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 .
Status of the Application
This action is a first action on the merits in response to the application filed on 06/22/2023.
Status of Claims
Claims 1-24 filed on 06/22/2023 are currently pending and have been examined in this application.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 06/22/2023, 10/12/2023, 10/19/2023, 03/08/2024, 12/09/2024, 04/01/2025, 08/06/2025, and on 12/08/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Allowable Subject Matter
Claims 5, 7, 13, 15, 19, 21, and 23 objected to as being dependent upon rejected base claims, but it appears they would be distinguished from the prior art references cited by the Examiner if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Objections
Claim 22 is objected to under 37 CFR 1.75 as being a substantial duplicate of claim 20. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Applicant is advised that should claim 22 be found allowable, claim 20will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
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 (claims 17 and 23) that use the word “means,” are 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: means for obtaining post-process step measured signals from a metrology, means for extracting post-process measurement results from a post-process physical model for the SOI, means for predicting a final value of the second parameter for the SOI, means for providing at least the final value of the second parameter for the SOI, and means for generating pre-conditioned signals that combines a pre-process step measured signals from the SOI.
Regarding claims 17 and 23, the use of means for obtaining post-process step measured signals from a metrology, means for extracting post-process measurement results from a post-process physical model for the SOI, means for predicting a final value of the second parameter for the SOI, means for providing at least the final value of the second parameter for the SOI, and means for generating pre-conditioned signals that combines a pre-process step measured signals from the SOI in the claims are considered to be supported by sufficient structures in the specification to perform the function. Obtaining, extracting, predicting, providing, and generating are performed by the metrology device 200 and interface with the processor 262 and memory 264 in computing system 260 shown in FIG. 2. See Paragraphs 0094-0106.
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.
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).
The Examiner notes that claim 24 does not invoke 35 U.S.C. 112(f) because the generic placeholder is modified by sufficient structure “using at least one of the post-process physical model or the trained post-process machine learning model”
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 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-24 are directed to an abstract idea without additional elements to integrate the claims into a practical application or to amount to significantly more than the abstract idea.
Claims 1-24 are directed to a process, machine, or manufacture (Step 1), however the claims are directed to the abstract idea of predicting a value from a trained machine learning model based on measurement results.
With respect to Step 2A Prong One of the frameworks, claim 1 recites an abstract idea. Claim 1 includes limitations for “A method for measuring at least one parameter of interest from a structure of interest (SOI), comprising: obtaining post-process step measured signals from a metrology device for a SOI on one or more samples at a post-process step; extracting post-process measurement results from a post-process physical model for the SOI based on the post-process step measured signals and at least one of a value of a first parameter for the SOI at a pre-process step that is fed forward to the post-process physical model, a value of a second parameter for the SOI at the post-process step that is fed back to the post-process physical model, and a combination thereof; predicting a final value of the second parameter for the SOI at the post-process step from a trained post-process machine learning model based on the post-process measurement results extracted from the post-process physical model; and providing at least the final value of the second parameter for the SOI”
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the limitations above recite Mental Process because the claimed limitations involve measurement observation and calculation. The claimed limitations can be reasonably performed by a skilled in the art using a pen and a paper. As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claims 9 and 17 recite substantially similar limitations to those presented with respect to claim 1. As a result, claims 9 and 17 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1. Similarly, claims 2-8, 10-16, and 18-24 recite a Mental Process because the claimed limitations involve measurement observation and calculation. As a result, claims 2-8, 10-16, and 18-24 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. When considered in view of the claim as a whole, the step of “obtaining” does not integrate the abstract idea into a practical application because “obtaining” is an insignificant extra solution activity to the judicial exception. Therefore, the claim is directed to an abstract idea.
As a result, claim 1 does not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claims 9 and 17 recite substantially similar limitations to those recited with respect to claim 1. Although claim 9 further recites “A computer system configured for measuring at least one parameter of interest from a structure of interest (SOI) comprising: at least one processor” and claim 17 further recites “a system comprising: means for obtaining post-process step measured signals from a metrology, means for extracting post-process measurement results from a post-process physical model for the SOI, means for predicting a final value of the second parameter for the SOI, means for providing at least the final value of the second parameter for the SOI, and means for generating pre-conditioned signals that combines a pre-process step measured signals from the SOI”, when considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 9 and 17 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2-8, 10-16, and 18-24 do not include any additional elements beyond those recited by independent claims 1, 9, and 17. As a result, claims 2-8, 10-16, and 18-24 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. The step of “obtaining” does not amount to significantly more than the abstract idea because “obtaining” is well-understood, routine, and conventional computer function in view of MPEP 2106.05(d)(ll). As a result, claim 1 does not include additional elements that amount to significantly more than the abstract idea under Step 2B.
