DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/17/26 has been entered.
Response to Arguments
Response: 35 U.S.C. § 101
1. Applicants argue:
The applicant argues that with the recent amendment the claimed features cannot be performed in the human mind without the associated hardware and the assistance of a special purpose computer programmed to apply specialized algorithms. The applicant also points to Step 2B of the 35 U.S.C. 101 guidelines and states that the limitation of claim 1 that states “at least one modification parameter for the computer simulation and/or the process is calculated from coordinates of the first distinguishing points and second distinguishing points at least partially mapped to each other” cannot be well-understood, routine or conventional. (Remarks: pages 21-23)
2. Examiner Response:
The examiner notes that the computer that has been recently amended into the claims is being viewed as an additional element. The computer is recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a computer and/or a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Also, the examiner notes that the amended limitation of claim 1 that states “at least one modification parameter for the computer simulation and/or the process is calculated from coordinates of the first distinguishing points and second distinguishing points at least partially mapped to each other” doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Also, the amended limitation of claim 1 that states “at least one modification parameter for the computer simulation and/or the process is calculated from coordinates of the first distinguishing points and second distinguishing points at least partially mapped to each other” is calculating at least one modification parameter for the computer simulation. Therefore, under MPEP 2106.04(a)(2), this limitation covers a mathematical concept, which falls in the “Mathematical Concept” grouping of abstract ideas.
The examiner notes that as shown above, the amended limitation of claim 1 that states “at least one modification parameter for the computer simulation and/or the process is calculated from coordinates of the first distinguishing points and second distinguishing points at least partially mapped to each other” would not be eligible under step 2A, prong 1, where the limitation can fall within the “Mental Process” and “Mathematical Concept” groupings of abstract ideas, where the computer is viewed as an additional element.
3. Applicants argue:
The applicant argues that the measuring, determined, calculated and modified recitations of claim 1 doesn’t require any specific mathematical calculations. The applicant pointed the Ex Parte Desjadins USPTO Patent Board decision and Recentive Analysis court case for support as to why the current claims are eligible under 35 U.S.C. 101. (Remarks: pages 25-28)
4. Examiner Response:
The examiner notes that the measuring limitation of claim 1 that states “measuring in the process really carried out at least one measurement progression of the characteristic variable with a sensor” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The claim limitation doesn’t state what the characteristic variable is. See MPEP 2106.05 (f) (1) Whether the claim 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. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
The examiner notes that the first distinguishing limitation of claim 1 that states “- first distinguishing points of the curve of the at least one simulation progression and second distinguishing points of the curve of the at least one measurement progression are determined” doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
The examiner notes that the amended limitation of claim 1 that states “at least one modification parameter for the computer simulation and/or the process is calculated from coordinates of the first distinguishing points and second distinguishing points at least partially mapped to each other” is calculating at least one modification parameter for the computer simulation. Therefore, under MPEP 2106.04(a)(2), this limitation covers a mathematical concept, which falls in the “Mathematical Concept” grouping of abstract ideas.
Also, the limitation that states “the at least one calculated modification parameter quantifying deviations between the at least one simulation progression and the at least one measurement progression” doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas. This limitation is also calculating the deviations between the at least one simulation progression and the at least one measurement progression. Therefore, under MPEP 2106.04(a)(2), this limitation covers a mathematical concept, which falls in the “Mathematical Concept” grouping of abstract ideas.
The examiner notes that the limitation of claim 1 that states “the process is modified on the basis of the at least one modification parameter and carried out again” doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
The examiner also notes that the applicant points to the Ex Parte Desjadins USPTO Patent Board decision for support as to why the current claims are eligible under 35 U.S.C. 101. The examiner notes that in Ex Parte Desjadins, it was determined that a limitation of claim 1 along with support within the specification reflects improvement to how machine learning operates. The limitation of claim 1 Desjadins states “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task.”. The claims of the current application and the specification do not mention machine learning modeling or a machine learning technique. Therefore, the limitations of claim 1 does not recite an improvement to machine learning models. Also, the examiner notes that the applicant argues that claim 1 presents an improvement to logical structures. The applicant points to the modification and process limitations of claim 1 as being the logical structures that are improved. As stated above, the limitation of “at least one modification parameter for the computer simulation and/or the process is calculated from coordinates of the first distinguishing points and second distinguishing points at least partially mapped to each other” is calculating at least one modification parameter for the computer simulation. Therefore, under MPEP 2106.04(a)(2), this limitation covers a mathematical concept, which falls in the “Mathematical Concept” grouping of abstract ideas.
The limitation of “the at least one calculated modification parameter quantifying deviations between the at least one simulation progression and the at least one measurement progression” doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas. This limitation is also calculating the deviations between the at least one simulation progression and the at least one measurement progression. Therefore, under MPEP 2106.04(a)(2), this limitation covers a mathematical concept, which falls in the “Mathematical Concept” grouping of abstract ideas.
Further, the limitation of “the process is modified on the basis of the at least one modification parameter and carried out again” doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Also, the applicant points to the Recentive Analytics decision as support for why the current claims are eligible under 35 U.S.C. 101. The examiner notes that in the Recentive Analytics decision it was determined that patents that claim the application of generic machine learning to new data environments are insufficient for patent eligibility. As stated above, the claims of the current application and the specification do not mention machine learning modeling or a machine learning technique. Therefore, the limitations of claim 1 does not recite an improvement to machine learning models. Also, as stated above, the modification and process limitations of claim 1 do not recite an improvement to machine learning models.
Response: 35 U.S.C. § 103
5. The examiner’s response regarding the applicant’s arguments to the newly added limitations are shown below.
Claim Rejections - 35 USC § 101
6. 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 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Under the broadest reasonable interpretation, the claims cover performance of the limitation in the mind or by pencil and paper and as a mathematical concept.
Claims 1 and 18
Regarding step 1, claims 1 and 18 are directed towards a method and computer program product, which has the claims fall within the eligible statutory categories of processes, machines, manufactures and composition of matter under 35 U.S.C. 101.
Regarding step 2A, prong 1, claim 1 recites “within a framework of the computer simulation at least one simulation progression of a variable that is characteristic of the process, is calculated”. This limitation doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Also, this limitation is calculating at least one simulation progression of at least one variable that is characteristic of the process. Therefore, under MPEP 2106.04(a)(2), this limitation covers a mathematical concept, which falls in the “Mathematical Concept” grouping of abstract ideas.
Claim 1 recites “- first distinguishing points of the curve of the at least one simulation progression and second distinguishing points of the curve of the at least one measurement progression are determined”. This limitation doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 1 recites “- the first distinguishing points and the second distinguishing points are at least partially mapped to each other”. The limitation doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper, where there are only examples within the specification of how the first distinguishing points of the curve of the at least one simulation progression and the second distinguishing points of the curve of the at least one measurement progression is determined. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 1 recites “- at least one modification parameter for the computer simulation and/or the process is calculated from coordinates of the first distinguishing points and second distinguishing points at least partially mapped to each other”. This limitation doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Also, this limitation is calculating at least one modification parameter for the computer simulation. Therefore, under MPEP 2106.04(a)(2), this limitation covers a mathematical concept, which falls in the “Mathematical Concept” grouping of abstract ideas.
Claim 1 recites “the at least one calculated modification parameter quantifying deviations between the at least one simulation progression and the at least one measurement progression”. This limitation doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Also, the limitation is calculating the deviations between the at least one simulation progression and the at least one measurement progression. Therefore, under MPEP 2106.04(a)(2), this limitation covers a mathematical concept, which falls in the “Mathematical Concept” grouping of abstract ideas.
