Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
DETAILED ACTION
This is a Final Office Action of the instant application 17/874,105 (hereinafter the ‘105 application), responsive to the amendment and argument submitted on 2/3/2026.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Independent claims 1, 9, and 18 (along with each or their corresponding dependent claims) are directed to the abstract idea of being directed to a mental process, using a mathematical concept of the model.
Subject Matter Eligibility Standard
In the Supreme Court’s decision, Alice Corporation Pty. Ltd. v. CLS Bank International, et al. (“Alice Corp."), the Supreme Court made clear that it applies the framework set forth in Mayo Collaborative Services v. Prometheus Laboratories, Inc., 566 U.S. __(2012) (Mayo), to analyze claims directed towards laws of nature and abstract ideas. Alice Corp. also establishes that the same analysis applies for all categories of claims (e.g., product and process claims).
When considering subject matter eligibility under 35 U.S.C. 101, the basic inquiries to determine subject matter eligibility remain the same as explained in MPEP 2106(I). First, it must be determined (in Step 1) whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. Second, if the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). Under the two-part analysis for judicial exceptions it is first determined (in Step 2A) whether the claims are directed to a judicial exception. Step 2A is broken down in to two parts:
Step 2A Prong 1- a determination is made if the claim recites an Abstract Idea, Law of Nature, or a Natural Phenomenon, under the 2019 PEG groupings of Abstract Ideas.
Step 2A Prong 2- a determination is made if the claim recites additional elements that integrate the judicial exception into a practical application.
If the claim is determined to be directed to a judicial exception, a determination is then made (in Step 2B) as to whether any element or combination of elements in the claim is sufficient to ensure that the claim amounts to significantly more than the abstract idea. See “2014 Interim Guidance on Patent Subject Matter Eligibility” 79 Fed. Reg. 241 (Dec. 16, 2014), pp. 74621-74624. As discussed below, the claims are directed to an abstract idea, and the claims do not recite additional elements or combination of elements that amount to significantly more than the abstract idea.
Examples of abstract ideas referenced in Alice Corp. Include:
a. Fundamental economic practices;
b. Certain methods of organizing human activities;
c. An idea itself; and
d. Mathematical relationships/formulas.
e. Mental Processes
Limitations reference in Alice Corp. that may be enough to qualify as “significantly more” when the claim features include, as non-exclusive examples:
a. Improvements to another technology or technical field;
b. Improvements to the functioning of the computer itself;
c. Meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment.
Examples that are NOT enough to qualify as “significantly more” when recited in a claim with an abstract idea include, as non-limiting or non-exclusive examples:
a. Adding words “apply it” (or an equivalent) with an abstract idea, or mere instructions to implement an abstract idea on a computer;
b. Requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry.
Analysis
The following analysis is based on the “2019 Revised Patent Subject Matter Eligibility Guidance” (hereinafter “2019 PEG”) published in January 2019.
Claims 1, 9, and 18 (the independent claims) pertain to a computer implemented system/method (Step 1), for receiving input data, providing it to a model, receiving output from the model, and graphically displaying the results. The systems/methods are said to include the steps/components for: receiving a first value; receiving a first plurality of values; providing the values to a model, receiving the results (outputs); and presenting the results on a graphical display.
Here the sequence of steps are directed to a familiar class of claims “directed to” a patent-ineligible concept which has been viewed by the courts to be abstract (Step 2A1). The focus of the asserted claims, as illustrated by the claims quoted above, is on collecting information, analyzing it, and outputting results of the collection and analysis.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (or by using a pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (e.g., see: MPEP 2106.04(a)(2)(III)). The present claims recite receiving, (processing), and preparing for display steps. Additionally, it is noted that claims can recite a “mental process” even if they are claimed as being performed on a computer. Accordingly, the claim recites an abstract idea.
Furthermore, the Examiner notes that said claim limitations equally fall within the “Mathematical Concepts” grouping of abstract ideas (e.g., see: MPEP 2106.04(a)(2)(I)). Said grouping including mathematical relationships and calculations, wherein the present claims “provid[e]… to a model the first value and the first plurality of values”. Accordingly, the claim recites an abstract idea.
The claims are therefore directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). Specifically, the case parallels the fact patterns in Electric Power Group (collecting information, analyzing it, and displaying certain results of the collection and analysis), Digitech (organizing information through mathematical correlations), and Benson (a mathematical procedure for converting one form of numerical representation to another / manipulating information using mathematical relationships).
