has been found by the courts to be well-understood, routine and conventional activity. See MPEP 2106.05(d), subsection II.
Step 2B (No).
Claim 1 is ineligible.
Claim 26 is similar in scope and rejected likewise.
Dependent Claims:
Claims 2-4, 17, 18-20, 22-25, 50: Each of these dependent claims merely discuss details of the data that is collected.
Claims 5, 7-11, 13, 14: These dependent claims elaborate on the mental step.
Claims 6, 12, 15, 16, 21: These dependent claims recite further mathematical concepts.
Thus, these claims are ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-8, 10, 11, 15, 17-32, 34, 35, 39, 41-50 are rejected under 35 U.S.C. 102 as being anticipated by Vann (US 20220282199 A1).
Regarding claim 1, Vann discloses “a data collection module collecting data” (See [0024]; Vann discloses a metrology system that measures and collects data)
“an analyzing component extracting at least one feature from the data, and processing the extracted at least one feature to produce at least one reduced feature” (See [0051]; Vann discloses a feature extractor (analyzing component) that extracts feature data and dimensionality reduces sensor data into groups or features)
“a learning component producing at least one mechanistic equation based on the at least one reduced feature” (See [0040], [0053], [Fig. 2]; Vann discloses a training engine 182 (learning component) that uses mechanistic modeling with Feature Extractor 208 to feed into Mechanistic Model 210 based off of reduced features. Mechanistic Model 210 can be used to produce a mechanistic equation, for example, producing a regression equation to model the relationship between individual data points)
Regarding claim 2, Vann discloses “the data is collected from at least one of the sources comprising measurement and sensor detection, computer simulation, existing databases and literatures” (See [0024]; Vann discloses using a metrology system that includes a variety of sensors to collect data)
Regarding claim 3, Vann discloses “the data is in one of formats comprising images, sounds, numeric numbers, mechanistic equations, and electronic signals” (See [0024]; Vann discloses that the data collected consists of numerical data, which includes temperature, pressure, acidity, density, etc.)
Regarding claim 4, Vann discloses “the data collected by the data collection module is multifidelity” (See [0024], [0035]; Vann discloses that the metrology system 110 uses metrology tools 114 and data engineering tools 116 to collect data. The tools can suffer from inaccuracy, so the sensor validation tool 128 identifies confidence levels with each sensor and recommends utilizing data with the highest confidence level of accuracy, implying that the data collected has multiple levels of accuracy)
Regarding claim 5, Vann discloses “the analyzing component further comprises: a feature extraction module extracting the at least one feature from the data; and” (See [0051]; Vann discloses a feature extractor 208 (analyzing component) that extracts feature data)
“a dimension reduction module reducing the size of the at least one feature” (See [0051]; Vann discloses that the feature extractor 208 (dimension reduction module) also reduces dimensionality of the features.)
Regarding claim 6, Vann discloses “the at least one extracted feature is extracted by a method comprising Fourier, wavelet, convolutional, or Laplace transformation” (See [0085]; Vann discloses using a convolutional neural network for feature extraction by the convolutional layers)
Regarding claim 7, Vann discloses “the at least one extracted feature has mechanistic and interpretable nature” (See [0040]; Vann discloses mechanistic features, which are extracted features that are mechanistic due mechanistic model 210 being associated with feature extractor 208 when features are being extracted. Mechanistic features are also interpretable as they are interpreted by training engine 182 to train a machine learning model 190)
Regarding claim 8, Vann discloses “dimension reduction module produces at least one reduced feature by reducing the size of the at least one extracted feature; wherein the dimension of the at least one extracted feature is reduced during the reducing process” (See [0051]; Vann discloses that the feature extractor 208 (dimension reduction module) reduces the dimensionality of the features.)
