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 .
Claims 1-26 are presented for examination based on the amended claims in the application filed on March 3, 2026.
Claims 1-26 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to judicial exception, an abstract idea, it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception.
Claims 1-10, 12-21, and 23-24 are rejected under 35 U.S.C. § 103 as being unpatentable over Shrivastava, Ankit, Jingxiao Liu, Kaushik Dayal, and Hae Young Noh. “Predicting Peak Stresses In Microstructured Materials Using Convolutional Encoder–Decoder Learning.” Mathematics and Mechanics of Solids Vol. 27, No. 7 (2022): p1336-1357 [herein, “Shrivastava”] in view of US 2006/0010427 A1 Hoffman, Edward [herein “Hoffman”].
Claim 25 is rejected under 35 U.S.C. § 103 as being unpatentable over Shrivastava in view of Hoffman as applied to claim 24, and in further view of Brink, Adam R., Robert J. Kuether, Matthew D. Fronk, Bryan L. Witt, and Brendan L. Nation. “Contact stress and linearized modal predictions of as-built preloaded assembly.” Journal of Vibration and Acoustics 142, no. 5 (2020) [herein “Brink”].
Claims 11, 22, and 26 are rejected under 35 U.S.C. § 103 as being unpatentable over Shrivastava in view of Hoffman as applied to claims 1, 12, and 24, respectively, and in further view of Yang, Chao, Zengyou He, and Weichuan Yu. “Comparison of public peak detection algorithms for MALDI mass spectrometry data analysis.” BMC bioinformatics Vol. 10 (2009): p1-13 [herein “Yang”].
This action is made Non-Final.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 March 3, 2026 has been entered.
Response to Amendment
The amendment filed March 3, 2026 has been entered. Claims 1-26 remain pending in the application. Applicant’s amendments to the Claims have overcome each and every objection, 112(b) rejection(s), and 112(a) rejection(s) previously set forth in the Non-Final Office Action mailed December 3, 2025. Examiner is persuaded by the applicant’s arguments regarding the 112(f) interpretation having structural modifier; and thus claim 26 will no longer be interpreted under 112(f).
Examiner’s Note: The examiner acknowledges that the status of the claims 9 and claim 10 have be amended to their original form which is the true status of the claims as filed in the submission on January 21, 2022. Examiner would like to thank the applicant for providing the clarification.
Claim Objections
Claims 26 is objected to because of the following informalities:
Claim 26 contains a recitation of elements with no previous recitation. For example, “the group of all types of critical points, only nominal strain points, and only nominal stress points, of the critical points” in Ln. 4-5, is improper because there has been no previous recitation of “the group of all types of critical points, only nominal strain points, and only nominal stress points, of the critical points”. For the purpose of examination, “the group of all types of critical points, only nominal strain points, and only nominal stress points, of the critical points” will be interpreted as “a ”.
Furthermore, claim 26, “the group of all types of critical points, only nominal strain points, and only nominal stress points, of the critical points” in Ln. 2-3 should be “a ”.
Appropriate correction is required.
Claim Rejections - 35 U.S.C. § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-26 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to judicial exception, an abstract idea, it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated the claims under the framework provided in the 2019 Patent Eligibility Guidance published in the Federal Register 01/07/2019 and has provided such analysis below.
Step 1:
Claims 1-11 are directed to a method and fall within the statutory category of a process; claims 12-22 are directed to a system and fall within the statutory category of a machine; claim 23 is directed to a non-transitory computer-readable medium and falls within the statutory category of an articles of manufacture; and claims 24-26 are directed to a method and fall within the statutory category of a process. Therefore, “Are the claims to a process, machine, manufacture or composition of matter?” Yes.
In order to evaluate the Step 2A inquiry “Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?” we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
Claims 1, 12, and 23: The limitations of:
“identifying one or more critical points among the test data points in the first test data set” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally determine or draw with pen and paper points that are local minimums, local maximums, and points of discontinuity being looking at a display of test data points. The specification discloses that this can be done by a person in the following sections:
Pg. 4 Ln. 12-14, “FIG. 5C(l) and 5C(2) show screenshots illustrating that a user can directly click on data points in a plot to highlight corresponding entries in a table where the checkbox can be toggled to facilitate quick visual-based specification of critical points”,
Pg. 9 Ln. 12-14, “These points can be defined by a computer (e.g., through a simple user interface) and refined by toggling test data points as critical points, either by selecting them from a table or clicking the points on a graph to commit these points to the computer memory”,
Pg. 12 Ln. 9-12, “In 10 a typical implementation, the systems and techniques disclosed herein enable a human user's ability to manually designate any critical test data points”,
Pg. 23 Ln-13-23 depicts the sequence in Fig. 5A that the user can refined the critical points such change which data points will be flagged as critical points,
Pg. 24 Ln. 21-22, “the user can directly toggle the check boxes in the table of test data (in FIG. 5A) to change which data points will or will not be flagged as critical points”, and
Pg. 37 Ln. 4-5 “the user has explicit manual control to specify individual data points as being critical or not”, and
“processing the first test data set to produce a second test data set with differing test data points than the first test data set, wherein the second test data set includes all the test data points that were identified as critical points in the first test data set and at least some other data points” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally create or draw with pen and paper a second data set by including both the critical points identified from the test data set and some sample points from test data set.
Pg. 24 Ln. 17-18, “The user also has the ability to keep previously-defined critical points, allowing the user to quickly build up critical points based on certain series”,
Pg. 25 Ln. 9-12, “the user manually designating one or more additional data points as critical. This may make sense if, for example, the user wants to ensure that those one or more additional data points are preserved in the test data set through subsequent data preprocessing for use in material model calibration”, and
Pg. 30 Ln. 20 – Pg. 31 Ln. 1, “the computer 100 enables the user to modify the results produced by the preprocessing (at 314) in much the same way that the user was able to refine candidate selections at 310. In a typical implementation, after preprocessing (at 314), the computer 100 also provides the user with a way to accept the preprocessing results as provided by the computer 100 and/or as modified by the user.”
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding Claims 1, 12, and 23: The limitation of “processing the first test data set to produce a second test data set with differing test data points than the first test data set, wherein the second test data set includes all the test data points that were identified as critical points in the first test data set and at least some other data points”, as drafted, is an operation that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation of mathematical evaluations. For example, calculating the test data set by smoothing the test data set can be accomplished using a smoothing mathematical algorithm (Pg. 4 Ln. 1-4, “phrase "critical point" or the like refers to a point in a data set that has been identified as a data point to be preserved throughout data processing (also referred to as "preprocessing"), which may include, for example, decimation, regularization, and/or smoothing, to be applied prior to material model calibration.”, Pg. 34 Ln. 19- Pg. 25 Ln. 2, “smoothing the data with critical points results in the Savitsky-Golay algorithm utilized by the smoothing tool being applied over intervals running between critical points, while the critical points themselves are not modified. Additionally, the smoothing is performed in a manner to preserve Cl discontinuities at the critical points. In FIGS. 16 and 17, smoothing, with critical points preserved, has been applied to the stress and strain time histories, and, as can be seen, the local minima/maxima are retained.”. Savitsky-Golay algorithm is mathematical algorithm that smooths a data set using the method of linear least squares1).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of mathematic operation but for the recitation of generic computer components, then it falls within the “Mathematical Operation” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Therefore, yes, claims 1, 12, and 23 recite judicial exceptions. The claims have been identified to recite judicial exceptions, Step 2A Prong 2 will evaluate whether the claims are directed to the judicial exception.
Step 2A Prong 2:
Claims 1, 12, and 23: The judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements: “with a computer processor”, “a system comprising: a computer system comprising: a computer processor; computer-based memory operatively coupled to the computer processor, wherein the computer-based memory stores computer-readable instructions that, when executed by the computer processor”, and “a non-transitory computer readable medium having stored thereon computer-readable instructions that, when executed by a computer-based processor” are merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) with the broadest reasonable interpretation, which does not integrate a judicial exception into elements. Further, the following additional elements of “producing a first test data set by performing a real-world test of a material that comprises: applying a load to a specimen
of the material using real-world physical machinery; and measuring real-world responses to the load
1Savitzky, Abraham, and Marcel JE Golay. “Smoothing and differentiation of data by simplified least squares procedures.” Analytical chemistry 36, no. 8 (1964): 1627-1639.
applied to the specimen of the material” and “receiving, in computer memory, the first test data set that comprises results of the real-world test of the material, wherein the first test data set comprises a plurality of test data points” from Claim 1 and its similar respective variants of “real-world physical machinery configured to apply a load to a specimen of a material and to measure real-world responses to the load applied to the specimen of the material to produce a first test data set” and “receiving, in computer memory, the first test data set wherein the first test data set comprises a plurality of test data points” from Claim 12 as well as “receive, in computer memory, a first test data set that comprises results of a real-world test of a material, wherein the first test data set comprises a plurality of test data points, and wherein the first test data set is from a real-world test that comprises applying a load to a specimen of the material using real-world physical machinery and measuring real-world responses to the load applied to the specimen of the material” from claim 23 and is merely a recitation of insignificant extra-solution data gathering activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application. The insignificant extra-solution activities are further addressed below under step 2B as also being Well-Understood, Routine, and Conventional (WURC).
