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 .
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: arithmetic processing section in claim 1-6.
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.
In regards to the arithmetic processing section in claims 1-6, the generic placeholder is given functional language without a corresponding structure capable of performing the claimed function. The instant specification describes the structure of the arithmetic processing section as being a generic processor. Figure 8 shows the algorithm of the generic processor. For the purpose of examination, the any electronic component that can perform the claimed function will correspond to the generic placeholder.
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 (i.e., changing from AIA to pre-AIA ) 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)(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.
Claim(s) 1 and 7 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Pack (US PUB. 20230135102).
Regarding claim 1, Pack teaches A support device that supports adjustment of values of parameters of a substrate processing apparatus that operates based on the parameters to perform substrate processing (0004 “substrate processing method has multiple different processes, with some advanced methods (e.g., plasma etching) having twenty or even more processes. Each process has a multitude of process control variables, also referred to as “knobs,” that can be used to tune and optimize performance. Therefore, the space available to tune and optimize a given process is theoretically extremely large”), the parameters including detection target parameters of which corresponding physical quantities are to be detected (0035 “The manufacturing equipment 124 can include sensors 126 configured to capture data for a substrate being processed at the manufacturing system. In some embodiments, the manufacturing equipment 124 and sensors 126 can be part of a sensor system that includes a sensor server (e.g., field service server (FSS) at a manufacturing facility) and sensor identifier reader (e.g., front opening unified pod (FOUP) radio frequency identification (RFID) reader for sensor system).”), the support device comprising
an arithmetic processing section (0078) that outputs parameter recommended values using a first machine learning model and a second learning model, the parameter recommended values being recommended values of the values of the parameters (0007 “he electronic device manufacturing system then generate metrology data associated with the plurality of features and inputs the metrology data into one or more Bayesian probabilistic models. The electronic device manufacturing system then receives an output from the one or more Bayesian probabilistic models, wherein the one or more Bayesian probabilistic models generate the output based on the metrology data and at least one settings parameter of the plurality of setting parameters. The electronic device manufacturing system then updates, based on the output of the one or more Bayesian probabilistic models, the process recipe by modifying at least one setting parameter of the plurality of setting parameters and performs a second process on a second substrate according to the updated process recipe”), wherein
the arithmetic processing section (0078)
builds a predictive model that predicts quality of the substrate processing performed by the substrate processing apparatus through the first machine learning model performing learning of first training data that includes detection data pieces relating to the physical quantities and quality data relating to the quality of the substrate processing (0035 “a predictive server 112 (e.g., to generate predictive data, to provide model adaptation, to use a knowledge base, etc.), and a data store 140. The predictive server 112 can be part of a predictive system 110. The predictive system 110 can further include server machines 170 and 180. The manufacturing equipment 124 can include sensors 126 configured to capture data for a substrate being processed at the manufacturing system. In some embodiments, the manufacturing equipment 124 and sensors 126 can be part of a sensor system that includes a sensor server (e.g., field service server (FSS) at a manufacturing facility) and sensor identifier reader (e.g., front opening unified pod (FOUP) radio frequency identification (RFID) reader for sensor system)”),
acquires detection data recommended values by executing Bayesian optimization based on the predictive model, an acquisition function (0059 “Once one or more models 190 (e.g., trained machine learning models and/or Bayesian models) are generated, they may be stored in predictive server 112 as predictive component 114 or as a component of predictive component”, 0035), and a search range (0071 “the numerical optimizer component 230 executes numerical search and optimization routines to generate an output in view of the set of feature models 220 and the set of target properties 210. In some implementations, the output of the numerical optimizer component 230 can include, or can be used to generate, predictive data, such as, for example, a probability graph 242, shown in FIG. 2C. The probability graph 242 can indicate a likely correlation between one or more features and one or more settings parameters. In some embodiments, the probability graph can be a heat map”), the detection data recommended values being recommended values of the detection data pieces that bring the quality of the substrate processing close to target quality (0033 “each feature model can model the correlation between a recipe setting (e.g., pressure, temperature, etc.) and certain metrology data (e.g., property data of a mandrel, such as, mandrel height, mandrel width, etc.). The output of each model and a set of target properties (e.g., the target properties of, for example, each mandrel) can be input into an optimizer component configured to output a Pareto frontier that includes at least one set of Pareto efficient manufacturing settings (or parameters). A Pareto front is a set of Pareto efficient solutions in which no objective can be improved without sacrificing at least one other objective.”),
causes the second machine learning model to perform learning of the second training data including the detection data pieces and values of the detection target parameters that are set at detection of the physical quantities (0068 “set of feature models 220 is shown including a number of feature models. In some embodiments, the set of feature models can be generated by predictive system 110. In some implementations, the set of feature models 220 includes a set of Bayesian probabilistic models…Input of a feature model can include manufacturing parameters (e.g., process parameters, hardware parameters), metrology data, target property data, etc. Output of a feature model can include a probabilistic function showing the correlation of two or more features”, ,
and converts the detection data recommended values to the parameter recommended values by inputting the detection data recommended values to the second machine learning model having performed the learning (0071 “ach feature model of the set of feature models 220 can be used to generate actual and/or predictive correlations based on the set of target properties 210 by capturing spatial relationships among corresponding features and/or recipe settings. To do this, the set of feature models 220 can be provided to the numerical optimizer component 230. In some embodiments, the numerical optimizer component 230 executes numerical search and optimization routines to generate an output in view of the set of feature models 220 and the set of target properties 210…area 246A can indicate a high probability of a correlation between the feature value and the setting value, while area 246B can indicate a low probability of a correlation between the feature value and the setting value. In some embodiments, the numerical optimizer is a component of predictive component 114”).
