CTNF 19/186,622 CTNF 93528 DETAILED ACTION 1. Claims 1-20 are pending in this application. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 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 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. Information Disclosure Statement 3. The information disclosure statement filed 04/01/2026 is in compliance with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609. It has been placed in the application file and the information referred to therein has been considered as to the merits. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 4. 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 1-15 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea (Mental Process) without significantly more. The claims similarly describe a way to help a large language model answer questions about a specific software codebase. The following is an analysis based on 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG). Step 1, Statutory Category? Claims 1-7 are directed to a system. Claims 8-15 are directed to a method. Therefore, claims 1-15 fall into at least one of the four statutory categories. Step 2A, Prong I: Judicial Exception Recited? The examiner submits that the foregoing claim limitations constitute a “Mental Process” , as the claims cover performance of the limitations in the human mind, given the broadest reasonable interpretation. As per independent claim 1, the claim recites the limitations of: “search for an example that is similar to the query and context from code segments of the codebase;” A human can observe a document and search for similar portions of a predefined portion within a document. For example, a human reviewing a page legal contract and need to find all sections that mention “liability waiver” or similar terms. There is nothing so complex in the limitation that could not be doing in the human mind. “generate a prompt to a large language model;” A human can mentally visualize instructions that will be used for large language models. For example, a human can mentally craft a prompt, such as one instructing an AI to act as a Michelin-star pastry chef. There is nothing so complex in the limitation that could not be doing in the human mind. As per independent claim 8, the claim recites the limitations of: “searching for a code segment from the codebase segment table that is similar to the query’” A human can observe a document and search for similar portions of a predefined portion within a document. For example, a human reviewing a page legal contract and need to find all sections that mention “liability waiver” or similar terms. There is nothing so complex in the limitation that could not be doing in the human mind. Accordingly, claims 1-15 recite at least one abstract idea. Step 2A, Prong II: Integrated into a Practical Application? The claims recite the following additional limitations/elements: As per independent claim 1, the claim recites the limitations of: “a processor; a memory; and a program;” These elements are merely applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). “receive a query and a context from a user interface” The courts have recognized that receiving or transmitting data over a network , e.g., using the Internet to gather data, as well ‐ understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See MPEP 2106.05(d)(II)(i). “wherein the query is related to data of a codebase, wherein the context identifies the codebase;” These elements are merely instructions applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). “wherein the search is based on an embedding of the query and context similar to an embedding associated with a code segment of the codebase and metadata associated with the code segment;” These elements are merely instructions applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). “a large language model” This element is merely applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). “wherein the prompt comprises the query, the context of the query and the example;” These elements are merely instructions applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). “receive a response from the large language model given the prompt;” The courts have recognized that receiving or transmitting data over a network , e.g., using the Internet to gather data, as well ‐ understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See MPEP 2106.05(d)(II)(i). “display the response in the user interface.” This additional element is an insignificant extra-solution activity of data gathering and/or output , and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). As per dependent claim 2, the claim recites the limitation of: “wherein the metadata of the code segment comprises a natural language summarization of the code segment.” This element is merely applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). As per dependent claim 3, the claim recites the limitation of: “wherein the metadata of the code segment comprises a file name associated with the code segment.” This element is merely applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). As per dependent claim 4, the claim recites the limitation of: “wherein the metadata of the code segment comprises a class definition associated with the code segment, a class associated with the code segment, and a method associated with the code segment.” These elements are merely applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). As per dependent claim 5, the claim recites the limitation of: “wherein the metadata includes a query associated with the code segment.” This element is merely applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). As per dependent claim 6, the claim recites the limitation of: “wherein the code segment is a file of the codebase, a method of the codebase or a class of the codebase.” These elements are merely applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). As per dependent claim 7, the claim recites the limitation of: “wherein the large language model comprises a neural transformer model with attention.” This element is merely applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). As per independent claim 8, the claim recites the limitations of: “obtaining a query and a context from a user interface” The courts have recognized that receiving or transmitting data over a network , e.g., using the Internet to gather data, as well ‐ understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See MPEP 2106.05(d)(II)(i). “wherein the context identifies a codebase, wherein the query comprises a question related to the codebase;” These elements are merely instructions applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). “wherein a code segment of the plurality of code segments is accessed by an embedding of the code segment and associated metadata;” These elements are merely instructions applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). “wherein the search is based on an embedding of the query and context being closely-similar to an embedding of a code segment and associated metadata from the codebase segment table;” These elements are merely instructions applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). “a large language model” This