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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/15/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: acquisition part, feature extraction part, and prediction part in claim 13; acquisition part, feature extraction part, and learning part in claim 14.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4, 7, 9 and 13-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al ("Property predictions for dual‐phase steels using persistent homology and machine learning." Advanced Theory and Simulations 3.3 (2020): 1900227, pages 1-6, retrieved from the Internet on 5/4/2026).
Regarding claim 1, Wang discloses a method comprising:
acquiring an image of a material (fig. 1(a); section 4, The microstructures of the achieved DP samples were observed using an optical microscope);
performing a topological data analysis on the image of the material to extract features of the material (fig. 1(b); abstract, A topological analysis of persistent homology and machine learning are combined to model microstructure–property
linkage for dual-phase steels, where a descriptor of persistent images is employed to characterize the microstructure); and
predicting a characteristic value of the material from the features of the material (fig. 3(b); abstract, stress–strain curves are predicted using an artificial neural network).
Regarding claim 2, Wang discloses a method comprising:
acquiring an image of a material (fig. 1(a); section 4, The microstructures of the achieved DP samples were observed using an optical microscope) and an actual measurement value of a characteristic value of the material (section 4, The properties of the DP samples were measured by a tensile test);
performing a topological data analysis on the image of the material to extract features of the material (fig. 1(b); abstract, A topological analysis of persistent homology and machine learning are combined to model microstructure–property
linkage for dual-phase steels, where a descriptor of persistent images is employed to characterize the microstructure); and
producing a machine learning model with the features of the material and the actual measurement value of the characteristic value of the material to generate a trained model (fig. 3(c); section 3, In the present work, microstructure–property linkage of DP samples has been successfully modeled by using the combination of persistent homology analysis and machine learning; section 4, A regular ANN model with three layers (input, hidden, and output) was used for regression analysis for the acquired microstructure and property data) for predicting a characteristic value of the material from the features of the material (fig. 3(b); section 2, Figure 3b illustrates experimental and ANN-predicted stress–strain curves of DP1. The experimental and predicted curves nearly coincide, indicating a good prediction performance of the present model).
Regarding claim 3, Wang discloses a method wherein the topological data analysis is persistent homology (abstract).
Regarding claim 4, Wang discloses a method wherein the material is a ceramic, a glass-ceramic, a polymer material, a composite resin, a glass ionomer, or a metal (section 4, The steel billets (Fe, C, Si, Mn, P, S, Al, and N in element) with 45 mm in thickness were used as the raw materials for preparing the DP1–DP6 samples).
Regarding claim 7, Wang discloses a method further comprising:
performing dimensionality reduction of the features of the material (section 2, Therefore, principal component analysis (PCA) was employed to reduce the dimension of the dataset).
Regarding claim 9, Wang discloses a method according to claim 2, further comprising:
visualizing the features of the material (figs. 1(b), 1(c), 3(b)).
Regarding claim 13, the claim recites similar subject matter as claim 1 and is rejected for the same reasons as stated above.
Regarding claim 14, the claim recites similar subject matter as claim 2 and is rejected for the same reasons as stated above.
Regarding claim 15, the claim recites similar subject matter as claim 1 and is rejected for the same reasons as stated above. Furthermore, a central processing unit, a read-only memory, a random-access memory, and an auxiliary memory device storing programs are all necessary components in a computer.
Regarding claim 16, the claim recites similar subject matter as claim 2 and is rejected for the same reasons as stated above. Furthermore, a central processing unit, a read-only memory, a random-access memory, and an auxiliary memory device storing programs are all necessary components in a computer.
Regarding claim 17, the claim recites similar subject matter as claim 1 and is rejected for the same reasons as stated above.
Regarding claim 18, the claim recites similar subject matter as claim 2 and is rejected for the same reasons as stated above.
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.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al ("Property predictions for dual‐phase steels using persistent homology and machine learning." Advanced Theory and Simulations 3.3 (2020): 1900227, pages 1-6, retrieved from the Internet on 5/4/2026) in view of Wang et al ("Biaxial flexural strength and translucent characteristics of dental lithium disilicate glass ceramics with different translucencies." Journal of prosthodontic research 64.1 (2019): pages 71-77, retrieved from the Internet on 5/4/2026), hereinafter referred to as Fu Wang.
