DETAILED ACTION
Claims 1-21 are presented for examination.
This office action is in response to submission of application on 16-JUNE-2023.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 16-JUNE-2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on 29-SEPTEMBER-2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on 02-DECEMBER-2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
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 1-2, 12-21 are rejected under 35 U.S.C. 103 as being unpatentable over Yoshida et al. (Pub. No. US 20080241360 A1, filed March 27th 2008, hereinafter Yoshida) in view of Sarlin et al. (Pub. No. US 20210150252 A1, filed November 13th 2020, hereinafter Sarlin).
Regarding claim 1:
Claim 1 recites:
A prediction method of predicting a behavior of droplets of a curable composition in a process of forming a film of the curable composition from a plurality of droplets of the curable composition arranged on a first member, the method comprising predicting the behavior of the droplets using a learning model, wherein an input of the learning model includes first information indicating positions on the first member to which the droplets of the curable composition are to be arranged.
Yoshida discloses predicting a behavior of droplets of a curable composition in a process of forming a film of the curable composition from a plurality of droplets of the curable composition arranged on a first member, the method comprising predicting the behavior of the droplets using a learning model, wherein an input of the learning model includes first information indicating positions on the first member to which the droplets of the curable composition are to be arranged:
Yoshida teaches forming a resin film on the surface of a substrate, i.e. forming a film of a curable composition arranged on a first member wherein droplets of the resin liquid are thermally cured and the diameter of the droplets after curing is predicted (Paragraph 21-22). The diameter of the resin droplets would be a form of behavior of the droplets of a curable composition, and their position is used to optimize a second resin liquid (Paragraph 22) wherein the position of the first droplets would therefore be the first information indicating positions on the first member.
Yoshida does not teach the use of a learning model, which is taught by Sarlin below.
Sarlin in the same field of endeavor of machine learning discloses a learning model:
Sarlin teaches the use of a graph neural network, which would be a type of learning model. Furthermore, this may be combined with the predictions of Yoshida in order to enable more efficient predictions (Sarlin, Paragraph 37).
Sarlin and the present application are analogous art because they are in the same field of endeavor of machine learning
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Yoshida and Sarlin. This would have provided the improvement of efficient prediction (Sarlin, Paragraph 37).
Regarding claim 2, which depends upon claim 1:
Claim 2 recites:
The method according to claim 1, wherein the input of the learning model includes second information indicating a relative positional relationship among adjacent droplets of the plurality of droplets.
Yoshida in view of Sarlin disclose the method of claim 1 upon which claim 2 depends. Furthermore, Sarlin discloses the limitations of claim 2:
Sarlin teaches a graph neural network that has an encoder to map position and visual descriptors together (Paragraph 5), indicating that the input of the learning model i.e. the neural network has information indicating a relative positional relationship among adjacent objects, wherein the adjacent objects may be the droplets of Yoshida.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Yoshida and Sarlin. This would have provided the improvement of reasoning about position as well as other factors in for a neural network (Sarlin, Paragraph 7).
Regarding claim 12, which depends upon claim 2:
Claim 12 recites:
The method according to claim 2, further comprising generating the learning model while using at least one of the first information and the second information as the input and using the predicted behavior of the droplets as learning data.
Yoshida in view of Sarlin disclose the method of claim 2 upon which claim 12 depends. Furthermore, Yoshida discloses using at least one of the first information and the second information as the input and using the predicted behavior of the droplets as learning data:
Yoshida teaches using the first information i.e. the position of the droplets in order to optimize the volume of a second series of droplets (Paragraph 22), wherein the optimization of the volume would be a form of prediction of behavior for the second series of resin droplets. Using the first information is such a manner would be using it as input.
Yoshida does not teach the use of a learning model or learning data, which is taught by Sarlin below.
Sarlin in the same field of endeavor of machine learning discloses a learning model:
Sarlin teaches the use of a graph neural network, which would be a type of learning model. Furthermore, this may be combined with the predictions of Yoshida in order to enable more efficient predictions (Sarlin, Paragraph 37).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Yoshida and Sarlin. This would have provided the improvement of reasoning about position as well as other factors in for a neural network (Sarlin, Paragraph 7).