101 COLLECTING INFORMATION is ABSTRACT:
Accordingly, we have treated collecting information, including when limited to particular content (which does not change its character as information), as within the realm of abstract ideas. See, e.g., Internet Patents, 790 F.3d at 1349; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1347 (Fed. Cir. 2014); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014); CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1370 (Fed. Cir. 2011).
101 ANALYZING INFORMATION is ABSTRACT:
In a similar vein, we have treated analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract-idea category. See, e.g., TLI Commc’ns, 823 F.3d at 613; Digitech, 758 F.3d at 1351; SmartGene, Inc. v. Advanced Biological Labs., SA, 555 F. App’x 950, 955 (Fed. Cir. 2014); Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012); CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372 (Fed. Cir. 2011); SiRF Tech., Inc. v. Int’l Trade Comm’n, 601 F.3d 1319, 1333 (Fed. Cir. 2010); see also Mayo, 132 S. Ct. at 1301; Parker v. Flook, 437 U.S. 584, 589–90 (1978); Gottschalk v. Benson, 409 U.S. 63, 67 (1972).
101 PRESENTING RESULTS is ABSTRACT:
And we have recognized that merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis. See, e.g., Content Extraction, 776 F.3d at 1347; Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014).
Furthermore, the claim does not recite additional elements that integrate the judicial exception into a practical application (Step 2A2). The claims merely use generic computer components to collect and display data, while running data through a model that is not described to any meaningful extent, while ‘updating a process recipe’ in an undescribed manner. With regard to the “storage medium”, the “processing device”, and the “one or more processors” they are each recited at a high level of generality in the specification such that they are merely implementing an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as such, the additional elements fail to integrate the judicial exception into a practical application.
The Examiner does not see improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a); Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo; Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b); Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c); Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo.
In summary -
The claims of the ‘276 application are clearly focused on the combination of those abstract-idea processes. The advance they purport to make is a process of gathering and analyzing information, then displaying the results, and not any particular assertedly inventive technology for performing those functions. They are therefore directed to an abstract idea.
The claims as a whole do not amount to significantly more than the abstract idea itself. In particular, the additional claim elements (Step 2B) of a ‘non-transitory machine-readable storage medium’; a ‘processing device’; ‘one or more processors’ are merely a generic computer components, where this claim element is not sufficient to amount to significantly more than the judicial exception. The claim as a whole does not amount to significantly more than the Judicial Exception.
Thus, the claim is not patent eligible.
Dependent claims 2-8, 10-17, and 19-20 do not add significantly more than the abstract idea presented in the parent claims and are therefore rejected for the same reasons.
Claim Rejections - 35 USC § 103
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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over David, U.S. Publication No. 2017/0109646 in further view of Pandev et al., U.S. Patent No. 10,935,893, hereinafter Pandev.
With regard to claim 1, which teaches “A non-transitory machine-readable storage medium storing instructions which, when executed, cause a processing device to perform operations comprising: receiving, by the processing device, a first value associated with a first input parameter of a model, wherein the first input parameter is associated with a first processing condition of a semiconductor wafer processing procedure;” David teaches a method and apparatus for utilizing machine learning to generate models to simulate and provide guidance to improve semiconductor manufacturing processes and specifically wafer manufacturing (see paragraph 29). In David a user provides input parameters corresponding to a lithography apparatus for use in wafer creation (see paragraphs 68-69).
With regard to claim 1, which teaches “receiving, by the processing device, a first plurality of values, wherein the first plurality of values ranges from a lowest value of the first plurality of values to a highest value of the first plurality of values, and wherein each of the first plurality of values is associated with a second input parameter of the model, wherein the second input parameter is associated with a second processing condition of the semiconductor wafer processing procedure;” David further teaches a user providing a range of input parameters corresponding to a lithography apparatus for use in wafer creation (see paragraphs 68-69). This in combination with the above noted input parameters form an input data set for the lithography apparatus or model thereof (see paragraphs 70-73).
With regard to claim 1, which teaches “providing, by the processing device, to the model the first value and the first plurality of values;” David teaches feeding the input values into an algorithm for training and generating a model based upon the results of the training (see paragraphs 83-87).
