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. 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 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. DETAILED ACTION Preliminary Amendment Preliminary Amendment filed on 03/15/2022 noted by the examiner, claims 1-20 are pending. Claim Rejections - 35 USC § 112 2. 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 appl icant regards as his invention. Claims 1-20 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 s 1-20, the term s “ high grade ” “ high reliability ” “ high value ” are vague and a relative term that renders the claim indefinite. The term s “ high grade ” “ high reliability ” “ high value ” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably appraised of the scope of the invention. An artisan doing measuring and testing would not know at what point “ high grade ” “ high reliability ” “ high value ” within the scope of the claim had been accomplished because nothing within the disclosure establishes when a sufficient “ high grade ” “ high reliability ” “ high value ” occur . Note: In view of the PTO compact prosecution, the Examiner notes that due to the indefiniteness issues described above all consideration of the merits of the claims in view of prior art is as best understood. 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. Claims 1- 20 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. Claim 1, Step 1 the claim is a process (or machine) ( Yes ), Step 2A Prong One , does the claim recite an abstract idea? current claim related to a n artificial intelligence-based device for recommending a composition-process of a composite material, the device comprising: a data collection unit configured to collect composition-process condition data for a target property input by a user and store the collected condition data in a collection database; an input grade classification unit configured to classify the collected condition data into different input grades according to input grade determination factors appears is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w] ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes . Step 2A Prong Two , is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of a training data supply unit configured to store the condition data classified into the input grades in a training database and input condition data of a predetermined high grade in the training database; a model generation unit configured to learn and verify the data input from the training data supply unit and generate a composition-process model are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application, Step 2A Prong Two: NO . Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? the additional element of a data output unit configured to derive one or more composition-process conditions for the target property and store the derived composition-process conditions in an output database appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 1 not eligible. Claim 1 0 , Step 1 the claim is a process (or machine) ( Yes ), Step 2A Prong One , does the claim recite an abstract idea? current claim related to a n artificial intelligence-based method of recommending a composition-process of a composite material, which is performed by an artificial intelligence-based device for recommending a composition-process of a composite material, implemented by a computer, the method comprising: collecting composition-process condition data for a target property input by a user; classifying the collected condition data into different input grades according to input grade determination factors appears is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w] ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes . Step 2A Prong Two , is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of storing the condition data classified into the input grades in a training database and inputting condition data of a predetermined high grade to a training data supply unit; learning and verifying the data input from the training data supply unit and generating a composition-process model are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application, Step 2A Prong Two: NO . Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? the additional element of deriving, by the model, one or more composition-process conditions for the target property and storing, by a data output unit, the derived composition-process conditions in an output database appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 1 0 not eligible. Claim 2 related to a data reasoning unit configured to extract reasoning condition data for the target property and send the extracted reasoning condition data to the model generation unit in order to compare and verify the reasoning condition data with the condition data supplied from the training data supply unit appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 2 not eligible . Claim 3 related to a model variation unit configured to vary the model generated by the model generation unit according to a variation condition input by the user appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 3 not eligible . Claim 4 related to : a relationship comparison unit configured to compare a relationship between a variant material condition or variant synthesis method input by the user and a material condition or synthesis method input by the training data supply unit; and a condition variation unit configured to replace one or more factors of a material combination condition and synthesis process condition generated by the model generation unit with other factor or vary the material combination condition and synthesis process condition generated by the model generation unit to include one or more additional factors appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 4 not eligible . Claim 5 related to wherein the relationship comparison unit compares and verifies a physical relationship additionally input by the user a ppears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 5 not eligible . Claim 6 related to wherein the data output unit comprises: a