Claim 1 recites “the process is modified on the basis of the at least one modification parameter and carried out again”. This limitation doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Regarding step 2A, prong 2, the limitation of “measuring in the process really carried out at least one measurement progression of the characteristic variable with a sensor” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The claim limitation doesn’t state what the characteristic variable is. See MPEP 2106.05 (f) (1) Whether the claim 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. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element of the shaping machine that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine, see MPEP 2106.05(b) 1. It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750, 1755-56 (Fed. Cir. 2014). See also TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) (noting that Alappat’s rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim was superseded by the Supreme Court’s Bilski and Alice Corp. decisions).
Further, the claim recites the additional elements of a computer and a shaping machine. The computer and shaping machine are recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a computer and/or a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Regarding Step 2B, the limitation of “measuring in the process really carried out at least one measurement progression of the characteristic variable with a sensor” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The claim limitation doesn’t state what the characteristic variable is. See MPEP 2106.05 (f) (1) Whether the claim 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. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element of the shaping machine that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine, see MPEP 2106.05(b) 1. It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750, 1755-56 (Fed. Cir. 2014). See also TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) (noting that Alappat’s rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim was superseded by the Supreme Court’s Bilski and Alice Corp. decisions).
Claim 18
Regarding step 2A, prong 1, claim 18 recites “- to calculate at least one simulation progression of at least one variable that is characteristic of the process, within a framework of a computer simulation”. This limitation doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Also, this limitation is calculating at least one simulation progression of at least one variable that is characteristic of the process. Therefore, under MPEP 2106.04(a)(2), this limitation covers a mathematical concept, which falls in the “Mathematical Concept” grouping of abstract ideas.
Claim 18 recites “- to determine first distinguishing points of the curve of the at least one simulation progression and second distinguishing points of the curve of the at least one measurement progression”. This limitation doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper, where there are only examples within the specification of how the first distinguishing points of the curve of the at least one simulation progression and the second distinguishing points of the curve of the at least one measurement progression is determined. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 18 recites “- to at least partially map the first distinguishing points and the second distinguishing points to each other”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 18 recites “- to calculate at least one modification parameter for the computer simulation and/or the process from coordinates of the first distinguishing points and second distinguishing points at least partially mapped to each other”. This limitation isn’t actually calculating the at least one modification parameter. This limitation doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 18 recites “the at least one calculated modification parameter quantifying deviations between the at least one simulation progression and the at least one measurement progression”. This limitation doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Also, the limitation is calculating the deviations between the at least one simulation progression and the at least one measurement progression. Therefore, under MPEP 2106.04(a)(2), this limitation covers a mathematical concept, which falls in the “Mathematical Concept” grouping of abstract ideas.
Claim 18 recites “and - to modify the process on the basis of the at least one modification parameter and to carry it out again”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Regarding step 2A, prong 2, the limitation of “measuring in the real process at least one measurement progression of the characteristic variable with a sensor” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The claim limitation doesn’t state what the characteristic variable is. See MPEP 2106.05 (f) (1) Whether the claim 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. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the limitations of “or to receive one from a separate simulation” and “- or to output instructions which include that the process is to be carried out again and what modifications are to be made to the process on the basis of the at least one modification parameter.” amounts to insignificant extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process, see MPEP 2106.05(g).
Also, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element of the shaping machine and computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine, see MPEP 2106.05(b) 1. It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750, 1755-56 (Fed. Cir. 2014). See also TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) (noting that Alappat’s rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim was superseded by the Supreme Court’s Bilski and Alice Corp. decisions).
Further, the claim recites the additional elements of a shaping machine, computer and medium. The shaping machine, computer and medium are recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a computer and/or a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Regarding Step 2B, the limitation of “measuring in the real process at least one measurement progression of the characteristic variable with a sensor” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The claim limitation doesn’t state what the characteristic variable is. See MPEP 2106.05 (f) (1) Whether the claim 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. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the limitations of “or to receive one from a separate simulation” and “- or to output instructions which include that the process is to be carried out again and what modifications are to be made to the process on the basis of the at least one modification parameter.” are also shown to reflect the court decisions of Versata Dev. Group, Inc. v. SAP Am., Inc. iv. Storing and retrieving information in memory, shown in MPEP 2106.05(d) (II).
Also, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element of the shaping machine and computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine, see MPEP 2106.05(b) 1. It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750, 1755-56 (Fed. Cir. 2014). See also TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) (noting that Alappat’s rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim was superseded by the Supreme Court’s Bilski and Alice Corp. decisions).
Claim 2
Dependent claim 2 recites “wherein the first distinguishing points and/or the second distinguishing points are determined using a Ramer-Douglas-Peucker algorithm”. This limitation is not actually determining the first distinguishing points and/or the second distinguishing points. This limitation doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Also, the Ramer-Douglas-Peucker algorithm involves is an algorithm to smooth polylines (lines that are composed of linear line segments) by reducing the number of points. Therefore, under MPEP 2106.04(a)(2), this limitation covers a mathematical concept, which falls in the “Mathematical Concept” grouping of abstract ideas.
Dependent claim 2 recites “wherein at least one additional criterion is preferably used to further reduce a point set reduced using the Ramer-Douglas-Peucker algorithm in order to obtain the first distinguishing points and/or the second distinguishing points”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Also, the Ramer-Douglas-Peucker algorithm involves is an algorithm to smooth polylines (lines that are composed of linear line segments) by reducing the number of points. Therefore, under MPEP 2106.04(a)(2), this limitation covers a mathematical concept, which falls in the “Mathematical Concept” grouping of abstract ideas.
Claim 3
Dependent claim 3 recites “wherein the first distinguishing points and/or the second distinguishing points are accordingly determined, if connecting lines to adjacent points of the simulation progression or of the measurement progression form an angle which deviates by a predefined angular amount from 180°”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 4
Dependent claim 4 recites “wherein at least one of the following conditions and/or criteria is used when determining the first distinguishing points and/or the second distinguishing points - a maximum number of reduced points and/or distinguishing points, - a minimum distance between the points of the reduced point set, - a maximum standardized error of squares of a distance between original data points of the measurement progression and/or of the simulation progression on the one hand and the points of the reduced point set on the other, - exceeding and/or reaching a threshold value through the characteristic variable - excluding a predefined partial range of the process, wherein the partial range is given by absolute or relative limits”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 5
Dependent claim 5 recites “wherein the first distinguishing points and the second distinguishing points are at least partially mapped to each other, in that - for all of the possible different options for mapping the first distinguishing points to the second distinguishing points, the first distinguishing points and/or the second distinguishing points are scaled and/or shifted such that in each case two of the first distinguishing points and of the second distinguishing points substantially lie on top of each other”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Dependent claim 5 recites “- in each case at least one characteristic number for the quality of the respective mapping option is calculated on the basis of at least one of the following: scaling parameter, shifting parameter, coordinate differences between the — optionally scaled and/or shifted — first distinguishing points, and the — optionally scaled and/or shifted — second distinguishing points”. The limitation doesn’t state how the at least one characteristic number for the quality of the respective mapping option is calculated. The limitation doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Also, at least one characteristic number for the quality of the respective mapping option is calculated. Therefore, under MPEP 2106.04(a)(2), this limitation covers a mathematical concept, which falls in the “Mathematical Concept” grouping of abstract ideas.
