CTFR 18/398,300 CTFR 87512 DETAILED ACTION Claims 1-20 are pending in the current application. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Response to Arguments Applicant’s arguments, see Remarks, filed 3/5/26, with respect to the rejection of claim 1 under 103 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 Therefore, the rejection has been withdrawn.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 Ramsl (Pub. No. US 2023/0040412 A1) [0042] lines 1-11 which shows the specifics of training the machine learning/AI language model based on training data/datasets that include paired/remapped source code elements with one written in a first programming language and the other written in the second/target programming language where it is trained to output source code in the second target programming language based on the first programming language input that in light of the teachings of Rei Col. 7 lines 26-40 and Col. 12 lines 36-65 which shows based on a calculated auditing score, viewed as type of validation score, associated with a machine learning model being able to determine to adjust/train model parameters associated with the model and retraining the model based on the audit score with the intent being to improving the model where the specifics of an AI language model is specifically seen in the teachings of Singh Col. 2 lines 5-8 and the specifics of the validation score is seen in the teachings of Balasubramanian [0010] lines 6-16 and [0012] lines 1-17. 07-37 AIA Applicant's arguments filed 3/5/26 have been fully considered but they are not persuasive. Applicant argues that (Argument 1; Remarks pg. 11 lines 1-5) that the claims are patent eligible subject matter under 35 USC 101. With respect to applicant’s argument examiner respectfully disagrees. As to argument 1, the discussion during the previous interview was that the provided amendment did not overcome the 101 rejection some potential examples of subject matter was discussed that could overcome the 101 depending on the detail recited/how it was claimed but no specific language was agreed to. This claims as amended are still viewed as being directed to an abstract idea mental process with the full mapping of the claim limitation seen below in the 101 rejection of those claims . Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 an abstract idea without significantly more. 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 1-20 are claims that are directed to a process, machine, manufacture or composition of matter. 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, 9 and 16: The limitation of “generating…output source code based on input source code;” “identifying,…, respective complexity scores for the input source code and the output source code using one or more complexity metrics;” “generating,… based on an evaluation of the respective complexity scores, a validation score for the output source code,” “adjusting one or more parameters of the AI language model based on the validation score to improve a quality and an accuracy of the code translations,” “identify respective complexity scores for input source code and output source code using one or more complexity metrics, wherein the output source code is generated …based on the input source code,” “generate, based on an evaluation of the respective complexity scores, a validation score for the output source code” and “adjust one or more parameters of the AI language model based on the validation score to improve a quality and an accuracy of the code translations” as drafted, is a function thus under its broadest reasonable interpretation recite the abstract idea of a mental process. The limitation encompasses a human mind carrying out the function of translation source code from one language to another and evaluation of source codes to identify and assign a complexity value and compare complexity value scores to determine a validation score and adjust parameters used in determining translation in light of validation calculation through observation, evaluation, judgment and/or opinion or even with the aid of pen and paper. Thus, this limitation recites and falls within the “Mental Process” grouping of abstract ideas under Prong 1. The claims have been identified to recite an abstract idea, Step 2A Prong 2 will evaluate whether the claims are directed to the judicial exception. Step 2A Prong 2: Claims 1, 9 and 16: The abstract idea is not integrated into a practical application. In particular the claims recite the following additional element “using a code analysis model,” “An apparatus comprising: a memory; and a processing device, operatively coupled to the memory, the processing device configured to” and “A non-transitory computer readable storage medium storing instructions which, when executed, cause a processing device to,” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer, and/or mere computer components. Further the additional elements of “…generated by an artificial intelligence (AI) language model… wherein the Al language is trained using datasets of original source code in one programming language that has been remapped to source code in a different programming language, and wherein the Al language is trained to perform code translations by generating the output source code based on the input source code,” and “retraining the AI language model, based on the validation score, to improve the quality and the accuracy of the code translations” fails to meaningfully limit the claim because it does not require any particular