With regard to claim 1, which teaches “receiving, by the processing device, a first plurality of outputs from the model, wherein each of the first plurality of outputs is associated with the first value and one value of the first plurality of values, and wherein each of the first plurality of outputs is associated with a first feature of one of a first plurality of simulated semiconductor wafers;” David teaches receiving outputs from the model corresponding to the respective inputs, where the output can be used to either generate better models (see paragraph 93) or used to make adjustments to the lithography apparatus parameters via control knobs used to adjust operation conditions (see paragraph 90).
With regard to claim 1, which teaches “preparing, by the processing device, the first plurality of outputs for presentation via a presentation element of a graphical user interface (GUI), wherein the presentation element comprises two axes, a first axis of the two axes corresponding to a first property of the first feature of the first plurality of simulated semiconductor wafers, and a second axis of the two axes corresponding to a second property of the first feature of the first plurality of simulated semiconductor wafers, and wherein preparing the first plurality of outputs for presentation comprises facilitating generation of a graphic for display in the presentation element that indicates a value of the first property of the first feature and a value of the second property of the first feature associated with each of the first plurality of outputs”, and “updating a process recipe for the semiconductor wafer processing procedure based on the first plurality of outputs”, David teaches the modeling process described above collection resulting outputs from varying inputs to determine how to adjust parameters of the lithography apparatus to improve the semiconductor (see paragraphs 67-72). Here ‘a first property of the first feature’, say ‘n’ is input and varied, while a ‘second property’, say ‘m’, is input an varied, while every ‘set of input data is associated with a specific output or target’ (see paragraph 69 and table II.) David teaches the use of models fed with inputs to generate better models and better data for input to improve wafer production (supra) and shows table II breaking down the relation between x and y components (‘n’ and ‘m’ components) with corresponding outputs, but doesn’t specifically teach a means of visualizing the output data derive from the varied input.
Pandev teaches a similar system for improving fabrication of wafers through use of modeling and machine learning algorithms (see column 2, line 63 through column 3, line 15 and column 13, line 62 through column 14, line 10). This enables the system to find “optimal lithography settings” (see column 6, lines 4-13). Pandev further specifically teaches visualization of the results of the given set of parameters on a wafer, where one parameter is on the X axis and another parameter is on the Y axis (see column 5, lines 20-64 and figure 2).
It would be obvious to one of ordinary skill in the art to utilize the data visualization in Pandev in the wafer creation optimization system of David. One would be motivated to do so to enable the user to see where the multiple conditions approach a desired result. Furthermore, both the David and Pandev patents are in the same art area and serve the same purpose to improve wafer manufacture processes.
With regard to claim 2, which teaches wherein the model comprises a machine learning model, David teaches use of machine learning algorithms to enable use of more and varied inputs to determine optimal parameters for a lithography apparatus (see paragraph 67).
With regard to claim 3, which teaches the operations further comprising: receiving a plurality of sets of input values; receiving a plurality of metrology measurements, wherein each of the plurality of metrology measurements is associated with one of the plurality of sets of input values; and training the model to generate simulated metrology measurements of a simulated semiconductor wafer using the plurality of sets of input values and the plurality of metrology measurements, wherein training the model comprises providing the plurality of sets of input values to the model as training input, and providing the plurality of metrology measurements to the model as target output, David teaches training using metrology data acquired from prior IC fabrication (see paragraphs 37, 38, and 46), while further enabling simulated input data to further train the model (see paragraph 46).
With regard to claim 4, which teaches wherein the first feature comprises at least one of: semiconductor wafer thickness; semiconductor wafer resistivity; semiconductor wafer sheet resistance; semiconductor wafer refractive index; semiconductor wafer extinction coefficient; or an indication of semiconductor wafer geometry, David further teaches features such as wafer thickness [50], wafer resistivity [42,76], wafer resistance [77], wafer refractivity [47,74], wafer geometry [71], etc.
With regard to claim 5, which teaches wherein the first property of the first feature comprises a statistical metric associated with simulated metrology measurements at a plurality of locations of the simulated semiconductor wafer, wherein the statistical metric comprises at least one of: an average of values of the first feature; a median of values of the first feature; a standard deviation of values of the first feature; or a uniformity of values of the first feature, David further teaches that the input data metrology can comprise statistical estimations of data such as the mean, a standard deviation (see paragraphs 38, 46, 75, and 188). Pandev further teaches taking the average of a plurality of parameter inputs (see column 8, lines 1-20).