Therefore, “Do the claims recite additional elements that integrate the judicial exception into a practical application?” No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
After having evaluated the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claims 1, 12, and 23 not only recite a judicial exception but that the claims are directed to the judicial exception as the judicial exception has not been integrated into practical application.
Step 2B:
Claims 1, 12, and 23: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components which do not amount to significantly more than the abstract idea. Further, the insignificant extra-solution data gathering, record update, and data transmission activities are also Well-Understood, Routine and Conventional (see MPEP § 2106.05(d)(II), “The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory”, and MPEP § 2106.05(g) recites “Below are examples of activities that the courts have found to be insignificant extra-solution activity: Mere Data Gathering: Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); Testing a system for a response, the response being used to determine system malfunction, In re Meyers, 688 F.2d 789, 794; 215 USPQ 193, 196-97 (CCPA 1982).”). The limitation of “producing a first test data set by performing a real-world test of a material that comprises: applying a load to a specimen of the material using real-world physical machinery; and measuring real-world responses to the load applied to the specimen of the material” is testing a system for a response and performing tests for an input, which is mere data gathering, an insignificant extra-solution activity as defined in the above MPEP citations, and the limitation of “receiving, in computer memory, the first test data set that comprises results of the real-world test of the material, wherein the first test data set comprises a plurality of test data points” is receiving data over a network or in a computer, which is mere data gathering, an insignificant extra-solution activity as defined in the above MPEP citations.
Therefore, “Do the claims recite additional elements that amount to significantly more than the judicial exception?” No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception. Having concluded the analysis within the provided framework, claims 1, 12, and 23 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claims 2 and 13, they recite an additional limitation of “calibrating a material model based on the second test data set to produce a calibrated material model, wherein the material model is an equation with parameters that describes a relationship between two or more characteristics of the material”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation of mathematical evaluations. For example, calculating a relationship between characteristics for a specific material can be conducted by using the generated second data set to alter a linear equation that describes a general relationship between characteristics of materials, such stress and strain (see Specification Pg. 10 Ln. 7-20, “A material model, in an exemplary implementation, is a virtual representation of an equation that describes the relationship between various material characteristics ( e.g., applied force ( or "stress") and resulting deformation ( or "strain")) for a particular material”).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of mathematic evaluations but for the recitation of generic computer components, then it falls within the “Mathematical Operation” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claims 2 and 13, they recite an additional limitation of “calibrating a material model based on the second test data set to produce a calibrated material model wherein the material model is an equation with parameters that describes a relationship between two or more characteristics of the material”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally determine or draw with pen and paper adjustments to the parameters of existing equation that describes between characteristics of a material, such has the relationship between stress and strain of a material, based on the generated second data set to tailor the parameters of the relationship.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claims 2 and 13, they recite additional element recitations of “computer-implemented material model” and “stored in a computer-readable medium” which is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. Further, these claims do not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, these claims also fail both Step 2A prong 2, thus the claims are directed to the judicial exception as they have not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, claims 2 and 13 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claims 3 and 14, they recite additional limitations of “analyzing, with the calibrated material model, a virtual design comprising a design geometry sourced from a computer-aided design program, of a real-world product that includes the material”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation of mathematical evaluations. For example, calculating the characteristics for a specific material contained in virtual design CAD program can be conducted using the tailored equation that describes the characteristics of the material in the analysis of design that uses the material (see Specification Pg. 32 Ln. 14-22 and see Specification Pg. 10 Ln. 7-20, “A material model, in an exemplary implementation, is a virtual representation of an equation that describes the relationship between various material characteristics ( e.g., applied force ( or "stress") and resulting deformation ( or "strain")) for a particular material”).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of mathematic evaluations but for the recitation of generic computer components, then it falls within the “Mathematical Operation” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claims 3 and 14, they recite additional limitations of “analyzing, with the calibrated material model, a virtual design comprising a design geometry sourced from a computer-aided design program, of a real-world product that includes the material”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally determine or draw with pen and paper the characteristics of a design from a CAD program that uses the modelled material using the simple tailored, linear equation that describes the relationship between stress and strain of a material.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claims 3 and 14, they recite additional element recitations of “wherein the computer-readable instructions, when executed by the computer processor,” which is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. Further, these claims do not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, these claims also fail both Step 2A prong 2, thus the claims are directed to the judicial exception as they have not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, claims 3 and 14 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claims 4 and 15, they recite additional limitations of “wherein identifying one or more critical points among the test data points in the first test data set comprises: identifying a data point in the first test data set for which an immediately preceding data point and an immediately following data point both have values that are greater than or both have values that are less than the data point” and “identifying a data point in the first test data set for which one of an immediately preceding data point or an immediately following data point has a value that is equal to a value of the data point and the other of the immediately preceding data point or the immediately following data point has a value that is not equal to a value of the data point”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally determine or draw with pen and paper points in a test data set that are local maximums or local minimums by finding a point at x (0.25 strain) with a value of y (1000 kN/m2 stress) where either points at both x-1 (0.24 strain) and x+1 (0.26 strain) are greater than y (1000 kN/m2 stress) or points at both x-1 (0.24 strain) and x+1 (0.26 strain) are less than y (1000 kN/m2 stress). Furthermore, for example, a person can mentally determine or draw with pen and paper points in a test data set where a slope in the data set begins by finding a point at x (0.25 strain) with a value of y (1000 kN/m2 stress) is equal to either the point at x-1 (0.24 strain) or the point at x+1 (0.26 strain) but not at both points x-1 (0.24 strain) and x+1 (0.26 strain).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regarding claims 5 and 16, they recite an additional limitation of “wherein the processing of the first test data set comprises individually processing each respective one of a plurality of intervals in the first test data set, wherein each one of the intervals is defined by one or more of the critical points in the first test data set”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally partition or draw with pen and paper sections of data in the test data set that include the critical points identified from the test data set.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regarding claims 6 and 17, they recite additional limitations of :
1) “a first one of the intervals is defined by an initial point in the first test data set and a first one of the critical points after the initial point”,
2) “a second one of the intervals is defined by a last one of the critical points before an end point in the first test data set and the end point in the first test data set”, and/or
3) “other intervals are defined by sequential critical points in the first data set” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, these limitations can be conducted as the following:
a) a person can mentally partition or draw with pen and paper sections of data in the test data set where the first section of data includes the first data point of the test data set and the critical point identified from the test data set that proceeds after the first data point in the test data set,
b) a person can mentally partition or draw with pen and paper sections of data in the test data set where the second section of data includes the last data point of the test data set and the critical point identified from the test data set that proceeds before the last data point in the test data set, and/or
c) a person can mentally partition or draw with pen and paper sections of data in the test data set where include other sections of data include a set of concurrent critical points identified from the test data.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regarding claims 7 and 18, they recite an additional limitation of “wherein the processing comprises decimating each respective one of the intervals in the first test data set, regularizing each respective one of the intervals in the first test data set, and/or smoothing each respective one of the intervals in the first test data set”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally alter or draw with pen and paper the test data set to include sections of data that is a set of concurrent critical points identified from the test data where the sampled data points in between the critical points are eliminated so the sampling data best fit between the critical points.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claims 7 and 18: The limitation of “wherein the processing comprises decimating each respective one of the intervals in the first test data set, regularizing each respective one of the intervals in the first test data set, and/or smoothing each respective one of the intervals in the first test data set”, as drafted, is an operation that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation of mathematical evaluations. For example, calculating the intervals in the test data set by smoothing the test data set can be accomplished using a smoothing mathematical algorithm (Pg. 4 Ln. 1-4, “phrase "critical point" or the like refers to a point in a data set that has been identified as a data point to be preserved throughout data processing (also referred to as "preprocessing"), which may include, for example, decimation, regularization, and/or smoothing, to be applied prior to material model calibration.”, Pg. 34 Ln. 19- Pg. 25 Ln. 2, “smoothing the data with critical points results in the Savitsky-Golay algorithm utilized by the smoothing tool being applied over intervals running between critical points, while the critical points themselves are not modified. Additionally, the smoothing is performed in a manner to preserve Cl discontinuities at the critical points. In FIGS. 16 and 17, smoothing, with critical points preserved, has been applied to the stress and strain time histories, and, as can be seen, the local minima/maxima are retained.”. Savitsky-Golay algorithm is mathematical algorithm that smooths a data set using the method of linear least squares1).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of mathematic operation but for the recitation of generic computer components, then it falls within the “Mathematical Operation” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regarding claims 8 and 19, they recite an additional limitation of “mapping each of the critical points in the first data set to a corresponding one of the data points in the second data set”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally create or draw with pen and paper every critical point identified in the test data set as a data point in the second set of data.