application of the recited “artificial intelligence (AI) language model” of “retraining” and is at best the equivalent of merely adding the words “apply it” to the judicial exception. Accordingly, the additional elements do not integrate the recited judicial exception into a practical application and the claim is therefore directed to the judicial exception. See MPEP 2106.05(g). After having evaluating the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claims, 1, 9 and 16 not only recite an abstract idea but that the claims are directed to the abstract idea as the abstract idea has not been integrated into practical application. Step 2B: Claims 1, 9 and 16: The claims do not include additional elements, alone or in combination that are sufficient to amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “using a code analysis model,” “An apparatus comprising: a memory; and a processing device, operatively coupled to the memory, the processing device configured to” and “A non-transitory computer readable storage medium storing instructions which, when executed, cause a processing device to,” amount to no more than mere instructions, or generic computer/computer components to carry out the exception. Further, the additional element of “…generated by an artificial intelligence (AI) language model… wherein the Al language is trained using datasets of original source code in one programming language that has been remapped to source code in a different programming language, and wherein the Al language is trained to perform code translations by generating the output source code based on the input source code” and “retraining the AI language model, based on the validation score, to improve the quality and the accuracy of the code translations” does not require any particular application of the recited AI language model and is at best the equivalent of merely adding the words “apply it” to the judicial exception. Mere instructions to apply an exception cannot provide an inventive concept. The recitation of generic computer instruction and computer components to apply the judicial exception and mere instructions to apply an exception, do not amount to significantly more, thus, cannot provide an inventive concept. Accordingly, the claims are not patent eligible under 35 USC 101. Having concluded analysis within the provided framework, claims 1, 9 and 16 do not recite patent eligible subject matter under 35 USC 101. With regard to claims 2, 10 and 18 they recite additional elements of “wherein the input source code is implemented in a first programming language and the output source code is implemented in a second programming language that is different from the first programming language” which is merely a field of use/technological environment which does not integrate the judicial exception into a practical application. Moreover, claims 2, 10 and 18 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 2, 10 and 18 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 2, 10 and 18 do not recite patent eligible subject matter under 35 USC 101. With regard to claims 3 and 11 they recite additional elements of “wherein the one or more complexity metrics include one or more of a cyclomatic complexity metric, one or more Halstead metrics, a live variable metric, a knot metric, an ultrametric topology metric, and a complexity index based on a plurality of complexity metrics” which is merely a field of use/technological environment which does not integrate the judicial exception into a practical application. Moreover, claims 3 and 11 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 3, and 11 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 3 and 11 do not recite patent eligible subject matter under 35 USC 101. As to claims 4 and 12 the limitation of “wherein identifying respective scores for the input source code and the output source code using one or more complexity metrics includes: calculating a first complexity score for the input source code using a plurality of complexity metrics, wherein the first complexity score represents a combination of the plurality of complexity metrics; and calculating a second complexity score for the output source code using the plurality of complexity metrics, wherein the second complexity score represents a combination of the plurality of complexity metrics” is an additional mental process element under prong 1. Moreover, claims 4 and 12 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 4, and 12 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 4 and 12 do not recite patent eligible subject matter under 35 USC 101. As to claims 5 and 13 the limitation of “wherein generating, based on an evaluation of the respective complexity scores, a validation score for the output source code includes: adjusting a weight of a complexity score of at least one of the input source code and the output source code based on its programming language” is an additional mental process element under prong 1. Moreover, claims 5 and 13 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 5, and 13 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 5 and 13 do not recite patent eligible subject matter under 35 USC 101. As to claims 6 and 14 the limitation of “regenerating, by the AI language model based on the validation score, the output source code from the input source code” is an additional mental process element under prong 1. Moreover, claims 6 and 14 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 6, and 14 