As noted above, claims 9 and 17 recite substantially similar limitations to those recited with respect to claim 1. Although claim 9 further recites “A computer system configured for measuring at least one parameter of interest from a structure of interest (SOI) comprising: at least one processor” and claim 17 further recites “a system comprising: means for obtaining post-process step measured signals from a metrology, means for extracting post-process measurement results from a post-process physical model for the SOI, means for predicting a final value of the second parameter for the SOI, means for providing at least the final value of the second parameter for the SOI, and means for generating pre-conditioned signals that combines a pre-process step measured signals from the SOI”, the recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 9 and 17 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 2-8, 10-16, and 18-24 do not include any additional elements beyond those recited by independent claims 1, 9, and 17. As a result, claims 2-8, 10-16, and 18-24 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-24 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
Claims 1-4, 6, 8-12, 14, 16-18, 20, and 24 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Yerushalmi Liran (TW1898147B) hereinafter Liran
Regarding claim 1. Liran teaches A method for measuring at least one parameter of interest from a structure of interest (SOI), comprising: obtaining post-process step measured signals from a metrology device for a SOI on one or more samples at a post-process step; extracting post-process measurement results from a post-process physical model for the SOI based on the post-process step measured signals and at least one of a value of a first parameter for the SOI at a pre-process step that is fed forward to the post-process physical model, a value of a second parameter for the SOI at the post-process step that is fed back to the post-process physical model, and a combination thereof; predicting a final value of the second parameter for the SOI at the post-process step from a trained post-process machine learning model based on thepost-process measurement results extracted from the post-process physical model; and providing at least the final value of the second parameter for the SOI [Liran, claim 1, Liran teaches “A system for semiconductor inspection and metrology, comprising: a first machine learning module configured to determine a set of recipes, wherein the first machine learning module receives measured signals, wherein each recipe in the set of recipes converts the measured signals into a parameter of interest; and a second machine learning module configured to determine a final recipe or settings from the set of recipes and a cost function, wherein the second machine learning module determines the settings if the set of recipes does not pass an evaluation using the cost function, whereby the second machine learning module guides the development of the first machine learning module, and wherein the second machine learning module determines the final recipe from the set of recipes that passes the evaluation using the cost function, wherein the second machine learning module is trained to evaluate the performance of multiple existing recipes, wherein the existing recipes are from at least one different production line running the same product, at least one different production line running a different product, at least one different production line running a different process step, or at least one different production line running a different goal” wherein a first machine learning module receives a first metrology signal and determine parameters, a second machine learning module receives a second metrology signal and determine parameters. Wherein the “wherein the existing recipes are from at least one different production line running the same product, at least one different production line running a different product, at least one different production line running a different process step” is equivalent to receiving and analyzing pre-process and post-process steps metrology signals. Wherein “whereby the second machine learning module guides the development of the first machine learning module, and wherein “the second machine learning module determines the final recipe from the set of recipes that passes the evaluation using the cost function” is equivalent to extracting post-process measurement results from a post-process physical model for the SOI based on the post-process step measured signals and at least one of a value of a first parameter for the SOI at a pre-process step and calculating a final value].
Regarding claim 2. wherein the value of the first parameter for the SOI is determined from at least one of a pre-process physical model and a trained pre-process machine learning model based on pre-process step measured signals obtained from the SOI on the one or more samples at the pre-process step [Liran, claim 1, Liran teaches “a first machine learning module configured to determine a set of recipes, wherein the first machine learning module receives measured signals…wherein the existing recipes are from at least one different production line running the same product, at least one different production line running a different product, at least one different production line running a different process step” emphasis added wherein samples from a pre-process step].
Regarding claim 3. further comprising: obtaining the pre-process step measured signals from the metrology device for the SOI on the one or more samples at the pre-process step; and determining the value of the first parameter from extracted pre-process measurement results for the SOI from the pre-process physical model based on the pre-process step measured signals [Liran, claim 1, Liran teaches “A system for semiconductor inspection and metrology, comprising: a first machine learning module configured to determine a set of recipes, wherein the first machine learning module receives measured signals, wherein each recipe in the set of recipes converts the measured signals into a parameter of interest…wherein the existing recipes are from at least one different production line running the same product, at least one different production line running a different product, at least one different production line running a different process step” emphasis added wherein samples from a pre-process step].
