CTNF 18/575,518 CTNF 101641 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statement The information disclosure statement (IDS) submitted on 02/06/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Drawings 06-22-06 AIA The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: 14, 18, 20 . Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance. 06-22-07 AIA The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 12a-c, 13’, IP, S, 0(N), 0(S), 21’, T’, 21a-b . Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance. Specification 07-29 AIA The disclosure is objected to because of the following informalities: Para[0025] recites “various preparatory steps carried out”, which should be “various preparatory steps can be carried out”. Para[0026] recites “apparatus and delivers then”, which should be “apparatuses and delivers them”. Para[0026] recites “apparatus”, which should be “apparatuses”. Para[0028] recites “inspection apparatus”, which should be “inspection apparatuses”. Para[0029] recites “examples forms”, which should be “example forms”. Para[0031] recites “+1(N)”, which should be “+1(13N)”. Para[0031] recites “1(S)”, which should be “-1(13S)”. Para[0037] recites “One reason for this is MTD offset is”, which should be “One reason for this MTD offset is”. Para[0038] recites “order to get signal”, which should be “order to get the signal”. Para[0055] recites “U c and U c ”, which should be “U x and U c ”. Para[0066] recites “such matrix”, which should be “such a matrix”. Para[0068] recites “in method”, which should be “in a method”. Para[0078] recites “and one or more monitoring KPIs calculated KPI”, which should be clarified to explain what this phrase means. Para[0080] recites “where e is an error measure, a wafer-level statistic and related threshold can be determined.”, which should be clarified to explain what this phrase means. Page 21 line 32 recites “A method as defined clause 20”, which should be “A method as defined in clause 20”. Para[0086] recites “outputted”, which should be “the output”. Para[0087] recites “parameter”, which should be “parameters” . Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 Claims 16, 23, 25, 27 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 16, 23, 25, and 27 recite “the reliability metric” even though Claim 16 recites “a reliability metric” and “at least one reliability metric”, rendering these limitations of “the reliability metric” indefinite. Claim 16 recites “a reliability” even though Claim 16 recites “a reliability” earlier so it is not clear if the second instance of “a reliability” is a separate instance, rendering the second limitation of “a reliability” indefinite. Claim 24 recites “an asymmetric measurement signal component” twice, so it is not clear if these instances of “an asymmetric measurement signal component” are separate, rendering these limitations in this claim indefinite. Claim 25 recites “A method of Claim 16”, however Claim 16 recites “A method”, so it is not clear if these instances of “A method” are separate, rendering the limitation of “A method” in Claim 25 as indefinite. Claims 26-27 recite “The method of Claim 25”, however Claims 16 and 25 recite “A method”, rendering these limitations of “The method of Claim 25” in these claims as indefinite. Claims 26 recite “the method further comprises”, however Claims 16 and 25 recite “A method”, rendering the limitation of “the method further comprises” in Claim 26 as indefinite. Claim 26 recites “an update”, however Claim 25 recites “an update”, so it is not clear if the second instance of “an update” is a separate instance, rendering this limitation of “an update” in Claim 26 as indefinite. Claim 28 recites “the at least one reliability metric”. There is insufficient antecedent basis for this limitation in the claim. Claims that depend on the above rejected claims are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 16-30 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract concept of performing mental steps without significantly more. The claim(s) recite(s) the following abstract concepts in BOLD of With regards to Claim 16, A method of determining a reliability metric describing a reliability of a measurement signal and/or a parameter of interest value derived therefrom, the method comprising; obtaining a trained inference model for inferring a value for the parameter of interest from the measurement signal; obtaining one or more measurement signals and/or respective one or more values of the parameter of interest derived therefrom using the trained inference model; and determining at least one reliability metric value for the one or more measurement signals and/or respective one or more values of the parameter of interest, the reliability metric describing a reliability of one or more measurement signals and/or respective one or more values of the parameter of interest with respect to an accurate prediction space associated with the trained inference model. With regards to Claim 28, A method of