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 6 and 14 do not recite patent eligible subject matter under 35 USC 101. With regard to claim 7 it recite additional elements of “indicating that the validation score is outside of an acceptable tolerance” which is merely insignificant extra-solution activity information of displaying information which does not integrate the abstract idea into a practical application. Further, the insignificant extra solution data activity is also WURC, see MPEP 2106.05(d)(II), where “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. presenting offers where the indicating the validation score is outside an acceptable tolerance limitation is akin to presenting/displaying/indicating information. Moreover, claim 7 does not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claim 7 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 7 does not recite patent eligible subject matter under 35 USC 101. As to claims 8, 15 and 20 the limitation of “generating, subsequent to retraining the AI language model, a second validation score for regenerated output source code; and quantifying an improvement of the AI language model based on at least the validation score and the second validation score” is an additional mental process element under prong 1. Moreover, claims 8, 15 and 20 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 8, 15 and 20 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 8, 15 and 20 do not recite patent eligible subject matter under 35 USC 101. With regard to claim 17 it recite additional elements of “wherein the output source code is generated by the AI language model in response to prompting the AI language model to generate the output source code using the input source code as part of a prompt” which is merely insignificant extra-solution activity information of transmitting and receiving information which does not integrate the abstract idea into a practical application. Further, the insignificant extra solution data activity is also WURC, see MPEP 2106.05(d)(II), where “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 where the in response to prompting limitation is akin to receiving or transmitting data information. Moreover, claim 17 does not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claim 17 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 17 does not recite patent eligible subject matter under 35 USC 101. With regard to claim 19 it recite additional elements of “wherein the instructions further cause the processing device to: prompt the AI language model, based on the validation score, to regenerate the output source code from the input source code” which is merely insignificant extra-solution activity information of transmitting and receiving information which does not integrate the abstract idea into a practical application. Further, the insignificant extra solution data activity is also WURC, see MPEP 2106.05(d)(II), where “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 where the prompting based on the validation score limitation is akin to receiving or transmitting data information. Moreover, claim 19 does not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claim 19 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 19 does not recite patent eligible subject matter under 35 USC 101. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-2, 4, 6-10, 12 and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Singh et al. (Patent No. US 11,693,637 B1) in view of Balasubramanian et al. (Pub. No. US 2022/0269580 A1) in view of Rei et al. (Patent No. US 11,514,349 B1) and further in view of Ramsl (Pub. No. US 2023/0040412 A1) . As to claims 1 and 16 Singh discloses a method of using complexity metrics to assess code generated using artificial intelligence comprising: generating, by an artificial intelligence (AI) language model, output source code based on input source code (Singh Col. 2 lines 5-8; which shows using AI/machine learning language type models to translates/generated from an input/source/base source code a target/output source code ); Identifying, using a code analysis module, respective complexity scores for the input source code and the output source code using one or more complexity metrics (Singh Col. 15 lines 40-53 and Col. 16 lines 1-6 and lines 30-36, Col. 23 lines 40-53 and 62-64 and Col. 25 lines 1-3; which shows being able to generate/determine, from an evaluation engine/code analysis module, how closely generate source code matches/conforms to original source code based on evaluation conditions that can include such information as a generated/identified embedding for the original source code, generated/identified embedding for the translated source code, and/or the complexity of the source code being evaluated as compared to complexity of other source code viewed as showing a type of identifying complexity score/value for the source codes base on one or more code complexity metrics/characteristics of the source code being compared ); and generating, using the code analysis module and based on an evaluation of the respective complexity scores, a validation for the output source code (Singh Col. 15 lines 15-26 and 40-53 and Col. 16 lines 1-6 and lines 30-36, Col. 23 lines 40-53 and Col. 25 lines 1-3; which shows how based on the determined evaluation conditions, evaluated with evaluation engine/code analysis module, associated with the code that can be associated with complexity, determine if the generated source code is selected viewed as showing the complexity score is used