CTNF 18/516,937 CTNF 92068 DETAILED ACTION This office action is responsive to the above identified application filed 11/21/2023. The application contains claims 1-20, all examined and rejected. 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. Information Disclosure Statement The Information Disclosure Statement with references submitted 12/28/2024, 12/12/2024, 3/26/2024, and 2/18/2024 have been considered and entered into the file. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 Claim 9-15 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. Claim 9 recites the limitation "the quality score". There is insufficient antecedent basis for this limitation in the claim. Dependent claims inherit the independent claim deficiency. 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 non-statutory subject matter. Claim 1 is rejected under 35 USC 101 because the claimed inventions are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. While independent claims 1, 9 and 16 are each directed to a statutory category, it recites a series of steps which appears to be directed to an abstract idea (mental process, mathematical concept). Claims 1-20 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below. When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts, (2019 PEG) STEP 1. Per Step 1, the claims are determined to include process, manufacture, and machine as in independent Claim 1, 9, and 16, and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category. At step 2A, prong 1, The invention is directed to Mental Process (see Alice), As such, the claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are: Claim 1 “tune the parameters of the deep learning model to learn to generate a unit test case having the one or more static code quality properties through reinforcement learning” (Mental process, observation, evaluation and judgment), Claim 9 “tuning the large language model to learn to generate a unit test case having the plurality of static code quality properties” (Mental process, observation, evaluation and judgment), Claim 16 “tuning the neural-based model to learn to predict a unit test case for a target focal method having one or more of the plurality of static code quality properties, wherein the tuning of the neural-based model comprises: iteratively sampling a predicted unit test case generated by the neural-based model and a predicted unit test case generated by the tuned neural-based model for a tuning sample” (Mental process, observation, evaluation and judgment); ”computing, by the reward model , a reward score for the predicted unit test case generated by the tuned neural-based model; computing a reward-based loss for the predicted unit test case generated by the neural-based model and the predicted unit test case generated by the tuned neural-based model, augmenting the reward-based loss with the reward score” (Mental process, observation, evaluation and judgment, Mathematical concept); “updating parameters of the tuned neural-based model using a policy loss that is based on the augmented reward-based loss” (Mental process, observation, evaluation and judgment) The claim recites additional elements as Claim 1 “A system comprising: a processor; and a memory that stores a program configured to be executed by the processor, the program includes instructions to perform actions“ (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C)); “obtain a reward model trained to generate a reward score for a model-generated unit test case for a focal method, wherein the reward score is based on a unit test case having one or more static code quality properties (insignificant extra-solution activity, MPEP 2106.05(g)), “wherein the one or more static code quality properties comprise an assertion, an invocation of the focal method, a name for the unit test case that describes the test case, and/or the existence of a comment that describes the test case” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)); “obtain a deep learning model trained to learn to generate a unit test case, wherein parameters of the deep learning model are learned through a cross-entropy loss” (insignificant extra-solution activity, MPEP 2106.05(g)), wherein the reinforcement learning generates a policy loss that is based on a reward that comprises a reward score from the reward model for a unit test case generated by the tuned deep learning model for a given tuning sample, wherein the policy loss is backpropagated through the tuned deep learning model (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h) and the deep learning model merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)) Claim 9 “A computer-implemented method, comprising” (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C)); “obtaining a reward model trained to generate a reward score for a model-generated unit test case for a focal method” (insignificant extra-solution activity, MPEP 2106.05(g)), “wherein the quality score is based on a unit test case having a plurality of static code quality properties, wherein the plurality of static code quality properties comprises an assertion, an invocation of the focal method, a name for the unit test case that describes the focal method, and/or the existence of a comment that describes the test case” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)); wherein the large language model was pre- trained to generate source code; tuning the large language model to learn to generate a unit test case having the plurality of static code quality properties, wherein the tuning uses a policy gradient method to determine a policy loss that is backpropagated to layers of the tuned large language model (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), “wherein the policy loss is based on a reward having a reward score generated by the reward model for a unit test case generated by the tuned large language model for a given tuning sample” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h) and the reward model and large language model merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)); “deploying the tuned large language model in a software development environment to predict a target unit test case for a target focal method” (insignificant extra-solution activity, MPEP 2106.05(g)), Claim 16, “A hardware storage device having stored thereon computer executable instructions that are structured to be executable by a processor of a computing device to thereby cause the computing device to perform actions” (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C)); “accessing a reward model trained to generate a reward score for a model-generated unit test case for a focal method” (insignificant extra-solution activity, MPEP 2106.05(g)), “wherein the reward score is based on a unit test case having one or more of a plurality of static code quality properties” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) “wherein the plurality of static code quality properties comprises an assertion, an invocation of the focal method, a name for the unit test case that describes the test case, and existence of a comment that describes the test case; accessing a neural-based model trained to generate a unit test case for a focal method wherein the neural-based model comprises a plurality of parameters generated via a cross- entropy loss” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG"). Thus, under Step 2A of the Mayo framework, the Examiner holds that the claims are directed to concepts identified as abstract. STEP 2B. Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts. The instant application includes in Claim 1 additional steps to those deemed to be abstract idea(s). When taken the steps individually, these steps are: Claim 1 “A system comprising: a processor; and a memory that stores a program configured to be executed by the processor, the program includes instructions to perform actions“ (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2)); “obtain a reward model trained to generate a reward score for a model-generated unit test case for a focal method, wherein the reward score is based on a unit test case having one or more static code quality properties” (well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)); “wherein the one or more static code quality properties comprise an assertion, an invocation of the focal method, a name for the unit test case that describes the test case, and/or the existence of a comment that describes the test case” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)); “obtain a deep learning model trained to learn to generate a unit test case, wherein parameters of the deep learning model are learned through a cross-entropy loss” (well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)); wherein the reinforcement learning generates a policy loss that is based on a reward that comprises a reward score from the reward model for a unit test case generated by the tuned deep learning model for a given tuning sample, wherein the policy loss is backpropagated through the tuned deep learning model (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h) and the deep learning model merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)) Claim 9 “A computer-implemented method, comprising” (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2)); “obtaining a reward model trained to generate a reward score for a model-generated unit test case for a focal method” (well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)); “wherein the quality score is based on a unit test case having a plurality of static code quality properties, wherein the plurality of static code quality properties comprises an assertion, an invocation of the focal method, a name for the unit test case that describes the focal method, and/or the existence of a comment that describes the test case” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)); wherein the large language model was pre- trained to generate source code; tuning the large language model to learn to generate a unit test case having the plurality of static code quality properties, wherein the tuning uses a policy gradient method to determine a policy loss that is backpropagated to layers of the tuned large language model (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), “wherein the policy loss is based on a reward having a reward score generated by the reward model for a unit test case generated by the tuned large language model for a given tuning sample” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h) and the reward model and large language model merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)); “deploying the tuned large language model in a software development environment to predict a target unit test case for a target focal method” (well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)). Claim 16 “A hardware storage device having stored thereon computer executable instructions that are structured to be executable by a processor of a computing device to thereby cause the computing device to perform actions” (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2)); “accessing a reward model trained to generate a reward score for a model-generated unit test case for a focal method” (well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)); “wherein the reward score is based on a unit test case having one or more of a plurality of static code quality properties” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) “wherein the plurality of static code quality properties comprises an assertion, an invocation of the focal method, a name for the unit test case that describes the test case, and existence of a comment that describes the test case; accessing a neural-based model trained to generate a unit test case for a focal method wherein the neural-based model comprises a plurality of parameters generated via a cross- entropy loss” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). In the instant case, Claims 1, 9 and 16 are directed to above mentioned abstract idea. Technical functions such as receiving, and extracting are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed. In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for analyzing model operations or updating the model that could then be pointed to as being "significantly more" than the abstract ideas themselves. Moreover, Examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional (WURC) in the related arts. Further, note that the limitations, in the instant claims, are done by the generically recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device that is recited in an abstract level and require no more than a generic computing devices to perform generic functions. CONCLUSION It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well-understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish). The dependent claims, when considered individually and as a whole, likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim. claims 2 disclose “wherein the program includes instructions to perform actions that: deploy the tuned deep learning model in a source code development system to generate a target unit test case for a given target focal method” (insignificant extra-solution activity, MPEP 2106.05(g) that is well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)) This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 3 disclose “the one or more static code quality properties comprises syntax correctness of the unit test case generated by the tuned first deep learning model” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 4-6 disclose “wherein the one or more static code quality properties comprises an absence of a duplicate assertion in the unit test case generated by the tuned first deep learning model”, “wherein the one or more static code quality properties comprise an absence of conditional logic in the unit test case generated by the tuned first deep learning model.”, “wherein the one or more static code quality properties comprise an absence of exception handling code, a print statement, and/or an empty test in the unit test case generated by the tuned first deep learning model” the claims disclose (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) These limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). they does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 7 “the reward model is a neural transformer model with attention” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 8 disclose “the deep learning model is a neural transformer model with attention.” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 10 disclose “wherein the plurality of static code properties further comprises one or more anti-pattern properties, wherein an anti- pattern property comprises existence of a duplicate assertion in the unit test case generated by the tuned large language model” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 11 disclose “wherein the one or more anti-pattern properties further comprise existence of conditional logic or exception handling code in the unit test case generated by the tuned large language model“(data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 12 disclose “wherein the one or more anti-pattern properties further comprise existence of a print statement in the unit test case generated by the tuned large language model” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 13 disclose “wherein the one or more anti-pattern properties further comprise existence of an empty test in the unit test case generated by the tuned large language model” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 14 disclose “generating the reward score as a combination of positive values for existence of the assertion, invocation of the focal method, existence of the name for the unit test case that describes the test case, and existence of a comment that describes the test case or the absence of anti-patterns, and a negative value for each syntactically incorrect unit test case generated by the tuned first large language model” (mental process, mathematical concept); This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, Claim 15 “wherein the reward model is a neural transformer model with attention” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 17 disclose “wherein the plurality of static code quality properties further comprises a plurality of anti-patterns, wherein the plurality of anti-patterns comprises existence of a duplicate assertion, existence of conditional logic or exception handling code, existence of print statement, and/or existence of an empty test in the unit test case generated by the tuned neural-based model” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 18 disclose “wherein the reward model is trained to generate the reward score for a select one of the static code quality properties”(data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 19 disclose “wherein the neural-based model comprises a neural transformer model with attention.” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 20 disclose “the reward model comprises a neural transformer model with attention.” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. The dependent claims which impose additional limitations also fail to claim patent eligible subject matter because the limitations cannot be considered statutory. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1 ; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims for the other statutory classes are similarly analyzed. For at least these reasons, the claimed inventions of each of dependent claims 2-8, 10-15, 17-20,are directed or indirect to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and are rejected 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 1-3, 7-9, 14-16, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over “CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning” [hereinafter D1] Published 11/2022 in view of “The secret life of test smells - an empirical study on test smell evolution and maintenance” Published 7/2021 [hereinafter D2] . With regard to Claim 1, D1 teach a system comprising: a processor; and a memory that stores a program configured to be executed by the processor (D1, P. 10, 4.1, “We perform our experiments on a kubernetes with 16 A100-40G GPUs on Google Cloud Platform”) , the program includes instructions to perform actions that: obtain a reward model trained to generate a reward score for a model-generated (D1, P. 8, 3.3.3., “we introduce a critic model. Figure 3 shows an overview of our critic model. The critic model is parameterized as a neural network with parameters ɸ that receives inputs as the problem description D and a sampled program PNG media_image1.png 32 202 media_image1.png Greyscale . The critic is trained to infer the unit test outcome”, P. 20, “CodeRL achieved consistent improvement upon the conventional pretrained LMs for code generation tasks. CodeRL is a general framework that integrates pretrained LMs and RL holistically for program synthesis, and can be extended and improved in various ways. For example, it can be easily integrated with other better pretrained LMs and can be improved with more fine-grained feedback from the environment, such as feedback received from a static code analyzer”) , tune the parameters of the deep learning model to learn through reinforcement learning, wherein the reinforcement learning generates a policy loss that is based on a reward that comprises a reward score from the reward model for a unit test case generated by the tuned deep learning model for a given tuning sample, wherein the policy loss is backpropagated through the tuned deep learning model (D1, P. 6-7, 3.3, “we can view the learned parameters of an LM model, _ as a stochastic policy, which decides an action as the prediction of each token. Following each action, an LM model updates its hidden state representations which are used by the policy to determine the next action in the next decoding step”, Fig. 3, “The learned hidden state representations from the critic are then used to estimate returns of synthetic samples to finetune the actor network. To improve and stabilize the training process, baseline programs are considered and relative returns are factored into the loss function to optimize the actor network”, P. 10, 3.4, “After training the critic, we then apply both Lce and Lrl with equal weights to finetune the actor network”, Eq. 14, Eq. 10, ) . D1 does not explicitly teach unit test case for a focal method, wherein the reward score is based on a unit test case having one or more static code quality properties, wherein the one or more static code quality properties comprise an assertion, an invocation of the focal method, a name for the unit test case that describes the test case, and/or the existence of a comment that describes the test case; generate a unit test case having the one or more static code quality properties. D2 teach a system comprising: a processor; and a memory that stores a program configured to be executed by the processor (P. 13, “deployment scenario in Visual Studio Code IDE backed by the Azure cloud compute”, “server-side module is deployed as a containerized web application to Azure Kubernetes Service [44] listening on a HTTPS endpoint. It processes completion requests and returns the model output”) , the program includes instructions to perform actions that: unit test case for a focal method, wherein the reward score is based on a unit test case having one or more static code quality properties, wherein the one or more static code quality properties comprise an assertion, an invocation of the focal method, a name for the unit test case that describes the test case, and/or the existence of a comment that describes the test case (D2, P. 3, 2.1, “test case names are often similar to the corresponding focal methods. Thus, the first heuristic attempts to match the test cases with a focal method having a name that matches, after removing possible Test prefix/suffix “. P. 4, 2.4, “We build different versions of the code input representation – with diverse degree of focal context – with the aim of empirically evaluating these code representations. We begin with the core information (i.e., focal method) and iteratively add contextual information such as class name, constructors, other method signatures, and fields.”, P. 6, “Test Annotation: the test case should declare the @Test annotation”, “Focal Method Invocation: to properly test a focal method, the test case should invoke the focal method”, “We check compliance to these properties using a Java parser, extracting annotations and method calls”, P. 6, “we consider two testing framework APIs: JUnit Assert APIs (e.g., assertTrue, assertEqual) as well as the Mockito Framework APIs (e.g., mock, verify)”) ; generate