DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the Application filed on June 13, 2023. Claims 1-18 are pending in the case. Claims 1, 7, and 13 are the independent claims.
This action is non-final.
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 limitation(s) is/are: “industrial facility configured to perform a certain industrial function in a production process” and “facility control device configured to acquire…preprocess and generate…input…and predict…” in claims 13-18.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-18 are 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.
With respect to claims 1, 7, and 13, these claims recite “acquiring[/obtaining] normal process data from at least one or more industrial facilities; preprocessing and generating the normal process data and discrete data as learning data…” It is unclear whether the recitation of “the normal process data” which is apparently generated subsequent to acquiring/obtaining normal process data, is intended to refer to the same normal process data which is acquired/obtained, or to if it is intended to refer to different data. For example, it is unclear whether normal process data which is generated is the same as, or different from, normal process data which is acquired/obtained. Further it is unclear whether the claim intends to recite that the generating is based on the normal process data which was previously obtained, or if the generating is separate and independent from the acquired/obtained normal process data. Therefore, this limitation is indefinite. In the interest of providing full examination on the merits these limitations are interpreted as referring to the same “normal process data,” i.e. such as acquiring/obtaining the normal process data and then using it in a preprocessing and generating step (along with discrete data) to generate learning data.
Claims 1, 7, and 13 further recite (in addition to one or more instances of “normal process data” as discussed above) “learning data,” “the preprocessed learning data,” and “inputting the data obtained from the industrial facility.” It is unclear which instance of “data” (i.e. one or more of the normal process data, the preprocessed learning data, etc.), the recitation of “the data” is intended to refer to. In the interest of providing full examination on the merits, this limitation is interpreted as referring to any data obtained from the industrial facility.
Claims 1, 7, and 13 also recite “learning the generative adversarial network” and “the pre-learned generative adversarial network.” It is unclear whether these limitations refer to the same generative adversarial network. For example, the limitation “pre-learned” may be interpreted as implying that the “pre-learned generative adversarial network” was trained/performed learning in some process which occurs outside of the claimed invention, while the other recited generative adversarial network is learned within the steps of the claimed invention. In the interest of providing full examination on the merits, these limitations are interpreted as referring to the same generative adversarial network (i.e. learning the generative adversarial network, and then inputting the data into the generative adversarial network resulting from the learning step).
Claims 2-6, 8-12, and 14-18 each depend upon claims 1, 7, and 13, respectively, and therefore inherit the deficiencies of the independent claims as discussed above. Therefore, claims 2-6, 8-12, and 14-18 are rejected on the same bases as are identified above with respect to independent claims 1, 7, and 13.
With respect to claims 2, 8, and 14, these claims recite “generates the normal process data when the data obtained from the industrial facility is input.” As discussed above, it is unclear which instance of recited “data” in the independent claims “the data” refers to, and this lack of clarity is also present in these dependent claims. Moreover, given that the independent claims recite “acquiring/obtaining normal process data” and “preprocessing and generating the normal process data,” it is further unclear whether the step “generates the normal process data when the data obtained from the industrial facility is input” refers to the same normal process data recited in the independent claims or if it refers to yet another separate instance of normal process data. For example, given that the independent claims already recite that normal process data is obtained/acquired, and then that some (possibly different) normal process data is also used in a preprocessing and generating step, it is unclear how or whether the normal process data recited in these dependent claims, where it is apparently generated in a subsequent step which is tied to the obtaining of “the data” is the same or different from the recited normal process data in the independent claims. In the interest of providing full examination on the merits, this limitation is interpreted as referring to any data which could be considered to be “normal process data,” and is not necessarily required to be the same as the normal process data recited in the independent claims.
With respect to claims 4, 10, and 16, these claims recite “the average number of pressing forces.” This limitation lacks antecedent basis.