With regard to claim 6, which teaches the operations further comprising: receiving, by the processing device, a second value associated with the second input parameter of the model; receiving, by the processing device, a second plurality of values, wherein the second plurality of values ranges from a lowest value of the second plurality of values to a highest value of the second plurality of values, and wherein each of the second plurality of values is associated with the first input parameter of the model; providing, by the processing device, to the model the second value and the second plurality of values; receiving, by the processing device, a second plurality of outputs from the model, wherein each of the second plurality of outputs is associated with the second value and one value of the second plurality of values, and wherein each of the second plurality of outputs is associated with the first feature of one of a second plurality of simulated semiconductor wafers; and preparing, by the processing device, the second plurality of outputs for presentation via the presentation element of the GUI, wherein preparing the second plurality of outputs for presentation comprises facilitating generation of a graphic for display in the presentation element that indicates the first property of the first feature and the second property of the first feature associated with each of the second plurality of outputs, <see the above rejection to claim 1>, where David allows for both first and second input to comprise a range of values that then result in the range of corresponding outputs (further see paragraphs 67-72)
With regard to claim 7, which teaches wherein preparing, by the processing device, the first plurality of outputs for presentation further comprises facilitating generation of a graphic for display in the presentation element that visually distinguishes an output of the first plurality of outputs associated with the highest value of the first plurality of values from an output of the first plurality of outputs associated with the lowest value of the first plurality of values, Pandev further specifically teaches visualization of the results of the given set of parameters on a wafer, where a visual distinction is shown between a first plurality of outputs associated with the highest value of the first plurality of values and an output of the first plurality of outputs associated with the lowest value of the first plurality of values (see column 5, lines 20-64 and figure 2).
With regard to claim 8, which teaches The non-transitory machine-readable storage medium of claim 1, the operations further comprising: receiving, by the processing device, a second value associated with the first input parameter of the model; providing, by the processing device, to the model the second value and the first plurality of values; receiving, by the processing device, a second plurality of outputs from the model, wherein each of the second plurality of outputs is associated with the second value and one of the first plurality of values, and wherein each of the second plurality of outputs is associated with a first feature of one of a second plurality of simulated semiconductor wafers; and preparing, by the processing device, the second plurality of outputs for presentation via the presentation element, wherein preparing the second plurality of outputs for presentation comprises facilitating generation of a graphic for display in the presentation element that indicates a value of the first property of the first feature and a value of the second property of the first feature associated with each of the second plurality of outputs, <see the above rejection to claim 1>, where David allows for both first and second input to comprise a range of values that then result in the range of corresponding outputs (further see paragraphs 67-72)
With regard to claim 9, which teaches A non-transitory machine-readable storage medium storing instructions which, when executed, cause a processing device to perform operations comprising: receiving, by the processing device, a first plurality of outputs from a first model, wherein each of the first plurality of outputs is associated with a first input value and one value of a first plurality of input values, and wherein each of the first plurality of outputs is associated with a first feature of one of a first plurality of simulated substrates; receiving, by the processing device, a second plurality of outputs from a second model, wherein each of the second plurality of outputs is associated with the first value and one value of the first plurality of values, and wherein each of the second plurality of outputs is associated with a second feature of one of the first plurality of simulated substrates;” David teaches a method and apparatus for utilizing machine learning to generate models to simulate and provide guidance to improve semiconductor manufacturing processes and specifically wafer manufacturing (see paragraph 29). In David, a user provides input parameters corresponding to a lithography apparatus for use in wafer creation (see paragraphs 68-69). David teaches receiving outputs from the model corresponding to the respective inputs, where the output can be used to either generate better models (see paragraph 93) or used to make adjustments to the lithography apparatus parameters via control knobs used to adjust operation conditions (see paragraph 90). David further notes the creation of multiple models to compare against each other to determine models with higher success rate (see paragraph 166 and 187).
With regard to claim 9, which teaches “preparing, by the processing device, the first and second pluralities of outputs for presentation via a presentation element of a graphical user interface (GUI), wherein the presentation element comprises two axes, a first axis of the two axes corresponding to a first property of the first feature, and a second axis of the two axes corresponding to a second property of the second feature, and wherein preparing the first and second pluralities of outputs for presentation comprises facilitating generation of a graphic for display in the presentation element that indicates, for each simulated substrate of the first plurality of simulated substrates, a value of the first property of the first feature and a value of the second property of the second feature”, and “updating a process recipe for the substrate processing procedure based on the first plurality of outputs and second plurality of outputs”, David teaches the modeling process described above collection resulting outputs from varying inputs to determine how to adjust parameters of the lithography apparatus to improve the semiconductor (see paragraphs 67-72). Here ‘a first property of the first feature’, say ‘n’ is input and varied, while a ‘second property’, say ‘m’, is input an varied, while every ‘set of input data is associated with a specific output or target’ (see paragraph 69 and table II.) David teaches the use of models fed with inputs to generate better models and better data for input to improve wafer production (supra) and shows table II breaking down the relation between x and y components (‘n’ and ‘m’ components) with corresponding outputs, but doesn’t specifically teach a means of visualizing the output data derive from the varied input.