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claims 8 and 19, they recite additional element recitations of “wherein the computer-readable instructions, when executed by the computer processor,” which is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. Further, these claims do not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, these claims also fail both Step 2A prong 2, thus the claims are directed to the judicial exception as they have not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, claims 8 and 19 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claims 9 and 20, they recite an additional limitation of “flagging each respective one of the mapped data points in the second data set as critical”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally identify or draw with pen and paper every critical point identified in the second set of data as being the same critical point in the test data set.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claims 9 and 20, they recite additional element recitations of “wherein the computer-readable instructions, when executed by the computer processor,” which is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. Further, these claims do not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, these claims also fail both Step 2A prong 2, thus the claims are directed to the judicial exception as they have not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, claims 9 and 20 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claims 10 and 21, they recite an additional element recitation of “wherein the computer-readable instructions, when executed by the computer processor” which is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. Further, the additional element of “after processing, returning the second test data set to a user interface for display, wherein the second test data set is displayed on a computer screen in a manner that visually distinguishes the critical points from the other data points in the second test data set” which is merely an insignificant extra-solution data outputting activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application. Further, the insignificant extra-solution data gathering, record update, and data transmission activities are also Well-Understood, Routine and Conventional (see MPEP § 2106.05(d)(II), “The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory”). Further, these claims do not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, these claims also fail both Step 2A prong 2, thus the claims are directed to the judicial exception as they have not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, claims 10 and 21 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claims 11 and 22, they recite an additional limitation of “adding to or deleting from the one or more critical points identified among the test data points in the first test data set prior to processing the first test data set, in response to a user input”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally determine or draw with pen and paper points in a test data set that are local minimums or local maximums and add additional critical points or remove unnecessary critical points before filtering the test data set in response to a specification.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claims 11 and 22, they recite an additional element recitation of “wherein the one or more critical points are identified automatically by a computer” which is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. Further, these claims do not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, these claims also fail both Step 2A prong 2, thus the claims are directed to the judicial exception as they have not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, claims 11 and 22 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Furthermore, for clarity, the analysis under the 2019 Patent Eligibility Guidance published in the Federal Register 01/07/2019 will continue for claims 24-26 at Step2A Prong 1:
Claim 24: The limitations of:
“identifying critical points in the first test data set”,
“processing the first test data set to produce a second test data set that comprises all the critical points and at least some additional data points”,
“calibrating the selected one of the uncalibrated material models using the second test data set, to produce a calibrated material model”, and
“analyzing a design geometry from a computer-aided design (CAD) file, using environmental and/or operational parameters entered by a human user into a computer-implemented input/output device of the computer system, and the calibrated material model from the storage for calibrated material models, to produce an output that for display on the computer screen of the computer system”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, the following limitation can be conducted as the following:
a person can mentally determine or draw with pen and paper points that are local minimums, local maximums, and points of discontinuity being looking at a display of test data points (the specification discloses that this can be done by a person in the following sections:
Pg. 4 Ln. 12-14, “FIG. 5C(l) and 5C(2) show screenshots illustrating that a user can directly click on data points in a plot to highlight corresponding entries in a table where the checkbox can be toggled to facilitate quick visual-based specification of critical points”,
Pg. 9 Ln. 12-14, “These points can be defined by a computer (e.g., through a simple user interface) and refined by toggling test data points as critical points, either by selecting them from a table or clicking the points on a graph to commit these points to the computer memory”,
Pg. 12 Ln. 9-12, “In 10 a typical implementation, the systems and techniques disclosed herein enable a human user's ability to manually designate any critical test data points”,
Pg. 23 Ln-13-23 depicts the sequence in Fig. 5A that the user can refined the critical points such change which data points will be flagged as critical points,
Pg. 24 Ln. 21-22, “the user can directly toggle the check boxes in the table of test data (in FIG. 5A) to change which data points will or will not be flagged as critical points”, and
Pg. 37 Ln. 4-5 “the user has explicit manual control to specify individual data points as being critical or not”.),
a person can mentally create or draw with pen and paper second data set by including both the critical points identified from the test data set and some sample points from test data set (the specification discloses that this can be done by a person in the following sections:
Pg. 24 Ln. 17-18, “The user also has the ability to keep previously-defined critical points, allowing the user to quickly build up critical points based on certain series”,
Pg. 25 Ln. 9-12, “the user manually designating one or more additional data points as critical. This may make sense if, for example, the user wants to ensure that those one or more additional data points are preserved in the test data set through subsequent data preprocessing for use in material model calibration”,
Pg. 30 Ln. 20 – Pg. 31 Ln. 1, “the computer 100 enables the user to modify the results produced by the preprocessing (at 314) in much the same way that the user was able to refine candidate selections at 310. In a typical implementation, after preprocessing (at 314), the computer 100 also provides the user with a way to accept the preprocessing results as provided by the computer 100 and/or as modified by the user.”,
a person can mentally determine or draw with pen and paper adjustments to the parameters of existing equation that describes between characteristics of a material, such has the relationship between stress and strain of a material, based on the generated second data set to tailor the parameters of the relationship, and
a person can mentally determine or draw with pen and paper the characteristics of a design from a CAD program that uses the modelled material using the simple tailored, linear equation that describes the relationship between stress and strain of a material.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 24, it recites additional limitations of:
“processing the first test data set to produce a second test data set that comprises all the critical points and at least some additional data points”,
“calibrating the selected one of the uncalibrated material models using the second test data set, to produce a calibrated material model”, and
“analyzing a design geometry from a computer-aided design (CAD) file, using environmental and/or operational parameters entered by a human user into a computer-implemented input/output device of the computer system, and the calibrated material model from the storage for calibrated material models, to produce an output that for display on the computer screen of the computer system”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation of mathematical evaluations. For example,
calculating the test data set by smoothing the test data set can be accomplished using a smoothing mathematical algorithm (Pg. 4 Ln. 1-4, “phrase "critical point" or the like refers to a point in a data set that has been identified as a data point to be preserved throughout data processing (also referred to as "preprocessing"), which may include, for example, decimation, regularization, and/or smoothing, to be applied prior to material model calibration.”, Pg. 34 Ln. 19- Pg. 25 Ln. 2, “smoothing the data with critical points results in the Savitsky-Golay algorithm utilized by the smoothing tool being applied over intervals running between critical points, while the critical points themselves are not modified. Additionally, the smoothing is performed in a manner to preserve Cl discontinuities at the critical points. In FIGS. 16 and 17, smoothing, with critical points preserved, has been applied to the stress and strain time histories, and, as can be seen, the local minima/maxima are retained.”. Savitsky-Golay algorithm is mathematical algorithm that smooths a data set using the method of linear least squares),
calculating a relationship between characteristics for a specific material can be conducted by using the generated second data set to alter a linear equation that describes a general relationship between characteristics of materials, such stress and strain (see Specification Pg. 10 Ln. 7-20, “A material model, in an exemplary implementation, is a virtual representation of an equation that describes the relationship between various material characteristics ( e.g., applied force ( or "stress") and resulting deformation ( or "strain")) for a particular material”), and
calculating the characteristics for a specific material contained in virtual design CAD program can be conducted using the tailored equation that describes the characteristics of the material in the analysis of design that uses the material (see Specification Pg. 32 Ln. 14-22 and see Specification Pg. 10 Ln. 7-20, “A material model, in an exemplary implementation, is a virtual representation of an equation that describes the relationship between various material characteristics ( e.g., applied force ( or "stress") and resulting deformation ( or "strain")) for a particular material”).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Therefore, yes, claim 24 recites judicial exceptions. The claim has been identified to recite judicial exceptions, Step 2A Prong 2 will evaluate whether the claim is directed to the judicial exception.
Step 2A Prong 2:
Claim 24: The judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements:
“providing a computer system comprising: a material model generator, a library of uncalibrated material models, a storage for calibrated material models, a design analyzer, a source of design geometry, a source of environmental/ operational parameters, and an output, wherein the material model generator comprises a critical points identifier, test data preprocessors, and a material model calibrator”, “automatically using the critical points identifier of the computer system”, “using the test data preprocessors of the computer system”, “with the material model calibrator of the computer system”, and “with the design analyzer of the computer system” which is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) with the broadest reasonable interpretation, which does not integrate a judicial exception into elements. Further, the following additional element,
“providing real-world physical machinery configured to perform real-world testing of real-world specimens”, “generating a first test data set by performing a real-world test of a specimen of material using the real-world physical machinery to apply a load that varies over time to the specimen of material, and measuring real-world responses to the load that varies over time in the specimen of material at different times during the real-world test”, “inputting the first test data set, to the critical points identifier of the material model generator of the computer system”, and “receiving a user selection identifying a selected one of the uncalibrated material models listed on the user interface” which is merely a recitation of insignificant extra-solution data gathering activities (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application. The insignificant extra-solution activities are further addressed below under step 2B as also being Well-Understood, Routine, and Conventional (WURC). Further, the following additional element,
“displaying, on a user interface of the computer system, a user-selectable listing of uncalibrated material models stored in the library of uncalibrated material models” which is merely a recitation of insignificant extra-solution data outputting activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application. Further, the following additional element,
“storing the calibrated material model in the storage for calibrated material models of the computer system” which is merely a recitation of insignificant extra-solution data storing activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application.
