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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, it recites “a plurality of predictive models each corresponding to a target variable” in line 3 and “the target variable includes a bending modulus, a bending strain, a bending strength, and a glass transition temperature” in lines 9-10. It is unclear whether each of the plurality of predictive models corresponds to a target variable respectively, or to the same target variable. The recitation of “the target variable” seems to indicate that the same target variable is involved. However, one target variable should have only one property, it cannot include “a bending modulus, a bending strain, a bending strength, and a glass transition temperature” as recited. This makes the claim unclear as to how many target variable(s) are involved.
For examination purpose, “a plurality of predictive models each corresponding to a target variable” in line 3 is assumed to be --a plurality of predictive models each corresponding to a target variable respectively--; and “the target variable includes a bending modulus, a bending strain, a bending strength, and a glass transition temperature” in lines 9-10 is assumed to be --the target variables includes a bending modulus, a bending strain, a bending strength, and a glass transition temperature--.
Regarding claim 4, it recites “The method of searching for a thermosetting epoxy resin composition according to Claim 3, wherein the feature value further includes a distance between oxygen atoms related to a thermosetting epoxy resin” in line 2-4. However, the feature value already includes “at least one of a molecular fingerprint and a molecular descriptor” as recited in claim 3. It is unclear how the same one feature value can further include different feature values.
For examination purpose, --The method of searching for a thermosetting epoxy resin composition according to Claim [[3]]2, wherein the feature value
Claims 6 and 7 are rejected for reasons similar to claim 1 discussed above.
The other claim(s) not discussed above, or depending on the above claim(s), are rejected for inheriting the issue(s) from their linking claim(s).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-3 and 5-9 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-12 of copending Application No. 18713700 in view of Jin, BONSER, and Lee (see full citations in rejection under 35 USC 103 below). The referenced claims teach substantially the same limitations of the current claims. Any differences are obvious in view of Jin, BONSER, and Lee. The rejection under 35 USC 103 below are incorporated herein by reference to address the differences.
This is a provisional nonstatutory double patenting rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
MPEP 2106 outlines a two-part analysis for Subject Matter Eligibility as shown in the chart below.
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Step 1, the claimed invention must be to one of the four statutory categories. 35 U.S.C. 101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter.
Step 2, the claimed invention also must qualify as patent-eligible subject matter, i.e., the claim must not be directed to a judicial exception unless the claim as a whole includes additional limitations amounting to significantly more than the exception.
Step 2A is a two-prong inquiry, as shown in the chart below.
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Prong One asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. If the claim recites a judicial exception (i.e., an abstract idea enumerated in MPEP § 2106.04(a), a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two. If the claim does not recite a judicial exception (a law of nature, natural phenomenon, or abstract idea), then the claim cannot be directed to a judicial exception (Step 2A: NO), and thus the claim is eligible at Pathway B without further analysis. Abstract ideas can be grouped as, e.g., mathematical concepts, certain methods of organizing human activity, and mental processes.
Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application? If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claim 1, Step 1: Is the claim to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea (judicially recognized exceptions)? Yes (see analysis below).
Prong one: Whether the claim recites a judicial exception? (Yes). The claim is directed to an abstract idea because it recites the limitations beginning from “creating time-lapse azimuth stacks between corresponding azimuth stacks in the first seismic survey and the second seismic survey” to the end of the claim. These limitations are directed to mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; and/or mental processes – concepts performed in the human mind (or with a pen and paper).
Prong two: Whether the claim recites additional elements that integrate the exception into a practical application of that exception? (No). The claim recites an additional element of “an information processor.” However, this is to invoke a generic computer for its computing power to facilitate the application of the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements are insufficient to integrate the abstract idea into a practical application of the abstract idea.
Step 2B: Does the claim recite additional elements (other than the judicial exception) that amount to significantly more than the judicial exception? No (see analysis below).
The claim does not include additional elements that are sufficient to make the claim significantly more than the judicial exception. As discussed with respect to Step 2A Prong Two above, the additional element(s) in the claim are to invoke a generic computer for its computing power to facilitate the application of the abstract idea. Also, it is routine and conventional to invoke a computer for data processing. See MPEP 2106.05(d). Considered as a whole, the claim does not amount to significantly more than the abstract idea.
Claims 6 and 7 are similarly rejected by analogy to claim 1.
