DETAILED ACTION
Claim Status
This is first office action on the merits in response to the application filed on 02/05/2024.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-13 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 02/05/2024 and 12/30/2025 is(are) 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 § 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.
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims are not directed to any of the statutory categories such as processes, machines, manufactures and compositions of matter. The system is recited as Products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations (MPEP 2106.03(I)). The client application, ephemeral compute instance, and payment service are interpreted not to have any physical or tangible form in accordance with their broadest reasonable interpretation (BRI).
Claims 1-11 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.
Under the Step 1 of the Section 101 analysis, Claims 1-8, and 10 are drawn to a method which is within the four statutory categories (i.e., a process), Claim 11 is drawn to a system which is within the four statutory categories (i.e. a machine), and Claim 9 is drawn to a non-transitory computer-readable medium which is within the four statutory categories (i.e., a manufacture).
Since the claims are directed toward statutory categories, it must be determined if the claims are directed towards a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea). Based on consideration of all of the relevant factors with respect to the claim as a whole, claims 1-11 are determined to be directed to an abstract idea. The rationale for this determination is explained below:
Regarding Claims 1, 9, and 11:
Claims 1, 9, and 11 are drawn to an abstract idea without significantly more. The claims recite “updating the arrangement of the plurality of droplets until an evaluation result of the arrangement of the plurality of droplets satisfies an end condition while evaluating the arrangement of the plurality of droplets using a learned model that receives the arrangement of the plurality of droplets and outputs an evaluation value of the arrangement of the plurality of droplets.”
Under the Step 2A Prong One, the limitations, as underlined above, are processes that, under its broadest reasonable interpretation, cover Mental Processes such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
For example, but for the “learned model” language, the underlined limitations in the context of this claim encompass the human activity or mental processes. The series of steps belong to a typical observation, evaluation, and judgment, because the arrangement of droplets are processed and updated accordingly to satisfy an end condition.
Under the Step 2A Prong Two, this judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – “An information processing method, applied to a film forming method of forming a cured film by arranging a plurality of droplets of a curable composition on a substrate and curing a liquid film formed by connecting the plurality of droplets, of deciding an arrangement of the plurality of droplets in the film forming method, comprising:”, “A non-transitory computer readable medium storing a program for causing a computer to execute processing, applied to a film forming method of forming a cured film by arranging a plurality of droplets of a curable composition on a substrate and curing a liquid film formed by connecting the plurality of droplets, of deciding an arrangement of the plurality of droplets in the film forming method, comprising:”, “An information processing apparatus, applied to a film forming method of forming a cured film by arranging a plurality of droplets of a curable composition on a substrate and curing a liquid film formed by connecting the plurality of droplets, of deciding an arrangement of the plurality of droplets in the film forming method, the information processing apparatus comprising: an updater configured to”, and “learned model”. The additional elements are recited at a high-level of generality (i.e., performing generic functions of an interaction) such that it amounts no more than mere instructions to apply the exception using a generic computer component, merely implementing an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. Additionally, regarding the specification and claims, there is no improvement in the functioning of a computer or an improvement to other technology or technical field present, there is no applying or using the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition present, there is no implementing the judicial exception with or using the judicial exception in conjunction with a particular machine or manufacture that is integral to the claim present, there is no effecting a transformation or reduction of a particular article to a different state or thing present, and there is no 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 present such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Accordingly, these additional elements, individually or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Under the Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements in the process amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Regarding Claim 10:
Claim 10 is drawn to an abstract idea without significantly more. The claims recite “the model is configured to receive the arrangement of the plurality of droplets and to output a merging probability between adjacent droplets among the plurality of droplets.”
Under the Step 2A Prong One, the limitations, as underlined above, are processes that, under its broadest reasonable interpretation, cover Mental Processes such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
For example, but for the “model” language, the underlined limitations in the context of this claim encompass the human activity or mental processes. The series of steps belong to a typical observation, evaluation, and judgment, because the arrangement of droplets are processed and updated accordingly to satisfy an end condition.
Under the Step 2A Prong Two, this judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – “A learned model, applied to a film forming method of forming a cured film by arranging a plurality of droplets of a curable composition on a substrate and curing a liquid film formed by connecting the plurality of droplets, of evaluating an arrangement of the plurality of droplets in the film forming method, wherein”, and “model”. The additional elements are recited at a high-level of generality (i.e., performing generic functions of an interaction) such that it amounts no more than mere instructions to apply the exception using a generic computer component, merely implementing an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. Additionally, regarding the specification and claims, there is no improvement in the functioning of a computer or an improvement to other technology or technical field present, there is no applying or using the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition present, there is no implementing the judicial exception with or using the judicial exception in conjunction with a particular machine or manufacture that is integral to the claim present, there is no effecting a transformation or reduction of a particular article to a different state or thing present, and there is no 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 present such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Accordingly, these additional elements, individually or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Under the Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements in the process amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Regarding Claim 2-8:
Dependent claims 3 and 5-8 only further elaborate the abstract idea and do not recite additional elements.