a unit test case having the one or more static code quality properties (D2, P. 3, 2.1, “test case names are often similar to the corresponding focal methods. Thus, the first heuristic attempts to match the test cases with a focal method having a name that matches, after removing possible Test prefix/suffix “. P. 4, 2.4, “We build different versions of the code input representation – with diverse degree of focal context – with the aim of empirically evaluating these code representations. We begin with the core information (i.e., focal method) and iteratively add contextual information such as class name, constructors, other method signatures, and fields.”, P. 6, “Test Annotation: the test case should declare the @Test annotation”, “Focal Method Invocation: to properly test a focal method, the test case should invoke the focal method”, “We check compliance to these properties using a Java parser, extracting annotations and method calls”, P. 6, “we consider two testing framework APIs: JUnit Assert APIs (e.g., assertTrue, assertEqual) as well as the Mockito Framework APIs (e.g., mock, verify) . D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of using actor-critic models to further improve output programs. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1resulting in resolutions as disclosed by D2 with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1 as described above to generate unit test cases by learning from real-world focal methods and developer- written test cases which could significantly improve developer productivity as D1 explicitly disclose that its CodeRL framework can be easily integrated with other better pretrained LMs and can be improved with more fine-grained feedback from the environment, such as feedback received from a static code analyzer (D1, P. 20, D2, Abstract) . With regard to Claim 2, D1-D2 teach the system of claim 1, wherein the program includes instructions to perform actions that: deploy the tuned deep learning model in a source code development system to generate a target unit test case for a given target focal method (D2, P. 13, 5.3, “possible deployment scenario in Visual Studio Code IDE backed by the Azure cloud compute … The server-side module is deployed as a containerized web application to Azure Kubernetes Service [44] listening on a HTTPS endpoint. It processes completion requests and returns the model output, which is implemented in PyTorch”) . With regard to Claim 3, D1-D2 teach the system of claim 1, wherein the one or more static code quality properties comprises syntax correctness of the unit test case generated by the tuned first deep learning model (D2, P. 6, Syntactic Correctness, “We begin by verifying that the sequence of tokens generated by the model represents a syntactically correct source code method”) . With regard to Claim 7, D1-D2 teach the system of claim 1, wherein the reward model is a neural transformer model with attention (D1, P. 8, 3.3.3, “We use Transformer models of smaller sizes than the actor model as the base architecture for the critic model”) . With regard to Claim 8, D1-D2 teach the system of claim 1, wherein the deep learning model is a neural transformer model with attention (D2, P. 3, 2.2, “ATHENATEST is based on a BART transformer model. BART [25] is a denoising autoencoder which utilizes the standard sequence-to-sequence transformer architecture from [26], substituting ReLUs with GeLU activation functions.”) . With regard to Claim 9, D1 teach a computer-implemented method, comprising: obtaining a reward model trained to generate a reward score for a model-generated (D1, Abstract, “Specifically, during training, we treat the code-generating LM as an actor network, and introduce a critic network that is trained to predict the functional correctness of generated programs and provide dense feedback signals to the actor”, Fig. 3, “The critic model is learned as an error predictor. The model receives problem specifications and programs as input sequences. For each program, the model is trained to predict one of four possible test outcomes: fCompileError; RuntimeError; FailedTest; PassedTestg. The learned hidden state representations from the critic are then used to estimate returns of synthetic samples to finetune the actor network.” P. 8, 3.3.3., “we introduce a critic model. Figure 3 shows an overview of our critic model. The critic model is parameterized as a neural network with parameters ɸ that receives inputs as the problem description D and a sampled program PNG media_image1.png 32 202 media_image1.png Greyscale . The critic is trained to infer the unit test outcome”, P. 20, “CodeRL achieved consistent improvement upon the conventional pretrained LMs for code generation tasks. CodeRL is a general framework that integrates pretrained LMs and RL holistically for program synthesis, and can be extended and improved in various ways. For example, it can be easily integrated with other better pretrained LMs and can be improved with more fine-grained feedback from the environment, such as feedback received from a static code analyzer.”) , wherein the large language model was pre-trained (D1, P. 6, “CodeT5. CodeT5 [Wang et al., 2021] is a multi-lingual code-aware language model pretrained on large-scale source code corpora curated from Github”, P. 10, 4.1, “We pretrain a CodeT5-large model (770M) from scratch following T5-large’s architecture”); [tuning] the large language model to learn, wherein the tuning uses a policy gradient method to determine a policy loss that is backpropagated to layers of the tuned large language model (D1, Fig. 2, “The learned hidden state representations from the critic are then used to estimate returns of synthetic samples to finetune the actor network”, P. 7, “Following the REINFORCE algorithm … and policy gradient theorem … we can define an estimate of the gradient PNG media_image2.png 33 92 media_image2.png Greyscale of the non-differentiable return r as” Eq. (3), P. 6, Fig. 2, “The returns are estimated based on critic scores and finally factored into the learning objective Lrl to finetune the actor LM network using synthetic samples”) , wherein the policy loss is based on a reward having a reward score generated by the reward model for a unit test case generated by the tuned large language model for a given tuning sample (D1, P. 8, “Given a learned critic, we use the probability distribution PNG media_image3.png 41 326 media_image3.png Greyscale to estimate the token-level value PNG media_image4.png 37 94 media_image4.png Greyscale in relation to the ground-truth unit test output … We use this estimate to train the actor LM model with intermediate returns” Eq(10)) ; D1 does not explicitly teach not teach unit test case for a focal method, wherein the quality score is based on a unit test case having a plurality of static code quality properties, wherein the plurality of static code quality properties comprises an assertion, an invocation of the focal method, a name for the unit test case that describes the focal method, and/or the existence of a comment that describes the test case, fine-tuning a large language model to learn to generate a unit test case for a given focal method and context of the focal method, model was pre-trained to generate source code, model to learn to generate a unit test case having the plurality of static code quality properties, and deploying the tuned large language model in a software development environment to predict a target unit test case for a target focal method. D2 teach unit test case for a focal method, wherein the quality score is based on a unit test case having a plurality