dNotice 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 .
Response to Amendment
Applicant's arguments filed 6/29/26 have been fully considered but they are not persuasive.
Applicant added the limitation of performing an undefined corrective action associated with the second substrate processing domain based on the analytic or predictive data which reads on any action a human can perform including redrawing a model using pen and paper. The corrective active is undefined and all the applicant’s disclosure specifies is e.g.,
“The client device 110 can include user interface (UI) component 112 and corrective action component 114. UI component 112 can receive user input (e.g., via a Graphical User Interface (GUI) displayed via the client device 110) associate with generating a machine-learning model, generating a transfer model, updating one or more machine-learning models, etc. The machine-learning model and transfer model can be generated by the predictive system 160, which is discussed with regards to FIG. 3. Each client device 110 can include an operating system that allows users to one or more of generate, view, or edit data (e.g., indication associated with manufacturing equipment 124, corrective actions associated with manufacturing equipment 124, etc.).”
Modeling is a mental process of modeling with assistance of pen and paper and training a neural network to learn (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Using the trained machine learning model to e.g., predict amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Applicant argues the USC 101 rejection.
Response to Arguments
Claim 1 amounts to e.g.,
Identifying a model
Obtaining data
Generating a model
Modifying the model
Performing an undefined “corrective action”.
It is well-settled that collecting and analyzing information by steps people go through in their minds or by mathematical algorithms, without more, are mental processes in the abstract-idea category. Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353-54 (Fed. Cir. 2016); see SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1167 (Fed. Cir. 2018) ("[S]electing certain information, analyzing it using mathematical techniques, and reporting or displaying the results of the analysis" is abstract); Intellectual Ventures I LLC v. Cap. One Fin. Corp., 850 F.3d 1332, 1341 (Fed. Cir. 2017) ("Organizing, displaying, and manipulating data of particular documents" is abstract.); FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1096-97 (Fed. Cir. 2016) (compiling and combining disparate data sources to generate a full picture of a user's activity, identity, frequency of activity, and the like in a computer environment to detect potential fraud does not differentiate a process from ordinary mental processes); In re Killian, 45 F.4th 1373, 1379 (Fed. Cir. 2022) ("These steps can be performed by a human, using 'observation, evaluation, judgment, [and] opinion,' because they involve making determinations and identifications, which are mental tasks humans routinely do"). Although the claims may specify an improvement they are only improving the abstract idea not a computer.
Using AI to generate analytic OR predictive data amounts to a mental process in the same way that a human can predict the weather with or without a computer.
"The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea." MPEP § 2106.04(a)(2).III. "Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions." Id. For the purposes of this abstract idea, "[t]he courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation."
If the application claims the inventive concept (from the disclosure) and the claims are drawn to the specifics of e.g., learning or training, such as the how and for what purpose the training occurs, the claims may be eligible/statutory. If the generic computer or processor is merely "used for", "applied to" or "using" an AI learning/training algorithm, process or equivalent without claimed details, it will most often fall into the "Mere Instructions to Apply an Exception" as set forth in MPEP 2106.05(f). When a claim merely recites only the idea of a solution or outcome, i.e. the claim fails to recite details of how a solution to a problem is accomplished, as described in 2106.05(f)(1), it is rejected as ineligible. Also, use of an existing AI or learning technique or method, set forth to be WURC (in accordance with MPEP 2106.05(d)), may also prove to be ineligible.
If the claims are merely "using" existing learning algorithms and/or "artificial intelligence techniques" then learning/training/AI is not the inventive concept but is merely a tool used to manipulate data. This can be a process that was previously performed by "human agents" and may now be automated. Learning, training, "updating" and/or "dynamically modifying" are insignificant computer activities, shown to be WURC in accordance with MPEP 2106.05(d)(II)(iii), for instance.