assessing a reliability metric, the method comprising; assessing the at least one reliability metric, the reliability metric describing a reliability of one or more measurement signals and/or respective one or more values of a parameter of interest with respect to an accurate prediction space associated with the trained inference model; and should the reliability metric indicate that at least one of the measurement signals and/or respective values of the parameter of interest is not reliable, prompting an update of a trained inference model for inferring a value for the parameter of interest from the measurement signal to include the at least one unreliable measurement signals and/or respective values of the parameter of interest, and/or measurement signals and/or respective values of the parameter of interest from one or more subsequently measured substrates. Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category. Under Step 2A, Prong One, we consider whether the claims recite a judicial exception (abstract idea). In the above claims, the bolded portions constitute an abstract idea because, under a broadest reasonable interpretation, they recite limitations that fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, they fall into the grouping of subject matter that, when recited as such in a claim limitation, cover performing mathematics or mental steps. The acts of obtaining one or more measurement signals and/or respective one or more values of the parameter of interest derived therefrom using the trained inference model; and determining at least one reliability metric value for the one or more measurement signals and/or respective one or more values of the parameter of interest, the reliability metric describing a reliability of one or more measurement signals and/or respective one or more values of the parameter of interest with respect to an accurate prediction space associated with the trained inference model, and assessing the at least one reliability metric, the reliability metric describing a reliability of one or more measurement signals and/or respective one or more values of a parameter of interest with respect to an accurate prediction space associated with the trained inference model; and should the reliability metric indicate that at least one of the measurement signals and/or respective values of the parameter of interest is not reliable, prompting an update of a trained inference model for inferring a value for the parameter of interest from the measurement signal to include the at least one unreliable measurement signals and/or respective values of the parameter of interest, and/or measurement signals and/or respective values of the parameter of interest from one or more subsequently measured substrates constitute mathematical relationships or mental steps, see MPEP 2106.04(a)(2). Additionally, the clam limitations merely indicate a field of use or technological environment in which the judicial exception is performed, which is the determination of the reliability metric of a measurement signal and/or a parameter of interest. Therefore, the steps recited in bold of claims 16 and 28 constitute mathematical relationships to perform steps that are otherwise abstract which do not integrate the abstract idea into a practical application. Next, under Step 2A, Prong Two, we consider whether the claims that recite a judicial exception are integrated into a practical application. In this step, we evaluate whether the claims recite additional elements that integrate the exception into a practical application of that exception. This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that the claimed method is not tied to a particular machine or apparatus, so it does not represent an improvement to another technology or technical field as no such equipment was recited, which does not indicate an improvement upon a component. Similarly, there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claim that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state. Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because every step in Claims 16 and 28 points to a mental or mathematical step. In addition, in Claim 16 the step of obtaining a trained inference model for inferring a value for the parameter of interest from the measurement signal amounts to nothing more than necessary data gathering as recited in MPEP section 2106.05(g). Necessary data gathering (i.e. receiving data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092- 93 (Fed. Cir. 2015). Examiner notes that the inference model was already trained prior to the step of obtaining it, and the model serves to only provide data, hence reducing the step to necessary data gathering. Claims 17-27 are further directed to abstract ideas and are rejected under 35 U.S.C. 101. Claims 29-30 are rejected under 35 U.S.C. 101. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection, see Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-12-aia AIA (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. 