Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This Office Action responds to the Application filed on 11/28/2023 and IDS filed on 11/28/2023, 6/3/2026, 9/2/2026. Claims 1-13 are pending.
Claim Rejections - 35 USC § 102
3. 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.
4. Claim(s) 1-3, 5, 7, 9, and 11-13 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Negoro et al. (U.S. Pub. No. 2023/0268208 A1).
As per claim 1, Negoro discloses:
An information processing apparatus comprising:
a learning trainer configured to train a machine learning model to learn a relationship between a process condition and a processing result of a substrate processing apparatus that has executed a processing based on the process condition (See Figures 11 and 12 & Para [0108]-[01113], i.e. the training data set 931 includes information pieces #A1 to #An each indicating a training processing condition and information pieces DA1 to DAn each indicating a processing result of actual substrate processing on a substrate W under a corresponding one of the training processing conditions)
an inferrer configured to infer a plurality of processing results depending on a plurality of process conditions using the trained machine learning model (See Figure 16, i.e. s22, s23, and s24 & Para [0140]-[0142], i.e. input condition ranges refer to ranges of processing conditions to be input to a trained model 23 in Step S24 later, See Para [0145]-[0146], i.e. inputs a plurality of processing conditions to a trained model 23 to acquire a plurality of estimation processing results);
a graph creator configured to plot the plurality of processing results inferred with the machine learning model on a graph (See Figure 16, i.e. S26 – display image & Para [0149], i.e. display an image based on the estimation processing results. Specifically, as illustrated in FIG. 18, the display section 105 displays a button i4 and a button i5. The button i4 is for selection of the “throughput uniformity” as a variable of the distribution chart) with an achievement level for a plurality of target values of the plurality of processing results as a plurality of axes (See Para [0149]-[0151], i.e. the display section 105 to display a three-dimensional distribution chart (see FIG. 13) using the variables selected by the use, See Figure 13 & Para [0125]-[-129], i.e. the amount of change in throughput uniformity around the point A1, See Para [0073], i.e. target throughput by calculating the difference between the target thickness distribution Pb and the thickness distribution Pa, See Para [0169][0173] & Figure 20 – [prior art graph processing result with Target processing result, illustrate of throughput uniformity, considered as the achievement level, marking points within target area and points outside target area with throughput uniformity is considered as the achievement level as cited above]); and
an information display configured to display, on the graph, information used by an operator to select an optimal solution for the process condition, based on the plot of the plurality of inferred processing results (See Para [0125]-[-129], i.e. a plurality of marks that are user selectable on the distribution chart… the user can easily select a processing condition under which a processing result close to the target processing result can be obtained, See Para [0153]-[0155], i.e. the user selects one of the processing conditions that corresponds to one of the estimation processing results through the input section… the user selects one of the processing conditions that corresponds to one of the first marks (the points A1 to A3) as an actual processing condition).
As per claim 2, Negoro discloses all of the features of claim 1 as discloses above wherein Negoro also discloses wherein the information display displays, on the graph, a limit of the substrate processing apparatus in terms of the achievement level for the plurality of target values of the plurality of processing results predicted based on the plot (See Para [0149]-[0151], i.e. the display section 105 to display a three-dimensional distribution chart (see FIG. 13) using the variables selected by the use, See Figure 13 & Para [0125]-[-129], i.e. the amount of change in throughput uniformity around the point A1, See Para [0073], i.e. target throughput by calculating the difference between the target thickness distribution Pb and the thickness distribution Pa, See Para [0169][0173] & Figure 20 –[prior art include target range (Figure 13, i.e. R) is considered as the limit of the achievement level as cited above]).
As per claim 3, Negoro discloses all of the features of claim 2 as discloses above wherein Negoro also discloses wherein the information display displays, on the graph, information on the optimal solution which is selected based on the plot displayed on the graph and the limit of the substrate processing apparatus in terms of the achievement level for the plurality of target values of the plurality of processing results (See Para [0125]-[-129], i.e. a plurality of marks that are user selectable on the distribution chart… the user can easily select a processing condition under which a processing result close to the target processing result can be obtained, See Para [0153]-[0155], i.e. the user selects one of the processing conditions that corresponds to one of the estimation processing results through the input section… the user selects one of the processing conditions that corresponds to one of the first marks (the points A1 to A3) as an actual processing condition).
As per claim 5, Negoro discloses all of the features of claim 3 as discloses above wherein Negoro also discloses wherein the graph creator plots the plurality of processing results inferred with the machine learning model on the graph with values calculated from a plurality of measured values of a substrate processed by the substrate processing apparatus based on the process condition as axes (See Para [0149]-[0151], i.e. the display section 105 to display a three-dimensional distribution chart (see FIG. 13) using the variables selected by the use, See Figure 13 & Para [0125]-[-129], i.e. the amount of change in throughput uniformity around the point A1, See Para [0073], i.e. target throughput by calculating the difference between the target thickness distribution Pb and the thickness distribution Pa, See Para [0169][0173] & Figure 20).
As per claim 7, Negoro discloses all of the features of claim 3 as discloses above wherein Negoro also discloses wherein the substrate processing apparatus is of a batch type or single wafer type (See Para [0048], i.e. single-wafer).
As per claim 9, Negoro discloses all of the features of claim 2 as discloses above wherein Negoro also discloses wherein the graph creator plots the plurality of processing results inferred with the machine learning model on the graph with values calculated from a plurality of measured values of a substrate processed by the substrate processing apparatus based on the process condition as axes (See Para [0149]-[0151], i.e. the display section 105 to display a three-dimensional distribution chart (see FIG. 13) using the variables selected by the use, See Figure 13 & Para [0125]-[-129], i.e. the amount of change in throughput uniformity around the point A1, See Para [0073], i.e. target throughput by calculating the difference between the target thickness distribution Pb and the thickness distribution Pa, See Para [0169][0173] & Figure 20).
