Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This action is response to the communication filed on June 16, 2026. Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments regarding art rejection filed on June 16, 2026 have been considered but are moot in the view of new ground of rejection. The argument regarding 101 rejection is not persuasive. The IDS objection has been withdrawn in the view of new IDS filed on July 12, 2026.
Regarding 101, applicant argument under Step 2A, prong one, Step 2A prong two, and Step 2B are not persuasive. The recited limitations of pre-training (both) and fine-tuning as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations providing ---- to generate, validate, eliminate, and output considered as additional limitation, but these limitations are insignificant extra solution activity. The “deep learning model” as recited is nothing a generic computer functions and it does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Further, the courts have recognized the functions of well‐understood, routine, and conventional are merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d) II, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding the claim 1, it recites pre-training a deep learning model on an unsupervised training dataset of natural language text; pre-training the deep learning model on an unsupervised training dataset of source code snippets; fine-tuning the deep learning model on a supervised training dataset, wherein the supervised training dataset includes a plurality of tuples, wherein a tuple includes a method signature of a focal method and a plurality of test cases for the focal method, the plurality of test cases specifying the behavior and functionality of the focal method; and providing the deep learning model for deployment in a test-driven development system to: generate a plurality of candidate method bodies for a target method given at least one test case for the target method, the at least one test case specifying the behavior and functionality of the target method, validate the plurality of candidate method bodies, eliminate any candidate method bodies that fail the validation, and output at least one candidate method body that passes the validation.
The limitations pre-training (both) and fine-tuning as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. User can mentally do these steps by thing in mind. If necessary, user can user physical aid such paper and pen. Hence, the limitations are a mental process. See MPEP 2106.04(a)(2) III, B, If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand.").
The claim recites four additional element: providing the deep learning model for deployment in a test-driven development system to: generate a plurality of candidate method bodies for a target method given at least one test case for the target method, the at least one test case specifying the behavior and functionality of the target method. The providing step as recited can be done with generic computer component. Hence, providing step is an insignificant extra-solution activity. Similarly, validate and eliminate is nothing but data manipulation which can be done with the generic computer component. Hence, validate and eliminate are an insignificant extra solution activity. Further, output step also data manipulation and organizing in the form insignificant extra solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of providing, validate, eliminate and output steps amounts to no more than mere instructions to apply the exception using a generic computer component. The courts have recognized these functions as well‐understood, routine, and conventional as they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d) II, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of neural transformer model of software development. The claim recites the limitations of obtaining a first plurality of source code snippets; and generating the unsupervised training dataset of source code snippets by applying a denoising function to each training sample of the first plurality of source code snippets, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 3 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of neural transformer model of software development. The claim recites the limitations of obtaining a first plurality of natural language text; and generating the unsupervised training dataset of natural language text by applying a denoising function to each sample of the first plurality of natural language text, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 4 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of neural transformer model of software development. The claim recites the limitations of wherein the deep learning model is deployed in an integrated development environment (IDE), which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 5 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 5 recites the same abstract idea of neural transformer model of software development. The claim recites the limitations of wherein the deep learning model is deployed in a source code editor, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 6 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of neural transformer model of software development. The claim recites the limitations of wherein the deep learning model is deployed as a web service that generates the method body for the target method when given the at least one test case for the target method, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 7 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 7 recites the same abstract idea of neural transformer model of software development. The claim recites the limitations of wherein the deep learning model is a neural transformer with attention, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 8 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 8 recites the same abstract idea of neural transformer model of software development. The claim recites the limitations of wherein the deep learning model is a neural transformer with attention in an encoder-decoder configuration, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
As to claim 9, it has similar limitations as of claim 1 above. Hence, claim 9 is rejected under the same rational as of claim 1 above.
Claim 10 is dependent on claim 9 and includes all the limitations of claim . Therefore, claim 10 recites the same abstract idea of neural transformer model of software development. The claim recites the limitations of wherein the program includes instructions to perform actions that: create the unsupervised training dataset of natural language text through application of a denoising function to each natural language training sample of the unsupervised training dataset of natural language text, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 11 is dependent on claim 9 and includes all the limitations of claim 9. Therefore, claim 11 recites the same abstract idea of neural transformer model of software development. The claim recites the limitations of wherein the program includes instructions to perform actions that: create the unsupervised training dataset of source code snippets through application of a denoising function to each source code snippet of the unsupervised training dataset of source code snippets, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
As to claims 12-16, they have similar limitations as of claims 4-8 above. Hence, they are rejected under the same rational as of claims 4-8 above.