Claims do not specify a clear practical application. It is true that making physical changes to at least a portion of hardware is not a mental process but if applicant merely states e.g., “making physical changes” without any more description it would be so broad that it would be directed to insignificant extra-solution activity. If the physical changes were more specifically laid out, examiner may agree that the physical changes make a claim eligible but if the examiner cannot easily identify what physical changes are positively made the claimed physical changes amount to mere extra-solution activity.
In order for an abstract idea to be integrated into a practical application, the improvement in a given technical field must be a byproduct of the additional elements. An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself”, as stated in MPEP 2106.5 (1). Applicant should state where within the claim limitations such an improvement is made.
Practical applications must be additional elements, not abstract ideas. Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception.
"It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception." paragraph is on 2106.05(a) Improvements to the Functioning of a Computer or To Any Other Technology or Technical Field [R-07.2022].
Limitations that are indicative of integration into a practical application:
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.
Example 47 claim 2 specifies: a method of using an artificial neural network (ANN) comprising:
(a) receiving, at a computer, continuous training data;
(b) discretizing, by the computer, the continuous training data to generate input data;
(c) training, by the computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm;
(d) detecting one or more anomalies in a data set using the trained ANN;
(e) analyzing the one or more detected anomalies using the trained ANN to generate anomaly data; and
(f) outputting the anomaly data from the trained ANN and was found to be ineligible.
step (b) recites discretizing continuous training data to generate input data by processes including rounding, binning, or clustering continuous data, which may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, the claimed discretizing of continuous data encompasses observing continuous data and performing an evaluation, such as rounding the continuous data;
steps (b), (d), and (e) fall within the mental process grouping of abstract ideas, and steps (b) and (c) fall within the mathematical concepts grouping of abstract ideas. Limitations (b)-(e) are considered together as a single abstract idea for further analysis.
In re pg. 11 applicant argues “perform a corrective action” amounts to a physical action.
In response, there is no mention of these limitations in the claims and the specification is not the measure of the invention. Therefore, limitations contained therein cannot be read into the claims for the purpose of avoiding the prior art; see In re Sprock, 55 CCPA 743, 386 F.2d 924, 155 USPQ 687 (1968). Although claims are read in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2D 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
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, 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: claims 1-20, 22 are directed to either a process, machine, manufacture or composition of matter.
With respect to claims 1, 13, 20, 22:
2A Prong 1:
identifying a machine-learning model trained to generate analytic or predictive data for a first substrate processing domain associated with a type of substrate processing system; (encompasses mental observations or evaluations, e.g., a computer programmer’s mental identification of data);
generating a transfer model for a second substrate processing domain associated with the type of substrate processing system, wherein the transfer model is generated based on the first trace data (collected data) pertaining to the first substrate processing domain and second trace data pertaining to the second substrate processing domain (mental process of modeling with assistance of pen and paper; A human- mind with pen and paper can generate/determine data/model);
modifying, using the transfer model, at least one of the machine-learning model or current trace data (modifying collected data) associated with the second substrate processing domain “to enable” (intended use) the machine-learning model to generate analytic or predictive data associated with the second substrate processing domain (Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data mental process of modeling with assistance of pen and paper);
performing an undefined corrective action associated with the second substrate processing domain based on the analytic or predictive data (reads on any action a human can perform including redrawing a model using pen and paper).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A system, memory device, processing device, (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
obtaining first trace data (collected data) pertaining to the first substrate processing domain, (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
input to a transfer model (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f));
the first trace data (collected data) used to train the machine-learning model (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A system, memory device, processing device, (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
obtaining first trace data (collected data) pertaining to the first substrate processing domain, (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
input to a transfer model (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f));
the first trace data (collected data) used to train the machine-learning model (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data).