Regarding claim 5, Wang fails to teach a method wherein the characteristic value is a biaxial flexural strength.
However Fu Wang teaches a method wherein the characteristic value is a biaxial flexural strength (abstract, The aim of this study was to evaluate the biaxial flexural strength and translucent characteristics of dental lithium disilicate glass ceramics with different translucencies. Lithium disilicate glass ceramics with different translucencies demonstrated different BFS and TP values).
Therefore taking the combined teachings of Wang and Fu Wang as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Fu Wang into the method of Wang. The motivation to combine Wang and Fu Wang would be to determine different BFS values (abstract of Fu Wang) to be used in increased applications of esthetics (section 1 of Fu Wang).
Claim(s) 6 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al ("Property predictions for dual‐phase steels using persistent homology and machine learning." Advanced Theory and Simulations 3.3 (2020): 1900227, pages 1-6, retrieved from the Internet on 5/4/2026) in view of Shen et al ("A deep learning method for extensible microstructural quantification of DP steel enhanced by physical metallurgy-guided data augmentation." Materials Characterization 180 (2021): 111392, pages 1-12, retrieved from the Internet on 5/4/2026).
Regarding claim 6, Wang fails to teach a method wherein the image is an SEM image.
However Shen teaches an SEM image (section 2.1.3).
Therefore taking the combined teachings of Wang and Shen as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Shen into the method of Wang. The motivation to combine Wang and Shen would be to accurately segment martensite and ferrite of various DP
steels and further quantify the phase fraction and grain size (section 4 of Shen).
Regarding claim 8, Wang fails to teach a method further comprising:
dividing the image and extracting the features of the material from the divided image.
However Shen teaches dividing an image (section 2.1.4, For model training, a standard SEM image was required to be continuously cut into many subimages of 128 × 128 pixels, i.e., each SEM image could produce 70 subimages, where 50 continuous subimages were assigned to the training set, and the other 20 subimages were assigned to the testing set; section 2.3) and extracting features of a material from the divided image (section 2.3; section 4, segment martensite and ferrite of various DP steels and further quantify the phase fraction and grain size).
Therefore taking the combined teachings of Wang and Shen as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Shen into the method of Wang. The motivation to combine Wang and Shen would be to accurately segment martensite and ferrite of various DP
steels and further quantify the phase fraction and grain size (section 4 of Shen).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al ("Property predictions for dual‐phase steels using persistent homology and machine learning." Advanced Theory and Simulations 3.3 (2020): 1900227, pages 1-6, retrieved from the Internet on 5/4/2026) in view of Kiyomura et al ("Characterization and optimization of pearlite microstructure using persistent homology and Bayesian optimization." ISIJ International 62.2 (9/27/2021): pages 307-312, retrieved from the Internet on 5/5/2026).
Regarding claim 10, Wang fails to teach a method further comprising:
determining parameters for extracting the features of the material through Bayesian optimization.
However Kiyomura teaches determining parameters (section 3.2, new vector was generated regarding the configuration parameters of the initial image and its Loss) for extracting features of a material (section 1, One promising method for topological analyses is persistent homology, which can capture potential topological features from a complex data space and concurrently provides a quantitative definition of the microstructure) through Bayesian optimization (section 3.2).
Therefore taking the combined teachings of Wang and Kiyomura as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Kiyomura into the method of Wang. The motivation to combine Wang and Kiyomura would be to minimize loss (section 3.2 of Kiyomura).
Related Art
Kim et al (US20230032952) – see para. [0078], [0127], [0131]
Rupela et al (US20200311100) – see para. [0032], [0060], [0069]-[0070]
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEON VIET Q NGUYEN whose telephone number is (571)270-1185. The examiner can normally be reached Mon-Fri 11AM-7PM.
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/LEON VIET Q NGUYEN/Primary Examiner, Art Unit 2663
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