Dependent claim 5 recites “- that mapping option is selected, at least one characteristic number of which indicates a best quality of the mapping”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 6
Dependent claim 6 recites “wherein the method is applied to results of the computer simulation modified on the basis of the at least one modification parameter carried out again and/or to measurements in the process carried out again”. This limitation amounts to mere instructions to apply an exception, where it recites an idea of a solution. The claim limitation doesn’t indicate what the results of the computer simulation are or what the modification parameter is. See MPEP 2106.05 (f) (1) Whether the claim 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. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Dependent claim 6 recites “wherein this is repeated until a simulation deviation between the at least one simulation progression and the at least one measurement progression is sufficiently small according to a predefined criterion”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 7
Dependent claim 7 recites “wherein the loop started by applying the method again is interrupted if: - values of the at least one modification parameter reach and/or fall below a first predefined limit value”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Dependent claim 7 recites “and/or - differences — in particular differences in amount — from areas under the at least one simulation progression and the at least one measurement progression reach and/or fall below a second predefined limit value”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Dependent claim 7 recites “and/or - the at least one simulation progression at least partially — preferably completely — proceeds within a predefined first tolerance range around the at least one measurement progression”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Dependent claim 7 recites “and/or - the at least one measurement progression at least partially — preferably completely — proceeds within a predefined second tolerance range around the at least one simulation progression.”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 8
Dependent claim 8 recites “wherein the at least one modification parameter relates to a magnitude of a time shift between the first distinguishing points and second distinguishing points mapped to each other”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Dependent claim 8 recites “wherein the time shift is in particular caused by an unknown volume of the molding material present in the shaping machine”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 9
Dependent claim 9 recites “wherein the simulation is modified by modifying an injection volume predefined for the simulation and/or an injection volume flow rate predefined for the simulation on the basis of the at least one modification parameter for the magnitude of the time shift”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 10
Dependent claim 10 recites “wherein the at least one modification parameter relates to a magnitude of a scaling of those coordinates of the first distinguishing points and second distinguishing points mapped to each other which correspond to the characteristic variable”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 11
Dependent claim 11 recites “wherein the simulation is modified by modifying a material parameter predefined for the simulation on the basis of the at least one modification parameter for the magnitude of the scaling”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 12
Dependent claim 12 recites “wherein the at least one modification parameter is calculated as a statistical parameter, in particular arithmetic mean, of the coordinates of the first distinguishing points and second distinguishing points at least partially mapped to each other”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 13
Dependent claim 13 recites “wherein a Cross-WLF model and/or a 2-domain Tait pvT model is used as material model for the simulation”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 14
Dependent claim 14 recites “wherein the at least one modification parameter is stored in a database and is used when simulating and/or setting a separate process”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 15
Dependent claim 15 recites “wherein several simulation progressions and/or several measurement progressions are taken into account when determining the first distinguishing points and/or the second distinguishing points”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 16
Dependent claim 16 recites “wherein the at least one simulation progression and/or the at least one measurement progression are parameterized by means of a time index or a position index of an actuator used in the process, in particular of a plasticizing screw”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 17
Dependent claim 17 recites “A shaping machine, which is set up to carry out the method according to claim 1”. The additional element of the shaping machine that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine, see MPEP 2106.05(b) 1. It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750, 1755-56 (Fed. Cir. 2014). See also TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) (noting that Alappat’s rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim was superseded by the Supreme Court’s Bilski and Alice Corp. decisions).
Also, the shaping machine is recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a computer and/or a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 19
Dependent claim 19 recites “wherein the predefined angular amount is 5° or more from 180°”. This limitation doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 20
Dependent claim 20 recites “wherein the predefined angular amount is 10° or more from 180°.”. This limitation doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 21
Dependent claim 21 recites “wherein the simulation is modified on the basis of the at least one modification parameter and carried out again.”. This limitation doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 22
Dependent claim 22 recites “- to modify the simulation on the basis of the at least one modification parameter and to carry it out again”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Dependent claim 22 recites “- or to output instructions which include that the simulation is to be carried out again and what modifications are to be made to the simulation on the basis of the at least one modification parameter.”. This limitation amounts to insignificant extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process, see MPEP 2106.05(g).
Claims 1-22 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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.
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.
Claim(s) 1, 5-6, 8-12, 14-18 and 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mensler et al. (U.S. PGPub 2018/0117817) (from IDS.3P dated 8/17/22) in view of Asaoka et al. (U.S. PGPub 2018/0281256).
With respect to claim 1, Mensler et al. discloses “A method for aligning a simulation of a process to be carried out with a shaping machine using simulation software executed by a computer with the process really carried out” as [Mensler et al. (paragraph [0063] “Particularly in the case of complex form part, the virtual part-specific pressure curve is determined by means of a numerical simulation. To this end, conventional FEM simulation software can be used, for example Autodesk Moldflow, Cadmold or Moldex3D.”, Mensler et al. paragraph [0064] “The assignment of virtual events of the part-specific event pattern to measurement events of the measurement event pattern may, in a preferred embodiment, be carried out with the aid of a suitable pattern recognition method, particularly an image recognition method”, Mensler et al. paragraph [0128] “It shall be noted that all steps of the method shown in FIG. 1 can be carried out by the control unit 18 itself but need not be. In particular, the simulation or computation of the part-specific event pattern Ms according to stages 1.I to 1.IV can be performed on a powerful external computer facility, while all other stages of the method are carried out for example by the control unit 18.”)];
“wherein - within a framework of the computer simulation at least one simulation progression of a variable that is characteristic of the process, is calculated” as [Mensler et al. (paragraph [0043] “With this particularly preferred method, a (theoretical) virtual part-specific pressure curve is first determined on the basis of the geometry data of the injection mould and/or the form part that is to be manufactured in the injection mould. As will be explained below, this can be done using an analytical computation or numerically, for example by means of a simulation.”, Mensler et al. paragraph [0128] “It shall be noted that all steps of the method shown in FIG. 1 can be carried out by the control unit 18 itself but need not be. In particular, the simulation or computation of the part-specific event pattern Ms according to stages 1.I to 1.IV can be performed on a powerful external computer facility, while all other stages of the method are carried out for example by the control unit 18.”)];
“- measuring in the process really carried out at least one measurement progression of the characteristic variable with a sensor” as [Mensler et al. (paragraph [0040] “Preferably, these measurement values comprise pressure values and/or temperature values. To this end, pressure sensors and/or temperature sensors may be arranged directly within the cavity of the injection mould.”, Mensler et al. paragraph [0047] “Using the measurement values obtained in the real injection moulding procedure carried out using the respective injection mould, a measurement pressure curve is then determined. This measurement pressure curve can as mentioned above be obtained for conventional injection mould arrangements directly in the machine, for example by means of a pressure sensor in the screw chamber or by means of an indirect pressure measurement of the hydraulic pressure at the injection cylinder, by measuring parameters of the injection motor etc., by strain gauges or other elements.”)];