to validate/select specific source code ). Singh does not specifically disclose wherein the Al language is trained using datasets of original source code in one programming language that has been remapped to source code in a different programming language, and wherein the Al language is trained to perform code translations by generating the output source code based on the input source code; the specifics of generating, using the code analysis module and based on an evaluation of the respective complexity scores, a validation score for the output source code; adjusting one or more parameters of the AI language model based on the validation score to improve a quality and an accuracy of the code translations; and retraining the AI language model, based on the validation score, to improve the quality and the accuracy of the code translations. However, Balasubramanian discloses the specifics of generating, using the code analysis module and based on an evaluation of the respective complexity scores, a validation score for the output source code (Balasubramanian [0010] lines 6-16 and [0012] lines 1-17; which shows being able to generate a specific validity/validation score for source code based on the analysis of the source code features/characteristics determined in the source code that are compared to source code features claimed and use that information to select source code where validation score can be a reflection of similarity score, that in light of the teachings of Singh above showing that being able to compare origin source code metric/complexity score to generated/translated source code and determine code feature/criteria/metric associated with it and based on evaluation and comparison between the two source code based on evaluation metric that can include metrics related to complexity to determine if code is valid/selected for use that uses emulation engine/code analysis module in its analysis, and thus together can together be viewed as showing generating, using the code analysis module and based on an evaluation of the respective complexity scores, a validation score for the output source code). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Balasubramanian showing the specifics of calculating a validation score for source code comparison into the evaluation of source code translations of Singh for the purpose of reducing the delay in verifying software for use as taught by Balasubramanian [0008] lines 1-6 and [0010] lines 6-16. Singh as modified by Balasubramanian do not specifically disclose wherein the Al language is trained using datasets of original source code in one programming language that has been remapped to source code in a different programming language, and wherein the Al language is trained to perform code translations by generating the output source code based on the input source code; adjusting one or more parameters of the AI language model based on the validation score to improve a quality and an accuracy of the code translations; and retraining the AI language model, based on the validation score, to improve the quality and the accuracy of the code translations. However, Rei discloses adjusting one or more parameters of the AI language model based on the validation score to improve a quality and an accuracy of the code translations; and retraining the AI language model, based on the validation score, to improve the quality and the accuracy of the code translations (Rei Col. 7 lines 26-40 and Col. 12 lines 36-65; that shows based on a calculated auditing score, viewed as type of validation score, associated with a machine learning model being able to determine to adjust/train model parameters associated with the model and thus retraining the model based on the audit score with the intent being to improving the model where the specifics of an AI language model is specifically seen in the teachings of Singh above and the specifics of the validation score is seen in the teachings of Balasubramanian above and thus together show the specifics of adjusting one or more parameters of the AI language model based on the validation score to improve a quality and an accuracy of the code translations; and retraining the AI language model, based on the validation score, to improve the quality and the accuracy of the code translations ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Rei showing the specifics of being able to determine improvement of a retrained model, into the evaluation of trained model results of Singh as modified by Balasubramanian for the purpose of increase ease of determining if a retrained model is an effective improvement for use over the original model and thus have improved efficiency, as taught by Rei Col. 12 line56- Col. 13 line 1. Singh as modified by Balasubramanian and Rei do not specifically disclose wherein the Al language is trained using datasets of original source code in one programming language that has been remapped to source code in a different programming language, and wherein the Al language is trained to perform code translations by generating the output source code based on the input source code. However, Ramsl discloses wherein the Al language is trained using datasets of original source code in one programming language that has been remapped to source code in a different programming language, and wherein the Al language is trained to perform code translations by generating the output source code based on the input source code (Ramsl [0042] lines 1-11; which shows the specifics of training the machine