element is merely applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). “wherein the prompt comprises the query, the context, and the similar code segment and associated metadata;” These elements are merely instructions applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). “obtaining an answer to the query from a large language model given a prompt;” The courts have recognized that receiving or transmitting data over a network , e.g., using the Internet to gather data, as well ‐ understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See MPEP 2106.05(d)(II)(i). “wherein the prompt comprises the query, the context, and the similar code segment and associated metadata;” These elements are merely instructions applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). “returning the answer to the user interface.” This additional element is an insignificant extra-solution activity of data gathering and/or output , and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). As per dependent claim 9, the claim recites the limitation of: “wherein the code segment comprises a file from the codebase, a class from the codebase, or a method from the codebase.” These elements are merely applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). As per dependent claim 10, the claim recites the limitation of: “wherein the metadata comprises a code summarization of the code segment.” This element is merely applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). As per dependent claim 11, the claim recites the limitation of: “wherein the metadata comprises a filename of the code segment and a local file path of a file containing the code segment.” These elements are merely applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). As per dependent claim 12, the claim recites the limitation of: “wherein the metadata comprises predicted queries for the code segment generated by the large language model.” This element is merely applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). As per dependent claim 13, the claim recites the limitation of: “wherein the metadata of the code segment comprises a class definition associated with the code segment, a class associated with the code segment, and a method associated with the code segment.” These elements are merely applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). As per dependent claim 14, the claim recites the limitation of: “wherein the large language model is a neural transformer model with attention.” This element is merely applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). As per dependent claim 15, the claim recites the limitation of: “wherein the answer comprises at least one code element from the codebase or a reference to a file of the codebase.” These elements are merely applied to an exception to implement an abstract idea. A recitation of the words “apply it” (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). Therefore, claims 1-15 do not integrate the recited abstract ideas into a practical application. Step 2B: Claim provides an Inventive Concept? With respect to the limitations identified as insignificant extra-solution activity above the conclusions are carried over, and both the “receiving …; and obtaining …” is well-understood, routine, and conventional operations. For support as being well-understood, routine, and conventional for “receiving ...; and obtaining …” as noted by the courts is well understood routine and conventional, see MPEP 2106.05(d)(ii) “i. 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); … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);” and/or MPEP 2106.05(d)(ii) “iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;”, and/or MPEP 2106.05(d)(II) “iii. Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);” . Looking at the limitations in combination and the claim as a whole does not change this conclusion and the claim is ineligible. Therefore, the claims 1-15 are not patent eligible. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 5. 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 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 of this title, 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. 07-23-fti The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 35 U.S.C. § 103(a) are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA 6. Claim s 1, 5, 16 and 17 are rejected under 35 U.S.C. § 103 as being unpatentable over Allamanis et al. (US 20190243622 A1) in view of Bird et al. (US 20210303989 A1) . As per claim 1, Allamanis teaches a system comprising (i.e. “ computer system 100 includes ”; para. [0028]) : a processor (i.e. “ hardware processing unit 105 (aka a “processor”) ”; para. [0028]) ; and a memory that stores a program configured to be executed by the processor (i.e . “hardware storage device” on which computer-executable instructions are stored.”; para. [0029]) , the program comprising instructions that when executed by the processor perform acts that: (i.e. “ instructions that, when executed by the hardware processing unit 105, cause the computer system 100 to perform the method 200 ”; para. [0048]) receive a query (i.e. “ receives a request to analyze a model codebase that has been identified as being a corpus of model data (act 205). ”; fig.2a, para. [0050]; Examiner note: using a BRI the query is interpreted as the request) and a context from a user interface (i.e. “ obtaining particular context ”; para. [0026]. Further, i.e. “ user interfaces, supporting illustrations, and methods for conducting a specific type of analysis on a codebase, namely a “variable analysis” that may include a variable renaming analysis or a variable misuse analysis. ”; para. [0119]) , wherein the query is related to data of a codebase (i.e. “ request to analyze a model codebase that has been identified as being a corpus of model data (act 205) .”; para. [0050]; Examiner note: using a BRI the data of a codebase is interpreted as the corpus of model data) , wherein the context identifies the codebase (i.e. “ a “context,” as used herein, describes a state of the codebase, ”; para. [0075]) ; However, it is noted that the prior art of Allamanis does not explicitly teach “search for an example that is similar to the query and context from code segments of the codebase, wherein the search is based on an embedding of the query and context similar to an embedding associated with a code segment of the codebase and metadata associated with the code segment; generate a prompt to a large language model, wherein the prompt comprises the query, the context of the query and the example; receive a response from the large language model given the prompt; and display the response in the user interface.” On the other hand, in the same field of endeavor, Bird teaches search for an example that is similar to the query and context from code segments of the codebase (i.e. “ The search component 332 searches the document embedding database 322 to find similar code snippets 334 as the document query embedding 330. ”; para. [0058]. Further, i.e. “ mining a large corpus of source code programs for closed and maximal frequent subtrees that represent the largest and frequently-used source code idioms ”; para. [0081]; Examiner note: using a