07-15-aia AIA Claim(s) 16, 18, 22-23, and 25-30 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Ophir (US 20210142466 A1) . With regards to Claim 16, Ophir teaches obtaining a trained inference model for inferring a value for the parameter of interest ( See Abstract “derive (i.e. obtain) an estimation model (i.e. a trained inference model, and this model is trained using training data 105, see Fig.1, where the machine learning algorithm(s) 110 is trained via the training step 115 using training data 105 to build the estimation model(s) 120 ) of the metrology metric(s) (i.e. the parameter of interest)”. Examiner notes that the estimation model infers a value of the metrology metric (i.e. the parameter of interest) ) from the measurement signal ( See Abstract “initial metrology measurements” ); obtaining one or more measurement signals and/or respective one or more values of the parameter of interest derived therefrom using the trained inference model ( See Abstract “using the estimation model (i.e. the trained inference model) to provide estimations (i.e. obtaining multiple parameter of interest values, where the measurement signals are the initial metrology measurements and they correspond to the respective parameters of interest in the Abstract “training data concerning metrology metric(s) from initial metrology measurements”) of the metrology metric(s)” ); and determining at least one reliability metric value for the one or more measurement signals and/or respective one or more values of the parameter of interest ( See Para[0021] “metrology module 101 may be configured to derive (i.e. determine) at least one confidence score 130 (i.e the set of multiple confidence scores 130 comprises the reliability metric) that quantifies a similarity between measurement data 122 and training data 105 (i.e. 122 and 105 are measurement signals)” ), the reliability metric describing a reliability of one or more measurement signals and/or respective one or more values of the parameter of interest with respect to an accurate prediction space associated with the trained inference model ( See Para[0021] “metrology module 101 may be configured to derive (i.e. determine) at least one confidence score 130 (i.e the set of multiple confidence scores 130 comprises the reliability metric) that quantifies a similarity (i.e. the similarity indicates a reliability of the measurement data 122 to training data 105 for each confidence score 130) between measurement data 122 and training data 105 (i.e. 122 and 105 are measurement signals)”. The accurate prediction space is that of the variation of training data used to train the model ). With regards to Claim 18 , Ophir teaches the limitations of Claim 16. Ophir further teaches wherein the accurate prediction space is related to a training of the trained inference model ( See Para[0021] “metrology module 101 may be configured to derive (i.e. determine) at least one confidence score 130 (i.e the set of multiple confidence scores 130 comprises the reliability metric) that quantifies a similarity between measurement data 122 and training data 105 (i.e. 122 and 105 are measurement signals)”. The accurate prediction space is that of the variation of training data used to train the model, therefore, the accurate prediction space is related to a training of the trained inference model. ). With regards to Claim 22 , Ophir teaches the limitations of Claim 16. Ophir further teaches wherein the accurate prediction space is defined by a training process window described by training data used in the training, the training process window describing the bounds of process variation comprised within the training data ( See Para[0021] “metrology module 101 may be configured to derive at least one confidence score 130 that quantifies a similarity between measurement data 122 and training data 105 (i.e. 122 and 105 are measurement signals)”. The accurate prediction space is that of the variation of training data used to train the model. The training process window is defined therefore as the amount of variation (i.e. bounds of process variation) within the training data that is used to train the model, therefore making the training process window described by training data (as in training data 105) used in the training ). With regards to Claim 23 , Ophir teaches the limitations of Claim 16. Ophir further teaches wherein the reliability metric ( See Para[0021] “the confidence score(s) (i.e the set of multiple confidence scores 130 comprises the reliability metric)” ) comprises at least one similarity metric that quantifies the similarity of the one or more measurement signals or respective components thereof to at least one of the training measurement signals or corresponding respective components thereof ( See Para[0021] “metrology module 101 may be configured to derive (i.e. determine) at least one confidence score 130 (i.e. a similarity metric, as the set of confidence scores is the reliability metric) that