As per claim 11, Negoro discloses all of the features of claim 2 as discloses above wherein Negoro also discloses wherein the substrate processing apparatus is of a batch type or single wafer type (See Para [0048], i.e. single-wafer).
As per claim 12, Negoro discloses:
A non-transitory computer-readable storage medium storing a program that causes an information processing apparatus to execute a process (See Figure 1 & Para [0089]) comprising:
training a machine learning model to learn a relationship between a process condition and a processing result of a substrate processing apparatus that has executed a processing based on the process condition (See Figures 11 and 12 & Para [0108]-[01113], i.e. the training data set 931 includes information pieces #A1 to #An each indicating a training processing condition and information pieces DA1 to DAn each indicating a processing result of actual substrate processing on a substrate W under a corresponding one of the training processing conditions);
inferring a plurality of processing results depending on a plurality of process conditions using the trained machine learning model (See Figure 16, i.e. s22, s23, and s24 & Para [0140]-[0142], i.e. input condition ranges refer to ranges of processing conditions to be input to a trained model 23 in Step S24 later, See Para [0145]-[0146], i.e. inputs a plurality of processing conditions to a trained model 23 to acquire a plurality of estimation processing results);
plotting the plurality of processing results inferred with the machine learning model on a graph (See Figure 16, i.e. S26 – display image & Para [0149], i.e. display an image based on the estimation processing results. Specifically, as illustrated in FIG. 18, the display section 105 displays a button i4 and a button i5. The button i4 is for selection of the “throughput uniformity” as a variable of the distribution chart) with an achievement level for a plurality of target values of the plurality of processing results as a plurality of axes (See Para [0149]-[0151], i.e. the display section 105 to display a three-dimensional distribution chart (see FIG. 13) using the variables selected by the use, See Figure 13 & Para [0125]-[-129], i.e. the amount of change in throughput uniformity around the point A1, See Para [0073], i.e. target throughput by calculating the difference between the target thickness distribution Pb and the thickness distribution Pa, See Para [0169][0173] & Figure 20 – [prior art graph processing result with Target processing result, illustrate of throughput uniformity, considered as the achievement level, marking points within target area and points outside target area with throughput uniformity is considered as the achievement level as cited above]); and
displaying, on the graph, information used by an operator to select an optimal solution for the process condition, based on the plot of the plurality of inferred processing results (See Para [0125]-[-129], i.e. a plurality of marks that are user selectable on the distribution chart… the user can easily select a processing condition under which a processing result close to the target processing result can be obtained, See Para [0153]-[0155], i.e. the user selects one of the processing conditions that corresponds to one of the estimation processing results through the input section… the user selects one of the processing conditions that corresponds to one of the first marks (the points A1 to A3) as an actual processing condition).
As per claim 13, Negoro discloses:
An optimal solution search method executed by an information processing apparatus (See Figure 1), the optimal solution search method comprising
training a machine learning model to learn a relationship between a process condition and a processing result of a substrate processing apparatus that has executed a processing based on the process condition (See Figures 11 and 12 & Para [0108]-[01113], i.e. the training data set 931 includes information pieces #A1 to #An each indicating a training processing condition and information pieces DA1 to DAn each indicating a processing result of actual substrate processing on a substrate W under a corresponding one of the training processing conditions);
inferring a plurality of processing results depending on a plurality of process conditions using the trained machine learning model (See Figure 16, i.e. s22, s23, and s24 & Para [0140]-[0142], i.e. input condition ranges refer to ranges of processing conditions to be input to a trained model 23 in Step S24 later, See Para [0145]-[0146], i.e. inputs a plurality of processing conditions to a trained model 23 to acquire a plurality of estimation processing results);
plotting the plurality of processing results inferred with the machine learning model on a graph (See Figure 16, i.e. S26 – display image & Para [0149], i.e. display an image based on the estimation processing results. Specifically, as illustrated in FIG. 18, the display section 105 displays a button i4 and a button i5. The button i4 is for selection of the “throughput uniformity” as a variable of the distribution chart) with an achievement level for a plurality of target values of the plurality of processing results as a plurality of axes (See Para [0149]-[0151], i.e. the display section 105 to display a three-dimensional distribution chart (see FIG. 13) using the variables selected by the use, See Figure 13 & Para [0125]-[-129], i.e. the amount of change in throughput uniformity around the point A1, See Para [0073], i.e. target throughput by calculating the difference between the target thickness distribution Pb and the thickness distribution Pa, See Para [0169][0173] & Figure 20 – [prior art graph processing result with Target processing result, illustrate of throughput uniformity, considered as the achievement level, marking points within target area and points outside target area with throughput uniformity is considered as the achievement level as cited above]); and
displaying, on the graph, information used by an operator to select an optimal solution for the process condition, based on the plot of the plurality of inferred processing results (See Para [0125]-[-129], i.e. a plurality of marks that are user selectable on the distribution chart… the user can easily select a processing condition under which a processing result close to the target processing result can be obtained, See Para [0153]-[0155], i.e. the user selects one of the processing conditions that corresponds to one of the estimation processing results through the input section… the user selects one of the processing conditions that corresponds to one of the first marks (the points A1 to A3) as an actual processing condition).
Allowable Subject Matter
5. Claims 4, 6, 8, and 10 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
6. The following is a statement of reasons for the indication of allowable subject matter: The prior art does not teach the limitations of claims 4, 6, 8, and/or 10.
Conclusion
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to NHA T NGUYEN whose telephone number is (571)270-1405. The examiner can normally be reached M-F 8:00AM-5:00PM.
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/NHA T NGUYEN/ Primary Examiner, Art Unit 2851
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