first output database configured to store one or more composition-process conditions derived by a model unit;an output grade classification unit configured to classify the conditions into separate grades according to an output grade determination factor; and a second output database configured to store an output grade according to a composition-process condition, which satisfy the output grade determination factor input by the user appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 6 not eligible . Claim 7 related to wherein the output grade determination factor of the output grade classification unit is one or more of a unit price, a yield, a processing time, a preferred process, and preexistence experience appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 7 not eligible . Claim 8 related to wherein the input grade determination factors of the input grade classification unit are one or more of a data type, a data characteristic, a property weight, and an error range appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 8 not eligible . Claim 9 related to a paper or report data characteristic grade classification unit configured to give a weight to each of qualitative grades of papers or reports or each of publication years of the papers or reports and give a characteristic grade according to the weight; a laboratory data characteristic grade classification unit and factory production data characteristic grade classification unit configured to give a characteristic grade according to a user input value or a similar value to a repeatedly input value; a patent data characteristic grade classification unit configured to give a characteristic grade according to the number of family countries, a patent application year, or the number of citations; and a weight giving unit configured to determine an input grade in consideration of weights and error ranges according to the order of priority or a collection path among a plurality of properties input by the user appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 9 not eligible . Claim 11 related to wherein the generating of the composition-process model further comprises comparing and verifying data for the target property derived from a reasoning database stored in a data reasoning unit with the data supplied from the training data supply unit a ppears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 11 not eligible . Claim 12 related to wherein the generating of the composition-process model further comprises a model variation operation of varying the model according to a variation condition input by the user appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 1 2 not eligible . Claim 13 related to wherein the model variation operation comprises: comparing a relationship between a variant material condition or variant synthesis method input by the user and a material condition or synthesis method input by the training data supply unit; and deriving a variation condition to replace one or more factors of a material combination condition and synthesis process condition generated by a model generation unit with other factors or include one or more additional factors appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 13 not eligible . Claim 14 related to wherein the comparing of the relationship comprises comparing and verifying a physical relationship additionally input by the user a ppears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 14 not eligible . Claim 15 related to wherein the storing of the derived composition-process conditions in the output database comprises: storing one or more composition-process conditions generated in a model unit in a first output database; classifying, by an output grade classification unit, the conditions into different grades according to an output grade determination factor; and storing an output grade according to a composition-process condition satisfying an output grade determination factor input by the user in a second output database appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 15 not eligible . Claim 16 related to , wherein in the storing of the derived composition-process conditions in the output database, the output grade determination factor is one or more selected from among a unit price, a yield, a processing time, a preferred process, and preexistence experience appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 16 not eligible . Claim 17 related to wherein in the classifying of the collected condition data into the different input grades, the input grade determination factor is one or more selected from among a data type, a data characteristic, a property weight, and an error range appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 17 not eligible . Claim 18 related to wherein in the classifying of the collected condition data into the different input grades, the data type is one or more selected from among paper or report data, laboratory data, factory production data, and patent data a ppears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 18 not eligible . Claim 19 related to wherein in the classifying of the collected condition data into the different input grades, the paper or report data gives a weight to each of qualitative grades of papers or reports or each of publication years of the papers or reports and gives a characteristic grade according to the weight, the laboratory data and the factory production data give a characteristic grade according to a user input value or a similarity to a repeatedly input value, and the patent data gives a characteristic grade according to the number of family countries, a patent application year, or the number of citations appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 19 not eligible . Claim 20 related to wherein, when the user gives a high reliability value to the laboratory data and the factory production data or when the laboratory data and the factory production data is a repeatedly input value, a reliability is set to a high value, a grade of data having a similar value to the high value is upgraded, a grade is lowered or a