Further, the obtaining step was considered to be extra-solution activity in Step 2A Prong 2, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The receiving and/or transmitting limitations constitute extra-solution activity. See buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) ("That a computer receives and sends the information over a network-with no further specification-is not even arguably inventive."). The court decisions cited in MPEP 2106.05(d)(II) indicate that merely Receiving and/or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Thereby, a conclusion that the claimed receiving/transmitting steps are well-understood, routine, conventional activity is supported under Berkheimer. The claim is not patent eligible.
2. The method of claim 1, wherein the first substrate processing domain comprises a first process chamber (a domain refers to a process chamber) and the second substrate processing domain comprises a second process chamber, wherein the first process chamber and the second process chamber are a same type of process chamber (further expand mental process user can model data using different inputs).
3. The method of claim 1, wherein the first substrate processing domain comprises a first process recipe and the second substrate processing domain comprise a second process recipe(further expand mental process user can model data using different inputs).
4. The method of claim 1, wherein the first trace data (collected data) comprises a first set of traces associated with the first substrate processing domain and the second trace data comprises a second set of traces associated with the second substrate processing domain(further expand mental process user can model data using different inputs).
5. The method of claim 4, further comprising: generating, from the first set of traces, a first fundamental trace; and generating, from the second set of traces, a second fundamental trace(further expand mental process user can model data using different inputs).
6. The method of claim 5, further comprising: generating, based on the first fundamental trace and the second fundamental trace, a transfer map reflecting a relationship between the first fundamental trace and the second fundamental trace (further expand mental process user can model data using different inputs; modeling with assistance of pen and paper).
7. The method of claim 6, where the transfer map (can be paper and pencil) provides feature-based scaling in reflecting the relationship between the first fundamental trace and second fundamental trace (further expand mental process user can model data using different inputs; modeling with assistance of pen and paper).
8. The method of claim 6, wherein the transfer map is used to generate the transfer model(further expand mental modeling with assistance of pen and paper).
9. The method of claim 1, further comprising: providing, as input to the transfer model, current trace data pertaining to the second substrate processing domain (data gathering); obtaining one or more first output values of the transfer model(data gathering); providing, as input to the machine-learning model, the one or more first output values(receiving/transmitting); and obtaining one or more second output values of the machine learning model, the one or more second output values reflecting the analytic or predictive data associated with the second substrate processing domain (receiving/transmitting steps are considered to be extra-solution activity).
10. The method of claim 9, further comprising: performing a corrective action based on the one or more second output values of the machine-learning model (using the trained machine learning model to make corrections (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a previously trained model to make a prediction).
11. The method of claim 1, further comprising: retraining the machine-learning model using the transfer model (the court finds that this training is generic and summarily states the process of the training a model is required for any e.g., machine learning model. Using a machine learning technique necessarily includes an iterative step training step; iterative training using selected training material and/or dynamic adjustments based on changes are incident to the very nature of machine learning); providing, as input to the retrained machine-learning model, current trace data pertaining to the second substrate processing domain; and obtaining one or more output values of the retrained machine-learning model reflecting the analytic or predictive data associated with the second substrate processing domain (receiving/transmitting steps are considered to be extra-solution activity).
12. The method of claim 11, further comprising: performing a corrective action based on the one or more output values of the machine-learning model(using the trained machine learning model to make corrections).
14. The system of claim 13, wherein the first trace data comprises a first set of traces associated with the first substrate processing domain and the second trace data comprises a second set of traces associated with the second substrate processing domain(receiving/transmitting steps are considered to be extra-solution activity).
15. The system of claim 14, wherein the operations further comprise: generating, from the first set of traces, a first fundamental trace; and generating, from the second set of traces, a second fundamental trace(Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
16. The system of claim 15, wherein the operations further comprise: generating, based on the first fundamental trace and the second fundamental trace, a transfer map reflecting a relationship between the first fundamental trace and the second fundamental trace (Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
17. The system of claim 13, wherein the operations further comprise: providing, as input to the transfer model, current trace data pertaining to the second substrate processing domain; obtaining one or more first output values of the transfer model; providing, as input to the machine-learning model, the one or more first output values; and obtaining one or more second output values of the machine learning model, the one or more second output values reflecting the analytic or predictive data associated with the second substrate processing domain(receiving/transmitting steps are considered to be extra-solution activity).