Regarding claim 10, Vann discloses “at least one extracted feature comprises a first extracted feature and a second extracted feature” (See [0051]; Vann discloses extracting multiple features)
Regarding claim 11, Vann discloses “the first extracted feature is reduced to produce a first reduced feature, and the second extracted feature is reduced to produce a second reduced feature” (See [0051]; Vann discloses that multiple features that are extracted can also be reduced)
Regarding claim 15, Vann discloses “the hidden mechanistic equation relates an input parameter to a target property” (See [0052]; Vann discloses processing cell growth data 206 as an input for mechanistic model 210, to obtain a target property, which is a determination of a viable cell density prediction of a cell culture of the cell cultivation system)
Regarding claim 17, Vann discloses “further comprising: a knowledge database module, wherein the knowledge database module stores knowledge comprising at least one component comprising: the collected data, the at least one extracted feature, the at least one reduced feature, the relationship between the reduced features, the hidden equation, and the model order reduction” (See [0030]; Vann discloses a data store (knowledge database module) that stores collected data)
Regarding claim 18, Vann discloses “a developer interface module in communication with the knowledge database module, wherein the developer interface module develops new knowledge for storing in the knowledge database module” (See [0019], [0030]; Vann discloses a network 160 (interface module) that can communicate with a data store 140 (knowledge database module) that also communicates with machine learning system 170 (learning component) to train, validate, and/or test a machine learning model)
Regarding claim 19, Vann discloses “the develop interface module is in communication with at least one of the collection module; the analyzing component, and the learning component” (See [0019], [0030]; Vann discloses a network 160 (interface module) that can communicate with a data store 140 (knowledge database module) that also communicates with machine learning system 170 (learning component) to train, validate, and/or test a machine learning model)
Regarding claim 20, Vann discloses “the develop interface module receives a data science algorithm input from an user” (See [0016], [0060]; Vann discloses that a graphical user interface takes prescriptive actions as input. Prescriptive actions are determined when an optimization model 218 receives cell growth data 206 and a target product yield model 216 (data science algorithm))
Regarding claim 21, Vann discloses “the analyzing component and the learning component process the collected data using the data science algorithm” (See [0058], [0059]; Vann discloses that the feature extractor 208 (analyzing component) and the training engine 182 (learning component) of machine learning system 170 are both used with target product yield model 216 to process collected data by using data science algorithms such as BFGS or MPC to generate optimization model 218)
Regarding claim 22, Vann discloses “a system design module in communication with knowledge database module” (See [0040]; Training engine 182 (system design module) communicates with data store 140 (knowledge database module))
Regarding claim 23, Vann discloses “the system design module produces a new system or a new design using the knowledge in the knowledge database module, and without using the data collection module, analyzing component, and learning component” (See [0040]; Training engine 182 (system design module) produces a new machine learning model 190 (system) using data from data store 140 (knowledge database module))
Regarding claim 24, Vann discloses “comprising: a user interface module for receiving inputs from the user and output knowledge, the new system, or new design to the user” (See [0071]; Vann discloses outputting a prescriptive action (new system) to a user interface. Any user interface will receive input and output knowledge, and any response to input would be "knowledge")
Regarding claim 25, Vann discloses “comprising: an optimized system module optimizing the new system or new design according to the received inputs” (See [0060]; Vann discloses that the optimization model 218 takes cell growth data 206 and data from the target product yield model 216 as inputs for optimizing the prescriptive actions taken by the system to determine what the best prescriptive action to take will be)
Regarding claim 50, Vann discloses “A non-transitory tangible computer-readable medium storing instructions which, when executed by one or more processors, cause a system to perform a method for design optimization and/or performance prediction of a material system” (See [0034]; Vann discloses that the yield optimization tool coordinates with the yield prediction tool to execute a prescriptive action that prescribes an optimized schedule for the system to be performed)
Regarding claim 1, this claim is similar in scope to claim 26.
Regarding claim 2, this claim is similar in scope to claim 27.
Regarding claim 3, this claim is similar in scope to claim 28.
Regarding claim 4, this claim is similar in scope to claim 29.
Regarding claim 5, this claim is similar in scope to claim 30.
Regarding claim 6, this claim is similar in scope to claim 31.
Regarding claim 8, this claim is similar in scope to claim 32.
Regarding claim 10, this claim is similar in scope to claim 34.
Regarding claim 11, this claim is similar in scope to claim 35.
Regarding claim 15, this claim is similar in scope to claim 39.
Regarding claim 17, this claim is similar in scope to claim 41.
Regarding claim 18, this claim is similar in scope to claim 42.
Regarding claim 19, this claim is similar in scope to claim 43.
Regarding claim 20, this claim is similar in scope to claim 44.
Regarding claim 21, this claim is similar in scope to claim 45.
Regarding claim 22, this claim is similar in scope to claim 46.
Regarding claim 23, this claim is similar in scope to claim 47.
Regarding claim 24, this claim is similar in scope to claim 48.
Regarding claim 25, this claim is similar in scope to claim 49.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 9, 33 are rejected under 35 U.S.C. 103 as being unpatentable over Vann (US 20220282199 A1), in view of Subramaniyan (US 20190287005 A1).
Regarding claim 9, Vann discloses “reducing the size of the at least one extracted feature” (See [0051]; Vann discloses reducing the size of extracted features with the feature extractor)
Vann fails to explicitly disclose, “at least one non-dimensional number is derived during the process of reducing the size of the at least one extracted feature”.
Subramaniyan teaches “at least one non-dimensional number is derived during the process of reducing the size of the at least one extracted feature” (See [0044]; Subramaniyan discloses that non-dimensional parameters are derived from a subset of features).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having Vann and Subramaniyan before them to modify Vann to derive at least one non-dimensional number during reduction of at least one extracted feature. One would be motivated to do so in order to because obtaining non-dimensional data from a reduced feature can contain valuable information about a feature that is not apparent prior to reduction, see e.g., [0044], where Subramaniyan teaches selecting non-dimensional parameters from derived information for the purpose of obtaining efficiency data on mechanical or electrical power components.
Regarding claim 9, this claim is similar in scope to claim 33.
Claim Rejections - 35 USC § 103
Claims 12, 13, 36, 37 are rejected under 35 U.S.C. 103 as being unpatentable over Vann (US 20220282199 A1), in view of Hayashi (US 20220044067 A1).