Pandev teaches a similar system for improving fabrication of wafers through use of modeling and machine learning algorithms (see column 2, line 63 through column 3, line 15 and column 13, line 62 through column 14, line 10). This enables the system to find “optimal lithography settings” (see column 6, lines 4-13). Pandev further specifically teaches visualization of the results of the given set of parameters on a wafer, where one parameter is on the X axis and another parameter is on the Y axis (see column 5, lines 20-64 and figure 2).
It would be obvious to one of ordinary skill in the art to utilize the data visualization in Pandev in the wafer creation optimization system of David. One would be motivated to do so to enable the user to see where the multiple conditions approach a desired result. Furthermore, both the David and Pandev patents are in the same art area and serve the same purpose to improve wafer manufacture processes.
With regard to claim 10, which teaches The non-transitory machine-readable storage medium of claim 9, wherein the first model comprises a first machine learning model, and wherein the second model comprises a second machine learning model, David teaches that the above referenced models are machine learning models (see paragraph 166).
With regard to claim 11, which teaches the operations further comprising: receiving a first plurality of sets of input values; receiving a second plurality of sets of input values; receiving a first plurality of metrology measurements, wherein each of the first plurality of metrology measurements is associated with one of the first plurality of sets of input values; receiving a second plurality of metrology measurements, wherein each of the second plurality of metrology measurements is associated with one of the second plurality of sets of input values; training the first model to generate first simulated metrology measurements of a simulated substrate using the first plurality of sets of input values and the first plurality of metrology measurements, wherein training the first model comprises providing the first plurality of sets of input values to the first model as training input, and providing the first plurality of metrology measurements to the first model as target output; and training the second model to generate second simulated metrology measurements of the simulated substrate using the second plurality of sets of input values and the second plurality of metrology measurements, wherein training the second model comprises providing the second plurality of sets of input values to the second model as training input, and providing the second plurality of metrology measurements to the second model as target output, David teaches a user providing input parameters corresponding to a lithography apparatus for use in wafer creation (see paragraphs 68-69). David further teaches a user providing a range of input parameters corresponding to a lithography apparatus for use in wafer creation (see paragraphs 68-69). This in combination with the above noted input parameters form an input data set for the lithography apparatus or model thereof (see paragraphs 70-73). David teaches simultaneously training two models with their own input parameters and own metrology measures (see paragraphs 165-169).
With regard to claim 12, which teaches The non-transitory machine-readable storage medium of claim 9, wherein the first feature comprises at least one of: substrate thickness; substrate resistivity; substrate sheet resistance; substrate refractive index; substrate extinction coefficient; or an indication of substrate geometry, David further teaches features such as wafer thickness [50], wafer resistivity [42,76], wafer resistance [77], wafer refractivity [47,74], wafer geometry [71], etc.
With regard to claim 13, which teaches wherein the first property of the first feature comprises a statistical metric associated with simulated metrology measurements at a plurality of locations of one of the first plurality of simulated substrates, wherein the statistical metric comprises at least one of: an average of values of the first feature; a median of values of the first feature; a standard deviation of values of the first feature; or a uniformity of values of the first feature, David further teaches that the input data metrology can comprise statistical estimations of data such as the mean, a standard deviation (see paragraphs 38, 46, 75, and 188). Pandev further teaches taking the average of a plurality of parameter inputs (see column 8, lines 1-20).
With regard to claim 14, which teaches wherein preparing, by the processing device, the first plurality of outputs and the second plurality of outputs for presentation further comprises facilitating generation of a graphic for display in the presentation element that visually distinguishes a presented data point associated with a first simulated substrate of the first plurality of simulated substrates that is associated with the highest value of the first plurality of values from a presented data point associated with a second simulated substrate of the first plurality of simulated substrates that is associated with the lowest value of the first plurality of values, Pandev further specifically teaches visualization of the results of the given set of parameters on a wafer, where a visual distinction is shown between a first plurality of outputs associated with the highest value of the first plurality of values and an output of the first plurality of outputs associated with the lowest value of the first plurality of values (see column 5, lines 20-64 and figure 2).