BRI the example is interpreted as the similar code snippets. Using a BRI the context from code segments is interpreted as the closed and maximal frequent subtrees that represent the largest and frequently-used source code idioms) wherein the search is based on an embedding of the query and context similar to an embedding associated with a code segment of the codebase (i.e. “ The document query embedding component 328 uses the pre-trained word embeddings from the word embedding database 316 to generate a document query embedding 330. The search component 332 finds similar document embeddings from the document embedding database 322 as the document query embedding 330”; para. [0032]; Examiner note: using a BRI the embedding of the query is interpreted as the document query embedding 330. Using a BRI the context similar to an embedding is interpreted as the similar document embeddings) and metadata associated with the code segment (i.e. “ a plurality of code snippets matching a query for a source code fragment”; para. [0038]; Examiner note: using a BRI the metadata is interpreted as the plurality of code snippets) ; generate a prompt to a large language model (i.e. “ generating the prompt for the large language model .”; para. [0051]) , wherein the prompt comprises the query (i.e. “ The encoder 134 encodes a query 302 containing source code into an embedding, Encode (Query), which the prompt generator 118 uses to search the repository database ”; para. [0037]) , the context of the query and the example (i.e. “ the prompt 428 which includes the focal context 430, the few-shot examples 432 ”; para. [0046]; Examiner note: using a BRI the context of the query is interpreted as the focal context); receive a response from the large language model given the prompt; and display the response in the user interface (i.e. “ The prompt generator 118 constructs a prompt 124 for the large language model 122 to autoregressively generate candidates to complete the partially-formed source code snippet. The candidates are ranked according to their respective probability with the candidates having the highest probability at the top. A select number of candidates 110 is then returned to the source code editor 102 and displayed in the user interface 106 .”; para. [0030]; Examiner note: using a BRI the response is interpreted as the select number of candidates 110 is then returned to the source code editor 102) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Bird that teaches a natural language code search service into the prior art of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to using a combination of the two mining datasets improves the quality of the idioms detected by the data mining technique, as it increases both context and frequency ( Bird , para. [0003], [0021]) . As per claim 5, Allamanis and Bird teach all the limitations as discussed in claim 1 above. However, it is noted that the prior art of Allamanis does not explicitly teach “wherein the metadata includes a query associated with the code segment.” On the other hand, in the same field of endeavor, Bird teaches wherein the metadata includes a query associated with the code segment (i.e. “ a plurality of code snippets matching a query for a source code fragment”; para. [0038]; Examiner note: using a BRI the metadata is interpreted as the plurality of code snippets) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Bird that teaches a natural language code search service into the prior art of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to using a combination of the two mining datasets improves the quality of the idioms detected by the data mining technique, as it increases both context and frequency ( Bird , para. [0003], [0021]) . As per claim 16, Allamanis teaches 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 that (i.e. “ the storage 115 may include computer-executable instructions that, when executed by the hardware processing unit 105, cause the computer system 100 to perform the method 200 ”; para. [0048]) : obtain a query (i.e. “ receives a request to analyze a model codebase that has been identified as being a corpus of model data (act 205). ”; fig.2a, para. [0050]) pertaining to data of a codebase from a user interface (i.e. “ request to analyze a model codebase that has been identified as being a corpus of model data (act 205) ”; para. [0026]. Further, i.e. “ user interfaces, supporting illustrations, and methods for conducting a specific type of analysis on a codebase, namely a “variable analysis” that may include a variable renaming analysis or a variable misuse analysis. ”; para. [0119]) , wherein the query comprises an identifier of the codebase (i.e. “ the analyzer 500A identifies and accesses the change context. ”; para. [0089]; Examiner note: using a BRI the identifier of the codebase is analyzer) ; access a large language model to generate a response to the query (i.e. “ the analysis results (e.g., the insights) provided by the analyzers 305 can be generated using a learning model ;”; para. [0110]) ; However, it is noted that the prior art of Allamanis does not explicitly teach “wherein the large language model is given a prompt, the prompt comprising the query, the identifier of the codebase and an example from the codebase, wherein the example comprises a code segment from a file of the codebase and associated metadata, wherein the example comprises an embedding based on the code segment and associated metadata that is similar to an embedding of the query and identifier of the codebase; and return the response to the user interface.” On the other hand, in the same field of endeavor, Bird teaches wherein the large language model is given a prompt (i.e. “ The prompt generator 118 constructs a prompt 124 for the large language model 122”; para. [0030]) , the prompt comprising the query (i.e. “ The encoder 134 encodes a query 302 containing source code into an embedding, Encode (Query), which the prompt generator 118 uses to search the repository database ”; para. [0037]) , the identifier of the codebase and an example from the codebase (i.e. “ the prompt 428 which includes the focal context 430, the few-shot examples 432 ”; para. [0046]) , wherein the example comprises a code segment from a file of the codebase and associated metadata (i.e. “ Code fragments are extracted from each file 204 of the repository. The files include modules, classes and methods used in various source code programs of the private repository. ”; para. [0036]) , wherein the example comprises an embedding based on the code segment and associated metadata that is similar to an embedding of the query and identifier of the codebase (i.e. “ The prompt generator 118 obtains the few-shot examples from the repository database (block 708). An embedding of the query, Encode(Q), is generated using the encoder, where Q is the query .”; para. [0053]) ; and return the response to the user interface (i.e. “ A select number of candidates 110 is then returned to the source code editor 102 and displayed in the user interface 106 .”; para. [0030]; Examiner note: using a BRI the answer is interpreted as the select number of candidates 110 is then returned to the source code editor102 and displayed in the user