“- first distinguishing points of the curve of the at least one simulation progression and second distinguishing points of the curve of the at least one measurement progression are determined” as [Mensler et al. (paragraph [0044] “On the basis of this virtual part-specific pressure curve, a part-specific event pattern is determined. This part-specific event pattern comprises a plurality of singular virtual events that are linked to characteristic event locations of the form part geometry.”, Mensler et al. paragraph [0048] “Similar to determining the part-specific event pattern on the basis of the virtual part-specific pressure curve, a measurement event pattern can now be determined on the basis of this real measurement pressure curve after carrying out the injection moulding procedure.”)];
“- the first distinguishing points and the second distinguishing points are at least partially mapped to each other” as [Mensler et al. (paragraph [0049] “Virtual events of the part-specific event pattern are then assigned to measurement events of the measurement event pattern. As will be explained below, this can be done by comparing the event patterns or by identifying the part-specific event pattern in the measurement event pattern, or similar.”)];
While Mensler et al. teaches having desired process parameter values based on the position data of the virtual events, Mensler et al. does not explicitly disclose “- at least one modification parameter for the computer simulation and/or the process is calculated from coordinates of the first distinguishing points and second distinguishing points at least partially mapped to each other, the at least one calculated modification parameter quantifying deviations between the at least one simulation progression and the at least one measurement progression; and - the process is modified on the basis of the at least one modification parameter and carried out again”
Asaoka et al. discloses “- at least one modification parameter for the computer simulation and/or the process is calculated from coordinates of the first distinguishing points and second distinguishing points at least partially mapped to each other” as [Asaoka et al. (paragraph [0060] “In the machine learning apparatus 20 of the state determination apparatus 10 shown in FIG. 2, the learning section 26 includes an error calculation section 32 that calculates an error E between a correlation model M that derives the state related to the abnormality of the injection molding machine from the state variable S and a correlation feature recognized from supervised data T prepared in advance, and a model update section 34 that updates the correlation model M so as to reduce the error E.”, Fig. 2, The examiner considers the error E to be the modification parameter, since the error E is calculated between a correlation model that derives the state related to the abnormality of the injection molding machine from the state variable S and a correlation feature recognized from supervised data T. The error E also quantifies the deviation between the correlation model and the correlation feature)];
“the at least one calculated modification parameter quantifying deviations between the at least one simulation progression and the at least one measurement progression” as [Asaoka et al. (paragraph [0060] “In the machine learning apparatus 20 of the state determination apparatus 10 shown in FIG. 2, the learning section 26 includes an error calculation section 32 that calculates an error E between a correlation model M that derives the state related to the abnormality of the injection molding machine from the state variable S and a correlation feature recognized from supervised data T prepared in advance, and a model update section 34 that updates the correlation model M so as to reduce the error E.”, Asaoka et al. paragraph [0061] “The error calculation 32 recognizes the correlation feature that suggests the correlation between the state related to the abnormality of the injection molding machine and the operation state of the injection molding machine from a large amount of the supervised data T given to the learning section 26, and determines the error E between the correlation feature and the correlation model M corresponding to the state variable S in the current state.”, Fig. 2)];
“and - the process is modified on the basis of the at least one modification parameter and carried out again” as [Asaoka et al. (paragraph [0060] “The learning section 26 learns the state related to the abnormality of the injection molding machine correlated with the operation state of the injection molding machine by causing the model update section 34 to repeat the update of the correlation model M.”, Asaoka et al. paragraph [0061] “The model update section 34 updates the correlation model M so as to reduce the error E in accordance with, e.g., a predetermined update rule.)];
Mensler et al. and Asaoka et al. are analogous art because they are from the same field
endeavor of analyzing the injection molding process.
Before the effective filing date of the invention, it would have been obvious to a person
of ordinary skill in the art to modify the teachings of Mensler et al. of having desired process parameter values based on the position data of the virtual events by incorporating - at least one modification parameter for the computer simulation and/or the process is calculated from coordinates of the first distinguishing points and second distinguishing points at least partially mapped to each other, the at least one calculated modification parameter quantifying deviations between the at least one simulation progression and the at least one measurement progression; and - the process is modified on the basis of the at least one modification parameter and carried out again as taught by Asaoka et al. for the purpose of determining a state related to an abnormality of an injection molding machine.
Mensler et al. in view of Asaoka et al. teaches - at least one modification parameter for the computer simulation and/or the process is calculated from coordinates of the first distinguishing points and second distinguishing points at least partially mapped to each other, the at least one calculated modification parameter quantifying deviations between the at least one simulation progression and the at least one measurement progression; and - the process is modified on the basis of the at least one modification parameter and carried out again.
The motivation for doing so would have been because Asaoka et al. teaches that by determining a state related to an abnormality of an injection molding machine based on an operation state of the injection molding machine, the ability to handle all pieces of sampling data acquired under different conditions can be accomplished. This allows a way to analyze injection molding data more efficiency (Asaoka et al. paragraph [0008] – [0010]).
With respect to claim 5, the combination of Mensler et al. and Asaoka et al. discloses the method of claim 1 above, and Mensler further discloses “wherein the first distinguishing points and the second distinguishing points are at least partially mapped to each other, in that - for all of the possible different options for mapping the first distinguishing points to the second distinguishing points, the first distinguishing points and/or the second distinguishing points are scaled and/or shifted such that in each case two of the first distinguishing points and of the second distinguishing points substantially lie on top of each other” as [Mensler et al. (paragraph [0135] “The events occurred at different absolute times, but the part-specific event pattern MS should be identifiable in the measurement event pattern Mm when the part-specific event pattern MS is scaled accordingly over time, i.e. distorted and offset.”, Mensler et al. [0137] “This scaling factor fk indicates by how much the time axis of the part-specific event pattern MS is to be stretched or scaled relative to that of the measurement event pattern Mm.”)];
“- in each case at least one characteristic number for the quality of the respective mapping option is calculated on the basis of at least one of the following: scaling parameter, shifting parameter, coordinate differences between the — optionally scaled and/or shifted — first distinguishing points, and the — optionally scaled and/or shifted — second distinguishing points” as [Mensler et al. (paragraph [0140] “To this end, two neighbouring virtual or simulated events ES,i, ES,i+1 of the part-specific event pattern are multiplied with the current scaling factor fk in stage 12.III. In stage 12.IV, the temporal difference between the scaled virtual events Ek,i, Ek,i+1 is calculated, and also the difference between the measurement events Em,i+j, E.sub.m,i+j+1. Here, i is the running index of the virtual event, and j is the index value by which the part-specific event pattern was already offset relative to the measurement event pattern in a higher-level loop (see stage 12.XIII). In the first pass with j=0, the indices of the simulated events and measurement events are therefore identical, i.e the part-specific event pattern is offset so that the first virtual event coincides with the first measurement event. With j=1, the part-specific event pattern can be offset so that the first virtual event coincides with the second measurement event, etc.”)];
“- that mapping option is selected, at least one characteristic number of which indicates a best quality of the mapping.” as [Mensler et al. (paragraph [0142] “It is then checked in stage 12.IX whether the maximum of the scaling factor fk has been reached. If not (“no” branch), the scaling factor fk is incremented by the amount Δf, and the entire loop makes another pass. Otherwise, (“y” branch) the smallest deviation value Smin,k of the deviation values determined in the previous iterations is identified in stage 12.XI.”, Mensler et al. (paragraph [0143] “Since the number of simulated virtual events is often not the same as the number of measurement events, the event pattern is offset by one event using index j in a higher-level loop as explained above, and the scaling or stretching is carried out again in order to see whether this results in a better fit. In stage 12.XII it is checked whether all possible offsets j have been carried out, whereby m is the number of measurement events. If not (“no” branch), the value of j is raised by 1 in stage 12.XIII and the scaling factor fk is initialised to its start value fstart and i is also reset to 1.”)];