Regarding claim 13, which depends upon claim 1:
Claim 13 recites:
The method according to claim 1, wherein in the predicting, a degree of merging of adjacent droplets of the plurality of droplets is predicted as the behavior of the droplets.
Yoshida in view of Sarlin disclose the method of claim 1 upon which claim 13 depends. Furthermore, Yoshida discloses the limitations of claim 13:
Yoshida teaches a goal of achieving uniform width and film thickness in a resin pattern (Paragraph 22) wherein the uniform width and film thickness would be an indication of the merging patterns of adjacent droplets of the plurality of droplets wherein the uniformity is predicted as an outcome of the method of Yoshida.
Regarding claim 14, which depends upon claim 1:
Claim 14 recites:
The method according to claim 1, wherein the process includes a process of bringing the curable composition arranged on the first member and a second member into contact with each other, thereby forming a film of the curable composition in a space between the first member and the second member.
Yoshida in view of Sarlin disclose the method of claim 1 upon which claim 14 depends. Furthermore, Yoshida discloses the limitations of claim 14:
Yoshida teaches that between multiple section of cured first resin, a second resin liquid may be spread in order to form a pattern (Paragraph 17). The different section of first resin may act as a first member and a second member wherein the second resin brings them into contact with each other, thereby forming a film of the curable composition in a space between the first member and the second member.
Regarding claim 15, which depends upon claim 14:
Claim 15 recites:
The method according to claim 14, wherein the input of the learning model includes at least one of information indicating a volume of the droplet, information concerning volatilization of the droplet, information concerning a shape of the first member, and information concerning a shape of a pattern provided on the second member.
Yoshida in view of Sarlin disclose the method of claim 14 upon which claim 15 depends. Yoshida in view of Sarlin has previously disclosed inputting information in a learning model Furthermore, Yoshida discloses the limitations of claim 15:
Yoshida teaches that with regards to predicting the behavior of second resin liquid the droplet volume of the second resin liquid may be increased in order to achieve uniformity, wherein the increased droplet volume would indicate the input of droplet volume into the prediction of uniformity (Paragraph 28), which would be at least one of the information of the above list.
Regarding claim 16, which depends upon claim 2:
Claim 16 recites:
The method according to claim 2, wherein the learning model uses a graph neural network.
Yoshida in view of Sarlin disclose the method of claim 2 upon which claim 16 depends. Furthermore, Sarlin discloses the limitations of claim 16:
Sarlin teaches the use of a graph neural network (Paragraph 5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Yoshida and Sarlin. This would have provided the improvement of reasoning about position as well as other factors in for a neural network (Sarlin, Paragraph 7).
Regarding claim 17, which depends upon claim 1:
Claim 17 recites:
The method according to claim 1, wherein the learning model uses a neural network.
Yoshida in view of Sarlin disclose the method of claim 1 upon which claim 17 depends. Furthermore, Sarlin discloses the limitations of claim 17:
Sarlin teaches the use of a graph neural network (Paragraph 5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Yoshida and Sarlin. This would have provided the improvement of reasoning about position as well as other factors in for a neural network (Sarlin, Paragraph 7).
Claims 18 recites an apparatus that parallels the method of claim 1. Therefore, the analysis discussed above with respect to claim 1 also applies to claim 18. Accordingly, claim 18 is rejected based on substantially the same rationale as set forth above with respect to claim 1.
Regarding claim 19, which depends upon claim 18:
Claim 19 recites:
A film forming apparatus incorporating an information processing apparatus defined in claim 18, wherein a process of forming a film of a curable composition from a plurality of droplets of the curable composition arranged on a first member is controlled based on prediction of a behavior of the droplets of the curable composition by the information processing apparatus.
Yoshida in view of Sarlin disclose the method of claim 18 upon which claim 19 depends. Furthermore, Yoshida discloses the limitations of claim 19:
Yoshida teaches the curing of resin from droplets (Paragraph 78), which would be the process of forming a film of curable composition from a plurality of droplets arranged on a first member, wherein the diameter of a first series of resin droplets is used to determine the optimal volume for a second series of resin droplets (Paragraph 22) which would be controlling based on the prediction of a behavior of the droplets of the curable composition i.e. of the diameter of the first resin droplets the forming of the film.
