DETAILED ACTION
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 .
The amendment filed on November 25, 2025 has been considered.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3 and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Handler (US 20160061748; hereinafter Handler) in view of Gaffin (US 4850715; hereinafter Gaffin), Wang et al. (US 20100235110; hereinafter Wang), and Carneiro Campello et al. (US 2023/0221266).
Regarding Claim 1, Handler teaches a process for testing material samples (Abstract, lines 1-2; the material test specimen may contain multiple components, paragraph 0041, lines 1-2), the method comprising:
acquiring, with an optical system (32), at least one image of a location of
interest within at least one of the plurality of material samples, the location of interest corresponding to a crack (paragraph 0005, lines 3-5) that either (i) initiates as a result of the performed testing (paragraph 0003, lines 8-12) or (ii) is introduced into the at least one of the plurality of material samples prior to the performed testing; and
using a data processing system (34) that is signally cooperative with the
optical system (32) (Fig. 1) to:
determine crack growth parameters of the location of interest from
the acquired at least one image (crack propagation, paragraph 0005, lines 3-5; paragraph 0049, lines 1-3, 6-9);
characterize microstructure parameters of the location of interest
from the acquired at least one image (implied by crack changes and crack lengths, paragraph 0049); and
correlate crack growth parameters and microstructure parameters
(paragraph 0049, lines 1-3).
Handler does not teach configuring a bending fatigue system to perform simultaneous fatigue testing on a plurality of material samples.
Gaffin discloses configuring a bending fatigue system (Fig. 1) to perform simultaneous fatigue testing (testing of specimen of dual materials by bending, Abstract) on a plurality of material samples (Abstract, lines 1-2);
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the invention was made to provide Handler et al. with configuring a bending fatigue system as suggested by Gaffin for the purpose of performing simultaneous fatigue testing by bending.
Handler further does not teach operating, using the data processing system, the predictive model to determine a lifetime of the at least one of the plurality of material samples.
Wang et al. teaches operating, using the data processing system, the predictive model to determine a lifetime of the at least one of the plurality of material samples.
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the invention was made to provide Handler et al. with a predictive model as suggested by Wang et al. for the purpose of determining a lifetime of the at least one of the plurality of material samples.
.
Handler further does not teach creating, with the data processing system, a predictive model based on the correlated crack growth parameters and microstructure parameters.
Carneiro Campello et al. discloses creating, with the data processing system (AI algorithm, claim 2), a predictive model (defining a service life predictive model, claim 2, lines 10-11) based on the correlated crack growth parameters and microstructure parameters (the predictive model is defined by crack growth, crack size, Fig. 3, claim 2, lines 4-11); and
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the invention was made to provide Handler et al. with creating a predictive model as suggested by Carneiro Campello et al. for the purpose of predicting life of materials (materials of flexible tubes).
Regarding claim 2, the combination of Handler, Gaffin, Wang, and Carneiro Campello et al. render obvious the methods of claim 1, and Handler further teaches: fatigue system includes color contrasting a surface of a test specimen and acquiring photos using a camera [see ¶0006-0007].
Regarding claim 3, the combination of Handler, Gaffin, Wang, and Carneiro Campello et al. render obvious the methods of claim 1, and Handler further teaches: wherein the creating crack growth parameters comprises creating a crack growth curve [see Fig. 5; ¶0043-0044 showing crack growth relation to load].
Handler does not teach: Paris fit parameters.
Wang teaches: Paris fit parameters [see ¶0005 fatigue crack growth Paris law exponent; equations 1-4].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Handler, Gaffin, Wang, and Carneiro Campello et al. with further teachings of Wang, namely by creating a crack growth curve and Paris fit parameters in order to gather information for predicting the lifespan of a sample material.
Regarding claim 14, the combination of Handler, Gaffin, Wang, and Carneiro Campello et al. render obvious the methods of claim 1, but Handler does not teach: wherein the predictive model predicts at least one of fracture and fatigue analysis for the material sample.
Wang teaches: a predictive model predicts fatigue analysis for a material sample [see ¶0022 micromechanics-based fatigue life model analyzes a material].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Handler, Gaffin, Wang, and Carneiro Campello et al. with further teachings of Wang, namely having the predictive model predict fatigue analysis for the material sample in order to analyze information for predicting the lifespan of a material.
Regarding claims 15-18, the combination of Handler, Gaffin, Wang, and Carneiro Campello et al. render obvious the methods of claim 14. Claims 15-18 do not need to be considered because they do not pertain to the fatigue analysis for the material sample and are descriptive of an alternative not required by the scope of the claim.
Regarding claim 19, the combination of Handler, Gaffin, Wang, and Carneiro Campello et al. render obvious the methods of claim 14, but Handler does not teach: wherein the fatigue analysis comprises generating at least one of a stress-strain curve, an endurance limit curve, and a Goodman curve.
Wang teaches: generating a stress-strain curve [see Fig. 2a, Fig. 2b, and Fig. 3].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Handler and Gaffin, Wang, and Carneiro Campello et al. with further teachings of Wang, namely generating a stress-strain curve in order to see how the material is deformed with applied stress.
Regarding Claim 20, Handler teaches a process for testing material samples (Abstract, lines 1-2; the material test specimen may contain multiple components, paragraph 0041, lines 1-2), the method comprising:
taking images of the samples with an optical system (32, Fig. 1) while the samples are being stressed (Fig. 1);
identifying cracks with a data processing system (paragraph 0005, lines 3-5) based on the images of the samples (Fig. 1); and
predicting crack growth rates (Abstract, lines 1-2) for untested microstructures (crack propagation, Abstract, lines 1-2z) based on an analysis of the images of the samples. (Fig. 1).