Claim 17 is dependent on claim 9 and includes all the limitations of claim 9. Therefore, claim 17 recites the same abstract idea of neural transformer model of software development. The claim recites the limitations of wherein validating the plurality of candidate method bodies comprises validating each candidate method body of the plurality of candidate method bodies for syntax correctness, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 18 is dependent on claim 9 and includes all the limitations of claim 9. Therefore, claim 18 recites the same abstract idea of neural transformer model of software development. The claim recites the limitations of wherein validating the plurality of candidate method bodies comprises testing each candidate method body of the plurality of candidate method bodies for semantic correctness, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 19 is dependent on claim 9 and includes all the limitations of claim 9. Therefore, claim 19 recites the same abstract idea of neural transformer model of software development. The claim recites the limitations of wherein validating the plurality of candidate method bodies comprises testing each candidate method body of the plurality of candidate method bodies using the at least one test case, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 20 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 20 recites the same abstract idea of neural transformer model of software development. The claim recites the limitations of wherein validating the plurality of candidate method bodies comprises testing each candidate method body of the plurality of candidate method bodies using the at least one test case, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Singh et al. (Patent No. : US 11693637 B1) in the view of Kurata et al. (Pub. No: US 20170061330 A1) and Gao (Pub. No. : US 20200401503 A1)
As to clam 1 Singh teaches a computer-implemented method for training a deep learning model to generate and filter a plurality of candidate method bodies for a target method given at least one test case associated with the target method, the method, comprising:
pre-training a deep learning model on an unsupervised training dataset of natural language text (Column 9 lines 11-12: training instance input that includes a natural language description of a source code snippet in PL1);
pre-training the deep learning model on an unsupervised training dataset of source code snippets (column 9 lines 3-4: training instance input that includes a source code snippet in PL1);
tuning the deep learning model on a supervised training dataset, wherein the supervised training dataset includes a plurality of tuples (column 12 lines 57-67: The augmentation engine 122 can be used to generate additional training instances that each include a modification of the source code snippet obtained from a repository).
Singh does not explicitly disclose but Kurata teaches the tuning being a fine-tuning (paragraph [0045]: a supervised fine-tuning is performed in deep learning architectures). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Singh by adding above limitation as taught by Kurata to improve the deep leaning training model.
Singh and Kurata do not explicitly disclose but Gao teaches wherein a tuple includes a method signature of a focal method and a plurality of test cases for the focal method, the plurality of test cases specifying the behavior and functionality of the focal method (paragraph [0083]-[0089], [0150]: create scripts and models for testing systems, wherein the AI-based test case engine automatically searches test cases and collects all effectively test cases depended on test cases history and model);
providing the deep learning model for deployment in a test-driven development system (paragraph [0070]-[0072], [0161]: run AI test for different groups. There are two different deploy models for services: a. Enterprise-oriented deploy model for enterprise users only, b. Crowed-sourced service deploy model for public users which are include Machine Learning: Supervision Learning, Un-supervision Learning, Reinforcement learning, Classification/Grouping, and Deep Learning and Prediction) to:
generate a plurality of candidate method bodies for a target method given at least one test case for the target method, the at least one test case specifying the behavior and functionality of the target method (paragraph [0083]-[0086]: The STAIS selects current test cases corresponding to different AI systems or AI applications. then STAIS starts to create scripts and models for testing systems or applications, a. An AI-based test modeling engine (501): automatically discovering test models based on existing AI test models and assisting derivation of new AI test model. b. An AI-based test scripting engine (502): automatically assisting on generation and derivation of new test scripting. c. An AI-based test selection engine (503): automatically selecting most frequency test cases on test cases history and testing all selected test cases.),
validate the plurality of candidate method bodies (Paragraph [0089]: automatically validates all test input data and map for expected outputs and events. on the forth step, an AI function classification test quality assessment, it automatically generates the quality assessment which includes test script, test result validation),
eliminate any candidate method bodies that fail the validation (paragraphs [0087], [0089], [0091]: automatically analyzing and detecting bug with selected test cases and generating the detailed information of bugs), and
output at least one candidate method body that passes the validation (paragraph [0152]: validates all test data, finally, generates the quality report based on test data score).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Singh and Kurata by adding above limitation as taught by Gao to automatically optimize a software test process (Gao, paragraph [0003]).