18. The system of claim 11, wherein the operations further comprise: retraining the machine-learning model using the transfer model; providing, as input to the retrained machine-learning model, current trace data pertaining to the second substrate processing domain; and obtaining one or more output values of the retrained machine-learning model reflecting the analytic or predictive data associated with the second substrate processing domain(receiving/transmitting steps are considered to be extra-solution activity).
19. The system of claim 18, wherein the operations further comprise: performing a corrective action based on the one or more output values (using a model).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-20,22 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Li (US 2023/0376373).
The applied reference has a common inventor and assignee with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2). This rejection under 35 U.S.C. 102(a)(2) might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C. 102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B) if the same invention is not being claimed; or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed in the reference and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement.
Li (US 2023/0376373) discloses:
13, 20, 22, 1. A system, comprising: a memory device (e.g., data store, 140, Fig. 1 or any inherent computer/server memories; 904, 906, 918, Fig. 9); and a processing device (e.g., servers/clients, 110, 170, 112, 120, Fig. 1; 902, Fig. 9), operatively coupled to the memory device, to perform operations comprising: identifying a machine-learning model (e.g., models, 190; 0036; 0040) trained (e.g., 746, Fig. 7C; “generate predictive data 168 using supervised machine learning (e.g., supervised data set, performance data 150 includes metrology data, the trace data 142 used to train a model 190 is associated with good substrates and bad substrates, etc.).”, 0036) to generate analytic (any data output by models 0036) or predictive data (0036) for a first substrate (substrates, 0035-0036) processing domain associated with a type of substrate processing system (“The present disclosure addresses false and missed positives and is adaptive to provide robustness over time and flexibility to address different domains (e.g., see FIGS. 8A-B).”, 0072; “Vertical and horizontal scaling is applied to address domain transfer (e.g., applying guardband to a different domain, such as a different recipe)”, 0240);
obtaining first trace data pertaining to the first substrate processing domain (“new trace data for substrates where it is to be determined whether the substrates are good or bad”, 0220; 0223, 0238), the first trace data used to train the machine-learning model (“At block 746, processing logic identifies a training set of trace data”, 0222, 0223);
generating a transfer model (models that can be used in different domains or for different substrates, 0197-0199; 0150; 0152 e.g., 190, Fig. 1) for a second substrate processing domain (“new trace data includes new sensor data associated with producing new substrates with the same substrate processing equipment as block 746 or with different substrate processing equipment”, 0223 or historical trace data, 144, Fig. 1) associated with the type of substrate processing system, wherein the transfer model is generated based on the first trace data pertaining to the first substrate processing domain and second trace data pertaining to the second substrate processing domain (“Trace data may include sets of sensor data associated with production of different substrates and from different types of sensors”, 0021; “new trace data includes new sensor data associated with producing new substrates with the same substrate processing equipment as block 746 or with different substrate processing equipment”, 0223 or historical trace data, 144, Fig. 1); and modifying, using the transfer model, at least one of the machine-learning model or current trace data associated with the second substrate processing domain to enable the machine-learning model to generate analytic or predictive data associated with the second substrate processing domain (plurality of models, 0057-8; models for different domains, “The present disclosure addresses false and missed positives and is adaptive to provide robustness over time and flexibility to address different domains (e.g., see FIGS. 8A-B).”, 0072; “Vertical and horizontal scaling is applied to address domain transfer (e.g., applying guardband to a different domain, such as a different recipe)”, 0240; “aspects of the disclosure describe the training of one or more machine learning models 190 using historical data (e.g., historical trace data 144, historical performance data 152) and inputting current data (e.g., current trace data 146) into the one or more trained machine learning models 190 to determine predictive data 168.”, 0064; “the corrective action includes updating a process recipe to produce subsequent substrates”, 0046);
performing an undefined corrective action associated with the second substrate processing domain based on the analytic or predictive data (see e.g., “the corrective action includes updating a process recipe to produce subsequent substrates”, 0046, 0135).