quantifies a similarity between measurement data 122 (i.e. the measurement signal) and training data 105 (i.e. the training measurement signal)”. ). With regards to Claim 25 , Ophir teaches the limitations of Claim 16. Ophir further teaches assessing the at least one reliability metric ( See Para[0021] “metrology module 101 may be configured to derive (i.e. determine) at least one confidence score 130 (i.e the set of multiple confidence scores 130 comprises the reliability metric) that quantifies a similarity (i.e. assessing the reliability metric for a reliability of the measurement data 122 for each confidence score 130) between measurement data 122 and training data 105 (i.e. 122 and 105 are measurement signals, so the reliability metric contains information of reliability, or similarity, between measurement data 122 and training data 105)” ); and should the reliability metric indicate that at least one of the measurement signals and/or respective values of the parameter of interest is not reliable, prompting an update of the trained model to include the at least one unreliable measurement signals ( See Para[0021] “Metrology module 101 may be further configured to reiterate (i.e. prompting an update) the estimation model (i.e. the trained inference model which infers a value of the parameter of interest, which is the metrology metric, from the measurement signal, i.e. the initial metrology measurements, see Abstract) derivation using additional calculated training data (where the additional data includes the unreliable measurement signals via the confidence score(s), see Fig. 1 step 140.), e.g., in, a re-training stage 140, to re-train machine learning algorithm(s) 110, in case confidence score(s) surpasses respective specified threshold(s) (i.e. if the reliability metric indicates the measurement signals are not reliable)” ) and/or respective values of the parameter of interest, and/or measurement signals and/or respective values of the parameter of interest from one or more subsequently measured substrates ( Examiner notes these limitations of “and/or” indicate options, and examiner chooses the “at least one unreliable measurement signals”. In addition, Examiner interprets “from one or more subsequently measured substrates” to be a limitation that is attached to the option of “respective values of the parameter of interest” ). With regards to Claim 26 , Ophir teaches the limitations of Claim 25. Ophir further teaches further measuring one or more structures corresponding to the at least one unreliable measurement signals ( See Fig. 1 the measurements of received wafer(s) (i.e substrates) 122, that can happen after the updating step 140 of retraining the model, wherein these wafer(s) can correspond to those that generated the unreliable measurement signals in the first place ) and/or respective values of the parameter of interest, and/or one or more structures on the one or more subsequently measured substrates to obtain respective ground truth values ( Examiner notes these limitations of “and/or” in this claim indicate options. In addition, Examiner interprets “ground truth values” to be a limitation that is attached to the option of “one or more structures” ); and further training the trained model with the at least one unreliable measurement signals ( See Para[0021] “Metrology module 101 may be further configured to reiterate (i.e. prompting an update) the estimation model (i.e. the trained inference model which infers a value of the parameter of interest, which is the metrology metric, from the measurement signal, i.e. the initial metrology measurements, see Abstract) derivation using additional calculated training data (where the additional data includes the unreliable measurement signals via the confidence score(s), see Fig. 1 step 140.), e.g., in, a re-training stage 140, to re-train machine learning algorithm(s) 110, in case confidence score(s) surpasses respective specified threshold(s) (i.e. if the reliability metric indicates the measurement signals are not reliable)” ) and/or respective values of the parameter of interest and the respective ground truth values ( Examiner notes these limitations of “and/or” in this claim indicate options. In addition, Examiner interprets “ground truth values” to be a limitation that is attached to the option of “respective values of the parameter of interest” ). With regards to Claim 27 , Ophir teaches the limitations of Claim 25. Ophir further teaches wherein the assessing the at least one reliability metric comprises assessing the at least one reliability metric against a respective threshold value for the reliability metric ( See Para[0021] “metrology module 101 may be configured to derive at least one confidence score 130 (i.e the set of multiple confidence scores 130 comprises the reliability metric)” and “Metrology module 101 may be further configured to reiterate the estimation model derivation using additional calculated training data, e.g., in, a re-training stage 140, to re-train