Claims 5, 6, 11, 12, 17, and 18 each depend upon claims 4, 10, and 16, respectively, and therefore inherit the deficiencies of those claims as discussed above. Therefore, claims 5, 6, 11, 12, 17, and 18 are rejected on the same bases as are identified above with respect to independent claims 4, 10, and 16.
With respect to claims 6, 12, and 18, these claims recite “the normal process data” and “the input data generated by the generator.” First, as is discussed above, it is unclear whether the various recitations of “the normal process data” (i.e. acquired/obtained, preprocessed and generated, etc.) are the same or different instances of “normal process data.” Further, the limitation “the input data generated by the generator,” and “the generator” both lack antecedent basis. Examiner notes that the independent claims do not recite “input data” or generating “the input data,” and further notes that the first recitation of the limitation “the generator” appears in dependent claims 2, 8, and 14. Claims 6, 12, and 18 do not depend upon claims 2, 8, and 14, and therefore do not acquire any antecedent basis for the limitation “the generator” from these claims. Further, even if there was proper antecedent basis for the limitation “the generator” in claims 2, 8, and 14, Examiner notes that these claims recite that the generator generates “the normal process data,” not “the input data.” Therefore, there does not appear to be any antecedent basis for the limitation “the input data generated by the generator” in any preceding claim, regardless of dependency, because no claim recites a generator generating input data. In the interest of providing full examination on the merits, these limitations are interpreted as if reciting “a generator” and any data which is generated by that generator.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental steps) without significantly more. This judicial exception is not integrated into a practical application because any additional elements amount to implementing the abstract idea on a generic computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding independent claims 1, 7, and 13, and relying on the evaluation flowchart in MPEP 2106:
Step 1 (Is the claim to a process, machine, manufacture, or composition of matter?): Yes. Claim is a method (process). Claim 7 is an apparatus (machine). Claim 13 is a system (machine).
Step 2a Prong One (Does the claim recite an abstract idea?): Yes. Claims 1, 7, and 13 recite:
predict a remaining life of an industrial facility (claim 1 preamble), predicting a remaining life of an industrial facility (claim 7 preamble), industrial facility remaining life prediction (claim 13 preamble) (a mental process of determination/prediction regarding remaining life an industrial facility);
preprocessing and generating the normal process data and discrete data as learning data (a mental process, including using a physical aid such as pen and paper, of mentally analyzing/determining the normal process data and discrete data to be learning data/learning data based on the normal process data and discrete data); and
predicting the remaining life of the industrial facility based on output data (a mental process of determination/prediction regarding remaining life an industrial facility).
Step 2a Prong Two (Does the claim recite additional elements that integrate the judicial exception into a practical application?): No. Claims 1, 7, and 13 additionally recite:
using a generative adversarial network, the apparatus comprising: a processor; a network interface; a memory; a computer program loaded into the memory and executed by the processor, wherein the processor includes instructions for performing the recited steps (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f));
an industrial facility remaining life prediction system, comprising: at least one industrial facility configured to perform a certain industrial function in a production process; and a facility control device configured to perform the method (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f));
the method performed by a facility control device (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)):
acquiring/obtaining normal process data from at least one or more industrial facilities (insignificant extra-solution activity as discussed in MPEP 2106.05(g));
learning the generative adversarial network based on the preprocessed learning data (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)); and
inputting the data obtained from the industrial facility into the pre-learned generative adversarial network to perform the prediction (insignificant extra-solution activity as discussed in MPEP 2106.05(g) with respect to inputting the obtained data; mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to the generative adversarial network performing the prediction),
the output data output from the generative adversarial network (insignificant extra-solution activity as discussed in MPEP 2106.05(g)).
Therefore, in view of the considerations set forth in MPEP 2106.04(d), 2106.05(a)-(c) and (e)-(h), the additional elements as disclosed above alone or in combination do not integrate the judicial exception into a practical application as they are mere insignificant extra solution activity, combined with implementing the abstract idea using generic computer components.
