DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/19/2026 has been entered.
Status of the claims
Claims 1-8 were pending. Therefore, claims 1-8 are currently pending for examination.
Claim Rejections - 35 USC § 103
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, 2 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20230376401, hereafter Wang) in view of Neves et al. (20220137959, hereafter Neves).
Regarding claim 1, Wang discloses: A processor-implemented method of generating a similarity determination model of programming codes performed by a computing device including at least a processor, the method comprising (Wang [0016]):
Performing, by the processor, preprocessing on raw data written in any one language (Wang [0013] discloses: deep learning-based approaches may automate the program repair process by training learning models to generate code repair patches, performance of such learning models is often limited by a fixed set of parameters to model the highly complex search space of program repair);
Generating, by the processor, positive pairs and negative pairs using the filtered data for use in training (Wang [0045-0046 and 0096] discloses: positive and negative training samples);
Training, by the processor, a pre-trained language model using the generated positive pairs and negative pairs to generate the similarity determination model for determining a similarity of the programming codes (Wang [0045-0046 and 0096] discloses: Each positive example may have |M|−1 negative samples. It is noted that various contrastive learning techniques, e.g., in-batch negatives strategy, hard negative mining strategy, etc. may be used, while in some embodiments, the contrastive learning with in-batch negatives as described above provides better performance than the hard negative mining strategy for noisier training data);
Wherein the training uses a cross-validation ensemble technique to eliminate redundancy in the training of the pre-trained language model (Wang [0068] discloses: During data processing, a duplication issue inside data splits and between data splits is observed. Specifically, there are 114, 2, and 4 duplicates in the train, validation, and test split respectively. For inter-split duplicates, there are 28, 34, and 4 duplicates between train and test, train and test, validation and test splits respectively. Those duplicates (243) are filtered, and a deduplicated version TFix (Dedup));
Wang didn’t disclose, but Neves discloses: Performing, by the processor, a three-step data filtering process on the preprocessed data, thereby removing duplicate data included in the preprocessed data (Neves [0064-0070] discloses: pattern mining process for detecting duplicated code patterns in visual programming language code instances and filter or deduplicate; pattern mining process is known as multiple steps for collection, preprocessing, pattern extracting and filtering duplicate data);
Determining, by the processor, a similarity of two programming codes using the similarity determination model to eliminate code duplication in programs (Neves [0047; 0062] discloses: a process for detecting duplicated code pattern(s) in visual programming language code instance; [0070] discloses: deduplicating process),
Wherein the two programming codes are written in different programming languages and the similarity between the two programming codes is determined at a sentence-level granularity (Neves [0017] discloses: the techniques include a scalable duplicated code pattern mining process that leverages the visual structure of visual programming languages to detect duplicated code).
Wang and Neves are analogous art because they are in the same field of endeavor, a software program running on a developer's computer . It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wang, to include the Teaching of Neves, in order to for detecting duplicated code patterns in visual programming language code instances. The suggestion/motivation to combine is to efficiently identifying duplicated code could reduce technical debt and thereby increase the maintainability of code.
Regarding claim 2, Wang as modified discloses: The method of claim 1, wherein in the performing of the preprocessing, comprises: at least one of removing a new line, removing a space, removing a comment, and removing a null space (Wang [0068; 0078]; Neves [0044; 0077; 0093]).
Regarding claim 7, Tao as modified didn’t disclose, but Wang discloses: The method of claim 1, wherein graphcodebert and codebert-mlm are used as the pre-trained language model (Wang [0075] discloses: pretrained programming language models based on Transformer architecture. One group of these models is the encoder-only models such as RoBERTa (code), CodeBERT, and GraphCodeBERT.).
Response to Arguments
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Allowable Subject Matter
Claim 8 have been considered and deemed allowable. The following is an examiner's statement of reasons for allowance: It is the examiner's opinion that the art of record considered as a whole, or alone or in combination, neither anticipates nor rendered obvious the specific details taught by the Applicant.
Examiner finds no single prior art reference teaching of the claims as recited in the independent claim 8. Furthermore, Examiner finds Applicant's arguments filed 05/19/2026 persuasive.
Hill (US 20110184938) - teaches system for determining similarity between a plurality of source code files. The computer system comprises a processor adapted to execute stored instructions, and a memory device that stores instructions for execution by the processor. The memory device comprises computer-implemented code adapted identify, in each of the plurality of source code files, data storage elements defined therein, determine which of the identified data storage elements are shared data storage elements, determine, for pairs of the source code files, the coincidence of the identified shared data storage elements, and identify pairs of the source code files as being similar based on the determined coincidence. However, it does not explicitly teach the claimed limitations.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CINDY NGUYEN whose telephone number is (571)272-4025. The examiner can normally be reached M-F 8:00-4:30.
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, Bhatia Ajay can be reached at 571-272-3906. 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.
/CINDY NGUYEN/Examiner, Art Unit 2156
Prosecution Insights