Dependent claims 2 and 4 include additional limitations, for example, “learned model” and “machine learning” (Claim 2); and “learned model” (Claim 4), but none of these limitations are deemed significantly more than the abstract idea because, as stated above, they require no more than generic computer structures or signals to be executed, and do not recite any Improvements to the functioning of a computer, or Improvements to any other technology or technical field.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation or implementing the judicial exception on a generic computer.
Therefore, whether taken individually or as an ordered combination, claims 2-8 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim(s) 1-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Narioka (KR20210070214A; refer to the English translation) in view of Guo (WO 2022008174 A1; already of record in IDS).
Regarding Claims 1, 9, and 11, Narioka teaches An information processing method, applied to a film forming method of forming a cured film by arranging a plurality of droplets of a curable composition on a substrate and curing a liquid film formed by connecting the plurality of droplets, of deciding an arrangement of the plurality of droplets in the film forming method, comprising (Narioka: Abstract; Page 3, lines 15-29): A non-transitory computer readable medium storing a program for causing a computer to execute processing (Narioka: Abstract; Page 20, lines 15-34), An information processing apparatus, applied to a film forming method (Narioka: Abstract; Page 3, lines 6-24)
updating the arrangement of the plurality of droplets until an evaluation result of the arrangement of the plurality of droplets satisfies an end condition while evaluating the arrangement of the plurality of droplets [using a learned model] that receives the arrangement of the plurality of droplets and outputs an evaluation value of the arrangement of the plurality of droplets (Narioka: Page 8, lines 6-17; Page 5, lines 26-41; Page 6, line 36 ~ Page 7, line 4 teach(es) Step S1 is a step of setting conditions (parameters) necessary for simulation. The parameters are the arrangement of the droplets of the curable composition IM on the substrate S, the volume of each droplet, the physical property values of the curable composition IM, and the unevenness of the surface of the mold M (for example, the pattern area PR ) of the pattern), and information about the unevenness of the surface of the substrate S. The parameters may include a profile of the pressure applied to the space SP (mold M) by the pressure control unit PC, and the like).
However, Narioka does not explicitly teach using a learned model.
Guo from same or similar field of endeavor teaches using a learned model (Guo: Abstract; Paragraph(s) 0076, 0122-0125 teach(es) receiving a control input for controlling a patterning process. A control output for the patterning process is generated, with a trained machine learning model, based on the control input. The machine learning model is trained with training data generated from simulation of the patterning process and/or actual process data; The training data also comprises training control outputs generated using a physical model based on the training control inputs and/or the plurality of operational conditions of the patterning process).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Narioka to incorporate the teachings of Guo for evaluating the arrangement of the plurality of droplets using a learned model that receives the arrangement of the plurality of droplets and outputs an evaluation value of the arrangement of the plurality of droplets.
There is motivation to combine Guo into Narioka because Guo’s teachings of trained machine learning model would facilitate simulation of the patterning process (Guo: Paragraph(s) 0076, 0122-0125).
Regarding Claim 10, Narioka teaches A [learned model], applied to a film forming method of forming a cured film by arranging a plurality of droplets of a curable composition on a substrate and curing a liquid film formed by connecting the plurality of droplets, of evaluating an arrangement of the plurality of droplets in the film forming method (Narioka: Abstract; Page 3, lines 15-29; Page 4, lines 1-6 & 15-18).
However, Narioka does not explicitly teach …learned model, and …wherein the model is configured to receive the arrangement of the plurality of droplets and to output a merging probability between adjacent droplets among the plurality of droplets.
Guo from same or similar field of endeavor teaches …learned model, …wherein the model is configured to receive the arrangement of the plurality of droplets and to output a merging probability between adjacent droplets among the plurality of droplets (Guo: Abstract; Paragraph(s) 0076, 0122-0125 teach(es) receiving a control input for controlling a patterning process. A control output for the patterning process is generated, with a trained machine learning model, based on the control input. The machine learning model is trained with training data generated from simulation of the patterning process and/or actual process data; The training data also comprises training control outputs generated using a physical model based on the training control inputs and/or the plurality of operational conditions of the patterning process).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Narioka to incorporate the teachings of Guo for …learned model, …wherein the model is configured to receive the arrangement of the plurality of droplets and to output a merging probability between adjacent droplets among the plurality of droplets.
There is motivation to combine Guo into Narioka because Guo’s teachings of trained machine learning model would facilitate simulation of the patterning process (Guo: Paragraph(s) 0076, 0122-0125).