learning/AI language model based on training data/datasets that include paired/remapped source code elements with one written in a first programming language and the other written in the second/target programming language where it is trained to output source code in the second target programming language based on the first programming language input ) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Ramsl showing the specifics of a machine learning model for source code translation from one programming language to another programming language trained based on paired remapped input source code languages into the machine learning source code programming language translation of Singh as modified by Balasubramanian and Rei for the purpose of improving adaptability of training of the machine learning model to allow for translation between plurality of programming language to that operations can be later performed without having to deal with multiple programming languages. As to claims 2 and 18, Singh discloses wherein the input source code is implemented in a first programming language and the output source code is implemented in a second programming language that is different from the first programming language (Singh Col. 1 lines 45-49 and Col. 2 lines 5-8; which shows the translation of base source code in one programming language to a target/output source code programming language and thus viewed as different from the first/base programming language of the source code ). As to claim 4, Singh discloses wherein identifying respective scores for the input source code and the output source code using one or more complexity metrics includes: calculating a first complexity score for the input source code using a plurality of complexity metrics, wherein the first complexity score represents a combination of the plurality of complexity metrics; and calculating a second complexity score for the output source code using the plurality of complexity metrics, wherein the second complexity score represents a combination of the plurality of complexity metrics (Singh Col. 15 lines 21-44, Col. 16 lines 1-6 and lines 30-57, Col. 19 lines 38-46 and Col. 25 lines 1-3; which shows being able to take into account a plurality of evaluation conditions for the comparison between the base/input source code and the target/output source code where the different evaluation conditions are viewed as different complexity metrics such as different generated embeddings for both the base and target source code and being able to determine between different generate source code which have better evaluation metric/conditions thus viewed as a plurality of evaluation conditions can be taken into account viewed as a type of combined complexity score/value of the evaluation metrics/conditions as the specifics of how the complexity score is calculated from the plurality of complexity metrics is not specified in the claim ). As to claim 6, Singh discloses regenerating, by the AI language model based on the validation score, the output source code from the input source code (Singh Col. 15 lines 15-26 and Col. 23 lines 40-53; which shows as part of evaluation the source code generated as part of natural language engine/AI model is able to determine based on evaluation and if generated source code satisfies an associated evaluation condition and based on that determination decide if the engine need to generate additional/regenerate source code output, where the condition can be a determination in the generated complexity measure is within a threshold degree of measurement of the base source code complexity measure viewed as a type of validation indicator, where the teachings of Balasubramanian disclosed above shows the specifics of a validity score determination of source code and thus together can be viewed as showing regenerating, by the AI language model based on the validation score, the output source code from the input source code ). As to claim 7, Singh discloses indicating that the validation score is outside of an acceptable tolerance (Singh Col. 15 lines 15-26 and Col. 23 lines 40-53; which shows as part of evaluation the source code being to determine based on evaluation and if generated source code satisfies an associated evaluation condition and based on that determination decide if the engine need to generate additional/regenerate source code output, where the condition can be a determination in the generated complexity measure is within a threshold degree of measurement of the base source code complexity measure viewed as a type of validation indicator and thus by generating additional source code viewed as a type of indication that the validation measure is not within a threshold value/outside acceptable tolerance where the teachings of Balasubramanian disclosed above shows the specifics of a validity score determination of source code and thus together can be viewed as showing indicating that the validation score is outside of an acceptable tolerance ). As to claims 8 and 20, Singh as modified by Balasubramanian do not specifically disclose however, Rei discloses generating, subsequent to retraining the AI language model, a second validation score for regenerated output source code; and quantifying an improvement of the AI language model based on at least the validation score and the second validation score (Rei Col. 12 lines 36-65; which shows for a retrained machine learning model being able to determine/calculate an auditing score, viewed as a type of validation score, for the retrained model