of static code quality properties, wherein the plurality of static code quality properties comprises an assertion, an invocation of the focal method, a name for the unit test case that describes the focal method, and/or the existence of a comment that describes the test case (D2, P. 3, 2.1, “test case names are often similar to the corresponding focal methods. Thus, the first heuristic attempts to match the test cases with a focal method having a name that matches, after removing possible Test prefix/suffix “. P. 4, 2.4, “We build different versions of the code input representation – with diverse degree of focal context – with the aim of empirically evaluating these code representations. We begin with the core information (i.e., focal method) and iteratively add contextual information such as class name, constructors, other method signatures, and fields.”, P. 6, “Test Annotation: the test case should declare the @Test annotation”, “Focal Method Invocation: to properly test a focal method, the test case should invoke the focal method”, “We check compliance to these properties using a Java parser, extracting annotations and method calls”, P. 6, “we consider two testing framework APIs: JUnit Assert APIs (e.g., assertTrue, assertEqual) as well as the Mockito Framework APIs (e.g., mock, verify)”) ; fine-tuning model to learn to generate a unit test case for a given focal method and context of the focal method (D2, Abstract, “supervised finetuning for a downstream translation task of generating unit tests”, P. 4, 2.4, “We begin with the core information (i.e., focal method) and iteratively add contextual information such as class name, constructors, other method signatures, and fields”, P.5, 2.5, “In this stage we finetune a model on the task of generating unit test cases for a given method. Specifically, we represent this task as a translation task, where the source is a focal method (i.e., the method we would like to test), and the target is the corresponding test case”) , model to learn to generate a unit test case having the plurality of static code quality properties (D2, P. 3, 2.1, “test case names are often similar to the corresponding focal methods. Thus, the first heuristic attempts to match the test cases with a focal method having a name that matches, after removing possible Test prefix/suffix “. P. 4, 2.4, “We build different versions of the code input representation – with diverse degree of focal context – with the aim of empirically evaluating these code representations. We begin with the core information (i.e., focal method) and iteratively add contextual information such as class name, constructors, other method signatures, and fields.”, P. 6, “Test Annotation: the test case should declare the @Test annotation”, “Focal Method Invocation: to properly test a focal method, the test case should invoke the focal method”, “We check compliance to these properties using a Java parser, extracting annotations and method calls”, P. 6, “we consider two testing framework APIs: JUnit Assert APIs (e.g., assertTrue, assertEqual) as well as the Mockito Framework APIs (e.g., mock, verify) ,and deploying the tuned model in a software development environment to predict a target unit test case for a target focal method (D2, P. 13, 5.3, “possible deployment scenario in Visual Studio Code IDE backed by the Azure cloud compute … The server-side module is deployed as a containerized web application to Azure Kubernetes Service [44] listening on a HTTPS endpoint. It processes completion requests and returns the model output, which is implemented in PyTorch”) . D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of using actor-critic models to further improve output programs. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1resulting in resolutions as disclosed by D2 with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1 as described above to generate unit test cases by learning from real-world focal methods and developer- written test cases which could significantly improve developer productivity as D1 explicitly disclose that its CodeRL framework can be easily integrated with other better pretrained LMs and can be improved with more fine-grained feedback from the environment, such as feedback received from a static code analyzer (D1, P. 20, D2, Abstract). With regard to Claim 14, D1-D2 teach the computer-implemented method of claim 10, further comprising: generating the reward score as a combination of positive values for existence of the assertion, invocation of the focal method, existence of the name for the unit test case that describes the test case, and existence of a comment that describes the test case or the absence of anti-patterns, and a negative value for each syntactically incorrect unit test case generated by the tuned first large language model (D1, 3.3.1, “From the outputs of the tests, we can determine the return r” Eqs (4), (5), (6), (7), P. 9-10, “We also include additional signals received from the unit test results, include the type of test outcomes (as defined in the return definitions in Eq. (4) to (7), and error subtypes (e.g. syntax errors, out-of-index errors)”, Table 11, “syntaxerror Raised when the parser encounters a syntax error. This may occur in an import statement, in a call to the built-in functions compile(), exec(), or eval().”, P. 7, 3.3.2, “Specifically, we use a greedy decoding strategy as a baseline and any generated samples that outperform this baseline are given positive return estimation, and negative return estimation otherwise”, D2, P. 3, 2.1, “test case names are often similar to the corresponding focal methods. Thus, the first heuristic attempts to match the test cases with a focal method having a name that matches, after removing possible Test prefix/suffix “. P. 4, 2.4, “We build different versions of the code input representation – with diverse degree of focal context – with the aim of empirically evaluating these code representations. We begin with the core information (i.e., focal method) and iteratively add contextual information such as class name, constructors, other method signatures, and fields.”, P. 6, “Test Annotation: the test case should declare the @Test annotation”, “Focal Method Invocation: to properly test a focal method, the test case should invoke the focal method”, “We check compliance to these properties using a Java parser, extracting annotations and method calls”, P. 6, “we consider two testing framework APIs: JUnit Assert APIs (e.g., assertTrue, assertEqual) as well as the Mockito Framework APIs (e.g., mock, verify)”) . With regard to Claim 15, Claim 15 is similar in scope to claim 7; therefore it is rejected under similar rationale. With regard to Claim 16, D1 teach a hardware storage device having stored thereon computer executable instructions that are structured to be executable by a processor of a computing device to thereby cause the computing device to perform actions (D1, P. 10, 4.1, “We perform our experiments on a kubernetes with 16 A100-40G GPUs on Google Cloud Platform”) for: accessing a reward model trained to generate a reward score for a model-generated (D1, Abstract, “Specifically, during training, we treat the code-generating LM as an actor network, and introduce a critic network that is trained to predict the functional correctness of generated programs and provide dense feedback signals to the actor”, Fig. 3, “The critic model is learned as an error predictor. The model receives problem specifications and programs as input sequences. For each program, the model is trained to predict one of four possible test outcomes: fCompileError; RuntimeError; FailedTest; PassedTestg. The learned hidden state representations from the critic are then used to estimate returns of synthetic samples to finetune the actor network.” P. 8, 3.3.3., “we introduce a critic model. Figure 3 shows an overview of our critic model. The critic model is parameterized as a neural network with parameters ɸ that receives inputs as the problem description D and a sampled program PNG media_image1.png 32 202 media_image1.png Greyscale . The critic is trained to infer the unit test outcome”, P. 20, “CodeRL achieved consistent improvement upon the conventional pretrained LMs for code generation tasks. CodeRL is a general framework that integrates pretrained LMs and RL holistically for program synthesis, and can be extended and improved in various ways. For example, it can be easily integrated with other better pretrained LMs and can be improved with more fine-grained feedback from the environment, such as feedback received from a static code analyzer.”) , accessing a neural-based model trained to generate, wherein the neural-based model comprises a plurality of parameters generated via a cross- entropy loss (D1, P. 10, 3.4, “we applied imitation learning to first warm-start a pretrained LM model with Lce only for up to 10 epochs”, P. 5, “the objective is to minimize the cross-entropy loss” Eq. (1)) ; and tuning the neural-based model to learn to predict, wherein the tuning of the neural-based model comprises: iteratively sampling a predicted unit test case generated by the neural-based model and a predicted [program] generated by the tuned neural-based model for a tuning sample, computing, by the reward model, a reward score for the predicted [program] generated by the tuned neural-based model (D1, P. 7, 3.3.2, “we adopt a “baseline” [Sutton and Barto, 2018]. Specifically, we use a greedy decoding strategy as a baseline and any generated samples that outperform this baseline are given positive return estimation, and negative return estimation otherwise. This relative normalization technique allows models to explore imperfect programs, as long as their returns are better than the baseline’s. Given a training sample, we denote the return of the baseline r(Wb) and the expected gradient is computed”, Eq. (8), P. 25, Algorithm 1, PNG media_image5.png 62 548 media_image5.png Greyscale , P. 8, 3.3.3, “we introduce a critic model … to estimate the token-level value ^q of wst in relation to the ground-truth unit test output … We use this estimate to train the actor LM model with intermediate returns” ,Eq(10)) ; computing a reward-based loss for the predicted [Program] unit test case generated by the neural-based model and the predicted [program] unit test case generated by the tuned neural-based model (D1, P. 7, 3.3.2, “Given a training sample, we denote the return of the baseline r(Wb) and the expected gradient is computed”, Eq. (8)) ; augmenting the reward-based loss with the reward score (D1, P. 8, 3.3.3, “We use this estimate to train the actor LM model with intermediate returns” Eq. (10)) ; and updating parameters of the tuned neural-based model using a policy loss that is based on the augmented reward-based loss (D1, P. 8, 3.3.3, “We use this estimate to train the actor LM model with intermediate returns” Eq. (10)) . D1 does not explicitly teach unit test case for a focal method, wherein the reward score is based on a unit test case having one or more of a plurality of static code quality properties, wherein the plurality of static code quality properties comprises an assertion, an invocation of the focal method, a name for the unit test case that describes the test case, and existence of a comment that describes the test case, a unit test case for a focal method, a unit test case for a target focal method having one or more of the plurality of static code quality properties, unit test case. D2 teach a hardware storage device having stored thereon computer executable instructions that are structured to be executable by a processor of a computing device to thereby cause the computing device to perform actions (D1, P. 10, 4.1, “We perform our experiments on a kubernetes with 16 A100-40G GPUs on Google Cloud Platform”) for: unit test case for a focal method, wherein the reward score is based on a unit test case having one or more of a plurality of static code quality properties, wherein the plurality of static code quality properties comprises an assertion, an invocation of the focal method, a name for the unit test case that describes the test case, and existence of a comment that describes the test case (D2, P. 3, 2.1, “test case names are often similar to the corresponding focal methods. Thus, the first heuristic attempts to match the test cases with a focal method having a name that matches, after removing possible Test prefix/suffix “. P. 4, 2.4, “We build different versions of the code input representation – with diverse degree of focal context – with the aim of empirically evaluating these code representations. We begin with the core information (i.e., focal method) and iteratively add contextual information such as class name, constructors, other method signatures, and fields.”, P. 6, “Test Annotation: the test case should declare the @Test annotation”, “Focal Method Invocation: to properly test a focal method, the test case should invoke the focal method”, “We check compliance to these properties using a Java parser, extracting annotations and method calls”, P. 6, “we consider two testing framework APIs: JUnit Assert APIs (e.g., assertTrue, assertEqual) as well as the Mockito Framework APIs (e.g., mock, verify)”), generate a unit test case for a focal method, a unit test case for a target focal method having one or more of the plurality of static code quality properties, unit test case generated (D2, Abstract, “supervised finetuning for a downstream translation task of generating unit tests”, P. 4, 2.4, “We begin with the core information (i.e., focal method) and iteratively add contextual information such as class name, constructors, other method signatures, and fields”, P.5, 2.5, “In this stage we finetune a model on the task of generating unit test cases for a given method. Specifically, we represent this task as a translation task, where the source is a focal method (i.e., the method we would like to test), and the target is the corresponding test case”) . With regard to Claim 18, D1-D2 teach the hardware device of claim 16, wherein the reward model is trained to generate the reward score for a select one of the static code quality properties (D1, Abstract, “Specifically, during training, we treat the code-generating LM as an actor network, and introduce a critic network that is trained to predict the functional correctness of generated programs and provide dense feedback signals to the actor”, Fig. 3, “The critic model is learned as an error predictor. The model receives problem specifications and programs as input sequences. For each program, the model is trained to predict one of four possible test outcomes: fCompileError; RuntimeError; FailedTest; PassedTestg. The learned hidden state representations from the critic are then used to estimate returns of synthetic samples to finetune the actor network.” P. 8, 3.3.3., “we introduce a critic model. Figure 3 shows an overview of our critic model. The critic model is parameterized as a neural network with parameters ɸ that receives inputs as the problem description D and a sampled program PNG media_image1.png 32 202 media_image1.png Greyscale . The critic is trained to infer the unit test outcome”, P. 20, “CodeRL achieved consistent improvement