interface) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Bird that teaches a natural language code search service into the prior art of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to using a combination of the two mining datasets improves the quality of the idioms detected by the data mining technique, as it increases both context and frequency ( Bird , para. [0003], [0021]) . As per claim 17, Allamanis and Bird teach all the limitations as discussed in claim 16 above. Additionally, Allamanis teaches having computer executable instructions that are structured to be executable by the processor of the computing device to thereby cause the computing device to perform actions that (i.e. “ the storage 115 may include computer-executable instructions that, when executed by the hardware processing unit 105, cause the computer system 100 to perform the method 200 ”; para. [0048]) : store code segments of files of the codebase in a codebase segment table, a code segment associated with metadata and indexed by an embedding of the code segment and metadata (i.e. “ the code entity or a reference to the code entity may be stored in a database and keywords associated with the code entity may be further filtered or selected according to a metric of relevance, uniqueness, rarity, and/or prominence such as described above. ”; para. [0073]. Further, i.e. “ database or corpus of code snippets is first indexed according to one or more indexing method ”; para. [0112]) ; and search the codebase segment table using the embedding of the code segment and metadata (i.e. “ At step 1002, the search query is executed against the database of indexed code snippets. T ”; para. [0114]) . 07-21-aia AIA 7. Claim s 2-3 are rejected under 35 U.S.C. § 103 as being unpatentable over Allamanis et al. (US 20190243622 A1) in view of Bird et al. (US 20210303989 A1) still in further view of Bahrami et al. (US 20230107242 A1) . As per claim 2, Allamanis and Bird teach all the limitations as discussed in claim 1 above. However, it is noted that the combination of the prior arts of Allamanis and Bird do not explicitly teach “wherein the metadata of the code segment comprises a natural language summarization of the code segment.” On the other hand, in the same field of endeavor, Bahrami teaches wherein the metadata of the code segment comprises a natural language summarization of the code segment (i.e. “ the package metadata 112B may include metadata about the software package in the form of a natural language text” ; para. [0026]. Further, i.e. “ the package metadata 112B may include at least one of a name of the software package, one or more classes used in the software package, a description of the software package, a summary of the software package, a programming language associated with the software package ,”; para. [0035]) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Bahrami that teaches operations for code enrichment through metadata for code synthesis into the combination of the prior arts of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code, and Bird that teaches a natural language code search service. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to generating a dataset of natural language (NL) text features and respective code features by using the updated one or more source code files because it can improve code development ( Bahrami , para. [0005]) . As per claim 3, Allamanis and Bird teach all the limitations as discussed in claim 1 above. However, it is noted that the combination of the prior arts of Allamanis and Bird do not explicitly teach “wherein the metadata of the code segment comprises a file name associated with the code segment.” On the other hand, in the same field of endeavor, Bahrami teaches wherein the metadata of the code segment comprises a file name associated with the code segment (i.e. “ The package metadata may include the source code files 112A and the package metadata 112B. In an embodiment, the package metadata 112B may include at least one of a name of the software package, ”; para. [0035]; Examiner note: using a BRI the file name is interpreted as the name of the software package which is a source code file) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Bahrami that teaches operations for code enrichment through metadata for code synthesis into the combination of the prior arts of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code, and Bird that teaches a natural language code search service. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to generating a dataset of natural language (NL) text features and respective code features by using the updated one or more source code files because it can improve code development ( Bahrami , para. [0005]) . 07-21-aia AIA 8. Claim s 4, 6, 8-9, 13, 15 and 19 are rejected under 35 U.S.C. § 103 as being unpatentable over Allamanis et al. (US 20190243622 A1) in view of Bird et al. (US 20210303989 A1) still in further view of Smith et al. (US 20200117446 A1) . As per claim 4, Allamanis and Bird teach all the limitations as discussed in claim 1 above. However, it is noted that the combination of the prior arts of Allamanis and Bird do not explicitly teach “wherein the metadata of the code segment comprises a class definition associated with the code segment, a class associated with the code segment, and a method associated with the code segment.” On the other hand, in the same field of endeavor, Smith teaches wherein the metadata of the code segment comprises a class definition associated with the code segment, a class associated with the code segment, and a method associated with the code segment (i.e. “ code entity may be a reference to any logical division of software source code such as but not limited to a class, a namespace, a function, a module, a method, a routine, a subroutine, a procedure, a library, or other such callable units of software source code. As new code entities are ingested and/or indexed by a programming co-pilot system, keywords associated with the code entities are determined and stored in an index .”; fig. 4a, para. [0056]; Examiner note: using a BRI the metadata of the code segment is interpreted as the keywords associated with the code entities) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Smith that teaches provide assistance to programmers during programming to reduce the number of routine tasks into the combination of the prior arts of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code, and Bird that teaches a natural language code search service. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to determine relatedness between embeddings in this joint tensor space for text and associated source code is used in some embodiments to facilitate code search ( Smith , para. [0005]-[0006]) . As per claim 6, Allamanis and Bird teach all the limitations as discussed in claim 1 above. However, it is noted that the combination of the prior arts of Allamanis and Bird do not explicitly teach “wherein the code segment is a file of the codebase, a method of the codebase or a class of the codebase.” On the other hand, in the same field of endeavor, Smith teaches wherein the code segment is a file of the codebase, a method of the codebase or a class of the codebase (i.e. “ these code snippets may be in separate files or separate programs .”; para. [0119]) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Smith that teaches provide assistance to programmers during programming to reduce the number of routine tasks into the combination of the prior arts of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code, and Bird that teaches a natural language code search service. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to determine relatedness between embeddings in this joint tensor space for text and associated source code is used in some embodiments to facilitate code search ( Smith , para. [0005]-[0006]) . As per claim 8, Allamanis teaches a computer-implemented method (i.e. “ Method 200 ”; fig. 2a-2c, para. [0046]) , comprising: obtaining a query (i.e. “ receives a request to analyze a model codebase that has been identified as being a corpus of model data (act 205). ”; fig.2a, para. [0050]) and a context from a user interface (i.e. “ obtaining particular context ”; para. [0026]. Further, i.e. “ user interfaces, supporting illustrations, and methods for conducting a specific type of analysis on a codebase, namely a “variable analysis” that may include a variable renaming analysis or a variable misuse analysis. ”; para. [0119]) , wherein the context identifies a codebase (i.e. “ a “context,” as used herein, describes a state of the codebase, ”; para. [0075]) , wherein the query comprises a question related to the codebase (i.e. “ When such a request is used, then the bot service 320 is able to open the code review request and determine the nature, scope, and impact of the codebase change. ”; para. [0079]; Examiner note: using a BRI the question is interpreted as the code review request) ; However, it is noted that the prior art of Allamanis does not explicitly teach “searching for a code segment from the codebase segment table that is similar to the query, wherein the search is based on an embedding of the query and context being closely-similar to an embedding of a code segment and associated metadata from the codebase segment table; obtaining an answer to the query from a large language model given a prompt, wherein the prompt comprises the query, the context, and the similar code segment and associated metadata; and returning the answer to the user interface.” On the other hand, in the same field of endeavor, Bird teaches searching for a code segment from the codebase segment table that is similar to the query (i.e. “ The search component 332 searches the document embedding database 322 to find similar code snippets 334 as the document query embedding 330. ”; para. [0058]) , wherein the search is based on an embedding of the query and context being closely-similar to an embedding of a code segment (i.e. “ The document query embedding component 328 uses the pre-trained word embeddings from the word embedding database 316 to generate a document query embedding 330. The search component 332 finds similar document embeddings from the document embedding database 322 as the document query embedding 330”; para. [0032]; Examiner note: using a BRI the embedding of the query is interpreted as the document query embedding 330. Using a BRI the context similar to an embedding is interpreted as the similar document embeddings) and associated metadata from the codebase segment table (i.e. “ a plurality of code snippets matching a query for a source code fragment”; para. [0038]; Examiner note: using a BRI the metadata is interpreted as the plurality of code snippets) ; obtaining an answer to the query from a large language model given a prompt (i.e. “ The prompt generator 118 constructs a prompt 124 for the large language model 122 to autoregressively generate candidates to complete the partially-formed source code snippet. The candidates are ranked according to their respective probability with the candidates having the highest probability at the top. A select number of candidates 110 is then returned to the source code editor 102” ; para. [0030]) , wherein the prompt comprises the query (i.e. “ The encoder 134 encodes a query 302 containing source code into an embedding, Encode (Query), which the prompt generator 118 uses to search the repository database ”; para. [0037]) , the context, and the similar code segment and associated metadata (i.e. “ the prompt 428 which includes the focal context 430, the few-shot examples 432 ”; para. [0046]) ; and returning the answer to the user interface (i.e. “ A select number of candidates 110 is then returned to the source code editor 102 and displayed in the user interface 106 .”; para. [0030]; Examiner note: using a BRI the answer is interpreted as the select number of candidates 110 is then returned to the source code editor 102 102 and displayed in the user interface) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Bird that teaches a natural language code search service into the prior art of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to using a combination of the two mining datasets improves the quality of the idioms detected by the data mining technique, as it increases both context and frequency ( Bird , para. [0003], [0021]) . However, it is noted that the combination of the prior arts of Allamanis and Bird do not explicitly teach “accessing a codebase segment table comprising a plurality of code segments, wherein a code segment of the plurality of code segments is accessed by an embedding of the code segment and associated metadata;” On the other hand, in the same field of endeavor, Smith teaches accessing a codebase segment table comprising a plurality of code segments (i.e. “s earch results that reference code entities in portions of a codebase that the user infrequently accesses may be ranked higher than search results in a portion of the same codebase that a user frequency accesses ”; para. [0140]. Further, i.e. “ the task of adding a new column to a database table may require the programmer to modify multiple non-contiguous sections of code ,”; para. [0123]) , wherein a code segment of the plurality of code segments is accessed by an embedding of the code segment and associated metadata (i.e. “ the database of code snippets and their embeddings is evaluated to identify embeddings of code snippets that are close to the embedding of the search query ”; para. [0135]) ; Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Smith that teaches provide assistance to programmers during programming to reduce the number of routine tasks into the combination of the prior arts of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code, and Bird that teaches a natural language code search service. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to determine relatedness between embeddings in this joint tensor space for text and associated source code is used in some embodiments to facilitate code search ( Smith , para. [0005]-[0006]) . As per claim 9, Allamanis , Bird and Smith teach all the limitations as discussed in claim 8 above. However, it is noted that the combination of the prior arts of Allamanis and Bird do not explicitly teach “wherein the code segment comprises a file from the codebase, a class from the codebase, or a method from the codebase.” On the other hand, in the same field of endeavor, Smith teaches wherein the code segment comprises a file from the codebase, a class from the codebase, or a method from the codebase (i.e. “ these code snippets may be in separate files or separate programs .”; para. [0119]) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Smith that teaches provide assistance to programmers during programming to reduce the number of routine tasks into the combination of the prior arts of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code, and Bird that teaches a natural language code search service. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to determine relatedness between embeddings in this joint tensor space for text and associated source code is used in some embodiments to facilitate code search ( Smith , para. [0005]-[0006]) . As per claim 13, Allamanis , Bird and Smith teach all the limitations as discussed in claim 8 above. However, it is noted that the combination of the prior arts of Allamanis and Bird do not explicitly teach “wherein the metadata of the code segment comprises a class definition associated with the code segment, a class associated with the code segment, and a method associated with the code segment.” On the other hand, in the same field of endeavor, Smith teaches wherein the metadata of the code segment comprises a class definition associated with the code segment, a class associated with the code segment, and a method associated with the code segment (i.e. “ code entity may be a reference to any logical division of software source code such as but not limited to a class, a namespace, a function, a module, a method, a routine, a subroutine, a procedure, a library, or other such callable units of software source code. As new code entities are ingested and/or indexed by a programming co-pilot system, keywords associated with the code entities are determined and stored in an index .”; fig. 4a, para. [0056]; Examiner note: using a BRI the metadata of the code segment is interpreted as the keywords associated with the code entities) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Smith that teaches provide assistance to programmers during programming to reduce the number of routine tasks into the combination of the prior arts of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code, and Bird that teaches a natural language code search service. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to determine relatedness between embeddings in this joint tensor space for text and associated source code is used in some embodiments to facilitate code search ( Smith , para. [0005]-[0006]) . As per claim 15, Allamanis , Bird and Smith teach all the limitations as discussed in claim 8 above. However, it is noted that the combination of the prior arts of Allamanis and Bird do not explicitly teach “wherein the answer comprises at least one code element from the codebase or a reference to a file of the codebase.” On the other hand, in the same field of endeavor, Smith teaches wherein the answer comprises at least one code element from the codebase or a reference to a file of the codebase (i.e. “ The machine learning model 200 generates an output 270 comprising information or data relevant to helping a programmer, such as code entities 272, code snippets 274, multiple related code snippets 276, code paths 278, or other data. ”; fig. 2b, para. [0050]; Examiner note: using a BRI the one code element is interpreted as the code entities 272, code snippets 274, multiple related code snippets 276, code paths 278, or other data) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Smith that teaches provide assistance to programmers during programming to reduce the number of routine tasks into the combination of the prior arts of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code, and Bird that teaches a natural language code search service. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to determine relatedness between embeddings in this joint tensor space for text and associated source code is used in some embodiments to facilitate code search ( Smith , para. [0005]-[0006]) . As per claim 19, Allamanis , Bird and Smith teach all the limitations as discussed in claim 16 above. However, it is noted that the combination of the prior arts of Allamanis and Bird do not explicitly teach “wherein the code segment is a file of the codebase, a class of the codebase or a method of the codebase.” On the other hand, in the same field of endeavor, Smith teaches wherein the code segment is a file of the codebase, a class of the codebase or a method of the codebase (i.e. “ The machine learning model 200 generates an output 270 comprising information or data relevant to helping a programmer, such as code entities 272, code snippets 274, multiple related code snippets 276, code paths 278, or other data. ”; fig. 2b, para. [0050]; Examiner note: using a BRI the one code element is interpreted as the code entities 272, code snippets 274, multiple related code snippets 276, code paths 278, or other data) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Smith that teaches provide assistance to programmers during programming to reduce the number of routine tasks into the combination of the prior arts of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code, and Bird that teaches a natural language code search service. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to determine relatedness between embeddings in this joint tensor space for text and associated source code is used in some embodiments to facilitate code search ( Smith , para. [0005]-[0006]) . 07-21-aia AIA 9. Claim s 7 and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Allamanis et al. (US 20190243622 A1) in view of Bird et al. (US 20210303989 A1) still in further view of Clement et al. (US 20220253712 A1) . As per claim 7, Allamanis and Bird teach all the limitations as discussed in claim 1 above. However, it is noted that the combination of the prior arts of Allamanis and Bird do not explicitly teach “wherein the large language model comprises a neural transformer model with attention.” On the other hand, in the same field of endeavor, Clement teaches wherein the large language model comprises a neural transformer model with attention (i.e. “ the deep learning model is a neural transformer model with attentio n.”; para. [0103]) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Clement that teaches an example generator tool generates an example illustrating correct usage of a command of a command line interface into the combination of the prior arts of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code, and Bird that teaches a natural language code search service. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to use command line interface because it is faster and more efficient than a GUI since it is composable, that is several tasks can be specified in a single text string thereby eliminating numerous interactions with the GUI ( Clement , para. [0002]) . As per claim 20, Allamanis and Bird teach all the limitations as discussed in claim 16 above. However, it is noted that the combination of the prior arts of Allamanis and Bird do not explicitly teach “wherein the large language model is a neural transformer model with attention.” On the other hand, in the same field of endeavor, Clement teaches wherein the large language model is a neural transformer model with attention (i.e. “ the deep learning model is a neural transformer model with attentio n.”; para. [0103]) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Clement that teaches an example generator tool generates an example illustrating correct usage of a command of a command line interface into the combination of the prior arts of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code, and Bird that teaches a natural language code search service. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to use command line interface because it is faster and more efficient than a GUI since it is composable, that is several tasks can be specified in a single text string thereby eliminating numerous interactions with the GUI ( Clement , para. [0002]) . 07-21-aia AIA 10. Claim s 12 and 14 are rejected under 35 U.S.C. § 103 as being unpatentable over Allamanis et al. (US 20190243622 A1) in view of Bird et al. (US 20210303989 A1) still in further view of Smith et al. (US 20200117446 A1) still in further view of Clement et al. (US 20220253712 A1) . As per claim 12, Allamanis, Bird and Smith teach all the limitations as discussed in claim 8 above. However, it is noted that the combination of the prior arts of Allamanis, Bird and Smith do not explicitly teach “wherein the metadata comprises predicted queries for the code segment generated by the large language model.” On the other hand, in the same field of endeavor, Clement teaches wherein the metadata comprises predicted queries for the code segment generated by the large language model (i.e. “ Turning to FIG. 5, there is shown an exemplary method 500 for predicting a parameter value using the neural transformer model. The example generation tool 116 receives a query for an example of the usage of a CLI command (block 502). The query contains a command name and optionally, a subcommand and/or parameters. The command is one that uses parameters with parameter values (block 502). ”; para. [0081]; Examiner note: Using a BRI the metadata is interpreted as the command name and optionally, a subcommand and/or parameters) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Clement that teaches an example generator tool generates an example illustrating correct usage of a command of a command line interface into the combination of the prior arts of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code, Bird that teaches a natural language code search service, and Smith that teaches provide assistance to programmers during programming to reduce the number of routine tasks. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to use command line interface because it is faster and more efficient than a GUI since it is composable, that is several tasks can be specified in a single text string thereby eliminating numerous interactions with the GUI ( Clement , para. [0002]) . As per claim 14, Allamanis, Bird and Smith teach all the limitations as discussed in claim 8 above. However, it is noted that the combination of the prior arts of Allamanis, Bird and Smith do not explicitly teach “wherein the large language model is a neural transformer model with attention.” On the other hand, in the same field of endeavor, Clement teaches wherein the large language model is a neural transformer model with attention (i.e. “ the deep learning model is a neural transformer model with attentio n.”; para. [0103]) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Clement that teaches an example generator tool generates an example illustrating correct usage of a command of a command line interface into the combination of the prior arts of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code, Bird that teaches a natural language code search service, and Smith that teaches provide assistance to programmers during programming to reduce the number of routine tasks. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to use command line interface because it is faster and more efficient than a GUI since it is composable, that is several tasks can be specified in a single text string thereby eliminating numerous interactions with the GUI ( Clement , para. [0002]) . 07-21-aia AIA 11. Claim s 10-11 and 18 are rejected under 35 U.S.C. § 103 as being unpatentable over Allamanis et al. (US 20190243622 A1) in view of Bird et al. (US 20210303989 A1) still in further view of Smith et al. (US 20200117446 A1) still in further view of Bahrami et al. (US 20230107242 A1) . As per claim 10, Allamanis, Bird and Smith teach all the limitations as discussed in claim 8 above. However, it is noted that the combination of the prior arts of Allamanis, Bird and Smith do not explicitly teach “wherein the metadata comprises a code summarization of the code segment.” On the other hand, in the same field of endeavor, Bahrami teaches wherein the metadata comprises a code summarization of the code segment (i.e. “ the package metadata 112B may include metadata about the software package in the form of a natural language text” ; para. [0026]. Further, i.e. “ the package metadata 112B may include at least one of a…, a summary of the software package, a programming language associated with the software package ”; para. [0035]) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Bahrami that teaches operations for code enrichment through metadata for code synthesis into the combination of the prior arts of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code, Bird that teaches a natural language code search service, and Smith that teaches provide assistance to programmers during programming to reduce the number of routine tasks. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to generating a dataset of natural language (NL) text features and respective code features by using the updated one or more source code files because it can improve code development ( Bahrami , para. [0005]) . As per claim 11, Allamanis, Bird and Smith teach all the limitations as discussed in claim 8 above. However, it is noted that the combination of the prior arts of Allamanis, Bird and Smith do not explicitly teach “wherein the metadata comprises a filename of the code segment and a local file path of a file containing the code segment.” On the other hand, in the same field of endeavor, Bahrami teaches wherein the metadata comprises a filename of the code segment (i.e. “the package metadata 306 may indicate the name of the software package as “ABC”, “; fig. 3b, para. [0057]) and a local file path of a file containing the code segment (i.e . “a path 310 of a first source code file (i.e., the PKG-INFO file)” ; fig. 3b, para. [0058]) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Bahrami that teaches operations for code enrichment through metadata for code synthesis into the combination of the prior arts of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code, Bird that teaches a natural language code search service, and Smith that teaches provide assistance to programmers during programming to reduce the number of routine tasks. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to generating a dataset of natural language (NL) text features and respective code features by using the updated one or more source code files because it can improve code development ( Bahrami , para. [0005]) . As per claim 18, Allamanis and Bird teach all the limitations as discussed in claim 16 above. However, it is noted that the combination of the prior arts of Allamanis and Bird do not explicitly teach “wherein the metadata includes a code summarization of the code segment, predicted queries of the code segment, a filename of a file containing the code segment, a file path of the file containing the code segment;” On the other hand, in the same field of endeavor, Bahrami teaches wherein the metadata includes a code summarization of the code segment (i.e. “ the package metadata 112B may include metadata about the software package in the form of a natural language text” ; para. [0026]. Further, i.e. “ the package metadata 112B may include … a summary of the software package, a programming language associated with the software package ,”; para. [0035]) , predicted queries of the code segment (i.e. “ the language model may be able to predict the likelihood of word “Deliver” appears after “Leverages” as such “ABC Leverages World's Fastest Supercomputer ‘XYZ ’”; para. [0018]) , a filename of a file containing the code segment (i.e. “ The package metadata may include the source code files 112A and the package metadata 112B. In an embodiment, the package metadata 112B may include at least one of a name of the software package, ”; para. [0035]; Examiner note: using a BRI the file name is interpreted as the name of the software package which is a source code file) , a file path of the file containing the code segment (i.e . “a path 310 of a first source code file (i.e., the PKG-INFO file);” fig. 3b, para. [0058]) ; Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Bahrami that teaches operations for code enrichment through metadata for code synthesis into the combination of the prior arts of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code, and Bird that teaches a natural language code search service. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to generating a dataset of natural language (NL) text features and respective code features by using the updated one or more source code files because it can improve code development ( Bahrami , para. [0005]) . However, it is noted that the combination of the prior arts of Allamanis , Bird and Bahrami do not explicitly teach “namespace data associated with the code segment.” On the other hand, in the same field of endeavor, Smith teaches namespace data associated with the code segment (i.e. “ A code entity may be a reference to any logical division of software source code such as but not limited to a class, a namespace, ”; fig. 4a, para. [0056]) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Smith that teaches provide assistance to programmers during programming to reduce the number of routine tasks into the combination of the prior arts of Allamanis that teaches a codebase is developed by analyzing the variables in the codebase' s source code, Bird that teaches a natural language code search service, and Bahrami that teaches operations for code enrichment through metadata for code synthesis. Additionally, this can improve the code's execution and to remove any coding bugs or errors. The motivation for doing so would be to determine relatedness between embeddings in this joint tensor space for text and associated source code is used in some embodiments to facilitate code search ( Smith , para. [0005]-[0006]) . 12-57 AIA Prior Art of Record 07-96 AIA 12. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Szwabe et al. (US 20250061287 A1), teaches provide enhanced machine learning model accuracy through post-hoc confidence score calibration. Clement et al. (US 20240419917 A1), teaches a customized prompt generation service automates prompts to a large language model to perform a specified software engineering task. Singh (US 20240095077 A1), teaches generate a prompt for one or more machine learning processes. Chen (US 20190272171 A1), teaches techniques for asynchronously displaying the results of a codebase analysis service . Conclusion 13. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANTONIO CAIADO whose telephone number is (469)295-9251. The examiner can normally be reached on Monday - Friday / 06:30 to 16:30. 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, Ng, Amy can be reached on (571) 270-1698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ANTONIO J CAIADO/ Examiner, Art Unit 2164 Application/Control Number: 19/186,622 Page 2 Art Unit: 2164 Application/Control Number: 19/186,622 Page 3 Art Unit: 2164 Application/Control Number: 19/186,622 Page 4 Art Unit: 2164 Application/Control Number: 19/186,622 Page 5 Art Unit: 2164 Application/Control Number: 19/186,622 Page 6 Art Unit: 2164 Application/Control Number: 19/186,622 Page 7 Art Unit: 2164 Application/Control Number: 19/186,622 Page 8 Art Unit: 2164 Application/Control Number: 19/186,622 Page 9 Art Unit: 2164 Application/Control Number: 19/186,622 Page 10 Art Unit: 2164 Application/Control Number: 19/186,622 Page 11 Art Unit: 2164 Application/Control Number: 19/186,622 Page 12 Art Unit: 2164 Application/Control Number: 19/186,622 Page 13 Art Unit: 2164 Application/Control Number: 19/186,622 Page 14 Art Unit: 2164 Application/Control Number: 19/186,622 Page 15 Art Unit: 2164 Application/Control Number: 19/186,622 Page 16 Art Unit: 2164 Application/Control Number: 19/186,622 Page 17 Art Unit: 2164 Application/Control Number: 19/186,622 Page 18 Art Unit: 2164 Application/Control Number: 19/186,622 Page 19 Art Unit: 2164 Application/Control Number: 19/186,622 Page 20 Art Unit: 2164 Application/Control Number: 19/186,622 Page 21 Art Unit: 2164 Application/Control Number: 19/186,622 Page 22 Art Unit: 2164 Application/Control Number: 19/186,622 Page 23 Art Unit: 2164 Application/Control Number: 19/186,622 Page 24 Art Unit: 2164 Application/Control Number: 19/186,622 Page 25 Art Unit: 2164 Application/Control Number: 19/186,622 Page 26 Art Unit: 2164 Application/Control Number: 19/186,622 Page 27 Art Unit: 2164 Application/Control Number: 19/186,622 Page 28 Art Unit: 2164 Application/Control Number: 19/186,622 Page 29 Art Unit: 2164 Application/Control Number: 19/186,622 Page 30 Art Unit: 2164 Application/Control Number: 19/186,622 Page 31 Art Unit: 2164 Application/Control Number: 19/186,622 Page 32 Art Unit: 2164 Application/Control Number: 19/186,622 Page 33 Art Unit: 2164 Application/Control Number: 19/186,622 Page 34 Art Unit: 2164 Application/Control Number: 19/186,622 Page 35 Art Unit: 2164 Application/Control Number: 19/186,622 Page 36 Art Unit: 2164 Application/Control Number: 19/186,622 Page 37 Art Unit: 2164 Application/Control Number: 19/186,622 Page 38 Art Unit: 2164 Application/Control Number: 19/186,622 Page 39 Art Unit: 2164 Application/Control Number: 19/186,622 Page 40 Art Unit: 2164 Application/Control Number: 19/186,622 Page 41 Art Unit: 2164 Application/Control Number: 19/186,622 Page 42 Art Unit: 2164 Application/Control Number: 19/186,622 Page 43 Art Unit: 2164 Application/Control Number: 19/186,622 Page 44 Art Unit: 2164
Prosecution Insights