2. The method of claim 1, wherein the first substrate processing domain comprises a first process chamber and the second substrate processing domain comprises a second process chamber, wherein the first process chamber and the second process chamber are a same type of process chamber (domain can refer to e.g., process chamber or process recipe; plurality of models, 0057-8; models for different domains, “The present disclosure addresses false and missed positives and is adaptive to provide robustness over time and flexibility to address different domains (e.g., see FIGS. 8A-B).”, 0072; “Vertical and horizontal scaling is applied to address domain transfer (e.g., applying guardband to a different domain, such as a different recipe)”, 0240).
3. The method of claim 1, wherein the first substrate processing domain comprises a first process recipe and the second substrate processing domain comprise a second process recipe(domain can refer to e.g., process chamber or process recipe; plurality of models, 0057-8; models for different domains, “The present disclosure addresses false and missed positives and is adaptive to provide robustness over time and flexibility to address different domains (e.g., see FIGS. 8A-B).”, 0072; “Vertical and horizontal scaling is applied to address domain transfer (e.g., applying guardband to a different domain, such as a different recipe)”, 0240).
4. The method of claim 1, wherein the first trace data comprises a first set of traces associated with the first substrate processing domain and the second trace data comprises a second set of traces associated with the second substrate processing domain(domain can refer to e.g., process chamber or process recipe; plurality of models, 0057-8; models for different domains, “The present disclosure addresses false and missed positives and is adaptive to provide robustness over time and flexibility to address different domains (e.g., see FIGS. 8A-B).”, 0072; “Vertical and horizontal scaling is applied to address domain transfer (e.g., applying guardband to a different domain, such as a different recipe)”, 0240).
5. The method of claim 4, further comprising: generating, from the first set of traces, a first fundamental trace; and generating, from the second set of traces, a second fundamental trace(domain can refer to e.g., process chamber or process recipe; plurality of models, 0057-8; models for different domains, “The present disclosure addresses false and missed positives and is adaptive to provide robustness over time and flexibility to address different domains (e.g., see FIGS. 8A-B).”, 0072; “Vertical and horizontal scaling is applied to address domain transfer (e.g., applying guardband to a different domain, such as a different recipe)”, 0240).
6. The method of claim 5, further comprising: generating, based on the first fundamental trace and the second fundamental trace, a transfer map reflecting a relationship between the first fundamental trace and the second fundamental trace (domain can refer to e.g., process chamber or process recipe; plurality of models, 0057-8; models for different domains, “The present disclosure addresses false and missed positives and is adaptive to provide robustness over time and flexibility to address different domains (e.g., see FIGS. 8A-B).”, 0072; “Vertical and horizontal scaling is applied to address domain transfer (e.g., applying guardband to a different domain, such as a different recipe)”, 0240).
7. The method of claim 6, where the transfer map provides feature-based scaling in reflecting the relationship between the first fundamental trace and second fundamental trace(scaling, 0237-0249; domain can refer to e.g., process chamber or process recipe; plurality of models, 0057-8; models for different domains, “The present disclosure addresses false and missed positives and is adaptive to provide robustness over time and flexibility to address different domains (e.g., see FIGS. 8A-B).”, 0072; “Vertical and horizontal scaling is applied to address domain transfer (e.g., applying guardband to a different domain, such as a different recipe)”, 0240).