As to claim 2 Singh together with Kurata and Gao teaches a computer-implemented method according to claim 1. Singh teaches obtaining a first plurality of source code snippets and generating the unsupervised training dataset of source code snippets by applying a denoising function to each training sample of the first plurality of source code snippets (column 9 lines 1-26).
As to claim 3 Singh together with Kurata and Gao teaches a computer-implemented method according to claim 1. Singh teaches obtaining a first plurality of natural language text; and generating the unsupervised training dataset of natural language text by applying a denoising function to each sample of the first plurality of natural language text (column 9 lines 1-26).
As to claim 4 Singh together with Kurata and Gao teaches a computer-implemented method according to claim 1. Singh teaches wherein the deep learning model is deployed in an integrated development environment (IDE) (column 8 lines 1-51).
As to claim 5 Singh together with Kurata and Gao teaches a computer-implemented method according to claim 1. Singh teaches wherein the deep learning model is deployed in a source code editor (column 5 lines 27-29).
As to claim 6 Singh together with Kurata and Gao teaches a computer-implemented method according to claim 1. Singh teaches wherein the deep learning model is deployed as a web service that generates the method body for the target method when given the at least one test case for the target method (column 8 lines 49-52).
As to claim 7 Singh together with Kurata and Gao teaches a computer-implemented method according to claim 1. Singh teaches wherein the deep learning model is a neural transformer with attention (column 9 lines 27-30).
As to claim 8 Singh together with Kurata and Gao teaches a computer-implemented method according to claim 1. Singh teaches wherein the deep learning model is a neural transformer with attention in an encoder-decoder configuration (column 9 lines 27-50).
As to claim 9, it has similar limitations as of claim 1 above. Hence, claim 9 is rejected under the same rational as of claim 1 above.
As to claim 10 Singh together with Kurata and Gao teaches a system according to claim 9. Singh teaches wherein the program includes instructions to perform actions that: create the unsupervised training dataset of natural language text through application of a denoising function to each natural language training sample of the unsupervised training dataset of natural language text (column 9 lines 27-50).
As to claim 11 Singh together with Kurata and Gao teaches a system according to claim 9. Singh teaches wherein the program includes instructions to perform actions that: create the unsupervised training dataset of source code snippets through application of a denoising function to each source code snippet of the unsupervised training dataset of source code snippets (column 9 line 61 to column 10 line 20).
As to claim 12 Singh together with Kurata and Gao teaches a system according to claim 9. Singh teaches wherein the deep learning model is deployed in an integrated development environment (IDE) (column 8 lines 35-52).
As to claims 12-16, they have similar limitations as of claims 4-8 above. Hence, they are rejected under the same rational as of claims 4-8 above.
As to claim 17 Singh together with Kurata and Gao teaches a system according to claim 9. Gao teaches wherein validating the plurality of candidate method bodies comprises validating each candidate method body of the plurality of candidate method bodies for syntax correctness (paragraph [0089]).
As to claim 18 Singh together with Kurata and Gao teaches a system according to claim 9. Gao teaches wherein validating the plurality of candidate method bodies comprises testing each candidate method body of the plurality of candidate method bodies for semantic correctness (paragraph [0065]).
As to claim 19 Singh together with Kurata and Gao teaches a system according to claim 9. Gao teaches wherein validating the plurality of candidate method bodies comprises testing each candidate method body of the plurality of candidate method bodies using the at least one test case (paragraph [0150]).
As to claim 20 Singh together with Kurata and Gao teaches a method according to claim 1. Gao teaches wherein validating the plurality of candidate method bodies comprises testing each candidate method body of the plurality of candidate method bodies using the at least one test case (paragraph [0150]).
Examiner's Note: Examiner has cited particular columns and line numbers or paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of 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 responses, to fully consider the references in its entirety as potentially teaching of all or part of the claimed invention, as well as the context.
Conclusion
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.
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant's disclosure.
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/MD I UDDIN/Primary Examiner, Art Unit 2169
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