With respect to claim 6, the combination of Mensler et al. and Asaoka et al. discloses the method of claim 1 above, and Asaoka et al. further discloses “wherein the method is applied to results of the computer simulation modified on the basis of the at least one modification parameter carried out again and/or to measurements in the process carried out again” as [Asaoka et al. (paragraph [0060] “In the machine learning apparatus 20 of the state determination apparatus 10 shown in FIG. 2, the learning section 26 includes an error calculation section 32 that calculates an error E between a correlation model M that derives the state related to the abnormality of the injection molding machine from the state variable S and a correlation feature recognized from supervised data T prepared in advance, and a model update section 34 that updates the correlation model M so as to reduce the error E. The learning section 26 learns the state related to the abnormality of the injection molding machine correlated with the operation state of the injection molding machine by causing the model update section 34 to repeat the update of the correlation model M.”, The examiner considers the error E to be the modification parameter, since the error E is calculated between a correlation model that derives the state related to the abnormality of the injection molding machine from the state variable S and a correlation feature recognized from supervised data T. The error E also quantifies the deviation between the correlation model and the correlation feature)];
“wherein this is repeated until a simulation deviation between the at least one simulation progression and the at least one measurement progression is sufficiently small according to a predefined criterion” as [Asaoka et al. (paragraph [0060] “In the machine learning apparatus 20 of the state determination apparatus 10 shown in FIG. 2, the learning section 26 includes an error calculation section 32 that calculates an error E between a correlation model M that derives the state related to the abnormality of the injection molding machine from the state variable S and a correlation feature recognized from supervised data T prepared in advance, and a model update section 34 that updates the correlation model M so as to reduce the error E. The learning section 26 learns the state related to the abnormality of the injection molding machine correlated with the operation state of the injection molding machine by causing the model update section 34 to repeat the update of the correlation model M.”, Asaoka et al. paragraph [0061] “The model update section 34 updates the correlation model M so as to reduce the error E in accordance with, e.g., a predetermined update rule.)];
With respect to claim 8, the combination of Mensler et al. and Asaoka et al. discloses the method of claim 1 above, and Mensler further discloses “wherein the at least one modification parameter relates to a magnitude of a time shift between the first distinguishing points and second distinguishing points mapped to each other, wherein the time shift is in particular caused by an unknown volume of the molding material present in the shaping machine.” as [Mensler et al. (paragraph [0065] “In a preferred method, it is attempted to at least partially (virtually) overlap the part-specific event pattern and the measurement event pattern in an iterative process for the assigning of virtual events of the part-specific event pattern to measurement events of the measurement event pattern. To this end, between the different iteration steps, the part-specific event pattern and/or the measurement event pattern may be temporally scaled and/or offset relative to each other according to defined rules in order to achieve a fit.”, The examiner considers the modification parameter to be the time difference, since the part-specific event pattern and/or the measurement event pattern are shifted in time to match them)];
With respect to claim 9, the combination of Mensler et al. and Asaoka et al. discloses the method of claim 8 above, and Mensler further discloses “wherein the simulation is modified by modifying an injection volume predefined for the simulation and/or an injection volume flow rate predefined for the simulation on the basis of the at least one modification parameter for the magnitude of the time shift.” as [Mensler et al. (paragraph [0058] “However, if it is considered expedient for reasons of the injection moulding procedure to adjust the volumetric flow, for example to vary the feed rate of the actuator (e.g. the screw), i.e. in the case of a variable volumetric flow during the injection moulding procedure, the determined measurement pressure curve is first converted to a fictitious, time-corrected measurement pressure curve…… It shall be noted that it is also possible in principle to assume a variable volumetric flow during the simulation, i.e. in the case of a well-defined variable volumetric flow during the measurement; the volumetric flow could be varied during a simulation. In this case also, a comparison of the event patterns is possible. For the sake of completeness, it shall be noted that also a virtual part-specific pressure curve, determined with an assumed variable volumetric flow, can be corrected according to the measurement pressure curve”)];
With respect to claim 10, the combination of Mensler et al. and Asaoka et al. discloses the method of claim 1 above, and Mensler further discloses “wherein the at least one modification parameter relates to a magnitude of a scaling of those coordinates of the first distinguishing points and second distinguishing points mapped to each other which correspond to the characteristic variable.” as [Mensler et al. (paragraph [0135] “The events occurred at different absolute times, but the part-specific event pattern MS should be identifiable in the measurement event pattern Mm when the part-specific event pattern MS is scaled accordingly over time, i.e. distorted and offset.”, Mensler et al. [0137] “This scaling factor fk indicates by how much the time axis of the part-specific event pattern MS is to be stretched or scaled relative to that of the measurement event pattern Mm.”, Mensler et al. paragraph [0159] “In FIG. 20, the measurement event pattern Mm (lower row) and the virtual part-specific event pattern Ms (middle row) are shown again over time t. A part-specific event pattern Ms′ is shown in the uppermost row, generated from the original virtual part-specific event pattern Ms by simply shifting it so that their first events overlap and by temporally scaling it with a scaling factor fk=0.90 (with fstart=0.1 and scaling increment Δf=0.01) to give the least deviation. This demonstrates how the virtual events of the part-specific event pattern Ms can be assigned to the measurement events of the measurement event pattern Mm within the predefined error bounds”)];
With respect to claim 11, the combination of Mensler et al. and Asaoka et al. discloses the method of claim 10 above, and Mensler further discloses “wherein the simulation is modified by modifying a material parameter predefined for the simulation on the basis of the at least one modification parameter for the magnitude of the scaling.” as [Mensler et al. (paragraph [0135] “The events occurred at different absolute times, but the part-specific event pattern MS should be identifiable in the measurement event pattern Mm when the part-specific event pattern MS is scaled accordingly over time, i.e. distorted and offset.”, Mensler et al. [0137] “This scaling factor fk indicates by how much the time axis of the part-specific event pattern MS is to be stretched or scaled relative to that of the measurement event pattern Mm.”, Mensler et al. paragraph [0159] “In FIG. 20, the measurement event pattern Mm (lower row) and the virtual part-specific event pattern Ms (middle row) are shown again over time t. A part-specific event pattern Ms′ is shown in the uppermost row, generated from the original virtual part-specific event pattern Ms by simply shifting it so that their first events overlap and by temporally scaling it with a scaling factor fk=0.90 (with fstart=0.1 and scaling increment Δf=0.01) to give the least deviation. This demonstrates how the virtual events of the part-specific event pattern Ms can be assigned to the measurement events of the measurement event pattern Mm within the predefined error bounds”)];
With respect to claim 12, the combination of Mensler et al. and Asaoka et al. discloses the method of claim 1 above, and Asaoka et al. further discloses “wherein the at least one modification parameter is calculated as a statistical parameter, in particular arithmetic mean, of the coordinates of the first distinguishing points and second distinguishing points at least partially mapped to each other.” as [Asaoka et al. (paragraph [0060] “In the machine learning apparatus 20 of the state determination apparatus 10 shown in FIG. 2, the learning section 26 includes an error calculation section 32 that calculates an error E between a correlation model M that derives the state related to the abnormality of the injection molding machine from the state variable S and a correlation feature recognized from supervised data T prepared in advance, and a model update section 34 that updates the correlation model M so as to reduce the error E. The learning section 26 learns the state related to the abnormality of the injection molding machine correlated with the operation state of the injection molding machine by causing the model update section 34 to repeat the update of the correlation model M.”, Fig. 2)];