difference between grades is increased when there is a greater difference, and a higher grade is given to the patent data when there are a greater number of family countries or a patent application has been filed more lately a ppears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 2 0 not eligible . Claim Rejections - 35 USC § 10 2 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) 1-20 a re rejected under 35 U.S.C. 102 (a) (1) as being anticipated by SI , CN 104748807 B , DATE PUBLISHED: 2017-11-03, CPC G01F 1/86. Regarding claim 1: SI described a n artificial intelligence-based device for recommending a composition-process of a composite material, the device comprising (abstract , correction at different load conditions , page 2, artificial intelligence model ): a data collection unit configured to collect composition-process condition data for a target property input by a user and store the collected condition data in a collection database ( page 2, collect field data ) ; an input grade classification unit configured to classify the collected condition data into different input grades according to input grade determination factors ( page 3, adjusting different level/grade of data ) ; a training data supply unit configured to store the condition data classified into the input grades in a training database and input condition data of a predetermined high grade in the training database (page 6, classified according to load the flow) ; a model generation unit configured to learn and verify the data input from the training data supply unit and generate a composition-process model (page 6, verify the dynamic characteristics of the model) ; and a data output unit configured to derive one or more composition-process conditions for the target property and store the derived composition-process conditions in an output database ( page 5-6, adjusting according to input to proper output stage , page 2, collecting establishing a sample database , the sample database in step (2), after classifying the flow according to load checking module) . . Regarding claim 1 0 : SI described a n artificial intelligence-based method of recommending a composition-process of a composite material, which is performed by an artificial intelligence-based device for recommending a composition-process of a composite material, implemented by a computer, the method comprising (abstract , correction at different load conditions , page 2, artificial intelligence model ) : collecting composition-process condition data for a target property input by a user ( page 2, collect field data ) ; classifying the collected condition data into different input grades according to input grade determination factors ( page 3, adjusting different level/grade of data ) ; storing the condition data classified into the input grades in a training database and inputting condition data of a predetermined high grade to a training data supply unit ( page 2, establishing a sample database ) ; learning and verifying the data input from the training data supply unit and generating a composition-process model ( page 2, building is a machine learning method ) ; and deriving, by the model, one or more composition-process conditions for the target property ( page 3, outputting the water supply flow quantity to calculate the main steam flow ) and storing, by a data output unit, the derived composition-process conditions in an output database ( page 5-6, adjusting according to input to proper output stage, page 2, collecting establishing a sample database , the sample database in step (2), after classifying the flow according to load checking module) . Regarding claim 2, SI further described a data reasoning unit configured to extract reasoning condition data for the target property and send the extracted reasoning condition data to the model generation unit in order to compare and verify the reasoning condition data with the condition data supplied from the training data supply unit ( page 2, obtain the condensed water flow quantity Dsn ) . Regarding claim 3 , SI further described a model variation unit configured to vary the model generated by the model generation unit according to a variation condition input by the user ( page 3, flow difference threshold ) . Regarding claim 4 , SI further described a relationship comparison unit configured to compare a relationship between a variant material condition or variant synthesis method input by the user and a material condition ( page 3, flow difference threshold) or synthesis method input by the training data supply unit; and a condition variation unit configured to replace one or more factors of a material combination condition and synthesis process condition generated by the model generation unit with other factor or vary the material combination condition and synthesis process condition generated by the model generation unit to include one or more additional factors. Regarding claim 5 , SI further described wherein the relationship comparison unit compares and verifies a physical relationship additionally input by the user ( page 3, judged water flow ) . Regarding claim 6 , SI further described a first output database configured to store one or more composition-process conditions derived by a model unit;an output grade classification unit configured to classify the conditions into separate grades according to an output grade determination factor; and a second output database configured to store an output grade according to a composition-process condition, which satisfy the output grade determination factor input by the user ( page 3-4, flow difference threshold , under the various load space, after adjusting the level represented by the pressure and temperature of the main steam flow ) . Regarding claim 7 , SI further described wherein the output grade determination factor of the output grade classification unit ( page 7, returning system each grade steam pressure ) is one or more of a unit price, a yield, a processing time, a preferred process (page 7, standardized input data to obtain X (n, m) ) , and preexistence experience (page 7, standardized input data