Step 2b (Does the claim recite additional elements that amount to significantly more than the judicial exception): No. Relying on the same analysis as Step 2a Prong Two (see MPEP 2106.05.I.A: Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:…Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP 2106.05(f));…Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception...; Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g);…)), claims 1 and 11 do not recite any additional elements that amount to significantly more than the abstract idea. As discussed above, Claims 1, 7, and 13 and 11 recite:
using a generative adversarial network, the apparatus comprising: a processor; a network interface; a memory; a computer program loaded into the memory and executed by the processor, wherein the processor includes instructions for performing the recited steps (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f));
an industrial facility remaining life prediction system, comprising: at least one industrial facility configured to perform a certain industrial function in a production process; and a facility control device configured to perform the method (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f));
the method performed by a facility control device (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)):
acquiring/obtaining normal process data from at least one or more industrial facilities (insignificant extra-solution activity as discussed in MPEP 2106.05(g), which can be reevaluated to be well-understood, routine, conventional activity such as receiving or transmitting data over a network and/or storing and retrieving information in memory as discussed in MPEP 2106.05(d));
learning the generative adversarial network based on the preprocessed learning data (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)); and
inputting the data obtained from the industrial facility into the pre-learned generative adversarial network to perform the prediction (insignificant extra-solution activity as discussed in MPEP 2106.05(g) with respect to inputting the obtained data, which can be reevaluated to be well-understood, routine, conventional activity such as receiving or transmitting data over a network and/or storing and retrieving information in memory as discussed in MPEP 2106.05(d); mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to the generative adversarial network performing the prediction),
the output data output from the generative adversarial network (insignificant extra-solution activity as discussed in MPEP 2106.05(g), which can be reevaluated to be well-understood, routine, conventional activity such as receiving or transmitting data over a network, storing and retrieving information in memory, and or presentation of information as discussed in MPEP 2106.05(d)).
The additional elements as discussed above, in combination with the abstract idea, are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination with generic computer functions and components used to implement the abstract idea.
Regarding dependent claims 2, 8, and 14:
Step 2a Prong One: incorporates the rejection of claims 1, 7, and 13.
Step 2a Prong Two: the claims additionally recite
wherein the generative adversarial network includes a generator that generates the normal process data (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f))
when the data obtained from industrial facility is input (insignificant extra-solution activity as discussed in MPEP 2106.05(g)).
Step 2b: the claims additionally recite
wherein the generative adversarial network includes a generator that generates the normal process data (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f))
when the data obtained from industrial facility is input (insignificant extra-solution activity as discussed in MPEP 2106.05(g), which can be reevaluated to be well-understood, routine, conventional activity such as receiving or transmitting data over a network and/or storing and retrieving information in memory as discussed in MPEP 2106.05(d)).
Regarding dependent claims 3, 9, and 15:
Step 2a Prong One: incorporates the rejection of claims 2, 8, and 14; the claims further recite
Step 2a Prong Two: the claims additionally recite wherein the generative adversarial network calculates a difference between the normal process data generated by the generator and the learning data (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Step 2b: the claims additionally recite wherein the generative adversarial network calculates a difference between the normal process data generated by the generator and the learning data (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Regarding dependent claims 4, 10, and 16:
Step 2a Prong One: incorporates the rejection of claims 1, 7, and 13.
Step 2a Prong Two: the claims additionally recite wherein the discrete data includes the average number of pressing forces utilized by at least one or more industrial facilities and data on the number of uses of industrial facility/number of times the industrial facilities are used (generally linking the use of a judicial exception to a particular technological environment or field of use as discussed in MPEP 2106.05(h)).
Step 2b: the claims additionally recite wherein the discrete data includes the average number of pressing forces utilized by at least one or more industrial facilities and data on the number of uses of industrial facility/number of times the industrial facilities are used (generally linking the use of a judicial exception to a particular technological environment or field of use as discussed in MPEP 2106.05(h)).