machine learning algorithm(s) 110, in case confidence score(s) surpasses respective specified threshold(s) (i.e. assessing the reliability metric against a respective threshold value, where Examiner interprets that the same threshold value is used for all confidence scores 130 (where each one is a similarity metric), as the reliability metric is the set of confidence scores 130). Hence the same respective threshold value is used for all the confidence score(s)” ). With regards to Claim 28, Ophir teaches assessing the at least one reliability metric, the reliability metric describing a reliability of one or more measurement signals and/or respective one or more values of a parameter of interest with respect to an accurate prediction space associated with the trained inference model ( See Para[0021] “metrology module 101 may be configured to derive (i.e. determine) at least one confidence score 130 (i.e the set of multiple confidence scores 130 comprises the reliability metric) that quantifies a similarity (i.e. assessing the reliability metric for a reliability of the measurement data 122 for each confidence score 130) between measurement data 122 and training data 105 (i.e. 122 and 105 are measurement signals, so the reliability metric is a measure of reliability, or similarity, between measurement data 122 and training data 105)”. The accurate prediction space is that of the variation of training data used to train the model ); and should the reliability metric indicate that at least one of the measurement signals and/or respective values of the parameter of interest is not reliable, prompting an update of a trained inference model for inferring a value for the parameter of interest from the measurement signal to include the at least one unreliable measurement signals ( See Para[0021] “Metrology module 101 may be further configured to reiterate (i.e. prompting an update) the estimation model (i.e. the trained inference model which infers a value of the parameter of interest, which is the metrology metric, from the measurement signal, i.e. the initial metrology measurements, see Abstract) derivation using additional calculated training data (where the additional data includes the unreliable measurement signals via the confidence score(s), see Fig. 1 step 140.), e.g., in, a re-training stage 140, to re-train machine learning algorithm(s) 110, in case confidence score(s) surpasses respective specified threshold(s) (i.e. if the reliability metric indicates the measurement signals are not reliable)” ) and/or respective values of the parameter of interest, and/or measurement signals and/or respective values of the parameter of interest from one or more subsequently measured substrates ( Examiner notes these limitations of “and/or” indicate options, and examiner chooses the “at least one unreliable measurement signals”. In addition, Examiner interprets “from one or more subsequently measured substrates” to be a limitation that is attached to the option of “respective values of the parameter of interest” ). With regards to Claim 29 , Ophir teaches the limitations of Claim 16. Ophir further teaches a computer program comprising processor readable instructions that, when run on suitable processor controlled apparatus, cause the processor controlled apparatus to perform the method of claim 16 ( See Para[0034] “Aspects of the present invention are described above with reference to flowchart illustrations and/or portion diagrams of methods, apparatus (systems) and computer program products (i.e. a computer program) according to embodiments of the invention. It will, be understood that each portion of the flowchart illustrations and/or portion diagrams, and combinations of portions in the flowchart illustrations and/or portion diagrams (i.e. referring to Fig.1, which encompasses the method of Claim 16), can be implemented by computer program instructions (i.e. processor readable instructions comprised by the computer program). These computer program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus (i.e. a processor controlled apparatus) to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or portion diagram or portions thereof (i.e. the processor controlled apparatus to perform the method of claim 16).” ). With regards to Claim 30, Ophir teaches the limitations of Claim 16. Ophir further teaches a processor ( See Fig. 1 the computer processer(s) 102 ); and a computer program comprising processor readable instructions that, when run on the processor, cause the processor to perform the method of claim 16 ( See Para[0034] “Aspects of the present invention are described above with reference to flowchart illustrations and/or portion diagrams of methods, apparatus (systems) and computer program products (i.e. a computer program) according to embodiments of the invention. It will, be understood that each portion of the flowchart illustrations and/or portion diagrams, and combinations of portions in the flowchart illustrations and/or portion diagrams (i.e. referring to Fig.1, which encompasses the method of Claim 16), can be implemented by computer program instructions (i.e. processor readable instructions comprised by the computer program). These computer program instructions may be provided to a processor (i.e. the processor readable instructions are run on the processor) of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or portion diagram or portions thereof (i.e. the processor performs the method of claim 16).” ) . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 17 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ophir as applied to Claim 16, and further in view of Fu (US 10101676 B2) . With regards to Claim 17, Ophir teaches the limitations of Claim 16. However, Ophir is silent to the language of wherein the measurement signal comprises a raw pupil image or a processed pupil image. Fu teaches wherein the measurement signal comprises a raw pupil image or a processed pupil image ( See Column 4 lines 17-19 “In some embodiments, both field and pupil measurement signals (i.e. a raw pupil image which is comprised by the pupil measurement signal) are simultaneously detected. The detected signals are iteratively processed”. In other words, the pupil measurement signals are initially detected in a raw form, then subsequently processed. ). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Ophir wherein the measurement signal comprises a raw pupil image or a processed pupil image like in Fu in order to have a more diversified set of pupil images that can be measured. With regards to Claim 19, Ophir teaches the limitations of Claim 16. Ophir further teaches the trained inference model ( See Abstract ). Ophir is silent to the language of wherein the trained inference model is operable to determine a weighting for each of the one or more measurement signals or respective components thereof. Fu teaches wherein the system ( See Abstract ) is operable to determine a weighting for each of the one or more measurement signals ( See Column 3 lines 25-28 “the spectroscopic beam profile metrology system (i.e. the system) is configured to weigh the acquired wavelengths and angles of incidence (in other words the system determines the weighting of the one or more measurement signals, which have wavelengths) to improve the accuracy and precision of the overlay measurement” ). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Ophir wherein the system is operable to determine a weighting for each of the one or more measurement signals like in Fu in order to accurately calibrate the trained inference model for inference of the reliability metric . 07-21-aia AIA Claim (s) 20-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ophir and Fu as applied to Claim 19, and further in view of Pandev (US 20160117847 A1) . With regards to Claim 20, Ophir and Fu teach the limitations of Claim 19. However, Ophir and Fu are silent to the language of wherein the parameter of interest is an asymmetric parameter and the trained inference model is operable to determine a weighting for an asymmetric measurement signal component of each of the one or more measurement signals. Nevertheless, Pandev teaches wherein the parameter of interest is an asymmetric parameter ( See Para[0098] “overlay target 210 including a bottom grating feature 211 and a top grating feature 212 having process induced asymmetry as described hereinbefore” and “the asymmetry reduction calculations described with respect to FIGS. 12 and 13 can be applied in any order, or together, to arrive at overlay signals having reduced process induced asymmetry for each azimuth angle”. In other words, the parameter of interest is some asymmetry attributed to the geometry, and this parameter is reduced. ) and asymmetric measurement signal component ( See Para[0042] “FIG. 12 depicts an overlay target 210 including a bottom grating feature 211 that includes a process induced asymmetry” and Para[0098] “the asymmetry reduction calculations described with respect to FIGS. 12 and 13 can be applied in any order, or together, to arrive at overlay signals having reduced process induced asymmetry for each azimuth angle”. In other words, the overlay signals are measurement signals with asymmetric signal components that are reduced. ) It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Ophir and Fu wherein the parameter of interest is an asymmetric parameter and Ophir and Fu use an asymmetric measurement signal component like in Pandev in order to identify and accurately infer the correct reliability metric from underlying geometric asymmetries. With regards to Claim 21, Ophir and Fu teach the limitations of Claim 19. Ophir further teaches parameter of interest ( See Abstract, the metrology metric ) Ophir and Fu are silent to the language of wherein the weighting translates each of the one or more measurement signals or respective components thereof to a respective value. Pandev teaches wherein the weighting translates each of the one or more measurement signals or respective components thereof to a respective value ( See Column 3 lines 25-28 “the spectroscopic beam profile metrology system is configured to weigh the acquired wavelengths and angles of incidence (in other words the system determines the weighting of the one or more measurement signals, which have wavelengths) to improve the accuracy and precision of the overlay measurement”. Therefore, the weighting translates each component of the measurement signals, like the wavelengths and angles of incidence, to a respective value that can be used to improve the accuracy. ). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Ophir and Fu wherein the weighting translates each of the one or more measurement signals or respective components thereof to a respective value like in Pandev in order to more systematically calculate and accurately estimate the reliability metric . 07-21-aia AIA Claim (s) 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ophir as applied to Claim 16, and further in view of Pandev (US 20160117847 A1) . With regards to Claim 24, Ophir teaches the limitations of Claim 16. Ophir further teaches wherein the at least one similarity metric ( See Fig. 1 a confidence score 130 of the set of confidence scores ) comprises a process variation metric ( See Fig. 1 one such confidence score within the set of confidence scores ) that quantifies the similarity of measurement signal component of each of the one or more measurement signals to a measurement signal component of at least one of the training measurement signals ( See Para[0021] “metrology module 101 may be configured to derive (i.e. determine) at least one confidence score 130 (i.e. a similarity metric, as the set of confidence scores is the reliability metric) that quantifies a similarity between measurement data 122 (i.e. the measurement signal) and training data 105 (i.e. the training measurement signal)”. ). Ophir is silent to the language of an asymmetric process variation metric and an asymmetric measurement signal component. Pandev teaches an asymmetric process variation metric ( See Para[0098] “overlay target 210 including a bottom grating feature 211 and a top grating feature 212 having process induced asymmetry (i.e. asymmetric process variation metric) as described hereinbefore” ) and an asymmetric measurement signal component ( See Para[0042] “FIG. 12 depicts an overlay target 210 including a bottom grating feature 211 that includes a process induced asymmetry” and Para[0098] “the asymmetry reduction calculations described with respect to FIGS. 12 and 13 can be applied in any order, or together, to arrive at overlay signals having reduced process induced asymmetry for each azimuth angle”. In other words, the overlay signals are measurement signals with asymmetric signal components that are reduced. ). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Ophir and Fu wherein an asymmetric process variation metric and an asymmetric measurement signal component is used like in Pandev in order to properly identify the causes of reliability issues in the parameter of interest and have the inference model more accurately infer the values of the parameter of interest. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOSTOFA AHMED HISHAM whose telephone number is (571)272-8773. The examiner can normally be reached Monday - Friday, 7:00 a.m. - 4 p.m. ET. 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, Catherine Rastovski can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MOSTOFA AHMED HISHAM/Examiner, Art Unit 2857 /Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857 Application/Control Number: 18/575,518 Page 2 Art Unit: 2857 Application/Control Number: 18/575,518 Page 3 Art Unit: 2857 Application/Control Number: 18/575,518 Page 4 Art Unit: 2857 Application/Control Number: 18/575,518 Page 5 Art Unit: 2857 Application/Control Number: 18/575,518 Page 6 Art Unit: 2857 Application/Control Number: 18/575,518 Page 7 Art Unit: 2857 Application/Control Number: 18/575,518 Page 8 Art Unit: 2857 Application/Control Number: 18/575,518 Page 9 Art Unit: 2857 Application/Control Number: 18/575,518 Page 10 Art Unit: 2857 Application/Control Number: 18/575,518 Page 11 Art Unit: 2857 Application/Control Number: 18/575,518 Page 12 Art Unit: 2857 Application/Control Number: 18/575,518 Page 13 Art Unit: 2857 Application/Control Number: 18/575,518 Page 14 Art Unit: 2857 Application/Control Number: 18/575,518 Page 15 Art Unit: 2857 Application/Control Number: 18/575,518 Page 16 Art Unit: 2857 Application/Control Number: 18/575,518 Page 17 Art Unit: 2857 Application/Control Number: 18/575,518 Page 18 Art Unit: 2857 Application/Control Number: 18/575,518 Page 19 Art Unit: 2857 Application/Control Number: 18/575,518 Page 20 Art Unit: 2857 Application/Control Number: 18/575,518 Page 21 Art Unit: 2857 Application/Control Number: 18/575,518 Page 22 Art Unit: 2857 Application/Control Number: 18/575,518 Page 23 Art Unit: 2857
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