Therefore, “Do the claims recite additional elements that integrate the judicial exception into a practical application?” No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
After having evaluated the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claim 24 not only recite a judicial exception but that the claim is directed to the judicial exception as the judicial exception has not been integrated into practical application.
Step 2B:
Claim 24: The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components which do not amount to significantly more than the abstract idea. Further, the insignificant extra-solution data gathering, record update, and data transmission activities are also Well-Understood, Routine and Conventional (see MPEP § 2106.05(d)(II), “The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory”, and MPEP § 2106.05(g) recites “Below are examples of activities that the courts have found to be insignificant extra-solution activity: Mere Data Gathering: Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); Testing a system for a response, the response being used to determine system malfunction, In re Meyers, 688 F.2d 789, 794; 215 USPQ 193, 196-97 (CCPA 1982).”). The limitations of:
“providing real-world physical machinery configured to perform real-world testing of real-world specimens” and “generating a first test data set by performing a real-world test of a specimen of material using the real-world physical machinery to apply a load that varies over time to the specimen of material, and measuring real-world responses to the load that varies over time in the specimen of material at different times during the real-world test” is testing a system for a response and performing tests for an input, which is mere data gathering, an insignificant extra-solution activity as defined in the above MPEP citations,
“inputting the first test data set, to the critical points identifier of the material model generator of the computer system” and “receiving a user selection identifying a selected one of the uncalibrated material models listed on the user interface” is receiving data over a network or in a computer, which is mere data gathering, an insignificant extra-solution activity as defined in the above MPEP citations;
“displaying, on a user interface of the computer system, a user-selectable listing of uncalibrated material models stored in the library of uncalibrated material models” is transmitting data, which is mere data outputting, an insignificant extra-solution activity as defined in the above MPEP citations, and
“storing the calibrated material model in the storage for calibrated material models of the computer system” is storing data, which is mere data storing, an insignificant extra-solution activity as defined in the above MPEP citations.
Therefore, “Do the claims recite additional elements that amount to significantly more than the judicial exception?” No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception. Having concluded the analysis within the provided framework, claim 24 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claim 25, it recites additional element recitation of “wherein the test data preprocessors in the computer system comprise a decimator, a regularizer, and a smoother, wherein the decimator is configured to decimate the second test data set, wherein the regularizer is configured to regularize the second test data set, and wherein the smoother is configured to smooth the second test data set” is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. Further, this claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional element amounts to significantly more, this claim also fails both Step 2A prong 2, thus this claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 25 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claim 26, it recites an additional limitation of “updating a listing of the critical points in response to the user selection”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally determine or draw with pen and paper points in a test data set that are nominal local minimum or nominal local maximum stress point and add additional critical points or remove unnecessary critical points before filtering the test data set in response to a specification.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 26, they recite additional element recitations of “presenting, on the user interface, a graphical element displaying selection choices from the group of all types of critical points, only nominal strain points, and only nominal stress points, of the critical points that were automatically identified in the first test data set by the critical points identifier of the computer system” and “receiving a user selection from the graphical element” an insignificant extra-solution data gathering and data outputting activities (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application. Further, the insignificant extra-solution data gathering, record update, and data transmission activities are also Well-Understood, Routine and Conventional (see MPEP § 2106.05(d)(II), “The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory”). Further, this claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional element amounts to significantly more, this claim also fails both Step 2A prong 2, thus this claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 26 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Therefore, having concluded the analysis within the provided framework, claims 1-26 do not recite patent eligible subject matter and are rejected under 35 U.S.C. § 101 because the claimed invention is directed to judicial exception, an abstract idea, that has not been integrated into a practical application. The claims further do not recite significantly more than the judicial exception. Claims 2-11, 13-22, and 25-26 are also rejected for incorporating the deficiency of their independent claims 1, 12, and 24, respectively.
Claim Rejections - 35 U.S.C. § 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 1-10, 12-21, and 23-24 are rejected under 35 U.S.C. § 103 as being unpatentable over Shrivastava, Ankit, Jingxiao Liu, Kaushik Dayal, and Hae Young Noh. “Predicting Peak Stresses In Microstructured Materials Using Convolutional Encoder–Decoder Learning.” Mathematics and Mechanics of Solids Vol. 27, No. 7 (2022): p1336-1357 [herein, “Shrivastava”] in view of US 2006/0010427 A1 Hoffman, Edward [herein “Hoffman”].
As per claim 1, Shrivastava teaches “A computer-implemented method comprising: receiving, in computer memory, the first test data set that comprises results of the real-world test of a material, wherein the first test data set comprises a plurality of test data points”. (Pg. 1 Sect. 1, “This paper aims to use a deep learning-based method” [computer-implemented method] “to predict peak-stress clusters in heterogeneous linear elastic materials”. Pg. 2, Sect. 1, “In these methods, the machine learning models were trained on the data (“ground truth”) obtained from numerical methods to learn the statistical relationship between the microstructure and the corresponding response to applied loads” [trained from data based on applied results (i.e., results of a real-world test of a material)]. Additionally, Pg. 2 Sect. 1, “Typical polycrystalline microstructures under an applied load show peak-stress clusters that are sparse and are governed by the local spatial features of the microstructure as suggested by [RLG+10]” [e.g., results of a real-world test of a material]. Pg. 8 Sect. 2.C., “the stress fields are fed into a peak detection algorithm” [receiving a first test data set comprises a plurality of test data points]. Pg. 19, Sect. 4.D., “It is also important to note that training the machine learning model requires a relatively large memory” [in computer memory]. Further see Sect. 1 and 2. The examiner has interpreted that a deep learning-based method that includes a model, algorithm, and memory that obtains ground truth data of a microstructure and its corresponding response to applied loads, stress fields, as a computer-implemented method comprising: receiving, in computer memory, a first test data set that comprises results of a real-world test of a material, wherein the first test data set comprises a plurality of test data points.)
Shrivastava also teaches “identifying one or more critical points among the test data points in the first test data set”. (Pg. 2, Sect. 1, “Then a cluster detection algorithm is used to detect peak-stress clusters within the predicted stress field”. Further see Sect. 1. The examiner has interpreted that detecting peak-stress clusters within the predicted stress field as identifying one or more critical points among the test data points in the first test data set.)
Shrivastava teaches “processing the first test data set with a computer processor to produce a second test data set with differing test data points than the first test data set, wherein the second test data set includes all the test data points that were identified as critical points in the first test data set and at least some other data points.” (Pg. 8 Sect. 2.C., “The peak-stress clusters are detected inside the stress fields predicted by the trained CED model. We characterize the peak-stress clusters as regions around a peak in the stress fields with values above a certain threshold. The low-resolution stress fields obtained by averaging von Mises stress fields, see Section 2.A,” [critical points in the first test data set] “and the predicted stress fields from the model are not smooth, and there are small fluctuations. The stress fields are smoothed using a Gaussian filter to avoid small fluctuation” [processing the first test data set to produce the second test data set includes all the test data points that were identified as critical points in the first test data set]. Further, Pg. 8 Sect. 2.C., “ “For every peak, the clusters around it” [hotspot, i.e. other data points] “are obtained by assigning the value of 1 if the pixel value is higher than the predefined threshold times the corresponding peak stress.” Figure 5 shows the stress peaks in yellow (e.g., critical points) and hotspots in cyan, being the area around the respective peak that is higher than a certain threshold (e.g., at least some other data points). Pg. 18-19 Sect. 4.D., “The computational cost of the CED model was analyzed by estimating the time taken to train the model on the training set and the time taken by the trained model to predict the low-resolution stress fields for the complete microstructure dataset. A CPU machine with the configuration Intel(R) i7-3770 CPU @ 3.40 GHz was used to analyze the computational cost.” [with a computer processor]. Further see Sect. 2 and 4. The examiner has interpreted that predicting stress field that are not smoothen then smoothening the stress fields and returning the stress field peaks with the clusters surrounding the corresponding peaks using a CPU machine as processing the first test data set with a computer processor to produce a second test data set with differing test data points than the first test data set, wherein the second test data set includes all the test data points that were identified as critical points in the first test data set and at least some other data points.)
Shrivastava does not specifically teach “producing a first test data set by performing a real-world test of a material that comprises: applying a load to a specimen of the material using real-world physical machinery; and measuring real-world responses to the load applied to the specimen of the material”.