With regard to claim 15, which teaches further comprising: receiving, by the processing device, the first input value, the first input value being associated with a first input parameter of: the first model; and the second model, wherein the first input parameter is associated with a first processing condition of a substrate processing procedure; and receiving, by the processing device, the first plurality of input values, wherein the first plurality of input values ranges from a lowest value of the first plurality of values to a highest value of the first plurality of values, and wherein each of the first plurality of values is associated with a second input parameter of: the first model; and the second model, wherein the second input parameter is associated with a second processing condition of the substrate processing procedure, <see the above rejection to claim 9>, where David allows for both first and second input to comprise a range of values that then result in the range of corresponding outputs (further see paragraphs 67-72)
With regard to claim 16, which teaches the operations further comprising: receiving, by the processing device, a second value associated with the second input parameter of the first and second models; receiving, by the processing device, a second plurality of values, wherein the second plurality of values ranges from a lowest value of the second plurality of values to a highest value of the second plurality of values, and wherein each of the second plurality of values is associated with the first input parameter of the first and second models; providing, by the processing device, to the first model the second value and the second plurality of values; providing, by the processing device, to the second model the second value and the second plurality of values; receiving, by the processing device, a third plurality of outputs from the first model, wherein each of the third plurality of outputs is associated with the second value and one value of the second plurality of values, and wherein each of the third plurality of outputs is associated with the first feature of one of a second plurality of simulated substrates; receiving, by the processing device, a fourth plurality of outputs from the second model, wherein each of the fourth plurality of outputs is associated with the second value and one value of the second plurality of values, and wherein each of the fourth plurality of outputs is associated with the second feature of one of the second plurality of simulated substrates; preparing, by the processing device, the third plurality of outputs and the fourth plurality of outputs for presentation via the presentation element of the GUI, wherein preparing the third and fourth pluralities of outputs for presentation comprises facilitating generation of a graphic for display in the presentation element that indicates, for each simulated substrate of the second plurality of simulated substrates, a value of the first property of the first feature and a value of the second property of the second feature, see the above rejection to claim 9, where David allows for both first and second input to comprise a range of values that then result in the range of corresponding outputs (further see paragraphs 67-72). The system further allows fur new models to be created with updated or new or different data (see paragraphs 93, 87, 167), where then the updated outputs can be visualized as per Pandev (see column 5, liens 44-66 and figure 2).
With regard to claim 17, which teaches the operations further comprising: receiving, by the processing device, a second value associated with the first input parameter of the first and second models; providing, by the processing device, to the first and second models the second value and the first plurality of values; receiving, by the processing device, a third plurality of outputs from the first model and a fourth plurality of outputs from the second model, wherein each of the third plurality of outputs and each of the fourth plurality of outputs is associated with the second value and one of the first plurality of values, and wherein each of the third plurality of outputs is associated with the first feature of one of a second plurality of simulated substrates, and wherein each of the fourth plurality of outputs is associated with the second feature of one of the second plurality of simulated substrates; and preparing, by the processing device, the third and fourth pluralities of outputs for presentation via the presentation element, wherein preparing the third and fourth pluralities of outputs for presentation comprises facilitating generation of a graphic for display in the presentation element that indicates, for each of the second plurality of simulated substrates, an associated value of the first property of the first feature and an associated value of the second property of the second feature, see the above rejection to claim 9, where David allows for both first and second input to comprise a range of values that then result in the range of corresponding outputs each put in the model / models individually and producing unique results (further see paragraphs 67-72)
With regard to claim 18, which teaches “A method, comprising: receiving, by one or more processors, a first value associated with a first input parameter of a first model, wherein the first input parameter is associated with a process recipe for processing a substrate;” David teaches a method and apparatus for utilizing machine learning to generate models to simulate and provide guidance to improve semiconductor manufacturing processes and specifically wafer manufacturing (see paragraph 29). In David a user provides input parameters corresponding to a lithography apparatus for use in wafer creation (see paragraphs 68-69).