to obtain X (n, m) ) Regarding claim 8 , SI further described wherein the input grade determination factors of the input grade classification unit are one or more of a data type ( page 2-3, classifying the flow according to load checking module ) , a data characteristic, a property weight, and an error range. Regarding claim 9 , SI further described a paper or report data characteristic grade classification unit configured to give a weight to each of qualitative grades of papers ( page 4, weighting factor) or reports or each of publication years of the papers or reports and give a characteristic grade according to the weight; a laboratory data characteristic grade classification unit and factory production data characteristic grade classification unit configured to give a characteristic grade according to a user input value or a similar value to a repeatedly input value; a patent data characteristic grade classification unit configured to give a characteristic grade according to the number of family countries, a patent application year, or the number of citations; and a weight giving unit configured to determine an input grade in consideration of weights and error ranges according to the order of priority or a collection path among a plurality of properties input by the user. Regarding claim 11 , SI further described comparing and verifying data for the target property derived from a reasoning database stored in a data reasoning unit with the data supplied from the training data supply ( page 2, obtain the condensed water flow quantity Dsn ) . Regarding claim 1 2 , SI further described a model variation operation of varying the model according to a variation condition input by the user ( page 3, flow difference threshold) . Regarding claim 1 3 , SI further described comparing a relationship between a variant material condition or variant synthesis method input by the user and a material condition ( page 3, flow difference threshold) or synthesis method input by the training data supply unit; and deriving a variation condition to replace one or more factors of a material combination condition and synthesis process condition generated by a model generation unit with other factors or include one or more additional factors. Regarding claim 1 4 , SI further described wherein the comparing of the relationship comprises comparing and verifying a physical relationship additionally input by the user ( page 3, judged water flow ) . Regarding claim 1 5 , SI further described storing one or more composition-process conditions generated in a model unit in a first output a tabase ; classifying, by an output grade classification unit, the conditions into different grades according to an output grade determination factor; and storing an output grade according to a composition-process condition satisfying an output grade determination factor input by the user in a second output database ( page 3-4, flow difference threshold , under the various load space, after adjusting the level represented by the pressure and temperature of the main steam flow) . Regarding claim 1 6 , SI further described wherein in the storing of the derived composition-process conditions in the output database, the output grade determination factor is one or more selected from among a unit price, a yield, a processing time, a preferred process (page 7, standardized input data to obtain X (n, m) ) , and preexistence experience (page 7, standardized input data to obtain X (n, m) ) . Regarding claim 1 7 , SI further described wherein in the classifying of the collected condition data into the different input grades ( page 3, adjusting different level/grade of data ) , the input grade determination factor is one or more selected from among a data type ( page 3, adjusting different level/grade of data ) , a data characteristic, a property weight, and an error range. Regarding claim 1 8 , SI further described wherein in the classifying of the collected condition data into the different input grades ( page 3, adjusting different level/grade of data ) , the data type is one or more selected from among paper or report data, laboratory data, factory production data, and patent data. Regarding claim 1 9 , SI further described wherein in the classifying of the collected condition data into the different input grades, the paper or report data gives a weight to each of qualitative grades of papers ( page 4, weighting factor ) or reports or each of publication years of the papers or reports and gives a characteristic grade according to the weight, the laboratory data and the factory production data give a characteristic grade according to a user input value or a similarity to a repeatedly input value, and the patent data gives a characteristic grade according to the number of family countries, a patent application year, or the number of citations. Regarding claim 20 , SI further described wherein, when the user gives a high reliability value to the laboratory data and the factory production data ( page 3, flow is reliable is calculated ) or when the laboratory data and the factory production data is a repeatedly input value, a reliability is set to a high value, a grade of data having a similar value to the high value is upgraded, a grade is lowered or a difference between grades is increased when there is a greater difference, and a higher grade is given to the patent data when there are a greater number of family countries or a patent application has been filed more lately. Contact information 5 . Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tung Lau whose telephone number is (571)272-2274, email is Tungs.lau@uspto.gov. The examiner can normally be reached on Tuesday-Friday 7:00 AM-5:00 PM EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice . If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TURNER SHELBY, can be reached on 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair- my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll- free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272- 1000. /TUNG S LAU/ Primary Examiner, Art Unit 2857 Technology Center 2800 March 24, 2026