Regarding claim 4. further comprising: obtaining the pre-process step measured signals from the metrology device for the SOI on the one or more samples at the pre-process step; extracting pre-process measurement results for the SOI from the pre-process physical model based on the pre-process step measured signals; and predicting the value of the first parameter for the SOI at the pre-process step from the trained pre-process machine learning model based on the pre-process measurement results extracted from the pre-process physical model [Liran, claim 1, Liran teaches “A system for semiconductor inspection and metrology, comprising: a first machine learning module configured to determine a set of recipes, wherein the first machine learning module receives measured signals, wherein each recipe in the set of recipes converts the measured signals into a parameter of interest…wherein the existing recipes are from at least one different production line running the same product, at least one different production line running a different product, at least one different production line running a different process step” wherein predicting parameters value from first (pre-process) metrology measurements].
Regarding claim 6. wherein the final value of the second parameter for the SOI at the post-process step is predicted from the trained post-process machine learning model further based on a pre-process step measured signals from the SOI at the pre-process step [Liran, claim 1, Liran teaches “and a second machine learning module configured to determine a final recipe or settings from the set of recipes and a cost function, wherein the second machine learning module determines the settings if the set of recipes does not pass an evaluation using the cost function, whereby the second machine learning module guides the development of the first machine learning module, and wherein the second machine learning module determines the final recipe from the set of recipes that passes the evaluation using the cost function, wherein the second machine learning module is trained to evaluate the performance of multiple existing recipes, wherein the existing recipes are from at least one different production line running the same product, at least one different production line running a different product, at least one different production line running a different process step, or at least one different production line running a different goal” wherein “whereby the second machine learning module guides the development of the first machine learning module, and wherein the second machine learning module determines the final recipe from the set of recipes that passes the evaluation using the cost function, wherein the second machine learning module is trained to evaluate the performance of multiple existing recipes” is equivalent to “wherein the final value of the second parameter for the SOI at the post-process step is predicted from the trained post-process machine learning model further based on a pre-process step measured signals from the SOI at the pre-process step”].
Regarding claim 8. further comprising determining one or more additional parameters for the SOI using at least one of the post-process physical model or the trained post- process machine learning model [Liran, claim 1, Liran teaches “and a second machine learning module configured to determine a final recipe or settings from the set of recipes and a cost function, wherein the second machine learning module determines the settings if the set of recipes does not pass an evaluation using the cost function, whereby the second machine learning module guides the development of the first machine learning module, and wherein the second machine learning module determines the final recipe from the set of recipes that passes the evaluation using the cost function, wherein the second machine learning module is trained to evaluate the performance of multiple existing recipes, wherein the existing recipes are from at least one different production line running the same product, at least one different production line running a different product, at least one different production line running a different process step, or at least one different production line running a different goal” wherein the second machine learning module determines a final recipe is equivalent to the determining an additional parameter from an additional machine learning model].
Regarding claim 9, the claim recites analogous limitations to claim 1 above, and is therefore rejected on the same premise. Claim 1 is a method claim while claim 9 is directed to a system which is anticipated by Liran claim 1.
Regarding claims 10-12, 14, and 16, claims 10-12, 14, and 16 recite substantially similar limitations as claim 2-4, 6, and 8, respectively; therefore, claims 10-12, 14, and 16 are rejected with the same rationale, reasoning, and motivation provided above for claims 2-4, 6, and 8, respectively. Claims 2-4, 6, and 8 are method claims while claims 10-12, 14, and 16 are directed to a system which is anticipated by Liran claim 1.
Regarding claim 17, the claim recites analogous limitations to claim 1 above, and is therefore rejected on the same premise. Claim 1 is a method claim while claim 17 is directed to a system which is anticipated by Liran claim 1.
Regarding claims 18, 20, and 24, claims 18, 20, and 24 recite substantially similar limitations as claim 2, 6, and 8, respectively; therefore, claims 18, 20, and 24 are rejected with the same rationale, reasoning, and motivation provided above for claims 2, 6, and 8, respectively. Claims 2, 6, and 8 are method claims while claims 18, 20, and 24 are directed to a system which is anticipated by Liran claim 1.
Conclusion
Any inquiry concerning this communication from the examiner should be directed to Abdallah El-Hagehassan whose contact information is (571) 272-0819 and Abdallah.el-hagehassan@uspto.gov The examiner can normally be reached on Monday- Friday 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-3734.
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/ABDALLAH A EL-HAGE HASSAN/
Primary Examiner, Art Unit 3623