Regarding claim 12, Vann discloses “the learning component further comprises: analyzing the at least one reduced feature” (See [0040]; Vann discloses that training engine 182 (learning component) comprises a trained machine learning model 190 that has reduced features due to containing a feature extractor 208)
“a discovery module producing at least one hidden mechanistic equation based on the analyzing results of the at least one reduced feature” (See [0051], [0052], [0053], [0056], [0085]; Vann discloses that cell growth data 206 can be processed by feature extractor 208 or mechanistic model 210, and also discloses that one or more of these two models and extractors can be combined to generate an input, therefore, processing cell growth data 206 by the feature extractor 208 to dimensionality reduces the data into reduced features, and then processing those reduced features by the mechanistic model 210 will yield a mechanistic representation (mechanistic equation) of the reduced feature. The combination of the feature extractor 208 and mechanistic model 210 form the discovery model. Additionally, hidden mechanistic equations can be produced when Vann discloses using artificial neural networks with regression layers, and these neural networks can be deep networks with multiple hidden layers. The multiple hidden layers can include regression layers, which is how Vann can produce hidden mechanistic equations from a regression layer’s regression equations)
Vann fails to explicitly disclose, “analyzing the at least one reduced feature” is done with “a regression module”.
Hayashi teaches “a regression module analyzing the at least one reduced feature” (See [0014]; Hayashi discloses performing regression of the second feature amount).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having Vann and Hayashi before them to modify Vann to analyze the at least one reduced feature using a regression module. One would be motivated to do so in order to establish relationships between variables, see e.g., [0014], where Hayashi teaches that an analyzer is configured to perform regression analysis on a feature.
Regarding claim 13, Vann fails to explicitly disclose, “relationship between the first reduced feature and the second reduced feature is established by the regression module during the analyzing process”.
Secondary teaches “relationship between the first reduced feature and the second reduced feature is established by the regression module during the analyzing process” (See [0014]; Hayashi discloses the relationship between a first feature and a second feature by determining a linear and a nonlinear relation between the two using regression).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having Vann and Hayashi before them to modify Vann to make use of determining the relationship between a first and second reduced feature. One would be motivated to do so in order to determine the correlation between the two features, see e.g., [0030], where Hayashi mentions that the DC value determines if there is a stronger relation between two variables, the two variables being the two features.
Regarding claim 12, this claim is similar in scope to claim 36.
Regarding claim 13, this claim is similar in scope to claim 37.
Claim Rejections - 35 USC § 103
Claims 14, 38 are rejected under 35 U.S.C. 103 as being unpatentable over Vann (US 20220282199 A1) in view of Hayashi (US 20220044067 A1), and further in view of Chittenden (US 20200327962 A1).
Regarding claim 14, Vann fails to explicitly disclose, “the analyzing process comprising a step of regression and classification of deep neural networks (DNNs)”.
Secondary teaches “the analyzing process comprising a step of regression and classification of deep neural networks (DNNs)” (See [0090]; Chittenden discloses using classification and regression for a deep neural network).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having Vann and Chittenden before them to modify Vann to add a regression step and a classification step when analyzing DNNs. One would be motivated to do so in order to analyze the relationships and importance of the features in the DNNs, see e.g., [0092], where Chittenden teaches that saliency maps that were derived from the trained LASSO DNN is used to evaluate relative importance of variables.
Regarding claim 14, this claim is similar in scope to claim 38.
Claim Rejections - 35 USC § 103
Claims 16, 40 are rejected under 35 U.S.C. 103 as being unpatentable over Vann (US 20220282199 A1) in view of Barrasso (Model order reduction of a multi-scale PBM-DEM description of a wet granulation process via ANN).
Regarding claim 16, Vann discloses “a model order reduction is produced by the discovery module” (See [0051], [0052]; Vann discloses using the feature extractor 208 with mechanistic model 210 as the discovery module to perform model order reduction)
Vann fails to explicitly disclose, “model order reduction” uses the “hidden mechanistic equation”.
Barrasso teaches “a model order reduction is produced by the discovery module based on the hidden mechanistic equation” (See [Page 1297, Section 1.3, Paragraph 1]; Barrasso discloses that a DEM (Discrete Element Method) model with mechanistic data can be used to develop a reduced order model (ROM), which is a reduced version of the DEM model used).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having Vann and Barrasso before them to modify Vann to use a hidden mechanistic equation when performing model order reduction. One would be motivated to do so in order to substitute time-consuming calculations with faster models to speed up model order reduction, see e.g., [Page 1297, Section 1.3, Paragraph 1], where Barrasso teaches replacing mechanistic DEM models with a reduced order model (ROM) that was developed from a DEM model to improve computational efficiency.
Regarding claim 16, this claim is similar in scope to claim 40.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID KIM whose telephone number is (571)272-4331. The examiner can normally be reached 7:30 AM - 4:30 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Ell can be reached at (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D.K./Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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