8. The method of claim 6, wherein the transfer map is used to generate the transfer model (e.g., “The training engine 182 may be capable of training machine learning model 190 or various machine learning models included in model 190 using one or more sets of elements associated with the training set from data set generator 172. The training engine 182 may generate multiple trained machine learning models 190, where each trained machine learning model 190 corresponds to a distinct set of elements of the training set (e.g., sensor data from a distinct set of sensors). For example, a first trained machine learning model may have been trained using all elements (e.g., X1-X5), a second trained machine learning model may have been trained using a first subset of elements (e.g., X1, X2, X4), and a third trained machine learning model may have been trained using a second subset of elements (e.g., X1, X3, X4, and X5) that may partially overlap the first subset of elements.”, 0057-0064).
9. The method of claim 1, further comprising: providing, as input to the transfer model, current trace data pertaining to the second substrate processing domain; obtaining one or more first output values of the transfer model; providing, as input to the machine-learning model, the one or more first output values; and obtaining one or more second output values of the machine learning model, the one or more second output values reflecting the analytic or predictive data associated with the second substrate processing domain(plurality of models, 0057-8; models for different domains, “The present disclosure addresses false and missed positives and is adaptive to provide robustness over time and flexibility to address different domains (e.g., see FIGS. 8A-B).”, 0072; “Vertical and horizontal scaling is applied to address domain transfer (e.g., applying guardband to a different domain, such as a different recipe)”, 0240; “aspects of the disclosure describe the training of one or more machine learning models 190 using historical data (e.g., historical trace data 144, historical performance data 152) and inputting current data (e.g., current trace data 146) into the one or more trained machine learning models 190 to determine predictive data 168.”, 0064; “the corrective action includes updating a process recipe to produce subsequent substrates”, 0046).
10. The method of claim 9, further comprising: performing a corrective action based on the one or more second output values of the machine-learning model(“the corrective action includes updating a process recipe to produce subsequent substrates”, 0046; Figs. 2).
11. The method of claim 1, further comprising: retraining the machine-learning model using the transfer model (“Sensor data associated with substrate processing operations is collected over time” 0018; “Responsive to the confidence data indicating a level of confidence below a threshold level for a predetermined number of instances (e.g., percentage of instances, frequency of instances, frequency of occurrence, total number of instances, etc.) the predictive component 114 may cause model 190 to be re-trained (e.g., based on current trace data 146, manufacturing parameters, current performance data 154, etc.).”, 0063; models are trained and retrained in order to optimize or reach convergence, “the selection engine 185 may be capable of selecting the trained machine learning model 190 that has the highest accuracy of the trained machine learning models 190. In some embodiments, validation engine 184 and selection engine 185 may repeat this process for each machine learning model include in model 190.”, 0058, 0060); providing, as input to the retrained machine-learning model, current trace data pertaining to the second substrate processing domain; and obtaining one or more output values of the retrained machine-learning model reflecting the analytic or predictive data associated with the second substrate processing domain(“Responsive to the confidence data indicating a level of confidence below a threshold level for a predetermined number of instances (e.g., percentage of instances, frequency of instances, frequency of occurrence, total number of instances, etc.) the predictive component 114 may cause model 190 to be re-trained (e.g., based on current trace data 146, manufacturing parameters, current performance data 154, etc.).”, 0063; “the selection engine 185 may be capable of selecting the trained machine learning model 190 that has the highest accuracy of the trained machine learning models 190. In some embodiments, validation engine 184 and selection engine 185 may repeat this process for each machine learning model include in model 190.”, 0058, 0060).
12. The method of claim 11, further comprising: performing a corrective action based on the one or more output values of the machine-learning model(e.g., “the corrective action includes updating a process recipe to produce subsequent substrates”, 0046; Figs. 2).