With respect to claim 14, the combination of Mensler et al. and Asaoka et al. discloses the method of claim 1 above, and Asaoka et al. further discloses “wherein the at least one modification parameter is stored in a database and is used when simulating and/or setting a separate process.” as [Asaoka et al. (paragraph [0086] “As another modification of the state determination apparatus 40, the state determination apparatus 40 may be operated after several patterns of parameters of the correlation model M (e.g., in the case where the correlation model M is the neural network, such parameter may be the weight value between neurons or the like) obtained as the result of the machine learning under a plurality of conditions by the learning section 26 are stored, and the pattern of parameters is set in the correlation model M in accordance with a situation in which the state determination apparatus 40 is used. At this point, the pattern of parameters of the correlation model M can be stored in, e.g., the parameter setting section 44.”)];
With respect to claim 15, the combination of Mensler et al. and Asaoka et al. discloses the method of claim 1 above, and Mensler further discloses “wherein several simulation progressions and/or several measurement progressions are taken into account when determining the first distinguishing points and/or the second distinguishing points.” as [Mensler et al. (paragraph [0132] “In the differentiated pressure curve dp.sub.s in the lower part of the diagram, the slope analysis is already indicated by the total of four significant events E.sub.S1, E.sub.S2, E.sub.S3, E.sub.S4 identified as occurring at four different times t.sub.S1, t.sub.S2, t.sub.S3, t.sub.S4. These times t.sub.S1, t.sub.S2, t.sub.S3, t.sub.S4 relate to the virtual progression of the injection moulding procedure as explained above.”, Fig. 8)];
With respect to claim 16, the combination of Mensler et al. and Asaoka et al. discloses the method of claim 1 above, and Mensler further discloses “wherein the at least one simulation progression and/or the at least one measurement progression are parameterized by means of a time index or a position index of an actuator used in the process, in particular of a plasticizing screw.” as [Mensler et al. (paragraph [0112] “In the usual manner, this shows a cylinder 13 in which a screw 14 is arranged as actuator.”, Mensler et al. paragraph [0120] “The resulting information, in particular the process parameter value as a function of injection time and/or actuator position, can be forwarded from the analysis unit 26 to a display control arrangement 27 that controls the display arrangement 31 of the user interface 30 accordingly in order to show the process parameter values inside the injection mould,”)];
With respect to claim 17, the combination of Mensler et al. and Asaoka et al. discloses the method of claim 1 above, and Mensler further discloses “A shaping machine, which is set up to carry out the method according to claim 1.” as [Mensler et al. (paragraph [0024] “With the aid of such a process parameter value determining apparatus and/or control arrangement, it is possible to construct an injection moulding arrangement according to the invention, which comprises the usual components in addition to the process parameter values determining arrangement, specifically an injection nozzle, an actuator such as a screw or similar to force injection mould material from the nozzle into an injection mould connected to the injection moulding arrangement, as well as a control unit for controlling the actuator. The process parameter values determining arrangement can for example be partially or entirely incorporated in the control unit.”)];
With respect to claim 18, Mensler et al. discloses “A non-transitory computer-readable recording medium storing a program for aligning a computer simulation of a process to be carried out with a shaping machine using simulation software executed by a computer with the process really carried out” as [Mensler et al. (paragraph [0023] “All components can all be realised in the form of software modules that act together on a dedicated processor unit, for example a central processing unit, or an a processor unit of an already existing controller of the injection molding arrangement.”, Mensler et al. paragraph [0064] “The assignment of virtual events of the part-specific event pattern to measurement events of the measurement event pattern may, in a preferred embodiment, be carried out with the aid of a suitable pattern recognition method, particularly an image recognition method”, Mensler et al. paragraph [0128] “It shall be noted that all steps of the method shown in FIG. 1 can be carried out by the control unit 18 itself but need not be. In particular, the simulation or computation of the part-specific event pattern Ms according to stages 1.I to 1.IV can be performed on a powerful external computer facility, while all other stages of the method are carried out for example by the control unit 18.”)];
“- or to output instructions which include that the process is to be carried out again and what modifications are to be made to the process on the basis of the at least one modification parameter” as [Mensler et al. (paragraph [0065] “In a preferred method, it is attempted to at least partially (virtually) overlap the part-specific event pattern and the measurement event pattern in an iterative process for the assigning of virtual events of the part-specific event pattern to measurement events of the measurement event pattern. To this end, between the different iteration steps, the part-specific event pattern and/or the measurement event pattern may be temporally scaled and/or offset relative to each other according to defined rules in order to achieve a fit.”, Mensler et al. paragraph [0159] “In FIG. 20, the measurement event pattern Mm (lower row) and the virtual part-specific event pattern Ms (middle row) are shown again over time t. A part-specific event pattern Ms′ is shown in the uppermost row, generated from the original virtual part-specific event pattern Ms by simply shifting it so that their first events overlap and by temporally scaling it with a scaling factor fk=0.90 (with fstart=0.1 and scaling increment Δf=0.01) to give the least deviation. This demonstrates how the virtual events of the part-specific event pattern Ms can be assigned to the measurement events of the measurement event pattern Mm within the predefined error bounds”, The examiner considers the time difference to be the modification parameter, since the part-specific event pattern and/or the measurement event pattern are shifted in time to match.)];
The other limitations of the claim recite the same substantive limitations as claim 1 above and are rejected using the same teachings.
With respect to claim 21, the combination of Mensler et al. and Asaoka et al. discloses the method of claim 1 above, and Asaoka et al. further discloses “wherein the simulation is modified on the basis of the at least one modification parameter and carried out again.” as [Asaoka et al. (paragraph [0060] “The learning section 26 learns the state related to the abnormality of the injection molding machine correlated with the operation state of the injection molding machine by causing the model update section 34 to repeat the update of the correlation model M.”, Asaoka et al. paragraph [0061] “The model update section 34 updates the correlation model M so as to reduce the error E in accordance with, e.g., a predetermined update rule.)];
With respect to claim 22, the claim recites the same substantive limitations as claim 18 above, and are rejected using the same teachings.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mensler et
al. in view of Asaoka et al. in further view of online reference Image analysis and the finite element method in the characterization of the influence of porosity parameters on the mechanical properties of porous EVA/PMMA polymer blends, written by Tomic et al.
With respect to claim 2, the combination of Mensler et al. and Asaoka et al. discloses the method of claim 1 above.
While Mensler et al. and Asaoka et al. teach determining first distinguishing points of the curve of the at least one simulation progression and second distinguishing points of the curve of the at least one measurement progression, Mensler et al. and Asaoka et al. do not explicitly disclose “wherein the first distinguishing points and/or the second distinguishing points are determined using a Ramer-Douglas-Peucker algorithm, wherein at least one additional criterion is preferably used to further reduce a point set reduced using the Ramer-Douglas-Peucker algorithm in order to obtain the first distinguishing points and/or the second distinguishing points”
Tomic et al. discloses “wherein the first distinguishing points and/or the second distinguishing points are determined using a Ramer-Douglas-Peucker algorithm, wherein at least one additional criterion is preferably used to further reduce a point set reduced using the Ramer-Douglas-Peucker algorithm in order to obtain the first distinguishing points and/or the second distinguishing points.” as [Tomic et al. (Abstract “The coordinates of pore contours were processed by the Ramer–Douglas–Peucker algorithm (RDP) to establish the models by the finite element method (FEM). This process is iterative and enables a parametric study of the problem so that the type of pore geometry responsible for the observed mechanical behavior could be revealed”, Tomic et al. Pg. 3, sec. 2.3.6 Python scripting for Abaqus CAE, 1st paragraph, “Scripts were obtained by processing the coordinates of the pore contours by the Ramer–Douglas–Peucker algorithm (RDP) of a binary ‟mask” image, as well as for creation of the geometry for the ‟mirrored” part of a physical polymer blend.”)];
Mensler et al., Asaoka et al. and Tomic et al. are analogous art because they are from the
same field endeavor of analyzing the volume of a material.
Before the effective filing date of the invention, it would have been obvious to a person
of ordinary skill in the art to modify the teachings of Mensler et al. and Asaoka et al. of determining first distinguishing points of the curve of the at least one simulation progression and second distinguishing points of the curve of the at least one measurement progression by incorporating wherein the first distinguishing points and/or the second distinguishing points are determined using a Ramer-Douglas-Peucker algorithm, wherein at least one additional criterion is preferably used to further reduce a point set reduced using the Ramer-Douglas-Peucker algorithm in order to obtain the first distinguishing points and/or the second distinguishing points as taught by Tomic et al. for the purpose of evaluating parameters of porous polymer blends EVA/PMMA.