and being able to compare it to the auditing score of the first/original machine learning model to determine in the retrained machine learning model improves by a predetermined value, viewed as a quantifying improvement of the AI/machine learning model based on at least the first and second validation/auditing score, where the specifics of an AI language model is specifically seen in the teachings of Singh above and the specifics of the validation score is seen in the teachings of Balasubramanian above ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Rei showing the specifics of being able to determine improvement of a retrained model, into the evaluation of trained model results of Singh as modified by Balasubramanian for the purpose of increase ease of determining if a retrained model is an effective improvement for use over the original model and thus have improved efficiency, as taught by Rei Col. 12 line56- Col. 13 line 1. As to claim 9, Singh discloses an apparatus comprising: a memory (Singh Col. 7 lines 58-63); and a processing device, operatively coupled to the memory, the processing device configured to (Singh Col. 7 lines 58-63): The remaining limitations of claim 9 are comparable to claim 1 above and rejected under the same reasoning. As to claim 10 it is comparable to claim 2 above and rejected under the same reasoning. As to claim 12 it is comparable to claim 4 above and rejected under the same reasoning. As to claim 14 it is comparable to claim 6 above and rejected under the same reasoning. As to claim 15 it is comparable to claim 8 above and rejected under the same reasoning. As to claim 17, Singh discloses, wherein the output source code is generated by the AI language model in response to prompting the AI language model to generate the output source code using the input source code as part of a prompt (Singh Col. 2 lines 5-8, Col. 14 lines 13-30 and Col. 18 lines 30-36; which shows response to a request/prompt to the target source code generation system, viewed as including the ai/machine learning language model, being able to translate/output source code snippet/part of source code from one/source programming language to another target programming language where the source code snippet to be translated is received/obtained as part of the translation thus viewed as part of the request/prompt ). As to claim 19, Singh discloses, wherein the instructions further cause the processing device to: prompt the AI language model, based on the validation score, to regenerate the output source code from the input source code (Singh Col. 14 lines 13-18 and lines 30-35Col. 15 lines 15-26, Co. 18 lines 30-36 and Col. 23 lines 40-53; which shows as part of evaluation the source code generated as part of natural language engine/AI model is able to determine based on evaluation and if generated source code satisfies an associated evaluation condition and based on that determination that the evaluation conditions are not satisfice trigger/prompt the source code generation system engine associated with the ai/machine learning language model to generate additional/regenerate source code output, where the condition can be a determination in the generated complexity measure is within a threshold degree of measurement of the base source code complexity measure viewed as a type of validation indicator, where the teachings of Balasubramanian disclosed above shows the specifics of a validity score determination of source code and thus together can be viewed as showing prompt the AI language model, based on the validation score, to regenerate the output source code from the input source code ) . 07-22-aia AIA Claim s 3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Singh, Balasubramanian, Rei and Ramsl as applied to claim s 1 and 9 above, and further in view of Kadam (Pub. No. US 2019/0079759 A1) . As to claims 3 and 11 Singh as modified by Balasubramanian, Rei and Ramsl do not specifically disclose wherein the one or more complexity metrics include one or more of a cyclomatic complexity metric, one or more Halstead metrics, a live variable metric, a knot metric, an ultrametric topology metric, and a complexity index based on a plurality of complexity metrics. However, Kadam discloses wherein the one or more complexity metrics include one or more of a cyclomatic complexity metric, one or more Halstead metrics, a live variable metric, a knot metric, an ultrametric topology metric, and a complexity index based on a plurality of complexity metrics (Kadam [0042] lines 11-21; which shows the evaluation of source code can be based on a plurality of metric including evaluating the code base on cyclomatic complexity metric and thus viewed as showing that the one or more complexity metrics include one or more of a cyclomatic complexity metric as claimed ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Kadam showing the specifics of being able to evaluate source code according to a cyclomatic complexity metric into the evaluation of source code base on complexity of Singh as modified by Balasubramanian, Rei and Ramsl for the purpose of increasing the adaptability of the evaluation of source code to take into account addition metrics and thus have a more accurate analysis of the source code, as taught by Kadam [0002] lines 38-45 and [0042] lines 11-21 . 