With regard to claim 18, which teaches “receiving, by the one or more processors, a first plurality of values, wherein the first plurality of values range from a lowest value of the first plurality of values to a highest value of the first plurality of values, and wherein each of the first plurality of values is associated with a second input parameter of the first model, wherein the second input parameter is associated with the process recipe;” David further teaches a user providing a range of input parameters corresponding to a lithography apparatus for use in wafer creation (see paragraphs 68-69). This in combination with the above noted input parameters form an input data set for the lithography apparatus or model thereof (see paragraphs 70-73).
With regard to claim 18, which teaches “providing, by the one or more processors, to the first model the first value and the first plurality of values;” David teaches feeding the input values into an algorithm for training and generating a model based upon the results of the training (see paragraphs 83-87).
With regard to claim 18, which teaches “receiving, by the one or more processors, a first plurality of outputs from the first model, wherein each of the first plurality of outputs is associated with the first value and one of the first plurality of values, and wherein each of the first plurality of outputs is associated with a first feature of a simulated substrate;” David teaches receiving outputs from the model corresponding to the respective inputs, where the output can be used to either generate better models (see paragraph 93) or used to make adjustments to the lithography apparatus parameters via control knobs used to adjust operation conditions (see paragraph 90).
With regard to claim 18, which teaches “preparing, by the one or more processors, the first plurality of outputs for presentation via a presentation element of a graphical user interface (GUI), wherein the presentation element comprises two independent axes, a first axis of the two independent axes corresponding to a first property of the first feature, and wherein preparing the first plurality of outputs for presentation comprises facilitating generation of a graphic in the presentation element that visually displays a relationship of the outputs of the first plurality of outputs to the first property of the first feature”, and “updating a process recipe for the substrate processing procedure based on the first plurality of outputs”, David teaches the modeling process described above collection resulting outputs from varying inputs to determine how to adjust parameters of the lithography apparatus to improve the semiconductor (see paragraphs 67-72). Here ‘a first property of the first feature’, say ‘n’ is input and varied, while a ‘second property’, say ‘m’, is input an varied, while every ‘set of input data is associated with a specific output or target’ (see paragraph 69 and table II.) David teaches the use of models fed with inputs to generate better models and better data for input to improve wafer production (supra) and shows table II breaking down the relation between x and y components (‘n’ and ‘m’ components) with corresponding outputs, but doesn’t specifically teach a means of visualizing the output data derive from the varied input.
Pandev teaches a similar system for improving fabrication of wafers through use of modeling and machine learning algorithms (see column 2, line 63 through column 3, line 15 and column 13, line 62 through column 14, line 10). This enables the system to find “optimal lithography settings” (see column 6, lines 4-13). Pandev further specifically teaches visualization of the results of the given set of parameters on a wafer, where one parameter is on the X axis and another parameter is on the Y axis (see column 5, lines 20-64 and figure 2).
It would be obvious to one of ordinary skill in the art to utilize the data visualization in Pandev in the wafer creation optimization system of David. One would be motivated to do so to enable the user to see where the multiple conditions approach a desired result. Furthermore, both the David and Pandev patents are in the same art area and serve the same purpose to improve wafer manufacture processes.
With regard to claim 19, which teaches further comprising: providing, by the one or more processors, to a second model the first value and the first plurality of values; receiving, by the one or more processors, a second plurality of outputs from the second model, wherein each of the second plurality of outputs is associated with a second feature; and preparing, by the one or more processors, the second plurality of outputs for presentation via the presentation element of the GUI, wherein the second of the two independent axes corresponds to a second property of the second feature, and wherein preparing the first and second pluralities of outputs for presentation comprises facilitating generation of a graphic in the presentation element that visually displays a relationship of the first plurality of outputs and the second plurality of outputs to the first property of the first feature and the second property of the second feature, David teaches repeating the steps of claim 18 with a different model (see paragraphs 93 and 99).
With regard to claim 20, which teaches further comprising: receiving, by the one or more processors, a second value associated with the second input parameter of the first model; receiving, by the one or more processors, a second plurality of values, wherein the second plurality of values range from a lowest value of the second plurality of values to a highest value of the second plurality of values, and wherein each of the second plurality of values is associated with the first input parameter of the first model; providing, by the one or more processors, to the first model the second value and the second plurality of values; receiving, by the one or more processors, a second plurality of outputs from the first model, wherein each of the second plurality of outputs is associated with the second value and one of the second plurality of values, and wherein each of the second plurality of outputs is associated with the first feature; and preparing, by the one or more processors, the second plurality of outputs for presentation via the presentation element of the GUI, wherein preparing the second plurality of outputs for presentation comprises facilitating generation of a graphic in the presentation element that visually displays a relationship of the first plurality of outputs and the second plurality of outputs to the first property of the first feature, David teaches repeating the steps of claim 18 with different inputs (training inputs, measured inputs, virtual training data, clean up inputs, etc.) (see paragraphs 69, 72, 83).
