CTNF 18/772,114 CTNF 80073 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. This action is responding to the application papers filed on 7/13/2024. Claims 1-20 are pending in the application. The information disclosure statements filed on 7/23/2025, 6/16/2025 and 8/23/2024 have been considered. 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 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Per claim 1, on line 8 and 10, it is unclear to which partial code state they are referring. Interpretation: each partial code state of the plurality of partial code states. “a training sample” on line 13 is interpreted as “each training sample of the plurality of training samples.” Per claim 4, on lines 1-4, wherein train a neural network … a target partially-formed code snippet” is interpreted as: “wherein train the neural network using the training dataset to learn to predict the expansion of the non-terminal symbol of the target partially-formed code snippet based on the current context of the target partially-formed code snippet.” Per claim 5, “the non-terminal expansion index” is interpreted as “the non-terminal expansion index corresponding to the given training sample.” Per claim 7, on line 6 and 8, it is unclear to which partial code state they are referring. Interpretation: each partial code state of the plurality of partial code states. “a training sample” on line 12 is interpreted as “each training sample of the plurality of training samples.” Per claim 15, on line 7 and 9, it is unclear to which partial code state they are referring. Interpretation: each partial code state of the plurality of partial code states. “a training sample” on line 12 is interpreted as “each training sample of the plurality of training samples.” Per claim 19, “given a training sample” on line 4 and “the training sample” on the last line are interpreted as “a given training sample” and “the given training sample” respectively. Per claims 19 and 20, on line 2, “a processor of a computing device“ is interpreted as “the processor of the computing device.” Per claims 2-6, 8-14 and 16-20 are rejected because they depend from claims 1, 7 and 15. 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 1-3, 6-12, and 15-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Specifically, claims 1-20 are directed to an abstract idea. Per claim 1, the claim is directed to an idea of itself, mental processes that can be performed in the human mind, or by a human using a pen and paper. The steps of transforming, extracting, and creating a training dataset, as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental process. The limitations encompass a human mind carrying out the functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas under Prong 1 Step 2A. Under Prong 2, the additional limitations, the processor and memory recited at a high-level of generality are generic computing components for applying or performing the abstract idea, and the steps of collecting a plurality of source code snippets and training a neural network are mere data gathering for the mental steps and outputting the result to apply it to the generic neural network. Therefore, they are insignificant extra solution activities and do not indicate any integration of the abstract idea into a practical application as the mental steps are merely applied with generic computing components. See MPEP see MPEP 2106.05(f) /2106.05(h). The neural network is used as a mere generic tool to process the training dataset and return a response. There is no detailed recitation(s) of how the network is trained in a particular manner. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components or insignificant extra solution activities (e.g. processors, devices, program instructions), then it falls within the "Mental Processes" grouping of abstract ideas (2019 PEG step 2A, Prong 1: Abstract idea grouping? Yes, Mental Process). At most, the steps of collecting and training are not found to include anything more than what is well-understood, routine, conventional activity in the field. In this case, it is noted that the claimed extra-solution of data gathering and outputting/applying is acknowledged to be a well-understood, routine, conventional activity court recognized as WURC examples in MPEP 2106.05(d)(ll), for example, data gathering and retrieving, storing data, updating, tran smitting, and displaying a result - Symantec, Versata Dev, Content extraction, Electric Power Group). Insignificant extra solution activities or mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Viewing the limitations individually and as a combination, the additional elements merely perform data gathering, outputting/applying using generic computing components as tools without integrating the abstract idea into a practical application. For at least these reasons, claim 1 is not patent eligible. Per claims 2, 3, and 6, the claims are directed to the same idea itself as in claim 1, reciting the details of the neural network and tree without adding any other additional element that is significantly more. Therefore, the claims are rejected for the same reasons as in claim 1. Per claim 7, the claim is directed to an idea of itself, mental processes that can be performed in the human mind, or by a human using a pen and paper. The steps of converting, generating, and creating a training dataset, as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental process. The limitations encompass a human mind carrying out the functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas under Prong 1 Step 2A. Under Prong 2, the additional limitations, the steps of obtaining a plurality of source code snippets and training a neural network are mere data gathering for the mental steps and outputting the result to apply it to the generic neural network which are insignificant extra solution activities, thus, they do not indicate any integration of the abstract idea into a practical application as the mental steps are merely applied with a generic computing component(s). See MPEP see MPEP 2106.05(f) /2106.05(h). The neural network is used as a mere generic tool to process the training dataset and return a response. There is no detailed recitation(s) of how the network is trained in a particular manner. Therefore, the additional limitations do not integrate the abstract idea into a practical application. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components or insignificant extra solution activities (e.g. processors, devices, program instructions), then it falls within the "Mental Processes" grouping of abstract ideas (2019 PEG step 2A, Prong 1: Abstract idea grouping? Yes, Mental Process). At most, the steps of obtaining and training are not found to include anything more than what is well-understood, routine, conventional activity in the field. In this case, it is noted that the claimed extra-solution of data gathering and outputting/applying is acknowledged to be a well-understood, routine, conventional activity court recognized as WURC examples in MPEP 2106.05(d)(ll), for example, data gathering and retrieving, storing data, updating, tran smitting, and displaying a result - Symantec, Versata Dev, Content extraction, Electric Power Group). Insignificant extra solution activities or mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Viewing the limitations individually and as a combination, the additional elements merely perform data gathering, outputting/applying using generic computing components as tools without integrating the abstract idea into a practical application. For at least these reasons, claim 7 is not patent eligible. Per claims 8-12, the claims are directed to the same idea itself as in claim 1, reciting the details of the neural network without adding any other additional element that is significantly more. Therefore, the claims are rejected for the same reasons as in claim 1. Per claims 15-18, the claims are directed to the same idea itself as in claims 7-12, reciting the same abstract idea and additional limitations (gathering, generic hardware storage, neural network) without adding any other additional element that is significantly more. Therefore, the claims are rejected for the same reasons as in claims 7-12. Allowable Subject Matter 13-03-01 AIA The following is a statement of reasons for the indication of allowable subject matter: While Chakraborty teaches a tree-based code editing machine learning model for code suggestion, expanding a tree in a depth-first, left-to-right fashion, Sun et al. teach code generation based on tree-based neural architecture using an attention mechanism of Transformers to alleviate the long dependency problem incorporating grammar rules and AST structures into the network, Wang et al. teaches code completion given a partially-completed code snippet, Ginzberg et al. teaches a deep learning approach to code completion for non-terminals, Svyatkovskiy et al. (“IntelliCode Compose: Code Generation using Transformer”) teach code generation using transformer , the prior arts of record, taken alone or in combination do not teach the combination of: extract a plurality of partial code states from the parse trees, wherein a partial code state represents a partial expansion of production rules applied to a source code snippet, wherein a partial code state includes at least one non-terminal symbol and zero or more terminal symbols, wherein the production rules are associated with a grammar of a programming language; create a training dataset comprising a plurality of training samples, wherein a training sample comprises a select partial code state from the plurality of partial code states, a non-terminal expansion index, and a true non-terminal expansion, wherein the non-terminal expansion index is a position in a training sample of a non-terminal symbol to expand without constraint to a left-to-right expansion order, wherein the true non-terminal expansion represents outcome of an expansion of the non-terminal symbol in the non-terminal expansion index; and train a neural network using the training dataset to learn to predict an expansion of a non-terminal symbol of a target partially-formed code snippet based on a current context of the target partially-formed code snippet . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Svyatkovskiy et al. (“Pythia: AI-assisted Code Completion System”) is related to code completion using deep learning models trained on code context extracted from ASTs. Any inquiry concerning this communication or earlier communications from the examiner should be directed to INSUN KANG whose telephone number is (571)272-3724. The examiner can normally be reached M-TR 9am-5pm. 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, Chat Do can be reached on 571-272-3721. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /INSUN KANG/Primary Examiner, Art Unit 2193 Application/Control Number: 18/772,114 Page 2 Art Unit: 2193 Application/Control Number: 18/772,114 Page 3 Art Unit: 2193 Application/Control Number: 18/772,114 Page 4 Art Unit: 2193 Application/Control Number: 18/772,114 Page 5 Art Unit: 2193 Application/Control Number: 18/772,114 Page 6 Art Unit: 2193 Application/Control Number: 18/772,114 Page 7 Art Unit: 2193 Application/Control Number: 18/772,114 Page 8 Art Unit: 2193
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