Regarding dependent claims 5, 11, and 17:
Step 2a Prong One: incorporates the rejection of claims 4, 10, and 16.
Step 2a Prong Two: the claims additionally recite wherein the generative adversarial network includes a label embedding layer that converts a data value of the discrete data into a weight applied to a feature map channel (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Step 2b: the claims additionally recite wherein the generative adversarial network includes a label embedding layer that converts a data value of the discrete data into a weight applied to a feature map channel (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Regarding dependent claims 6, 12, and 18:
Step 2a Prong One: incorporates the rejection of claims 5, 11, and 17.
Step 2a Prong Two: the claims additionally recite
wherein an inspection signal transmission period of the industrial facility is set (insignificant extra-solution activity as discussed in MPEP 2106.05(g))
based on a difference value between the normal process data and the input data generated by the generator in the generative adversarial network (generally linking the use of a judicial exception to a particular technological environment or field of use as discussed in MPEP 2106.05(h)).
Step 2b: the claims additionally recite
wherein an inspection signal transmission period of the industrial facility is set (insignificant extra-solution activity as discussed in MPEP 2106.05(g), which can be reevaluated to be well-understood, routine, conventional activity such as storing and retrieving information in memory and/or electronic recordkeeping as discussed in MPEP 2106.05(d))
based on a difference value between the normal process data and the input data generated by the generator in the generative adversarial network (generally linking the use of a judicial exception to a particular technological environment or field of use as discussed in MPEP 2106.05(h)).
Claim Rejections – 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(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 1-4, 6-10, 12-16, and 18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Maher (US 20230281439 A1).
With respect to claims 1, 7, and 13, Maher teaches
an apparatus for predicting a remaining life of an industrial facility (e.g. paragraph 0043, predictive data may be an indication of an end of life of a component of manufacturing equipment; paragraph 0047, corrective action associated with SPC to predict useful lifespan of components) using a generative adversarial network (e.g. paragraph 0115, trained machine learning model is a GAN; first machine learning model may be a GAN), the apparatus comprising: a processor; a network interface; a memory; a computer program loaded into the memory and executed by the processor, wherein the processor includes instructions for performing a method (e.g. paragraphs 0132-0136, Fig. 6, computer system 600 connected via network, etc., to other computer systems, including processing device 602, volatile memory 604, non-volatile memory 606, data storage device 618; network interface device 622; storing instructions 626 encoding described methods or functions; execution of instructions by computer system);
an industrial facility remaining life prediction system (e.g. paragraph 0043, predictive data may be an indication of an end of life of a component of manufacturing equipment; paragraph 0047, corrective action associated with SPC to predict useful lifespan of components), comprising: at least one industrial facility configured to perform a certain industrial function in a production process (e.g. paragraph 0034, manufacturing equipment associated with producing corresponding products such as substrates); and a facility control device configured to perform the method (e.g. paragraphs 0132-0136, Fig. 6, computer system 600 connected via network, etc., to other computer systems, including processing device 602, volatile memory 604, non-volatile memory 606, data storage device 618; network interface device 622; storing instructions 626 encoding described methods or functions; execution of instructions by computer system); and
the method performed by a facility control device to predict a remaining life of an industrial facility (e.g. paragraph 0043, predictive data may be an indication of an end of life of a component of manufacturing equipment; paragraph 0047, corrective action associated with SPC to predict useful lifespan of components) using a generate adversarial network (e.g. paragraph 0115, trained machine learning model is a GAN; first machine learning model may be a GAN), the method comprising:
acquiring/obtaining normal process data from at least one or more industrial facilities (e.g. paragraph 0109, Fig. 4A, generating first data input that may include sensor, manufacturing parameters, metrology data, etc.; input data may be historical data; paragraph 0118, Fig. 4C, provide historical including trace sensor data to train a first machine learning model 420);
preprocessing and generating the normal process data and discrete data as learning data (e.g. paragraphs 0110-0113, Fig. 4A, generating first target output for the data inputs/first data input; generating mapping data indicative of input/output mapping; adding mapping data to the data set; determining whether data set T is sufficient for training/validating/testing machine learning model; paragraph 0118, Fig. 4C, provide historical including trace sensor data to train a first machine learning model 420; input data provided with associated attributes; trace data including data from many types of sensors, including measure of energy provided, frequency, power, voltage, or current supplied, pressure and temperature data);
learning the generative adversarial network based on the preprocessed learning data (e.g. paragraph 0114, Fig. 4A, providing data set T to train, validate, and test machine learning model; paragraph 0115, trained machine learning model is a GAN; paragraph 0118, Fig. 4C, provide historical including trace sensor data to train a first machine learning model 420; first machine learning model may be a GAN; training generator model using discriminator); and
inputting the data obtained from the industrial facility into the pre-learned generative adversarial network and predicting the remaining life of the industrial facility based on output data output from the generative adversarial network (e.g. paragraph 0043, predictive data may be an indication of an end of life of a component of manufacturing equipment; paragraph 0047, corrective action associated with SPC to predict useful lifespan of components; paragraph 0120, providing output from trained first machine learning model as training input to train second machine learning model; output data provided with historical sensor data, data indicative of attributes as target output, synthetic data generated for training model to predict a fault, anomaly, or performance of equipment that has been in service for an amount of time; synthetic data generated associated with particular tool, sensor, design, etc.; information provided to train second machine learning model as attribute data; paragraph 0121, current sensor data provided to trained second machine learning model which is configured to accept the input sensor data and output predictive data; receiving predictive data output from trained second machine learning model; second machine learning model configured to accept as input sensor data and produce as output predictive data, e.g. predicted metrology data, predicted anomalies in a product, predicted faults in a manufacturing system , predicted processing operation progress, etc.; i.e. data obtained from the industrial facility/equipment, including historical data, current data, etc., is provided to the first model/GAN, and the output of the first model (trained GAN) is provided to the second model, which is trained using the output of the GAN, and subsequently predicts various aspects regarding the facility/equipment based on additionally input current sensor data, where this prediction is also “based on” the output of the GAN, since the model is trained on this output and this training provides the basis for the model’s ability to make the prediction).
With respect to claims 2, 8, and 14, Maher teaches all of the limitations of claims 1, 7, and 13 as previously discussed, and further teaches wherein the generative adversarial network includes a generator that generates the normal process data when the data obtained from industrial facility is input (e.g. paragraph 0027, generation of synthetic data by GAN, which includes a generator part; paragraph 0064, generator of GAN attempts to generate data similar to input data (i.e. true sensor data); paragraph 0118, training GAN using historical data, including training generator model of GAN; paragraph 0119-0120, generation of synthetic data (i.e. by generator of GAN), such as for training a model (i.e. second model) to predict fault, anomaly, performance, etc.).
With respect to claims 3, 9, and 15, Maher teaches all of the limitations of claims 2, 8, and 14, and further teaches wherein the generative adversarial network calculates a difference between the normal process data generated by the generator and the learning data (e.g. paragraph 0027, discriminator part of GAN provided with synthetic and true data and attempts to distinguish/label the provided data as true or synthetic; paragraph 0064, discriminator of GAN attempts to distinguish true data from synthetic data).