Regarding claim 20, which depends upon claim 1:
Claim 20 recites:
An article manufacturing method comprising: determining, while repeating a prediction method defined in claim 1, a condition for a process of forming a film of a curable composition from a plurality of droplets of the curable composition arranged on a first member, and executing the process in accordance with the condition.
Yoshida in view of Sarlin disclose the method of claim 1 upon which claim 20 depends. Furthermore, Yoshida discloses the limitations of claim 20:
Yoshida teaches determining if the condition for series of first resin droplets to cure has been fulfilled (e.g., temperature of the surface of the substrate) the further spreading the second resin droplets (Paragraph 78), which would be executing the process as described above
Claims 21 recites a non-transitory storage medium that parallels the method of claim 1. Therefore, the analysis discussed above with respect to claim 1 also applies to claim 21. Accordingly, claim 21 is rejected based on substantially the same rationale as set forth above with respect to claim 1.
Claims 3-11 are rejected under 35 U.S.C. 103 as being unpatentable over Yoshida in view of Sarlin, further in view of Nair et al. (Pub. No. US 20200143005 A1, filed November 2nd 2018, hereinafter Nair).
Regarding claim 3, which depends upon claim 2:
Claim 3 recites:
The method according to claim 2, wherein the second information is expressed by a graph formed from nodes and a link connecting the nodes.
Yoshida in view of Sarlin disclose the method of claim 2 upon which claim 3 depends. Furthermore, Nair in the same field of endeavor of machine learning discloses the limitations of claim 3:
Nair teaches that use of a Voronoi diagram is known in the art, which would be a type of graph formed from nodes and links connecting the nodes by way of adjacent regions (Paragraph 49).
Nair and the present application are analogous art because they are in the same field of endeavor of machine learning
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Yoshida in view of Sarlin and Nair. This would have provided the improvement of further modelling flexibility (Nair, Paragraph 2).
Regarding claim 4, which depends upon claim 3:
Claim 4 recites:
The method according to claim 3, wherein in the graph, a position corresponding to each of the plurality of droplets is set as the node, and a line connecting adjacent nodes is set as the link.
Yoshida in view of Sarlin further in view of Nair disclose the method of claim 3 upon which claim 4 depends. Furthermore, Yoshida discloses the limitations of claim 4:
Yoshida teaches knowledge of the position of the first series of droplets after they have cured in order to drop the second series of droplets, wherein the adjacent droplets to each first droplet are also known in order to optimize the volume of the second resin droplets (Paragraph 77). Therefore, a position corresponding to each of the plurality of droplets acts as the node wherein adjacent droplets are linked in order to determine optimal second droplet volume.
Regarding claim 5, which depends upon claim 4:
Claim 5 recites:
The method according to claim 4, wherein the position includes a center position of each of the plurality of droplets.
Yoshida in view of Sarlin further in view of Nair disclose the method of claim 4 upon which claim 5 depends. Furthermore, Yoshida discloses the limitations of claim 5:
Yoshida teaches that the diameter of a droplet is known, wherein the diameter would include knowledge of the center position of each of the plurality of droplets as diameter indicates a circle (Paragraph 77).
Regarding claim 6, which depends upon claim 3:
Claim 6 recites:
The method according to claim 3, wherein in the graph, a position in a region defined by adjacent droplets of the plurality of droplets is set as the node, and a line connecting adjacent nodes is set as the link.
Yoshida in view of Sarlin further in view of Nair disclose the method of claim 3 upon which claim 6 depends. Furthermore, Nair discloses the limitations of claim 6:
Nair teaches the use of a Voronoi diagram as known in the art (Paragraph 49), wherein a Voronoi diagram is a graph that would link adjacent nodes wherein regions are defined by adjacent nodes. Furthermore, in combination with the teachings of Yoshida and Sarlin, the graph of Nair may be used for the forming of a film from resin droplets of Yoshida in view of Sarlin.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Yoshida in view of Sarlin and Nair. This would have provided the improvement of further modelling flexibility (Nair, Paragraph 2).