However, in the same field of endeavor namely using data to update models for materials, Hoffman teaches “producing a first test data set by performing a real-world test of a material that comprises: applying a load to a specimen of the material using real-world physical machinery; and measuring real-world responses to the load applied to the specimen of the material”. (Para. 0023, “Materials characterization tests are specialized tests performed to produce the material response represented by the constitutive model. These tests are performed on a material sample of specific shape and under loads within the load-range of interest. The material response as a function of the applied load is measured and recorded”. Para. 0024, “An example of a materials characterization test is a `uniaxial tensile test` performed to characterize the elastic-plastic response of a metal. A small cylindrical sample of the metal is strain-loaded in an axial direction until it breaks. A one-dimensional load-displacement response is recorded in a stress-versus-strain plot”. Further see 0023-0024 and 0071-0072. The examiner has interpreted that performing a uniaxial tensile test on a sample of a metal to measure and record the material response of the metal under the load as producing a first test data set by performing a real-world test of a material that comprises: applying a load to a specimen of the material using real-world physical machinery; and measuring real-world responses to the load applied to the specimen of the material.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “producing a first test data set by performing a real-world test of a material that comprises: applying a load to the specimen of the material using real-world physical machinery; and measuring real-world responses to the load applied to the specimen of the material” as conceptually seen from the teaching of Hoffman, into that of Shrivastava because this modification of performing and measuring the response after applying a load for the advantageous purpose of updating models from material test data (Hoffman Para. 0027, 0084, and 0102). Further motivation to combine be that Shrivastava and Hoffman are analogous art to the current claim and are directed to using data to update models for materials.
As per claim 2, Shrivastava teaches “calibrating a computer-implemented material model based on the second test data set to produce a calibrated material model, wherein the computer-implemented material model is an equation with parameters, stored in a computer-readable medium, that describes a relationship between two or more characteristics of the material.” (Pg. 19 Sect. 4.D., “The machine took 8 hours, 32 minutes, and 14 seconds to train the CED model using 8000 training samples and training parameters” [calibrating a computer-implemented material model based on the second test data set to produce a calibrated material model, wherein the computer-implemented material model contains parameters] “as shown in Table 4. For predicting the low-resolution stress fields for 10000 microstructures using the trained CED model,” [based on the second test data]. Pg. 2, Sect. 1, “the machine learning models were trained on the data (“ground truth”) obtained from numerical methods to learn the statistical relationship between the microstructure and the corresponding response to applied loads” [describes a relationship between two or more characteristics of the material]. Pg. 3 Sect. 2.A., “The input therefore contains information about the orientation and grain boundaries” [first and second characteristics]. Pg. 2, Sect. 1, “a cluster detection algorithm is used to detect peak-stress clusters within the predicted stress field” [characteristic]. Further see Sect. 1 and 4. The examiner has interpreted that training the CED model using samples and microstructures that predict stress fields to learn the statistical relationship between the microstructure and the corresponding response to applied loads as calibrating a computer-implemented material model based on the second test data set to produce a calibrated material model, wherein the computer-implemented material model is an equation with parameters, stored in a computer-readable medium, that describes a relationship between two or more characteristics of the material.)
As per claim 3, Shrivastava teaches “analyzing, with the calibrated material model, a virtual design comprising a design geometry sourced from a computer-aided design program, of a real-world product that includes the material.” (Pg. 19, Sect. 5, “The output of the filters from the first layer of the CED model was analyzed to investigate the microstructure features extracted by the model while predicting the stress fields” [analyzing with the calibrated material model]. Pg. 19, Sect. 5, “Since the microstructure had many complexities in such as grain boundaries, Euler angles, and grain junctions, we also study the behavior of these filters on simple manually-designed microstructures. In the first case, we considered a single crystal microstructure with the fixed Euler angle values” [analyzing a virtual design comprising a design geometry of a real-world product that includes the material] “The values were varied from 0 to 1 since the scaled microstructure is fed into the model” and further Figure 19 shows the geometry of the microstructures implemented in the computing system, e.g. sourced from a computer-aided design program. Further see Sect. 5. The examiner has interpreted that analyzing the model filters to investigate the microstructure features extracted by the model while predicting the stress fields on a on single crystal manually-designed microstructure as implemented in a computer for use in the model as analyzing, with the calibrated material model, a virtual design comprising a design geometry sourced from a computer-aided design program, of a real-world product that includes the material.)
As per claim 4, Shrivastava teaches “wherein identifying one or more critical points among the test data points in the first test data set comprises: identifying a data point in the first test data set for which an immediately preceding data point and an immediately following data point both have values that are greater than or both have values that are less than the data point, or identifying a data point in the first test data set for which one of an immediately preceding data point or an immediately following data point has a value that is equal to a value of the data point and the other of the immediately preceding data point or the immediately following data point has a value that is not equal to a value of the data point.” (Pg. 8, Sec. 2.C., “the stress fields are fed into a peak detection algorithm which then returns the location of multiple peaks inside stress fields” [identifying a peak data point (e.g., a data point in the first test data set for which an immediately preceding data point and an immediately following data point have values that are both have values that are less than the data point)]. Further see Sect. 2. The examiner has interpreted that being fed stress fields and returning the peak stress fields as identifying a data point in the first test data set for which an immediately preceding data point and an immediately following data point have values that are both have values that are less than the data point.)
As per claim 5, Shrivastava teaches “wherein the processing of the first test data set comprises individually processing each respective one of a plurality of intervals in the first test data set, wherein each one of the intervals is defined by one or more of the critical points in the first test data set.” (Pg. 8 Sect. 2.C., “After the smoothing, the stress fields are fed into a peak detection algorithm which then returns the location of multiple peaks inside stress fields” [processing the first test data set]. “For every peak, the clusters around it are obtained by assigning the value of 1 if the pixel value is higher than the predefined threshold times the corresponding peak stress. The result is a binary image with multiple disconnected regions representing stress clusters.” Figure 5 shows the stress peaks in yellow (e.g., critical points) and hotspots in cyan, being the area around the respective peak that is higher than a certain threshold (e.g., at least some other data points). Further see Sect. 2. The examiner has interpreted that representing stress clusters as regions comprised of the peak stresses and the area surrounding the corresponding peaks wherein the processing of the first test data set comprises individually processing each respective one of a plurality of intervals in the first test data set, wherein each one of the intervals is defined by one or more of the critical points in the first test data set.)
As per claim 6, Shrivastava teaches “wherein: a first one of the intervals is defined by an initial point in the first test data set and a first one of the critical points after the initial point, a second one of the intervals is defined by a last one of the critical points before an end point in the first test data set and the end point in the first test data set, and/or other intervals are defined by sequential critical points in the first data set.” (Fig. 6 shows the peak clusters regions as color-coded in the order of their peak stress value. Further see Fig. 6 and Sect. 2. The examiner has interpreted that ordering the peak clusters in regions according to their respective peak stress value as intervals are defined by sequential critical points in the first data set.)
As per claim 7, Shrivastava teaches “wherein the processing comprises decimating each respective one of the intervals in the first test data set, regularizing each respective one of the intervals in the first test data set, and/or smoothing each respective one of the intervals in the first test data set.” (Pg. 8 Sect. 2.C., “For every peak, the clusters around it are obtained by assigning the value of 1 if the pixel value is higher than the predefined threshold times the corresponding peak stress.” Figure 5 shows the stress peaks in yellow and hotspots in cyan, signifying the regions for different thresholds (e.g., regularizing each respective one of the intervals in the first test data set). Pg. 8, Sect. 2.C., “The stress fields are smoothed using a Gaussian filter to avoid small fluctuations” [smoothing each respective one of the intervals in the first test data set]. Further see Sect. 2. The examiner has interpreted that modifying the thresholds that decrease the region of the cluster corresponding to the peak stress in addition to smoothening the stress fields and returning the stress field peaks with the clusters surrounding the corresponding peaks as regularizing each respective one of the intervals in the first test data set, and/or smoothing each respective one of the intervals in the first test data set.)
As per claim 8, Shrivastava teaches “mapping each of the critical points in the first data set to a corresponding one of the data points in the second data set.” (Pg. 8 Sect. 2.C., “After the smoothing, the stress fields are fed into a peak detection algorithm which then returns the location of multiple peaks inside stress fields”. Pg. 8 Sect. 2.C., “The stress clusters are then separately labeled using the connected component labeling algorithm as shown in Figure 5.” Further see Sect. 2. The examiner has interpreted that feeding the stress fields, outputting the peaks, and separately labeling the clusters surrounding the peak as mapping each of the critical points in the first data set to a corresponding one of the data points in the second data set.)
As per claim 9, Shrivastava teaches “flagging each respective one of the mapped data points in the second data set as critical.” (Pg. 8 Sect. 2.C., “The stress clusters are then separately labeled using the connected component labeling algorithm as shown in Figure 5.” Figure 5 shows the stress peaks in yellow (e.g., critical points). Further see Sect. 2 and Figure 5. The examiner has interpreted that separately labeling the stress peaks from the returned stress fields as yellow to indicate a peak as flagging each respective one of the mapped data points in the second data set as critical.)
As per claim 10, Shrivastava teaches “after processing, returning the second test data set to a user interface for display, wherein the second test data set is displayed on a computer screen in a manner that visually distinguishes the critical points from the other data points in the second test data set”. (Pg. 8 Sect. 2.C., “After the smoothing, the stress fields are fed into a peak detection algorithm which then returns the location of multiple peaks inside stress fields” [after processing, returning the second test data set]. Pg. 23 Acknowledgements, “We thank Pittsburgh Supercomputing Center for XSEDE computing resources” [e.g., to a user interface for display]. Figure 5 shows the stress peaks in yellow and hotspots in cyan (e.g., wherein the second test data set is displayed on a computer screen in a manner that visually distinguishes the critical points from the other data points in the second test data set). Further see Sect. 2 and Figure 5. The examiner has interpreted that returning the location of the peaks that are inside the stress fields representing the peaks in yellow after the smoothing through the use of XSEDE computing resources as after processing, returning the second test data set to a user interface for display, wherein the second test data set is displayed on a computer screen in a manner that visually distinguishes the critical points from the other data points in the second test data set.)