With respect to claims 6, 12, and 18, Maher teaches all of the limitations of claims 5, 11, and 17, and further teaches wherein an inspection signal transmission period of the industrial facility is set based on a difference value between the normal process data and the input data generated by the generator in the generative adversarial network (e.g. paragraph 0048, corrective action may include retraining machine learning model; paragraph 0068, causing predictive model to be retrained based on confidence level that predictive data is accurate being below a threshold level; retraining including generating one or more data sets utilizing historical data and/or synthetic data; paragraph 0100, performing model retraining based on model not meeting threshold accuracy; paragraph 0103, generating new model/retraining based on model deteriorating; paragraph 0124, determining accuracy of discriminator and generator based on feedback indicative of how accurately the discriminator distinguished synthetic from measured data, and updating the generator and discriminator in view of this information; training processes repeated until accuracy threshold reached; i.e. where model accuracy is measured based on performance of the discriminator (i.e. its determined difference between normal and generated data), and additional training data is requested/received in order to perform additional training/retraining of the models until a threshold level is reached, this is analogous to setting an inspection signal transmission period based on this difference value (i.e. the number of times, within a given time period, that data is accessed/requested for continuing the training/retraining), such as a given number of times during a training time period, and then no times during a subsequent period during which the models perform with threshold accuracy, and then another given number of times during a retraining period)).
With respect to claims 4, 10, and 16, Maher teaches all of the limitations of claims 1, 7, and 13, and further teaches wherein the discrete data includes the average number of pressing forces utilized by at least one or more industrial facilities and data on the number of uses of industrial facility/number of times the industrial facilities are used (e.g. paragraph 0016, large volume of time trace data associated with hundreds of processing runs; paragraph 0028, generating data sets over many processing runs; paragraph 0034, manufacturing equipment performing runs over a period of time; sensor data may include pressure; paragraph 0035, historical processing runs; paragraph 0049, manufacturing parameters include manufacturing parameters such as component age and process parameters such as pressure; paragraph 0052, time trace sensor data including pressure in a processing chamber; paragraph 0056, sensor data may also include preprocessed data such as averages and composite data indicative of sensor performance over time; paragraph 0118, time trace sensor data including pressure; i.e. the data may include pressure information corresponding to a given component/facility, including in the form of an average (analogous to average number of pressing forces), and may additionally include data indicative of a number of processing runs, equipment/facility age, etc., indicative of number of uses of the facility).
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102€, (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 5, 11, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Maher in view of Li et al. (US 20200210773 A1).
With respect to claims 5, 11, and 17, Maher teaches all of the limitations of claims 4, 10, and 16. Although Maher teaches adjusting weights associated with input data of the model such as connection weights in a neural network (e.g. paragraph 0082, 0096), Maher does not explicitly disclose wherein the generative adversarial network includes a label embedding layer that converts a data value of the discrete data into a weight applied to a feature map channel.
However, Li teaches wherein the generative adversarial network includes a label embedding layer that converts a data value of the discrete data into a weight applied to a feature map channel (e.g. paragraph 0041, content label training data set; paragraph 0080-0081, first and second prediction probability of content label summed and averaged to obtain prediction probability of the content label; paragraph 0111, neural network includes weight full connection layer configured to weight each channel of the feature map with the prediction probability of the content label; i.e. content labels are provided as part of the training data (analogous to discrete data/data values), and these are then converted to prediction probabilities which are used as weights that are applied to feature map channels).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Maher and Li in front of him to have modified the teachings of Maher (directed to synthetic time series data associated with processing equipment to predict a lifespan, faults, etc. associated with the processing equipment), to incorporate the teachings of Li (directed to neural networks) to include, within the neural network (i.e. the GAN of Maher), a layer that converts discrete data provided as training data (such as label data) into weights which are applied to feature map channels (as taught by Li). One of ordinary skill would have been motivated to perform such a modification in order to utilize content labels to enhance and correlate category features as described in Li (paragraph 0110-0111).
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain,” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting in re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (GCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co, v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert, denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F,3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir, 2005): Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEREMY L STANLEY whose telephone number is (469)295-9105. The examiner can normally be reached on Monday-Friday from 9:00 AM to 5:00 PM CST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar, can be reached at telephone number (571) 270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JEREMY L STANLEY/
Primary Examiner, Art Unit 2127