Dependent claims 2-5 and 8-10 when analyzed as a whole respectively are held to be patent ineligible under 35 U.S.C. 101 because they either extend (or add more details to) the abstract idea or the additional recited limitation(s) (if any) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as discussed below: there is no additional element(s) in the dependent claims that sufficiently integrates the abstract idea into a practical application of, or makes the claims significantly more than, the judicial exception (abstract idea). The additional element(s) (if any) are mere instructions to apply an except, field of use, and/or insignificant extra-solution activities (applied to Step 2A_Prong Two and Step 2B; see MPEP 2016.05(f)-(h)) and/or well-understood, routine, or conventional (applied to Step 2B; see MPEP 2106.05(d)) to facilitate the application of the abstract idea.
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 5-9 are rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. ("Composition optimization of a high-performance epoxy resin based on molecular dynamics and machine learning" Materials and Design 194 (2020) 108932; cited in IDS; hereinafter “Jin”) in view of BONSER et al (WO 2023174891 A1; hereinafter “BONSER”) and Lee et al. ("Comparison of Machine Learning Methods towards Developing Interpretable Polyamide Property Prediction" Polymers 2021, 13, 3653; hereinafter “Lee”).
Regarding claim 1, Jin teaches a method of searching for a thermosetting epoxy resin composition (i.e., “Composition optimization of a high-performance epoxy resin based on molecular dynamics and machine learning”; see title) executed by an information processor (a processor is implied or obvious for machine learning), the method comprising the steps of:
training a (i.e., “The task of ML for the resin system design was realized by an artificial neural network (ANN)”; see p. 3, col. 1, ¶ 7) each corresponding to a target variable (i.e., “These material properties as output parameters”; see p. 4, col. 1, ¶ 1) using actual data related to a thermosetting epoxy resin composition (i.e., “The raw data set was divided randomly into a training data set (70%) and testing data set (30%)”; see p. 5, col. 1 ¶ 2); and
searching for a thermosetting epoxy resin composition with a desired balance of physical properties by inverse analysis using the (i.e., “An optimized high-performance epoxy resin system considered Young's modulus (E), Ultimate Tensile Strength (UTS), Elongation (δ), and the glass transition temperature (Tg) together was designed by using the proposed method”; see Abstract),
wherein the actual data include a polymer composition, a structural formula, an epoxy equivalent, and a blending condition related to a thermosetting epoxy resin (i.e., “the ratios of matrix resin, curing agent and toughening agent as input features… the content of six components of DGEBA, TGDDM, DICY, DDS, PES, and PEI were selected as the input”; see p. 4, col. 2, ¶ 1), and
the target variable includes (i.e., “considered Young's modulus (E), Ultimate Tensile Strength (UTS), Elongation (δ), and the glass transition temperature (Tg) together was designed by using the proposed method”; see Abstract; “The output layer was the four key performances, which are Tg, E, UTS, and δ”; see p. 4, col. 2, ¶ 1; see the rejection under 35 USC 112(b) above for claim interpretation).
Jin does not explicitly disclose (see only the underlined):
the target variable includes a bending modulus, a bending strain, a bending strength, and a glass transition temperature.
But BONSER teaches:
a bending modulus, a bending strain, and a bending strength as properties of interest (i.e., “The adhesive bond between the blade shells or between the shear web and spars is required to exhibit high structural strength, in particular high tensile strength and high flexural strength, and high fatigue strength”; see [007]; “the flexural strength, flexural modulus and flexural strain which were measured under the test protocol of ISO 178”; see [071]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Jin in view of BONSER by incorporating a bending modulus, a bending strain, and a bending strength as the target variables, as claimed. The rationale would be to account for the other known properties in designing the epoxy resin.
Jin does not explicitly disclose (see only the underlined):
training a plurality of predictive models each corresponding to a target variable using actual data related to a thermosetting epoxy resin composition;
searching for a thermosetting epoxy resin composition with a desired balance of physical properties by inverse analysis using the plurality of predictive models.
The difference is that Jin uses one predictive model for predicting all properties (i.e., target variables) of interest, rather than one models for each property.
But Lee teaches:
training a model for predicting each property (i.e., “we first train a set of 5 RF models (80% train, 20% test) for a given property”; see p. 6, ¶ 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Jin in view of BONSER, further in view of Lee, by training a plurality of predictive models each corresponding to a target variable using actual data related to a thermosetting epoxy resin composition; and searching for a thermosetting epoxy resin composition with a desired balance of physical properties by inverse analysis using the plurality of predictive models, as claimed. The rationale would be to provide flexibility of reusing the individual models for discovering a material composition when considering a different combination of target properties.
Regarding claims 2 and 3, the prior art applied to the preceding linking claim(s) teaches the features of the linking claim(s).