Regarding claim 7, which depends upon claim 6:
Claim 7 recites:
The method according to claim 6, wherein the region is a region divided by Voronoi boundaries of a Voronoi diagram in which each of the plurality of droplets is set as a generating point.
Yoshida in view of Sarlin further in view of Nair disclose the method of claim 6 upon which claim 7 depends. Furthermore, Nair discloses the limitations of claim 7:
Nair teaches the use of a Voronoi diagram as known in the art (Paragraph 49). Furthermore, in combination with the teachings of Yoshida and Sarlin, the graph of Nair may be used for the forming of a film from resin droplets of Yoshida in view of Sarlin.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Yoshida in view of Sarlin and Nair. This would have provided the improvement of further modelling flexibility (Nair, Paragraph 2).
Regarding claim 8, which depends upon claim 6:
Claim 8 recites:
The method according to claim 6, wherein the position includes a centroid position of the region.
Yoshida in view of Sarlin further in view of Nair disclose the method of claim 6 upon which claim 8 depends. Furthermore, Nair discloses the limitations of claim 8:
Nair teaches the use of K-means clustering (Paragraph 49), wherein K-means clustering involves the use of a centroid position of the region.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Yoshida in view of Sarlin and Nair. This would have provided the improvement of further modelling flexibility (Nair, Paragraph 2).
Regarding claim 9, which depends upon claim 6:
Claim 9 recites:
The method according to claim 6, wherein the position includes a Voronoi boundary of a Voronoi diagram in which each of the plurality of droplets is set as a generating point.
Yoshida in view of Sarlin further in view of Nair disclose the method of claim 6 upon which claim 9 depends. Furthermore, Nair discloses the limitations of claim 9:
Nair teaches the use of a Voronoi diagram as known in the art (Paragraph 49). Furthermore, in combination with the teachings of Yoshida and Sarlin, the graph of Nair may be used for the forming of a film from resin droplets of Yoshida in view of Sarlin as the generating point.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Yoshida in view of Sarlin and Nair. This would have provided the improvement of further modelling flexibility (Nair, Paragraph 2).
Regarding claim 10, which depends upon claim 3:
Claim 10 recites:
The method according to claim 3, wherein in the graph, a position corresponding to each of the plurality of droplets, a position in a region defined by adjacent droplets of the plurality of droplets, and a position where Voronoi boundaries of a Voronoi diagram intersect, in which each of the plurality of droplets is set as a generating point, are set as nodes, and a line connecting adjacent nodes is set as the link.
Yoshida in view of Sarlin further in view of Nair disclose the method of claim 3 upon which claim 10 depends. Yoshida and Sarlin have previously taught the forming of a film through the curing of resin droplets. Furthermore, regarding the limitations of claim 10:
Nair teaches a Voronoi diagram (Paragraph 77). Yoshida and Sarlin may use the graphing method of Nair wherein the droplets are set as nodes and adjacent droplets are connected as link in the graph for reasons of the improvement provided below. Therefore, the position corresponding to each of the plurality of droplets, a position in a region defined by adjacent droplets of the plurality of droplets, and a position where Voronoi boundaries of a Voronoi diagram would intersect, as the position of the droplet and its adjacent droplets in a region would correspond to the Voronoi boundaries of a Voronoi diagram, thus intersecting with each other at the Voronoi boundaries.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Yoshida in view of Sarlin and Nair. This would have provided the improvement of further modelling flexibility (Nair, Paragraph 2).
Regarding claim 11, which depends upon claim 3:
Claim 11 recites:
The method according to claim 3, wherein in the graph, at least one of the node and the link holds, as a feature amount, one of information concerning a position and a volume of the droplet and information concerning a shape of the first member.
Yoshida in view of Sarlin further in view of Nair disclose the method of claim 3 upon which claim 11 depends. Furthermore, Yoshida discloses the limitations of claim 11:
Yoshida teaches that for a first series of resin droplets that may act as graph nodes as previously seen, the position of the droplet is known in order to determine the volume of a second series of droplets (Paragraph 77) which would be one of the information as determined by the list above. This would therefore act a feature of the first droplet when predicting the second droplet patterns.
Conclusion
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/A.J.M./Examiner, Art Unit 2142 /Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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