Claim 7 is rejected using similar reasoning as the rejection of claim 1 due to reciting similar limitations but directed towards a method.
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.
Claim(s) 2-6 and 8-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pack (US PUB. 20230135102) in view of Groteke et al (US PUB. 20230170069, herein Groteke).
Regarding claim 2, the cited prior art teach The support device according to claim 1.
The cited prior art do not teach wherein the detection data pieces include preprocessed data pieces that are acquired by performing preprocessing on raw data pieces each indicating a corresponding one of the physical quantities, the arithmetic processing section generates the preprocessed data pieces by performing the preprocessing on each of the raw data pieces, and a number of data pieces of each of the preprocessed data pieces is smaller than that of each of the raw data pieces.
Groteke teaches wherein the detection data pieces include preprocessed data pieces that are acquired by performing preprocessing on raw data pieces each indicating a corresponding one of the physical quantities, the arithmetic processing section generates the preprocessed data pieces by performing the preprocessing on each of the raw data pieces, and a number of data pieces of each of the preprocessed data pieces is smaller than that of each of the raw data pieces (0104 “the user's device has a poor internet connection by preprocessing the collected raw data from assessments, tests, etc. on the user's device, allowing only the smaller file size of the processed data to be forwarded over the internet”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to have modified the teachings of Pack with the diagnostic system with its processing and communication system of Groteke since it teaches a means for preprocessing raw data in order to expedite data transmission times (0104).
Regarding claim 3, the cited prior art teach 3 The support device according to claim 2.
Pack teaches wherein the preprocessing includes processing of extracting a feature amount of each of the raw data pieces (0050 “mapping top layer features extracted by the convolutional layers to decisions (e.g. classification outputs). Deep learning is a class of machine learning algorithms that use a cascade of multiple layers of nonlinear processing units for feature extraction and transformation”).
Regarding claim 4, the cited prior art teach The support device according to claim 3.
Pack teaches wherein the processing of extracting a feature amount of each of the raw data pieces includes processing of reducing a dimension of each of the raw data pieces (0052 “one or more machine-learning models may be or use one or more probabilistic models. In some embodiments, server machine 180 can generate the probabilistic model using one or more operations including pre-processing input data (e.g., sensor data, metrology data, parameter data, settings data, etc.) to generate statistics data (or obtain statistics data from data store 140, manufacturing equipment 124, metrology equipment 128, etc.), reduce the dimensionality of the input data or statistics data, process the reduced representations by one or more statistical methods or models, normalize the data, and/or process the data using one or more models”).
Regarding claim 5, the cited prior art teach The support device according to claim 3.
Pack teaches wherein the processing of extracting a feature amount of each of the raw data pieces includes processing of calculating a summary statistic of each of the raw data pieces (0052 “one or more machine-learning models may be or use one or more probabilistic models. In some embodiments, server machine 180 can generate the probabilistic model using one or more operations including pre-processing input data (e.g., sensor data, metrology data, parameter data, settings data, etc.) to generate statistics data (or obtain statistics data from data store 140, manufacturing equipment 124, metrology equipment 128, etc.), reduce the dimensionality of the input data or statistics data, process the reduced representations by one or more statistical methods or models, normalize the data, and/or process the data using one or more models”).
Regarding claim 6, the cited prior art teach The support device according to claim 2.
Pack teaches wherein the preprocessing includes processing of extracting any one or more data pieces from each of the raw data pieces (0052 “one or more machine-learning models may be or use one or more probabilistic models. In some embodiments, server machine 180 can generate the probabilistic model using one or more operations including pre-processing input data (e.g., sensor data, metrology data, parameter data, settings data, etc.) to generate statistics data (or obtain statistics data from data store 140, manufacturing equipment 124, metrology equipment 128, etc.), reduce the dimensionality of the input data or statistics data, process the reduced representations by one or more statistical methods or models, normalize the data, and/or process the data using one or more models”).
Regarding claim 13, the cited prior art teach A substrate processing system comprising: the support device according to claim 1 (see rejection of claim 1)
Pack teaches and the substrate processing apparatus that operates based on the parameters to perform the substrate processing (0005 “only learning of the relationship between the setting values of the parameters and the result of the substrate processing limits improvement in quality of the substrate processing. Therefore, there is room for further improvements in the method for supporting adjustment of the parameter setting values in order to further improve quality of the substrate processing”).
Claims 8-12 and 14 are rejected using similar reasoning as the rejection of claims 2-6 and 13 due to reciting similar limitations but directed towards a method.
Conclusion
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/TAMEEM D SIDDIQUEE/
Primary Examiner
Art Unit 2116