14. The system of claim 13, wherein the first trace data comprises a first set of traces (collected/senor data “sensor data is summarized across a particular recipe or recipe operation associated with the production of a substrate by equipment”, 0019; “Trace data may include sets of sensor data associated with production of different substrates and from different types of sensors.”, 0021) associated with the first substrate processing domain and the second trace data comprises a second set of traces associated with the second substrate processing domain (“new trace data for substrates where it is to be determined whether the substrates are good or bad”, 0220; 0223, 0238plurality of models, 0057-8; models for different domains, “The present disclosure addresses false and missed positives and is adaptive to provide robustness over time and flexibility to address different domains (e.g., see FIGS. 8A-B).”, 0072; “Vertical and horizontal scaling is applied to address domain transfer (e.g., applying guardband to a different domain, such as a different recipe)”, 0240; “aspects of the disclosure describe the training of one or more machine learning models 190 using historical data (e.g., historical trace data 144, historical performance data 152) and inputting current data (e.g., current trace data 146) into the one or more trained machine learning models 190 to determine predictive data 168.”, 0064; “the corrective action includes updating a process recipe to produce subsequent substrates”, 0046).
15. The system of claim 14, wherein the operations further comprise: generating, from the first set of traces, a first fundamental trace; and generating, from the second set of traces, a second fundamental trace(collected/senor data “sensor data is summarized across a particular recipe or recipe operation associated with the production of a substrate by equipment”, 0019; “Trace data may include sets of sensor data associated with production of different substrates and from different types of sensors.”, 0021).
16. The system of claim 15, wherein the operations further comprise: generating, based on the first fundamental trace and the second fundamental trace, a transfer map reflecting a relationship between the first fundamental trace and the second fundamental trace (collected/senor data “sensor data is summarized across a particular recipe or recipe operation associated with the production of a substrate by equipment”, 0019; “Trace data may include sets of sensor data associated with production of different substrates and from different types of sensors. In some embodiments, the data is analyzed at the trace level (e.g., as opposed to just providing summary statistics of a sensor across a recipe or recipe operation) by using guardbands.”, 0021; domain can refer to e.g., process chamber or process recipe; plurality of models, 0057-8; models for different domains, “The present disclosure addresses false and missed positives and is adaptive to provide robustness over time and flexibility to address different domains (e.g., see FIGS. 8A-B).”, 0072; “Vertical and horizontal scaling is applied to address domain transfer (e.g., applying guardband to a different domain, such as a different recipe)”, 0240).
17. The system of claim 13, wherein the operations further comprise: providing, as input to the transfer model, current trace data pertaining to the second substrate processing domain; obtaining one or more first output values of the transfer model (collected/senor data “Sensor data associated with substrate processing operations is collected over time”, 0018; “sensor data is summarized across a particular recipe or recipe operation associated with the production of a substrate by equipment”, 0019); providing, as input to the machine-learning model, the one or more first output values; and obtaining one or more second output values of the machine learning model, the one or more second output values reflecting the analytic or predictive data associated with the second substrate processing domain (models are trained and retrained in order to optimize or reach convergence, “the selection engine 185 may be capable of selecting the trained machine learning model 190 that has the highest accuracy of the trained machine learning models 190. In some embodiments, validation engine 184 and selection engine 185 may repeat this process for each machine learning model include in model 190.”, 0058, 0060).
18. The system of claim 11, wherein the operations further comprise: retraining the machine-learning model using the transfer model; providing, as input to the retrained machine-learning model, current trace data pertaining to the second substrate processing domain; and obtaining one or more output values of the retrained machine-learning model reflecting the analytic or predictive data associated with the second substrate processing domain (models are trained and retrained in order to optimize or reach convergence, “the selection engine 185 may be capable of selecting the trained machine learning model 190 that has the highest accuracy of the trained machine learning models 190. In some embodiments, validation engine 184 and selection engine 185 may repeat this process for each machine learning model include in model 190.”, 0058, 0060).
19. The system of claim 18, wherein the operations further comprise: performing a corrective action based on the one or more output values(e.g., “the corrective action includes updating a process recipe to produce subsequent substrates”, 0046; Figs. 2).