Re Claim 12, it is a system claim, having similar limitations of claim 1. Thus, claim 12 is also rejected under the similar rationale as cited in the rejection of claim 1.
Furthermore, as per claim 12, Shrivastava teaches “A system comprising: a computer processor and computer-based memory operatively coupled to the computer processor, wherein the computer-based memory stores computer-readable instructions that, when executed by the computer processor”. (Pg. 23 Acknowledgements, “We thank Pittsburgh Supercomputing Center for XSEDE computing resources” [e.g., a system comprising: a computer processor and computer-based memory operatively coupled to the computer processor, wherein the computer-based memory stores computer-readable instructions that, when executed by the computer processor]. The examiner has interpreted that using XSEDE computing resources as a computer system comprising: a computer processor and computer-based memory operatively coupled to the computer processor, wherein the computer-based memory stores computer-readable instructions that, when executed by the computer processor.)
Shrivastava does not specifically teach “real-world physical machinery configured to apply a load to a specimen of a material and to measure real-world responses to the load applied to the specimen of the material to produce a first test data set”.
However, Hoffman teaches “real-world physical machinery configured to apply a load to a specimen of a material and to measure real-world responses to the load applied to the specimen of the material to produce a first test data set”. (Para. 0023, “Materials characterization tests are specialized tests performed to produce the material response represented by the constitutive model. These tests are performed on a material sample of specific shape and under loads within the load-range of interest. The material response as a function of the applied load is measured and recorded”. Para. 0024, “An example of a materials characterization test is a `uniaxial tensile test` performed to characterize the elastic-plastic response of a metal. A small cylindrical sample of the metal is strain-loaded in an axial direction until it breaks. A one-dimensional load-displacement response is recorded in a stress-versus-strain plot”. Further see 0023-0024 and 0071-0072. The examiner has interpreted that performing a uniaxial tensile test on a sample of a metal to measure and record the material response of the metal under the load as real-world physical machinery configured to apply a load to a specimen of a material and to measure real-world responses to the load applied to the specimen of the material to produce a first test data set.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “real-world physical machinery configured to apply a load to a specimen of a material and to measure real-world responses to the load applied to the specimen of the material to produce a first test data set” as conceptually seen from the teaching of Hoffman, into that of Shrivastava because this modification of performing and measuring the response after applying a load for the advantageous purpose of updating models from material test data (Hoffman Para. 0027, 0084, and 0102). Further motivation to combine be that Shrivastava and Hoffman are analogous art to the current claim and are directed to using data to update models for materials.
Re Claim 13, it is a system claim, having similar limitations of claim 2. Thus, claim 13 is also rejected under the similar rationale as cited in the rejection of claim 2.
Re Claim 14, it is a system claim, having similar limitations of claim 3. Thus, claim 14 is also rejected under the similar rationale as cited in the rejection of claim 3.
Re Claim 15, it is a system claim, having similar limitations of claim 4. Thus, claim 15 is also rejected under the similar rationale as cited in the rejection of claim 4.
Re Claim 16, it is a system claim, having similar limitations of claim 5. Thus, claim 16 is also rejected under the similar rationale as cited in the rejection of claim 5.
Re Claim 17, it is a system claim, having similar limitations of claim 6. Thus, claim 17 is also rejected under the similar rationale as cited in the rejection of claim 6.
Re Claim 18, it is a system claim, having similar limitations of claim 7. Thus, claim 18 is also rejected under the similar rationale as cited in the rejection of claim 7.
Re Claim 19, it is a system claim, having similar limitations of claim 8. Thus, claim 19 is also rejected under the similar rationale as cited in the rejection of claim 8.
Re Claim 20, it is a system claim, having similar limitations of claim 9. Thus, claim 20 is also rejected under the similar rationale as cited in the rejection of claim 9.
Re Claim 21, it is a system claim, having similar limitations of claim 10. Thus, claim 21 is also rejected under the similar rationale as cited in the rejection of claim 10.
Re Claim 23, it is an articles of manufacture claim, having similar limitations of claim 1. Thus, claim 23 is also rejected under the similar rationale as cited in the rejection of claim 1.
Furthermore, as per claim 23, Shrivastava teaches “A non-transitory computer readable medium having stored thereon computer-readable instructions that, when executed by a computer-based processor” (Pg. 23 Acknowledgements, “We thank Pittsburgh Supercomputing Center for XSEDE computing resources” [e.g., a non-transitory computer readable medium having stored thereon computer-readable instructions that, when executed by a computer-based processor]. The examiner has interpreted that using XSEDE computing resources as a non-transitory computer readable medium having stored thereon computer-readable instructions that, when executed by a computer-based processor.)
Re Claim 24, it is a method, having similar limitations of claim 13. Thus, claim 24 is also rejected under the similar rationale as cited in the rejection of claim 13.
Furthermore, regarding claim 24, Shrivastava teaches “providing a computer system comprising: a material model generator, a library of uncalibrated material models, a storage for calibrated material models, a design analyzer, a source of design geometry, a source of environmental/ operational parameters, and an output, wherein the material model generator comprises a critical points identifier, test data preprocessors, and a material model calibrator”. (Pg. 23 Acknowledgements, “We thank Pittsburgh Supercomputing Center for XSEDE computing resources” [in light of claim 13, e.g., providing a computer system comprising: a material model generator, a library of uncalibrated material models, a storage for calibrated material models, a design analyzer, a source of design geometry, a source of environmental/ operational parameters, and an output, wherein the material model generator comprises a critical points identifier, test data preprocessors, and a material model calibrator]. The examiner has interpreted that using XSEDE computing resources as providing a computer system comprising: a material model generator, a library of uncalibrated material models, a storage for calibrated material models, a design analyzer, a source of design geometry, a source of environmental/ operational parameters, and an output, wherein the material model generator comprises a critical points identifier, test data preprocessors, and a material model calibrator.)
Shrivastava teaches “storing the calibrated material model in the storage for calibrated material models of the computer system”. (Pg. 9 Sect. 3, “Initially, the model was trained using a dataset with 40000 samples. However, the test error using 40000 samples was 0:0188618, whereas, for 10000 samples, it was 0:0197408. Hence, increasing the data did not improve the accuracy significantly but increased the training time by a factor of four, which acted as a bottleneck to the tuning of model architectures. It also required a large amount of memory to store the data and results for post-prediction analysis” [storing the calibrated material model in the storage for calibrated material models of the computer system]. Further se Sect. 3. The examiner has interpreted that training the model and requiring a large amount of memory to store the data and results for post-prediction analysis as storing the calibrated material model in the storage for calibrated material models of the computer system.)
Shrivastava teaches “analyzing a design geometry from a computer-aided design (CAD) file, using environmental and/or operational parameters entered by a human user into a computer-implemented input/output device of the computer system, and the calibrated material model from the storage for calibrated material models, to produce an output that for display on the computer screen of the computer system”. (Pg. 19, Sect. 5, “The output of the filters from the first layer of the CED model was analyzed to investigate the microstructure features extracted by the model while predicting the stress fields” [analyzing using the calibrated material model from the storage for calibrated material models]. Pg. 19, Sect. 5, “Since the microstructure had many complexities in such as grain boundaries, Euler angles, and grain junctions, we also study the behavior of these filters on simple manually-designed microstructures. In the first case, we considered a single crystal microstructure with the fixed Euler angle values” [analyzing a design geometry of a real-world product that includes the material] “The values were varied from 0 to 1 since the scaled microstructure is fed into the model” and further Figure 19 shows the geometry of the microstructures implemented in the computing system, e.g. from a computer-aided design (CAD) file to produce an output that for display on the computer screen of the computer system. Pg. 9 Sect. 3,” For the current problem, microstructures of size 128x128x3 were generated using Dream3D with parameters in Table 1” [using operational parameters entered by a human user into a computer-implemented input/output device of the computer system]. Further see Sect. 3 and 5. The examiner has interpreted that analyzing the model filters to investigate the microstructure features extracted by the model while predicting the stress fields on a on single crystal manually-designed microstructure generated using Dream3D for the microstructure have statical parameters as analyzing a design geometry from a computer-aided design (CAD) file, using environmental and/or operational parameters entered by a human user into a computer-implemented input/output device of the computer system, and the calibrated material model from the storage for calibrated material models, to produce an output that for display on the computer screen of the computer system.)
Shrivastava does not specifically teach “generating a first test data set by performing a real-world test of a specimen of material using the real-world physical machinery to apply a load that varies over time to the specimen of material, and measuring real-world responses to the load that varies over time in the specimen of material at different times during the real-world test”, “displaying, on a user interface of the computer system, a user-selectable listing of uncalibrated material models stored in the library of uncalibrated material models” and “receiving a user selection identifying a selected one of the uncalibrated material models listed on the user interface”.