Mensler et al. in view of Asaoka et al. in further view of Tomic et al. teaches wherein the first distinguishing points and/or the second distinguishing points are determined using a Ramer-Douglas-Peucker algorithm, wherein at least one additional criterion is preferably used to further reduce a point set reduced using the Ramer-Douglas-Peucker algorithm in order to obtain the first distinguishing points and/or the second distinguishing points.
The motivation for doing so would have been because Tomic et al. teaches by evaluating parameters of porous polymer blends EVA/PMMA, the ability to correlate them with experimental mechanical behavior to design a structure of a material and its processing, can be accomplished (Tomic et al. (Pg. 2, left col., last paragraph, “The aim of this study was to evaluate, etc.”)).
Claim(s) 3 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Mensler et al. in view of Asaoka et al. in further view of Okawa (JP 2002192589).
With respect to claim 3, the combination of Mensler et al. and Asaoka et al. discloses the method of claim 1 above.
While Mensler et al. and Asaoka et al. teaches determining first distinguishing points of the curve of the at least one simulation progression and second distinguishing points of the curve of the at least one measurement progression, Mensler et al. and Asaoka et al. do not explicitly disclose “wherein the first distinguishing points and/or the second distinguishing points are accordingly determined, if connecting lines to adjacent points of the simulation progression or of the measurement progression form an angle which deviates by a predefined angular amount from 180°.”
Okawa discloses “wherein the first distinguishing points and/or the second distinguishing points are accordingly determined, if connecting lines to adjacent points of the simulation progression or of the measurement progression form an angle which deviates by a predefined angular amount from 180°.” as [Okawa (paragraph [0009] “the coordinates at which the flow velocity vector association angle of the resin flow tips is within a predetermined value range, and the association angle of the resin flow tips are within a predetermined value range.”, Okawa paragraph [0034] “The flow velocity vector association angle at the resin flow front is an angle α formed by the flow velocity vectors V1 and V2 at the resin flow fronts R1 and R2 to be merged, as shown in FIG. On the other hand, the association angle of the resin flow fronts is the angle β formed by the resin flow fronts R1 and R2 to be joined, as shown in FIG 4”, Fig. 4)];
Mensler et al., Asaoka et al. and Okawa are analogous art because they are from the same
field endeavor of analyzing parameters of injection molding.
Before the effective filing date of the invention, it would have been obvious to a person
of ordinary skill in the art to modify the teachings of Mensler et al. and Asaoka et al. of determining first distinguishing points of the curve of the at least one simulation progression and second distinguishing points of the curve of the at least one measurement progression by incorporating wherein the first distinguishing points and/or the second distinguishing points are accordingly determined, if connecting lines to adjacent points of the simulation progression or of the measurement progression form an angle which deviates by a predefined angular amount from 180° as taught by Okawa for the purpose of deciding on a parameter of injection molding.
Mensler et al. in view of Asaoka et al. in further view of Okawa teaches wherein the first distinguishing points and/or the second distinguishing points are accordingly determined, if connecting lines to adjacent points of the simulation progression or of the measurement progression form an angle which deviates by a predefined angular amount from 180°.
The motivation for doing so would have been because Okawa teaches by deciding on a parameter of injection molding, the ability to enable an easy setting of a weld position of the molding at a desired position can be accomplished, which allows the user to identify a position of the injection molding (Okawa (Abstract, paragraph [0003] - [0004])).
With respect to claim 19, the combination of Mensler et al., Asaoka et al. and Okawa discloses the method of claim 3 above, and Okawa further discloses “wherein the predefined angular amount is 5° or more from 180°” as [Okawa (paragraph [0009] “the coordinates at which the flow velocity vector association angle of the resin flow tips is within a predetermined value range, and the association angle of the resin flow tips are within a predetermined value range.”, Okawa paragraph [0034] “The flow velocity vector association angle at the resin flow front is an angle α formed by the flow velocity vectors V1 and V2 at the resin flow fronts R1 and R2 to be merged, as shown in FIG. On the other hand, the association angle of the resin flow fronts is the angle β formed by the resin flow fronts R1 and R2 to be joined, as shown in FIG 4”, Fig. 4, Fig. 4 of the Okawa reference displays the different angles of the flow velocity vector)];
With respect to claim 20, the combination of Mensler et al., Asaoka et al. and Okawa discloses the method of claim 3 above, and Okawa further discloses “wherein the predefined angular amount is 10° or more from 180°.” as [Okawa (paragraph [0009] “the coordinates at which the flow velocity vector association angle of the resin flow tips is within a predetermined value range, and the association angle of the resin flow tips are within a predetermined value range.”, Okawa paragraph [0034] “The flow velocity vector association angle at the resin flow front is an angle α formed by the flow velocity vectors V1 and V2 at the resin flow fronts R1 and R2 to be merged, as shown in FIG. On the other hand, the association angle of the resin flow fronts is the angle β formed by the resin flow fronts R1 and R2 to be joined, as shown in FIG 4”, Fig. 4, Fig. 4 of the Okawa reference displays the different angles of the flow velocity vector)];
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mensler et
al. in view of Asaoka et al. in further view of Tomic et al. in further view of Stoehr et al. (U.S. PGPub 2018/0178430) (from IDS dated 4/16/21).
With respect to claim 4, the combination of Mensler et al., Asaoka et al. and Tomic et al. discloses the method of claim 2 above.
While the combination of Mensler et al., Asaoka et al. and Tomic et al. discloses determining first distinguishing points of the curve of the at least one simulation progression and second distinguishing points of the curve of the at least one measurement progression, Mensler et al., Asaoka et al. and Tomic et al. do not explicitly disclose “wherein at least one of the following conditions and/or criteria is used when determining the first distinguishing points and/or the second distinguishing points - a maximum number of reduced points and/or distinguishing points, - a minimum distance between the points of the reduced point set, - a maximum standardized error of squares of a distance between original data points of the measurement progression and/or of the simulation progression on the one hand and the points of the reduced point set on the other, - exceeding and/or reaching a threshold value through the characteristic variable, - excluding a predefined partial range of the process, wherein the partial range is given by absolute or relative limits.”
Stoehr et al. discloses “wherein at least one of the following conditions and/or criteria is used when determining the first distinguishing points and/or the second distinguishing points
- a maximum number of reduced points and/or distinguishing points,
- a minimum distance between the points of the reduced point set,
- a maximum standardized error of squares of a distance between original data points of the measurement progression and/or of the simulation progression on the one hand and the points of the reduced point set on the other,
- exceeding and/or reaching a threshold value through the characteristic variable” as [Stoehr et al. (paragraph [0074] – [0075] “It can be provided that the at least one quality parameter concerns at least one of the following: [0075] process properties, in particular individual process times, overall cycle time, robustness, tool loading, energy consumption, necessary closing force, melt temperature, maximum injection pressure”, Stoehr et al. (paragraph [0109] “Optionally it is possible to change the value of a second parameter in dependence on the input of a value for a first setting parameter so that in accordance with the performance maps the value of a quality parameter is maximized.”)];
- excluding a predefined partial range of the process, wherein the partial range is given by absolute or relative limits.
Mensler et al., Asaoka et al., Tomic et al. and Stoehr et al. are analogous art because they
are from the same field endeavor of analyzing the volume of a material.