20. A method, comprising: providing, as input to a transfer model, current trace data associated with a target substrate processing domain, wherein the transfer model is generated based on historical trace data associated with the target substrate processing domain and historical trace data associated with a source substrate processing domain (e.g., 144, Fig. 1; “identifying trace data including a plurality of data points, the trace data being associated with production, via a substrate processing system, of substrates. The method further includes comparing the trace data to a guardband generated based on historical trace data and a plurality of allowable types of variance associated with the historical trace data, the historical trace data being associated with historical production, via the substrate processing system, of historical substrates having historical property values that meet threshold values, the guardband including an upper limit and a lower limit for fault detection”, 0005), wherein the source substrate processing domain and the target substrate processing domain are both associated with a type of substrate processing system; obtaining one or more first output values of the transfer model reflective of the current trace data modified by a set of offset values; providing, as input to a machine-learning model trained to generate analytic or predictive data for the source substrate processing domain, the one or more first output values from the transfer model; and obtaining one or more second output values of the machine learning model, the one or more second output values representing analytic or predictive data associated with the target substrate processing domain(plurality of models that are trained and retrained to reach optimization or convergence, 0057-8; models for different domains, “The present disclosure addresses false and missed positives and is adaptive to provide robustness over time and flexibility to address different domains (e.g., see FIGS. 8A-B).”, 0072; “Vertical and horizontal scaling is applied to address domain transfer (e.g., applying guardband to a different domain, such as a different recipe)”, 0240; “aspects of the disclosure describe the training of one or more machine learning models 190 using historical data (e.g., historical trace data 144, historical performance data 152) and inputting current data (e.g., current trace data 146) into the one or more trained machine learning models 190 to determine predictive data 168.”, 0064).
22. A method, comprising: retraining a machine-learning model using a transfer model, wherein the transfer model is generated based on historical trace data associated with a target substrate processing domain and historical trace data associated with a source substrate processing domain(“Sensor data associated with substrate processing operations is collected over time”, 0018; models are trained and retrained in order to optimize or reach convergence, “the selection engine 185 may be capable of selecting the trained machine learning model 190 that has the highest accuracy of the trained machine learning models 190. In some embodiments, validation engine 184 and selection engine 185 may repeat this process for each machine learning model include in model 190.”, 0058, 0060), wherein the source substrate processing domain and the target substrate processing domain are associated with a type of substrate processing system, wherein the machine-learning model is trained to generate analytic or predictive data for the source substrate processing domain (predictive, Fig. 9 or 168, Fig. 1); providing, as input to the retrained machine-learning model, current trace data pertaining to the target substrate processing domain; and obtaining one or more output values of the retrained machine-learning model reflecting analytic or predictive data associated with the target substrate processing domain(plurality of models, 0057-8; models for different domains, “The present disclosure addresses false and missed positives and is adaptive to provide robustness over time and flexibility to address different domains (e.g., see FIGS. 8A-B).”, 0072; “Vertical and horizontal scaling is applied to address domain transfer (e.g., applying guardband to a different domain, such as a different recipe)”, 0240; “aspects of the disclosure describe the training of one or more machine learning models 190 using historical data (e.g., historical trace data 144, historical performance data 152) and inputting current data (e.g., current trace data 146) into the one or more trained machine learning models 190 to determine predictive data 168.”, 0064).
Response to Arguments
Applicant stated Applicant will file a separate statement on a separate sheet, signed in accordance with 37 CFR 1.33(b), establishing common ownership pursuant to 35 U.S.C. § 102(b)(2)(C) and 37 CFR 1.104(c)(4)(ii). (See MPEP § 717.02(a) (setting forth the requirements for invoking the prior art exception under 35 U.S.C. § 102(b)(2)(C) based on common ownership).)
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID R VINCENT whose telephone number is (571)272-3080. The examiner can normally be reached ~Mon-Fri 12-8:30.
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/DAVID R VINCENT/Primary Examiner, Art Unit 2123
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