Response to Arguments
Applicant's arguments filed 2/3/2026 have been fully considered but they are not persuasive.
101
Applicant argues that “The updating of a process recipe, which is based on model data to improve performance of a processing procedure, integrates features of the claim into a practical application.”
In response, the Examiner respectfully submits that there is a complete disconnect between how the ‘outputs for presentation’ are used in ‘updating a process recipe’. Is someone to just look at the graph and manually make adjustments to the recipe? The claims leaves this ambiguous as does the specification, making this amendment unable to ground the claims in a practical application.
Applicant argues that “Even assuming arguendo that it could be determined that the claims somehow are not integrated into a practical application under the second prong of Step 2A, the claims add significantly more than the judicial exception under Step 2B. The claims recite a specific technological improvement in semiconductor wafer processing visualization.”
In response, the Examiner respectfully submits that this section does not provide any reference to the specification of a specific improvement this claim language provides. The only cite to the specification provided previous to this argument is to paragraph 70, which describes several options for corrective actions. The only relevant sentence from that paragraph notes: “In some embodiments, the corrective action includes updating a processing recipe (e.g., modifying one or more manufacturing parameters based on the predictive data 168).” However this does not describe a specific technological improvement, nor is there any elaboration by the applicant, only noting “visualizing model learning by generating graphic” which doesn’t pertain to the cited paragraph.
103
Applicant argues that “Pandev does teach "visualizations," such as the visualization shown in FIG. 2. However, the visualizations discussed by Pandev include axes related to process parameters, e.g., "focus" and "dose" of FIG. 2, or eigenvalues "pertaining to changes in process parameters, such as focus/dose" corresponding to signals S1, S2, and S3 of FIG. 4A. The visualizations of Pandev include axes (e.g., axes corresponding to process variables dose and focus of FIG. 2) that do not include "a first axis of the two axes corresponding to a first property of the first feature of the first plurality of simulated semiconductor wafers, and a second axis of the two axes corresponding to a second property of the first feature of the first plurality of simulated semiconductor wafers," as recited in amended claims 1. Thus, Pandev does not cure the deficiencies of David.”
In response, the Examiner respectfully submits that Applicant argues that Pandev’s visualization of features via a graph for a semiconductor wafer model are not the same as "a first axis of the two axes corresponding to a first property of the first feature of the first plurality of simulated semiconductor wafers, and a second axis of the two axes corresponding to a second property of the first feature of the first plurality of simulated semiconductor wafers," without providing any articulation. The Examiner is left to guess why they are not the same. Note these argued independent claims broadly claim anywhere from the axis including “a first property of the first feature of a simulated substrate” to the quote cited above.
The argument skips over David who teaches the modeling process described above collection resulting outputs from varying inputs to determine how to adjust parameters of the lithography apparatus to improve the semiconductor (see paragraphs 67-72). Here ‘a first property of the first feature’, say ‘n’ is input and varied, while a ‘second property’, say ‘m’, is input an varied, while every ‘set of input data is associated with a specific output or target’ (see paragraph 69 and table II.) David teaches the use of models fed with inputs to generate better models and better data for input to improve wafer production (supra) and shows table II breaking down the relation between x and y components (‘n’ and ‘m’ components) with corresponding outputs, but doesn’t specifically teach a means of visualizing the output data derive from the varied input.
Pandev teaches a similar system for improving fabrication of wafers through use of modeling and machine learning algorithms (see column 2, line 63 through column 3, line 15 and column 13, line 62 through column 14, line 10). This enables the system to find “optimal lithography settings” (see column 6, lines 4-13). Pandev further specifically teaches visualization of the results of the given set of parameters on a wafer, where one parameter is on the X axis and another parameter is on the Y axis (see column 5, lines 20-64 and figure 2).
Clearly Pandev provides axis related to features of semiconductor wafer (see 5:21-6:14). Furthermore, these features are used to determine “optimal lithography settings” (6:7-9), similar to how the claims are amended.
Summary
Claims 1-20 are REJECTED.
Conclusion
Applicant's amendment necessitated any new/modified ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DENNIS G BONSHOCK/Primary Examiner, Art Unit 3992