Handler does not disclose stressing samples simultaneously under test by bending the samples with a bending fatigue system.
Gaffin discloses stressing samples simultaneously under test by bending the samples with a bending fatigue system (Fig. 1; testing of specimen of dual materials by bending, Abstract).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the invention was made to provide Handler et al. with configuring a bending fatigue system as suggested by Gaffin for the purpose of performing simultaneous fatigue testing by bending.
Handler does not disclose predicting crack growth rates with the predictive model for untested microstructures.
Wang discloses predicting crack growth rates with the predictive model for untested microstructures (Abstract, lines 1-7; paragraph 0002, lines 1-7).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the invention was made to provide Handler et al. with a predicting model as suggested by Wang et al. for the purpose of predicting crack growth rates for untested microstructures.
Handler does not disclose a data processing system that is configured to generate a predictive model based on correlated crack growth parameters and microstructure parameters within the samples.
Carneiro Campello et al.discloses a data processing system that is configured to generate a predictive model (defining a service life predictive model, claim 2, lines 10-11) based on correlated crack growth parameters and microstructure parameters within the samples (the predictive model is defined by crack growth, crack size, Fig. 3, claim 2, lines 4-11).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the invention was made to provide Handler et al. with creating a predictive model as suggested by Carneiro Campello et al. for the purpose of predicting life of materials (materials of flexible tubes).
Claims 4-13 are rejected under 35 U.S.C. 103 as being unpatentable over Handler in view of Gaffin, Wang, and Carneiro Campello et al. according to claim 1 above, and further in view of and Mohamed Shibley et al. (US 20210340857; hereinafter Mohamed).
Regarding claim 4, the combination of Handler, Gaffin, Wang, and Carneiro Campello et al. render obvious the methods of claim 1, but Handler does not teach: wherein the predictive model comprises a machine learning model.
Mohamed teaches: a predictive model comprises a machine learning model [see ¶0029; ¶0039-42].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Handler, Gaffin, Wang, and Carneiro Campello et al. with the teachings of Mohamed, namely having a predictive model that comprises a machine learning model in order to efficiently make predictions of crack propagation.
Regarding claims 5-7, the combination of Handler, Gaffin, Wang, Carneiro Campello et al. and Mohamed render obvious the methods of claim 4, but Handler does not teach: wherein an algorithm used to create the machine learning model comprises a classification algorithm;
wherein the classification algorithm comprises a convolutional neural network.
Mohamed teaches: an algorithm used to create the machine learning model comprises a classification algorithm [see ¶0029 classify each crack or defect];
wherein the classification algorithm comprises a convolutional neural network [see ¶0029; ¶0039].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Gaffin, Wang, Carneiro Campello et al. and Mohamed with further teachings of Mohamed, namely having an algorithm used to create the machine learning model comprise a classification algorithm that is a convolutional neural network.
One of ordinary skill in the art would have been motivated to do this in order to analyze image data, detect cracks or defects in the images, identify and classify each crack or defect, track or monitor the cracks or defects over time [see ¶0029].
Regarding claim 8, the combination of Handler, Gaffin, Wang, Carneiro Campello et al. and Mohamed render obvious the methods of claim 5, but Handler does not teach: wherein the classification algorithm is selected from the group consisting of instance-based algorithms, Bayesian-based algorithms, dimensionality reduction algorithms, support vector machine algorithms, decision tree algorithms and ensemble-based algorithms.
Mohamed teaches: the classification algorithm is a support vector machine [see ¶0029].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Gaffin, Wang, Carneiro Campello et al. and Mohamed with further teachings of Mohamed, namely the classification algorithm is a support vector machine.
One of ordinary skill in the art would have been motivated to do this in order to analyze image data, detect cracks or defects in the images, identify and classify each crack or defect, track or monitor the cracks or defects over time [see ¶0029].
Regarding claims 9-13, the combination of Handler, Gaffin, Wang, Carneiro Campello et al. and Mohamed render obvious the methods of claim 8. Claims 9-13 do not need to be considered because they do not pertain to the selected classification algorithm of a support vector machine algorithm and are descriptive of an alternative not required by the scope of the claim.
Response to Arguments
Applicant's arguments filed on November 25, 2025 have been fully considered.
Applicant’s arguments and amendment with respect to the claim objection have been fully considered and are persuasive. The claim objection has been withdrawn.
Applicant’s arguments and amendment with respect to the rejections under 35 USC 112(b) have been fully considered and are persuasive. The rejections under 35 USC 112(b) have been withdrawn.
Applicant’s arguments and amendment with respect to the rejections under 35 USC 101 have been fully considered and are persuasive. The rejections under 35 USC 101 have been withdrawn.
With respect to the rejections under USC 103, Applicants argue “[a]s presently amended, claim 1 (and claim 20) now recites that its process for testing material samples performs such testing on numerous material samples simultaneously. … Handler is completely as to such a feature, as are each of the secondary references to Gaffin, Wang and Shilby. As such, at least this recited feature is not taught in the present combination of references, necessitating withdrawal of the present rejection.”
Examiner’s position is that, as discussed above, Handler teaches a process for testing material samples (Abstract, lines 1-2; the material test specimen may contain multiple components, paragraph 0041, lines 1-2).
Applicant’s remaining arguments and amendments have been considered but are traversed in view of the grounds of rejection discussed above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Nghiem whose telephone number is (571) 272-2277. The examiner can normally be reached on M-F.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached at (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MICHAEL P NGHIEM/Primary Examiner, Art Unit 2857 July , 2026
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