Before the effective filing date of the invention, it would have been obvious to a person
of ordinary skill in the art to modify the teachings of Mensler et al., Asaoka et al. and Tomic et al. of determining first distinguishing points of the curve of the at least one simulation progression and second distinguishing points of the curve of the at least one measurement progression by incorporating wherein at least one of the following conditions and/or criteria is used when determining the first distinguishing points and/or the second distinguishing points - a maximum number of reduced points and/or distinguishing points, - a minimum distance between the points of the reduced point set, - a maximum standardized error of squares of a distance between original data points of the measurement progression and/or of the simulation progression on the one hand and the points of the reduced point set on the other, - exceeding and/or reaching a threshold value through the characteristic variable, - excluding a predefined partial range of the process, wherein the partial range is given by absolute or relative limits as taught by Stoehr et al. for the purpose of setting a shaping machine.
Mensler et al. in view of Asaoka et al. in view of Tomic et al. in further view of Stoehr et al. teaches wherein at least one of the following conditions and/or criteria is used when determining the first distinguishing points and/or the second distinguishing points - a maximum number of reduced points and/or distinguishing points, - a minimum distance between the points of the reduced point set, - a maximum standardized error of squares of a distance between original data points of the measurement progression and/or of the simulation progression on the one hand and the points of the reduced point set on the other, - exceeding and/or reaching a threshold value through the characteristic variable, - excluding a predefined partial range of the process, wherein the partial range is given by absolute or relative limits.
The motivation for doing so would have been because Stoehr et al. teaches by carrying out a shaping process that involves establishing control of controllable components of a shaping machine, the ability to set a shaping machine can be accomplished, where the injection molding can be established (Stoehr et al. (paragraph [0001] – [0008])).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mensler et
al. view of Asaoka et al. in further view of Stoehr et al. (U.S. PGPub 2018/0178430) (from IDS dated 4/16/21).
With respect to claim 7, the combination of Mensler et al. and Asaoka et al. discloses the method of claim 6 above.
While Mensler et al. and Asaoka et al. teaches determining first distinguishing points of the curve of the at least one simulation progression and second distinguishing points of the curve of the at least one measurement progression, Mensler et al. and Asaoka et al. do not explicitly disclose “wherein the loop started by applying the method again is interrupted if: - values of the at least one modification parameter reach and/or fall below a first predefined limit value, and/or - differences — in particular differences in amount — from areas under the at least one simulation progression and the at least one measurement progression reach and/or fall below a second predefined limit value, and/or - the at least one simulation progression at least partially — preferably completely — proceeds within a predefined first tolerance range around the at least one measurement progression, and/or - the at least one measurement progression at least partially — preferably completely — proceeds within a predefined second tolerance range around the at least one simulation progression.”
Stoehr et al. discloses “wherein the loop started by applying the method again is interrupted if:
- values of the at least one modification parameter reach and/or fall below a first predefined limit value, and/or
- differences — in particular differences in amount — from areas under the at least one simulation progression and the at least one measurement progression reach and/or fall below a second predefined limit value, and/or
- the at least one simulation progression at least partially — preferably completely — proceeds within a predefined first tolerance range around the at least one measurement progression” as [Stoehr et al. (paragraph [0011] “It is also state of the art to find by means of simulations ranges of setting parameters (process windows), within which a process delivers products enjoying properties within predetermined tolerances.”, Stoehr et al. paragraph [0046] “Process and machine parameters (setting parameters) are varied in the individual simulations and thus a predetermined parameter range is covered.”)];
and/or
- the at least one measurement progression at least partially — preferably completely — proceeds within a predefined second tolerance range around the at least one simulation progression.
Mensler et al., Asaoka et al. and Stoehr et al. are analogous art because they are from the
same field endeavor of analyzing a shaping machine (injection molding machine).
Before the effective filing date of the invention, it would have been obvious to a person
of ordinary skill in the art to modify the teachings of Mensler et al. and Asaoka et al. of determining first distinguishing points of the curve of the at least one simulation progression and second distinguishing points of the curve of the at least one measurement progression by incorporating wherein the loop started by applying the method again is interrupted if: - values of the at least one modification parameter reach and/or fall below a first predefined limit value, and/or - differences — in particular differences in amount — from areas under the at least one simulation progression and the at least one measurement progression reach and/or fall below a second predefined limit value, and/or - the at least one simulation progression at least partially — preferably completely — proceeds within a predefined first tolerance range around the at least one measurement progression, and/or - the at least one measurement progression at least partially — preferably completely — proceeds within a predefined second tolerance range around the at least one simulation progression as taught by Stoehr et al. for the purpose of setting a shaping machine.
Mensler et al. in view of Asaoka et al. in further view of Stoehr et al. teaches wherein the loop started by applying the method again is interrupted if: - values of the at least one modification parameter reach and/or fall below a first predefined limit value, and/or - differences — in particular differences in amount — from areas under the at least one simulation progression and the at least one measurement progression reach and/or fall below a second predefined limit value, and/or - the at least one simulation progression at least partially — preferably completely — proceeds within a predefined first tolerance range around the at least one measurement progression, and/or - the at least one measurement progression at least partially — preferably completely — proceeds within a predefined second tolerance range around the at least one simulation progression.
The motivation for doing so would have been because Stoehr et al. teaches by carrying out a shaping process that involves establishing control of controllable components of a shaping machine, the ability to set a shaping machine can be accomplished, where the injection molding can be established (Stoehr et al. (paragraph [0001] – [0002])).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mensler et
al. view of Asaoka et al. in further view of online reference Improving the Predictions of Injection Molding Simulation Software, written by Vietri et al. (from IDS.3P dated 8/17/22).
With respect to claim 13, the combination of Mensler et al. and Asaoka et al. the method of claim 1 above.
While Mensler et al. and Asaoka et al. teach having the simulation and/or process modified on the basis of the at least one modification parameter, Mensler et al. Asaoka et al. do not explicitly disclose “wherein a Cross-WLF model and/or a 2-domain Tait pvT model is used as material model for the simulation.”
Vietri et al. discloses “wherein a Cross-WLF model and/or a 2-domain Tait pvT model is used as material model for the simulation.” as [Vietri et al. (Pg. 8, 2nd paragraph “Each test was simulated by setting the injection and mold temperature, the filling time and the experimental packing pressure profile at the nozzle node location. The filling time used in the simulation was determined directly by experimental pressure curves: it was chosen as the time taken by the polymer to reach position P4 (cavity tip). As a first attempt, pressure curves were simulated by adopting the parameters for Cross WLF equation and Tait equation already present in the standard Moldflow® database.”)];
Mensler et al., Asaoka et al. and Vietri et al. are analogous art because they are from the
same field endeavor of analyzing parameters of injection molding.
Before the effective filing date of the invention, it would have been obvious to a person
of ordinary skill in the art to modify the teachings of Mensler et al. and Asaoka et al. of having the simulation and/or process modified on the basis of the at least one modification parameter by incorporating wherein a Cross-WLF model and/or a 2-domain Tait pvT model is used as material model for the simulation as taught by Vietri et al. for the purpose of improving predictions of the description of pressure profiles.
Mensler et al. in view of Asaoka et al. in further view of Vietri et al. teaches wherein a Cross-WLF model and/or a 2-domain Tait pvT model is used as material model for the simulation.
The motivation for doing so would have been because Vietri et al. teaches by introducing the effect of pressure on viscosity and the effect of cavity deformation during molding, the ability to improve the predictions of the description of pressure curves inside a cavity can be accomplished, where the predictions gives the user a closer description of a physical mold design (Vietri et al. (Abstract, Pg. 13, Conclusion “In this work, a method is present to take, etc.”)).
Conclusion
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/BERNARD E COTHRAN/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188