upon the conventional pretrained LMs for code generation tasks. CodeRL is a general framework that integrates pretrained LMs and RL holistically for program synthesis, and can be extended and improved in various ways. For example, it can be easily integrated with other better pretrained LMs and can be improved with more fine-grained feedback from the environment, such as feedback received from a static code analyzer”, D2, P. 3, 2.1, “test case names are often similar to the corresponding focal methods. Thus, the first heuristic attempts to match the test cases with a focal method having a name that matches, after removing possible Test prefix/suffix “. P. 4, 2.4, “We build different versions of the code input representation – with diverse degree of focal context – with the aim of empirically evaluating these code representations. We begin with the core information (i.e., focal method) and iteratively add contextual information such as class name, constructors, other method signatures, and fields.”, P. 6, “we consider two testing framework APIs: JUnit Assert APIs (e.g., assertTrue, assertEqual) as well as the Mockito Framework APIs (e.g., mock, verify).”) . With regard to Claim 19, Claim 19 is similar in scope to claim 8; therefore it is rejected under similar rationale. With regard to Claim 20, Claim 20 is similar in scope to claim 7; therefore it is rejected under similar rationale . 07-21-aia AIA Claim s 4-6, 10-13, 17 are rejected under 35 U.S.C. 103 as being unpatentable over “CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning” [hereinafter D1] Published 11/2022 in view of “The secret life of test smells - an empirical study on test smell evolution and maintenance” Published 7/2021 [hereinafter D2] in view of “The secret life of test smells - an empirical study on test smell evolution and maintenance” Published 7/2021 [hereinafter D3] . With regard to Claim 4, D1-D2 teach the system of claim 1, wherein the one or more static code quality properties (D2, P. 3, 2.1, “test case names are often similar to the corresponding focal methods. Thus, the first heuristic attempts to match the test cases with a focal method having a name that matches, after removing possible Test prefix/suffix “. P. 4, 2.4, “We build different versions of the code input representation – with diverse degree of focal context – with the aim of empirically evaluating these code representations. We begin with the core information (i.e., focal method) and iteratively add contextual information such as class name, constructors, other method signatures, and fields.”, P. 6, “we consider two testing framework APIs: JUnit Assert APIs (e.g., assertTrue, assertEqual) as well as the Mockito Framework APIs (e.g., mock, verify).”) . D1-D2 does not explicitly teach an absence of a duplicate assertion in the unit test case generated by the tuned first deep learning model. D3 teach one or more static code quality properties comprises an absence of a duplicate assertion in the unit test case generated by the tuned first deep learning model (Table 1, P. 4, “Duplicate Assert”, “DA Occurs when a test case tests the same condition multiple times, which may increase test overhead.”, P. 23, Fig. 3, “Assertion refactoring, removing duplicate assertion and Assertion Roulette test smells.” ) . D1- D2 and D3 are analogous art to the claimed invention because they are from a similar field of endeavor of software quality assurance. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-D2 resulting in resolutions as disclosed by D3 with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1-D2 as described above to allow focusing on the specific types of test smells that may have a higher correlation with defect-proneness when helping developers with test code maintenance (D3, Abstract). With regard to Claim 5, D1-D2 teach the system of claim 1, wherein the one or more static code quality properties (D2, P. 3, 2.1, “test case names are often similar to the corresponding focal methods. Thus, the first heuristic attempts to match the test cases with a focal method having a name that matches, after removing possible Test prefix/suffix “. P. 4, 2.4, “We build different versions of the code input representation – with diverse degree of focal context – with the aim of empirically evaluating these code representations. We begin with the core information (i.e., focal method) and iteratively add contextual information such as class name, constructors, other method signatures, and fields.”, P. 6, “we consider two testing framework APIs: JUnit Assert APIs (e.g., assertTrue, assertEqual) as well as the Mockito Framework APIs (e.g., mock, verify).”) . D1-D2 does not explicitly teach an absence of conditional logic in the unit test case generated by the tuned first deep learning model. D3 teach one or more static code quality properties comprise an absence of conditional logic in the unit test case generated by the tuned first deep learning model. (D3, Table 1, P. 4, “Conditional Test Logic CTL There exist conditions in a test case that may alter the behavior of the test and its expected output”, P. 23, “the developer simplifies the test smell’s verbosity with assertion statements. Namely, assert That() & is() is used to improve the readability of the test logic”). D1- D2 and D3 are analogous art to the claimed invention because they are from a similar field of endeavor of software quality assurance. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-D2 resulting in resolutions as disclosed by D3 with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1-D2 as described above to allow focusing on the specific types of test smells that may have a higher correlation with defect-proneness when helping developers with test code maintenance (D3, Abstract). With regard to Claim 6, D1-D2 teach the system of claim 1, wherein the one or more static code quality properties (D2, P. 3, 2.1, “test case names are often similar to the corresponding focal methods. Thus, the first heuristic attempts to match the test cases with a focal method having a name that matches, after removing possible Test prefix/suffix “. P. 4, 2.4, “We build different versions of the code input representation – with diverse degree of focal context – with the aim of empirically evaluating these code representations. We begin with the core information (i.e., focal method) and iteratively add contextual information such as class name, constructors, other method signatures, and fields.”, P. 6, “we consider two testing framework APIs: JUnit Assert APIs (e.g., assertTrue, assertEqual) as well as the Mockito Framework APIs (e.g., mock, verify).”) . D1-D2 does not explicitly teach an absence of exception handling code , a print statement, and/or an empty test in the unit test case generated by the tuned first deep learning model. D3 teach one or more static code quality properties comprise an absence of exception handling code (D3, Table 1, P. 4, “Exception Catch/Throw ECT Passing or failing of a test case depends on custom exception handling code or exception throwing, which may hide real problems and hamper debugging” , a print statement (D3, Table 1, P. 4, “Print Statement PS Print statements in unit tests are redundant as unit tests are executed as part of an automated script and do not affect the failing or passing of test cases. Furthermore, they can increase execution time if the developer calls a long-running method from within the print method (i.e., as a parameter).”) , and/or an empty test in the unit test case generated by the tuned first deep learning model (D3, Table 1, P. 4, “Empty Test ET Occurs when test code has no executable statements) . D1- D2 and D3 are analogous art to the claimed invention because they are from a similar field of endeavor of software quality assurance. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-D2 resulting in resolutions as disclosed by D3 with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1-D2 as described above to allow focusing on the specific types of test smells that may have a higher correlation with defect-proneness when helping developers with test code maintenance (D3, Abstract) . With regard to Claim 10, Claim 10 is similar in scope to claim 4; therefore it is rejected under similar rationale. With regard to Claim 11, Claim 11 is similar in scope to claims 5 and 6; therefore it is rejected under similar rationale. With regard to Claim 12, Claim 12 is similar in scope to claim 6; therefore it is rejected under similar rationale. With regard to Claim 13, Claim 13 is similar in scope to claim 6; therefore it is rejected under similar rationale. With regard to Claim 17, Claim 17 is similar in scope to claims 4, 5 and 6; therefore it is rejected under similar rationale . Claim Rejections - 35 USC § 102 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. 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15 AIA Claim s 1-3, 9 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by US 2023/0195428 A1 [hereinafter D4] . Regarding claim 1, D4 disclose a system comprising ([0027] and following, system 100, Fig. 1) : a processor; and a memory that stores a program configured to be executed by the processor (§ [0119], exemplary operating environment, Fig. 5, processor 510, memory 514) , the program includes instructions to perform actions that: obtain a reward model (code-specific reward engine 128, Fig. 1) trained to generate a reward score for a model-generated unit test case for a focal method (it is understood that the reward score, unit test case and focal method are defined with respect to generated source code, see [0027], [0029], code-quality reward based on code-quality factors and a set of supervised metrics) , wherein the reward score is based on a unit test case having one or more static code quality properties, wherein the one or more static code quality properties comprise an assertion, an invocation of the focal method, a name for the unit test case that describes the test case, and/or the existence of a comment that describes the test case ([0095], code-quality reward includes code-quality factors and source code metrics, [0096], invocation of focal method) ; obtain a deep learning model trained to learn to generate a unit test case, wherein parameters of the deep learning model are learned through a cross-entropy loss ([0027], [0029] Model-0 training phase 102, model-0 118, Fig. 1, cross entropy loss) ; and tune the parameters of the deep learning model to learn to generate a unit test case having the one or more static code quality properties through reinforcement learning (Fig. 1, Model-RL tuning phase, [0030]) , wherein the reinforcement learning generates a policy loss that is based on a reward that comprises a reward score from the reward model for a unit test case generated by the tuned deep learning model for a given tuning sample ([0030], PPO engine 130, tuning samples 120, Fig. 1) , wherein the policy loss is backpropagated through the tuned deep learning model ([0030], generate a policy loss that is backpropagated to update the parameters of Model-RL) . Regarding claim 2; D4 teach the system of claim 1, wherein the program includes instructions to perform actions that: deploy the tuned deep learning model in a source code development system to generate a target unit test case for a given target focal method ([0118]) . Regarding claim 3; D4 teach the system of claim 1, wherein the one or more static code quality properties comprises syntax correctness of the unit test case generated by the tuned first deep learning model ([0096]) . Regarding claim 9; Claim 9 is similar in scope to claim 1, further D4 discloses the deep learning model being a language model pretrained to generate source code given a context ([0084]) and a step of deploying the tuned language model ([0118], generate unit test cases) . Conclusion 07-96 The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure. US Patent Application Publication No. 20220036186 filed by De Carvalho Evangelista et al. that disclose training actor-critic model See ¶¶48-49, . Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck , 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) ( quoting In re Lemelson , 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT. 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, Michelle Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148 Application/Control Number: 18/516,937 Page 2 Art Unit: 2148 Application/Control Number: 18/516,937 Page 3 Art Unit: 2148 Application/Control Number: 18/516,937 Page 4 Art Unit: 2148 Application/Control Number: 18/516,937 Page 5 Art Unit: 2148 Application/Control Number: 18/516,937 Page 6 Art Unit: 2148 Application/Control Number: 18/516,937 Page 7 Art Unit: 2148 Application/Control Number: 18/516,937 Page 8 Art Unit: 2148 Application/Control Number: 18/516,937 Page 9 Art Unit: 2148 Application/Control Number: 18/516,937 Page 10 Art Unit: 2148 Application/Control Number: 18/516,937 Page 11 Art Unit: 2148 Application/Control Number: 18/516,937 Page 12 Art Unit: 2148 Application/Control Number: 18/516,937 Page 13 Art Unit: 2148 Application/Control Number: 18/516,937 Page 14 Art Unit: 2148 Application/Control Number: 18/516,937 Page 15 Art Unit: 2148 Application/Control Number: 18/516,937 Page 16 Art Unit: 2148 Application/Control Number: 18/516,937 Page 17 Art Unit: 2148 Application/Control Number: 18/516,937 Page 18 Art Unit: 2148 Application/Control Number: 18/516,937 Page 19 Art Unit: 2148 Application/Control Number: 18/516,937 Page 20 Art Unit: 2148 Application/Control Number: 18/516,937 Page 21 Art Unit: 2148 Application/Control Number: 18/516,937 Page 22 Art Unit: 2148 Application/Control Number: 18/516,937 Page 23 Art Unit: 2148 Application/Control Number: 18/516,937 Page 24 Art Unit: 2148 Application/Control Number: 18/516,937 Page 25 Art Unit: 2148 Application/Control Number: 18/516,937 Page 26 Art Unit: 2148 Application/Control Number: 18/516,937 Page 27 Art Unit: 2148 Application/Control Number: 18/516,937 Page 28 Art Unit: 2148 Application/Control Number: 18/516,937 Page 29 Art Unit: 2148 Application/Control Number: 18/516,937 Page 30 Art Unit: 2148 Application/Control Number: 18/516,937 Page 31 Art Unit: 2148 Application/Control Number: 18/516,937 Page 32 Art Unit: 2148 Application/Control Number: 18/516,937 Page 33 Art Unit: 2148 Application/Control Number: 18/516,937 Page 34 Art Unit: 2148 Application/Control Number: 18/516,937 Page 35 Art Unit: 2148 Application/Control Number: 18/516,937 Page 36 Art Unit: 2148 Application/Control Number: 18/516,937 Page 37 Art Unit: 2148 Application/Control Number: 18/516,937 Page 38 Art Unit: 2148 Application/Control Number: 18/516,937 Page 39 Art Unit: 2148 Application/Control Number: 18/516,937 Page 40 Art Unit: 2148 Application/Control Number: 18/516,937 Page 41 Art Unit: 2148 Application/Control Number: 18/516,937 Page 42 Art Unit: 2148 Application/Control Number: 18/516,937 Page 43 Art Unit: 2148 Application/Control Number: 18/516,937 Page 44 Art Unit: 2148 Application/Control Number: 18/516,937 Page 45 Art Unit: 2148 Application/Control Number: 18/516,937 Page 46 Art Unit: 2148 Application/Control Number: 18/516,937 Page 47 Art Unit: 2148
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