Regarding Claim 12, the combination of Narioka and Guo teaches A film forming method of forming a cured film of a curable composition on a substrate, comprising: deciding an arrangement of a plurality of droplets of the curable composition in accordance with an information processing method defined in claim 1, as stated above with respect to claim 1; and Narioka further teaches forming the cured film by arranging the plurality of droplets on the substrate in accordance with the arrangement decided in the deciding and curing a liquid film formed by connecting the plurality of droplets (Narioka: Abstract; Page 8, lines 11-17).
Regarding Claim 13, the combination of Narioka and Guo teaches An article manufacturing method of manufacturing an article, comprising:
deciding an arrangement of a plurality of droplets of a curable composition in accordance with an information processing method defined in claim 1, as stated above with respect to claim 1; and Narioka further teaches forming a cured film by arranging the plurality of droplets on a substrate in accordance with the arrangement decided in the deciding and curing a liquid film formed by connecting the plurality of droplets; and obtaining the article by processing the substrate on which the cured film has been formed (Narioka: Abstract; Page 8, lines 11-17 teach(es) The simulation method includes the steps of: inputting a physical property value of a gas between the first member and the second member; inputting a movement profile of the second member for the first member when bringing the plurality of droplets of the curable composition arranged on the first member into contact with the second member; determining a pressure of the gas between the first member and the second member based on the input physical property value and the input movement profile; and predicting, based on the determined pressure, an amount of a residual gas trapped between the plurality of droplets due to the contact of the plurality of droplets with the second member).
Regarding Claim 2, the combination of Narioka and Guo teaches all the limitations of claim 1 above; however the combination does not explicitly teach further comprising generating the learned model by machine learning.
Guo further teaches further comprising generating the learned model by machine learning (Guo: Page 0076, 0122-0125).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Narioka to incorporate the teachings of Guo for further comprising generating the learned model by machine learning.
There is motivation to combine Guo into Narioka because Guo’s teachings of trained machine learning model would facilitate simulation of the patterning process (Guo: Paragraph(s) 0076, 0122-0125).
Regarding Claim 3, the combination of Narioka and Guo teaches all the limitations of claim 2 above; and Narioka further teaches wherein the evaluation value output by the learned model includes an index indicating a state of the liquid film (Narioka: Page 8, lines 11-17).
Regarding Claim 4, the combination of Narioka and Guo teaches all the limitations of claim 3 above; and Narioka further teaches wherein in the updating, the arrangement of the plurality of droplets is changed until a value of an objective function for evaluating the arrangement of the plurality of droplets satisfies the end condition while calculating the value of the objective function using the index obtained using the learned model (Narioka: Page 3, lines 15-24 teach(es) it is important to form a film of the curable composition having a uniform thickness and not to include air bubbles in the film. To achieve this, the placement of the droplets, the method and conditions for pressing the mold against the droplets, and the like can be adjusted).
Regarding Claim 5, the combination of Narioka and Guo teaches all the limitations of claim 4 above; and Narioka further teaches wherein the index includes a merging probability between adjacent droplets among the plurality of droplets (Narioka: Abstract; Page 3, lines 15-29; Page 4, lines 1-6 & 15-18).
Regarding Claim 6, the combination of Narioka and Guo teaches all the limitations of claim 5 above; and Narioka further teaches wherein the objective function includes a term for evaluating a size of a bubble surrounded by adjacent droplets among the plurality of droplets (Narioka: Page 16, lines 24-32; Page 7, lines 22-29 teach(es) A core-out portion (concave portion) subjected to a spot facing process is formed on the opposite side of the mesa portion MS of the mold M. When the cavity space SP as a closed space formed by the core-out portion and the mold holder MH holding the mold M is pressed, the core-out portion can be bent toward the substrate S).
Regarding Claim 7, the combination of Narioka and Guo teaches all the limitations of claim 6 above; and Narioka further teaches wherein the objective function includes a term for evaluating a thickness of the liquid film (Narioka: Page 17, lines 22-29 teach(es) When the core-out portion has a rectangular shape, the size (length of each side) and thickness of the core-out portion may be input to the simulation device).
Regarding Claim 8, the combination of Narioka and Guo teaches all the limitations of claim 1 above; and Narioka further teaches wherein the updating includes deciding a direction in which a droplet is moved to update the arrangement of the plurality of droplets (Narioka: Page 7, line 30 ~ Page 8, line 5 teach(es) in order to accurately predict the unfilled defect caused by the residual gas, the change in the pressure of the gas caused by the motion of the mold M in contact with the liquid is considered).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Helander (US 12672476 B2) teaches Opto-electronic Device With Nanoparticle Deposited Layers, including evaluating, and thin film.
Chang (US 12150374 B2) teaches Method For Patterning A Coating On A Surface And Device Including A Patterned Coating, including vapor deposition on a surface of a substrate.
Cortie (US 10620544 B2) teaches Fluid Handling Structure, Lithographic Apparatus And Device Manufacturing Method, including droplet, liquid film, and merge.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Neha Patel can be reached at (571)270-1492. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CLAY C LEE/Primary Examiner, Art Unit 3699
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