However, Hoffman teaches “generating a first test data set by performing a real-world test of a specimen of material using the real-world physical machinery to apply a load that varies over time to the specimen of material, and measuring real-world responses to the load that varies over time in the specimen of material at different times during the real-world test”. (Para. 0023, “Materials characterization tests are specialized tests performed to produce the material response represented by the constitutive model. These tests are performed on a material sample of specific shape and under loads within the load-range of interest. The material response as a function of the applied load is measured and recorded”. Para. 0024, “An example of a materials characterization test is a `uniaxial tensile test` performed to characterize the elastic-plastic response of a metal. A small cylindrical sample of the metal is strain-loaded in an axial direction until it breaks. A one-dimensional load-displacement response is recorded in a stress-versus-strain plot”. Further see 0023-0024 and 0071-0072. The examiner has interpreted that performing a uniaxial tensile test on a sample of a metal to measure and record the material response of the metal under the loads within a load-range until the metal sample breaks as generating a first test data set by performing a real-world test of a specimen of material using the real-world physical machinery to apply a load that varies over time to the specimen of material, and measuring real-world responses to the load that varies over time in the specimen of material at different times during the real-world test.)
Hoffman teaches “displaying, on a user interface of the computer system, a user-selectable listing of uncalibrated material models stored in the library of uncalibrated material models” and “receiving a user selection identifying a selected one of the uncalibrated material models listed on the user interface”. (Para. 0078, “The application includes a library of various constitutive models of materials. While these constitutive models may provide a general response pattern of a class of materials, the model parameters must be estimated to provide an accurate response of a specific material” [listing of uncalibrated material models stored in the library of uncalibrated material models]. Para. 0045, “When a product is selected from the catalog (by a double-click), various information about the product including test data, property sets for a variety of constitutive models, are displayed in the product information window (216). A parameter estimation window 220 shows a model response after the model parameters have been calculated. The parameter estimation window shows that the model response matches the test data after the parameter estimation process. A units conversion window 224 allows a user to select the units that the user want to view data in. All data displayed are automatically converted to the selected units system. A material list window 228 is a virtual `shopping list` that allows a user to select model-based property sets from the application database and build simulation code input” [e.g., displaying, on a user interface of the computer system, a user-selectable listing of uncalibrated material models stored in the library of uncalibrated material models and receiving a user selection identifying a selected one of the uncalibrated material models listed on the user interface]. Furthermore, Para. 0097, “Once a material is selected for use in a system component, the designer can identify the vulnerability of the system through modeling and simulation. The designer can identify relevant constitutive models for the simulation to represent the subject matter involved in the vulnerability study.” Further Para. 0045, 0078, and 0097. The examiner has interpreted that a designer that selects a constitutive models of materials based on a product from a catalog that provides a general response pattern of a class of materials to have the model response calculated and math test data as displaying, on a user interface of the computer system, a user-selectable listing of uncalibrated material models stored in the library of uncalibrated material models and receiving a user selection identifying a selected one of the uncalibrated material models listed on the user interface.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “generating a first test data set by performing a real-world test of a specimen of material using the real-world physical machinery to apply a load that varies over time to the specimen of material, and measuring real-world responses to the load that varies over time in the specimen of material at different times during the real-world test”, “displaying, on a user interface of the computer system, a user-selectable listing of uncalibrated material models stored in the library of uncalibrated material models” and “receiving a user selection identifying a selected one of the uncalibrated material models listed on the user interface” as conceptually seen from the teaching of Hoffman, into that of Shrivastava because this modification of performing and measuring the response after applying a load and calibrating model from a group of models for the advantageous purpose of updating models from material test data (Hoffman Para. 0027, 0084, and 0102). Further motivation to combine be that Shrivastava and Hoffman are analogous art to the current claim and are directed to using data to update models for materials.
Claim 25 is rejected under 35 U.S.C. § 103 as being unpatentable over Shrivastava in view of Hoffman as applied to claim 24, and in further view of Brink, Adam R., Robert J. Kuether, Matthew D. Fronk, Bryan L. Witt, and Brendan L. Nation. “Contact stress and linearized modal predictions of as-built preloaded assembly.” Journal of Vibration and Acoustics 142, no. 5 (2020) [herein “Brink”].
As per claim 25, Shrivastava teaches “the computer system comprise [a decimator,] a regularizer, and a smoother, [wherein the decimator is configured to decimate the second test data set], wherein the regularizer is configured to regularize the second test data set, and wherein the smoother is configured to smooth the second test data set.” (Pg. 8 Sect. 2.C., “For every peak, the clusters around it are obtained by assigning the value of 1 if the pixel value is higher than the predefined threshold times the corresponding peak stress.” Figure 5 shows the stress peaks in yellow and hotspots in cyan, signifying the regions for different thresholds (e.g., the computer system comprise a regularizer wherein the regularizer is configured to regularize the second test data set). Pg. 8, Sect. 2.C., “The stress fields are smoothed using a Gaussian filter to avoid small fluctuations” [a smoother, wherein the smoother is configured to smooth the second test data set]. Further see Sect. 2. The examiner has interpreted that modifying the thresholds that decrease the region of the cluster corresponding to the peak stress in addition to smoothening the stress fields and returning the stress field peaks with the clusters surrounding the corresponding peaks as the computer system comprise a regularizer and a smoother, wherein the regularizer is configured to regularize the second test data set, and wherein the smoother is configured to smooth the second test data set.)
Shrivastava and Hoffman do not teach “comprises a decimator” and “wherein the decimator is configured to decimate the second test data set”.
However, in the same field of endeavor namely using data to update models for materials, Brink teaches “comprises a decimator” and “wherein the decimator is configured to decimate the second test data set”. (Pg. 6 Sect. 2, “With these surface profile measurements, the data were decimated to a more workable size for postprocessing and mapping to the finite element mesh. With the in-plane camera resolution being about 6.1 µm, every ten points are retained resulting in a new in-plane resolution of 61 µm. This decimated dataset is further smoothed in MATLAB® using a moving average filter with 5% of the data used in each window. Without this smoothing, the reduced dataset had peaks with a single node harsh rise. The decimated and smoothed point cloud is overlaid onto the finite element mesh of the interface surface” [comprises a decimator and wherein the decimator is configured to decimate the second test data set]. Further see Sect. 3. The examiner has interpreted that decimating the data that is overlaid into the model as comprises a decimator and wherein the decimator is configured to decimate the second test data set.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “comprises a decimator” and “wherein the decimator is configured to decimate the second test data set” as conceptually seen from the teaching of Brink, into that of Shrivastava and Hoffman because this modification of decimating the data for the advantageous purpose of creating a more workable size of data (Brink Sect. 1-2). Further motivation to combine be that Shrivastava, Hoffman, and Brink are analogous art to the current claim and are directed to using data to update models for materials.
Claims 11, 22, and 26 are rejected under 35 U.S.C. § 103 as being unpatentable over Shrivastava in view of Hoffman as applied to claims 1, 12, and 24, respectively, and in further view of Yang, Chao, Zengyou He, and Weichuan Yu. “Comparison of public peak detection algorithms for MALDI mass spectrometry data analysis.” BMC bioinformatics Vol. 10 (2009): p1-13 [herein “Yang”].
As per claim 11, Shrivastava teaches “wherein the one or more critical points are identified automatically by a computer”. (Pg. 2 Sect. 1, “we propose an unsupervised learning-based approach” [automatically] “to this problem. Specifically, we propose an approach to predict peak-stress clusters” [the critical points are identified] “in a given microstructure using a combination of a deep learning-based Convolutional Encoder-Decoder (CED) model” [i.e., by a computer]. Further see Sect. 1 The examiner has interpreted that using a deep learning-based Convolutional Encoder-Decoder (CED) model to predict peak stresses in an unsupervised learning-based approach as wherein the one or more critical points are identified automatically by a computer.)
Shrivastava and Hoffman do not teach “adding to or deleting from the one or more critical points identified among the test data points in the first test data set prior to processing the first test data set, in response to a user input.”
However, in the same field of endeavor namely detecting critical points from test data, Yang teaches “adding to or deleting from the one or more critical points identified among the test data points in the first test data set prior to processing the first test data set, in response to a user input.” (Pg. 8 Col. 1, “False peaks are removed if the length of their ridge lines are smaller than a given threshold supplied by users” [deleting from the one or more critical points identified among the test data points in the first test data set, in response to a user input]. Pg. 2 Col. 2, “To facilitate peak detection, we often use the framework shown in Figure 1. It should be noted that smoothing and baseline correction may switch their locations in the pipeline” [moving smoothing and baseline correction after peak picking/finding (e.g., prior to processing the first test data set)]. “Figure 2 gives a concrete example of peak detection by showing the result after each step of the pipeline.” Further see Col. 1-2. The examiner has interpreted that removing false peaks by a user input before the smoothening and baseline correction steps in the process as adding to or deleting from the one or more critical points identified among the test data points in the first test data set prior to processing the first test data set, in response to a user input.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “adding to or deleting from the one or more critical points identified among the test data points in the first test data set prior to processing the first test data set, in response to a user input” as conceptually seen from the teaching of Yang, into that of Shrivastava and Hoffman because this modification of altering the number of critical points to process for the advantageous purpose of accurately and efficiently identifying the peak positions in the data (Yang, Pg. 12 Col. 1). Further motivation to combine be that Shrivastava, Hoffman, and Yang are analogous art to the current claim are directed to detecting critical points from test data.