07-21-aia AIA Claim s 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Singh, Balasubramanian, Rei and Ramsl as applied to claims 1 and 9 above, and further in view Burke et al. (Pub. No. US 2021/0194891 A1) As to claims 5 and 13 Singh as modified by Balasubramanian, Rei and Ramsl does not specifically disclose wherein generating, based on an evaluation of the respective complexity scores, a validation score for the output source code includes: adjusting a weight of a complexity score of at least one of the input source code and the output source code based on its programming language. However, Burke discloses wherein generating, based on an evaluation of the respective complexity scores, a validation score for the output source code includes: adjusting a weight of a complexity score of at least one of the input source code and the output source code based on its programming language (Burke [0024] lines 1-9 and [0054] lines 4-18; which shows being able to assign different weights to different program language representations to adjusted associated classification score, viewed as a score to show similarity, where the specifics of a complexity score being used to show type of similarity between different source code languages is seen specifically disclosed above in the teachings of Singh and therefore together are viewed as showing adjusting a weight of a complexity score of at least one of the input source code and the output source code based on its programming language ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Burke showing the specifics of being able assign weights to parameter values used to determine similarity between two comparable elements, into the determination of valid similarity between two programming language source code of Singh as modified by Balasubramanian, Rei and Ramsl for the purpose of improving the efficiency of classifying parameters for comparison, as taught by Burke [0024] lines 1-13 . 07-21-aia AIA Claim s 8, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Singh and Balasubramanian as applied to claims 1, 9 and 16 above, and further in view Rei et al. (Patent No. US 11,514,349 B1) . As to claims 8, 15 and 20 Singh as modified by Balasubramanian do not specifically disclose generating, subsequent to retraining the AI language model, a second validation score for regenerated output source code; and quantifying an improvement of the AI language model based on at least the validation score and the second validation score. However, Rei discloses generating, subsequent to retraining the AI language model, a second validation score for regenerated output source code; and quantifying an improvement of the AI language model based on at least the validation score and the second validation score (Rei Col. 12 lines 36-65; which shows for a retrained machine learning model being able to determine/calculate an auditing score, viewed as a type of validation score, for the retrained model and being able to compare it to the auditing score of the first/original machine learning model to determine in the retrained machine learning model improves by a predetermined value, viewed as a quantifying improvement of the AI/machine learning model based on at least the first and second validation/auditing score, where the specifics of an AI language model is specifically seen in the teachings of Singh above and the specifics of the validation score is seen in the teachings of Balasubramanian above ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Rei showing the specifics of being able to determine improvement of a retrained model, into the evaluation of trained model results of Singh as modified by Balasubramanian for the purpose of increase ease of determining if a retrained model is an effective improvement for use over the original model and thus have improved efficiency, as taught by Rei Col. 12 line56- Col. 13 line 1. Conclusion 07-40 AIA 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. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRADFORD F WHEATON whose telephone number is (571)270-1779. The examiner can normally be reached Monday-Friday 8:00-5:00 EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /BRADFORD F WHEATON/Examiner, Art Unit 2193 Application/Control Number: 18/398,300 Page 2 Art Unit: 2193 Application/Control Number: 18/398,300 Page 3 Art Unit: 2193 Application/Control Number: 18/398,300 Page 4 Art Unit: 2193 Application/Control Number: 18/398,300 Page 5 Art Unit: 2193 Application/Control Number: 18/398,300 Page 6 Art Unit: 2193 Application/Control Number: 18/398,300 Page 7 Art Unit: 2193 Application/Control Number: 18/398,300 Page 8 Art Unit: 2193 Application/Control Number: 18/398,300 Page 9 Art Unit: 2193 Application/Control Number: 18/398,300 Page 10 Art Unit: 2193 Application/Control Number: 18/398,300 Page 11 Art Unit: 2193 Application/Control Number: 18/398,300 Page 12 Art Unit: 2193 Application/Control Number: 18/398,300 Page 13 Art Unit: 2193 Application/Control Number: 18/398,300 Page 14 Art Unit: 2193 Application/Control Number: 18/398,300 Page 15 Art Unit: 2193 Application/Control Number: 18/398,300 Page 16 Art Unit: 2193 Application/Control Number: 18/398,300 Page 17 Art Unit: 2193 Application/Control Number: 18/398,300 Page 18 Art Unit: 2193 Application/Control Number: 18/398,300 Page 20 Art Unit: 2193 Application/Control Number: 18/398,300 Page 21 Art Unit: 2193 Application/Control Number: 18/398,300 Page 22 Art Unit: 2193 Application/Control Number: 18/398,300 Page 23 Art Unit: 2193 Application/Control Number: 18/398,300 Page 24 Art Unit: 2193 Application/Control Number: 18/398,300 Page 25 Art Unit: 2193 Application/Control Number: 18/398,300 Page 26 Art Unit: 2193
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