Jin does not explicitly disclose:
(claim 2) wherein in the step of training the plurality of predictive models, a feature value is calculated on the basis of the actual data and is used as an explanatory variable of the plurality of predictive models.
(claim 3) wherein the feature value includes at least one of a molecular fingerprint and a molecular descriptor.
But Lee further teaches:
a feature value is calculated on the basis of the actual data and is used as an explanatory variable of the plurality of predictive models; wherein the feature value includes at least one of a molecular fingerprint and a molecular descriptor (i.e., “We use extended connectivity fingerprints (ECFP) and traditional QSPR fingerprints to develop machine learning models to perform high fidelity prediction of glass transition temperature (Tg), melting temperature (Tm), density (ρ), and tensile modulus (E)”; see Abstract; “These descriptors were calculated using RDKit… The resulting fingerprint is a one-hot encoded vector of these descriptors”; see p. 4, ¶ 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Jin in view of BONSER and Lee, by adapting the input variables, such that in the step of training the plurality of predictive models, a feature value is calculated on the basis of the actual data and is used as an explanatory variable of the plurality of predictive models; and that the feature value includes at least one of a molecular fingerprint and a molecular descriptor, as claimed. The rationale would be to use a known type of input feature for its ability of representing a material composition.
Regarding claim 5, the prior art applied to the preceding linking claim(s) teaches the features of the linking claim(s).
Jin does not explicitly disclose:
wherein the actual data include actual data of a thermosetting epoxy resin composition used for a predetermined application and actual data of a thermosetting epoxy resin composition used for an application other than the predetermined application, and
the step of training the plurality of predictive models includes training the plurality of predictive models using the actual data of the thermosetting epoxy resin composition used for the application other than the predetermined application and then relearning the plurality of predictive models using the actual data of the thermosetting epoxy resin composition used for the predetermined application.
This is a method of transfer learning in machine learning.
However, Lee further teaches:
multitask learning and transfer leaning (see p. 2, ¶ 5 and p. 13, ¶ 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Jin in view of BONSER and Lee, such that the actual data include actual data of a thermosetting epoxy resin composition used for a predetermined application and actual data of a thermosetting epoxy resin composition used for an application other than the predetermined application, and that the step of training the plurality of predictive models includes training the plurality of predictive models using the actual data of the thermosetting epoxy resin composition used for the application other than the predetermined application and then relearning the plurality of predictive models using the actual data of the thermosetting epoxy resin composition used for the predetermined application, as claimed. The rationale would be to help overcome the issue of small dataset (see Lee p. 2, ¶ 5).
Regarding claim 6, the claim recites the same substantive limitations as claim 1 and is rejected by applying the same teachings.
Regarding claim 7, the claim recites the same substantive limitations as claim 1 and is rejected by applying the same teachings.
Regarding claim 8, the claim recites the same substantive further limitations as claim 5 and is rejected by applying the same teachings.
Regarding claim 9, the claim recites the same substantive further limitations as claim 5 and is rejected by applying the same teachings.
Notes
Claim 4 distinguishes over the closest prior art of record as discussed below.
Regarding claim 4, the closest prior art of record fails to teach the feature: “wherein the feature value [” in combination with the rest of the claim limitations as claimed and defined by the Applicant.
Jin teaches the ratios of matrix resin, curing agent and toughening agent as the input features. Lee teaches fingerprints or descriptors as the input features. None of the prior art of record teaches or suggests a distance between oxygen atoms related to a thermosetting epoxy resin as the input feature as claimed.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
KATSUKI (WO 2020148588 A1; see, also, US 20200227143 A1) teaches a method for discovering new material candidates from a chemical database, involving extracting a feature vector from a chemical formula, learning a prediction model for predicting property values from the feature vector with a sparse kernel model employing the chemical database.
KAKUDA et al. (US 20240378355 A1) teaches a method of predicting property of a resin composite material, involving predicting the property while searching a combination of one or more raw materials to be optimized and changing mixing amounts of the one or more raw materials included in the combination, and identifying a combination of one or more raw materials and mixing amounts of the one or more raw materials included in the combination such that the predicted property approximates to a target property of the composite material corresponding to the prediction data.
FUJISHIMA et al. (US 20230252319 A1) teaches a system for material discovery, involving a machine learning model that predicts a characteristic or composition of a thermosetting resin composition.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN C KUAN whose telephone number is (571)270-7066. The examiner can normally be reached M-F: 9:00AM-5:30PM.
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/JOHN C KUAN/Primary Examiner, Art Unit 2857