Re Claim 22, it is a system claim, having similar limitations of claim 11. Thus, claim 22 is also rejected under the similar rationale as cited in the rejection of claim 11.
Re Claim 26, it is a method, having similar limitations of claim 22. Thus, claim 26 is also rejected under the similar rationale as cited in the rejection of claim 22.
Response to Arguments
Applicant's arguments filed on March 3, 2026 have been fully considered but they are not persuasive for the following reasons:
Applicant argues that claim 1 features are patent eligible under 35 U.S.C. § 101 because the claims do not recite mental processes as they are not performed through by a human in the mind (See Applicant’s response, Pg. 14-16).
MPEP § 2106.04(a)(2)(III)(A) recites “claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions”, “claims can recite a mental process even if they are claimed as being performed on a computer”, and “in evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.” MPEP § 2106.07(a)(I) recites “When the examiner has determined the claim recites an abstract idea, the rejection should identify the abstract idea as it is recited (i.e., set forth or described) in the claim, and explain why it falls within one of the groupings of abstract ideas (i.e., mathematical concepts, mental processes, or certain methods of organizing human activity) enumerated in MPEP § 2106.04(a)(2).”
The examiner has provided the rational for the claim limitations that are being directed to a mental process in the rejection above. For the limitation of “identifying one or more critical points among the test data points in the first test data set”, a person mentally determine or draw with pen and paper points that are local minimums, local maximums, and points of discontinuity being looking at a display of test data points. This is supported by the applicant’s disclosure as the examiner has provided a plethora of evidence from the specification to support that that a human can identify critical process for processing in the rejection above. A person can visually point out a maximum or minimum in the data, which is a critical point. While the applicant points out the examiner’s example for a mental process assumes a graphical plot, a person can mentally identify these points even it where in tabular or list form. Additionally, the claims do not limit the form in which the test data is received, i.e., a plot, table, list, etc. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). For the limitation of “processing the first test data set to produce a second test data set with differing test data points than the first test data set, wherein the second test data set includes all the test data points that were identified as critical points in the first test data set and at least some other data points”, a person can mentally create or draw with pen and paper a second data set by including both the critical points identified from the test data set and some sample points from test data set. Including is combining the critical points identified with sample points, such as into a plot or in a table of data to create a new data set, which can be done by a person without the use of a computer.
The examiner has properly identified that the claims recite a mental concept as provided in the rejection above is proper under the framework provided in the 2019 Patent Eligibility Guidance and MPEP § 2106.04(a)(2)(III)(C). The claims are directed to judicial exception, an abstract idea.
Applicant argues that claim 1 features are patent eligible under 35 U.S.C. § 101 because the claim is integrated into a practical application as claim features recite the steps are performed reliably without a human (See Applicant’s response, Pg. 17).
MPEP § 2106.05(I) recites “An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016).” MPEP § 2106.04(I) recites “Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a new abstract idea is still an abstract idea")”; MPEP § 2106.05(a) recites “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.”; MPEP § 2106.05(a) also recites “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality: vii. Providing historical usage information to users while they are inputting data, in order to improve the quality and organization of information added to a database, because "an improvement to the information stored by a database is not equivalent to an improvement in the database’s functionality" BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018)”; and furthermore MPEP § 2106.04(d)(II) recites “examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application”.
The examiner has provided the rational for the independent claim limitations that are being directed to a mental process in the rejection above. Additionally, the independent claim limitation also includes the abstract idea of a mathematical concept. The abstract idea cannot provide the improvement to integrate the claims into a practical application. Integration into a practical application can only be provided by an additional element (see further below for the additional element that is argued to being the limitation that integrates the claimed invention into a practical application).
The examiner has properly identified that the claims recite a mental concept as provided in the rejection above is proper under the framework provided in the 2019 Patent Eligibility Guidance and MPEP § 2106.04(a)(2)(III)(C). The claims are directed to judicial exception, an abstract idea.
Applicant argues that the claim 1 features are patent eligible under 35 U.S.C. § 101 because the
claim is integrated into a practical application as the claim recites additional elements that are not insignificant extra-solution data gathering nor generic computing components (See Applicant’s response, Pg. 17).
MPEP § 2106.05(d)(II) recites “The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory”. MPEP § 2106.05(g) recites “Below are examples of activities that the courts have found to be insignificant extra-solution activity: Mere Data Gathering: Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); Testing a system for a response, the response being used to determine system malfunction, In re Meyers, 688 F.2d 789, 794; 215 USPQ 193, 196-97 (CCPA 1982).”
With regards to claim 1, the additional element of “producing a first test data set by performing a real-world test of a material that comprises: applying a load to a specimen of the material using real-world physical machinery; and measuring real-world responses to the load applied to the specimen of the material” has been interpreted, not as generic computing components, but as testing of a material for a response, which has been defined by MPEP § 2106.05(d)(II) as insignificant extra-solution activity and well-understood, routine, or conventional. This is because applying a load to a material to measure a response of the material has been interpreted as testing a system for a response, which has been defined by as insignificant extra-solution activity in MPEP § 2106.05(g). Therefore, this additional element is an insignificant extra-solution activity that is well-understood, routine, or conventional as defined in MPEP § 2106.05(g) and does not integrate the claims into a practical application.
Therefore, the examiner has properly identified that the claims recite a feature that is well-understood, routine, or conventional in the art and does not integrate the abstract idea into a practical application since the feature is an insignificant extra-solution activity.
Applicant argues that reference does not teach each and every limitation in the claim 1 because cited reference fails to teach “identifying one or more critical points among the test data points in the first test data set” (See Applicant’s response, Pg. 18-20).
MPEP § 2143.03 that “All words in a claim must be considered in judging the patentability of that claim against the prior art” and “Examiners must consider all claim limitations when determining patentability of an invention over the prior art.”
As mapped in the previous Office Action in claim 1, Shrivastava discloses “identifying one or more critical points among the test data points in the first test data set” as detecting peak-stress clusters within the predicted stress field. Peak stress points are critical data points. While the applicant argues that the cited reference does not close critical points being discontinuities in the data set, the claims do not limit the critical point to be a discontinuity. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Therefore, this limitations of the claim 1 are disclosed in Shrivastava. Therefore, applicant’s arguments are not persuasive.
Applicant argues that reference does not teach each and every limitation in the claim 1 because cited reference fails to teach “processing the first test data set with a computer processor to produce a second test data set with differing test data points than the first test data set, wherein the second test data set includes all the test data points that were identified as critical points in the first test data set and at least some other data points” (See Applicant’s response, Pg. 21-22).
MPEP § 2143.03 that “All words in a claim must be considered in judging the patentability of that claim against the prior art” and “Examiners must consider all claim limitations when determining patentability of an invention over the prior art.”
As mapped in the previous Office Action in claim 1, Shrivastava discloses “processing the first test data set with a computer processor to produce a second test data set with differing test data points than the first test data set, wherein the second test data set includes all the test data points that were identified as critical points in the first test data set and at least some other data points” as a predicting stress field that are not smoothen then smoothening the stress fields and returning the stress field peaks with the clusters surrounding the corresponding peaks using a CPU machine. First, the peaks stress clusters are detected by a trained model in a stress field, e.g., identify the critical points. Second, the stress field is smoothened using a Gaussian filter, e.g., processing the first data set. Finally, hotspots around the peak clusters at added to the stress field, which is further shown in Fig. 5, e.g., adding at least some other data points to the critical points to obtain a second data set. Thus, since the processing, e.g., filtering, comes after the detection of peaks in the stress fields the claimed limitation is taught. Addition emphasis has been added to the citations provided in the rejection above.
Therefore, this limitations of the claim 1 are disclosed in Shrivastava. Therefore, applicant’s arguments are not persuasive.
Applicant’s arguments, filed March 3, 2026, with respect to the rejection(s) of claims 1-10, 12-21, and 23-25 under 35 U.S.C. 103 with regards to the combination of references, namely Brink (see Pg. 21), have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of newly found art reference.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wells, Garth. "Discontinuous modelling of strain localisation and failure." PhD diss., 2011 teaches analyzing discontinuities in the stress and strain curves of material properties models and further processing the data discontinuities through the regularization and smoothening of the data to be applied to the material models.
Examiner’s Note: The examiner has cited particular columns and line numbers in the reference that applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. In the case of amending the claimed invention, the applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for the proper interpretation and also to verify and ascertain the metes and bound of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Simeon P Drapeau whose telephone number is (571)-272-1173. The examiner can normally be reached Monday - Friday, 8 a.m. - 5 p.m. ET.
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/SIMEON P DRAPEAU/ Examiner, Art Unit 2188
/RYAN F PITARO/ Supervisory Patent Examiner, Art Unit 2188
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