DETAILED ACTION
This action is responsive to application filed on April 24, 2024.
Claims 1-20 are pending and are presented to examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Drawings
The drawings filed on April 24, 2024 are acceptable for examination purposes.
Information Disclosure Statement
As required by M.P.E.P. 609, the applicant’s submission of the Information Disclosure Statement dated April 30, 2024 is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, is directed to that judicial exception, an abstract idea, as it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated the claims under the framework provided in the 2019 Patent Eligibility Guidance published in the Federal Register 01/07/2019 and has provided such analysis below.
Step 1: Claims 1-7 are directed to systems and fall within the statutory category of machines; Claims 8-14 are directed to methods and fall within the statutory category of processes; and Claims 15-20 are directed to media and fall withing the statutory category of manufactures. Therefore, “Are the claims to a process, machine, manufacture or composition of matter?” Yes.
In order to evaluate the Step 2A inquiry “Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?” we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
Claims 1, 8 and 15 as drafted, recite a process that, under its broadest reasonable interpretation, covers steps that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. That is, the limitations: a) “receiving a request to generate computer code for insertion into source code of a software application, the request including a description of the computer code;” – Mental processes (see MPEP 2106.04(a)(2), III), this limitation can be reasonable performed by a human, wherein a person can write with the aid of pen and paper a description of a code to be pass over a computer tool.
b) “generating a prompt based on the description of the computer code and contextual information regarding the computer code;” – Mental processes (see MPEP 2106.04(a)(2), III), this limitation can be reasonable performed by a human mind, where a person using pen and paper can generate a prompt/instructions/input based on data. c) “ - Mental processes (see MPEP 2106.04(a)(2), III), this limitation can be reasonable performed by a human mind, wherein a person using pen and paper can provide different/plurality of suggestions on how to fix a code having an error.
d) “
e) “validating each of the plurality of suggested computer code blocks for syntax, security, and functionality;” - Mental processes (see MPEP 2106.04(a)(2), III), this limitation can be reasonable performed by a human mind, wherein a person using pen and paper can validate suggested code to fix an error.
f) “ranking any valid suggested computer code blocks based on one or more quality metrics;” - Mental processes (see MPEP 2106.04(a)(2), III), this limitation can be reasonable performed by a human mind, wherein a person using pen and paper can create a ranking list based on the evaluation of the suggestions to fix an error in the code. g) “ That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, (i.e., “receiving”, “generating”, “validating” and, “ranking”) can be performed in the human mind through observation, evaluation, judgment, opinion. Thus, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Therefore, Yes, claims 1, 8 and 15 recite judicial exceptions.
The claims have been identified to recite judicial exceptions, Step 2A Prong 2 will evaluate whether the claims are directed to the judicial exception.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. The claims recite the following additional elements: “a system”, “at least one hardware processor”, “a computer-readable medium”, “a non-transitory machine-readable medium” and “one or more processors”. The additional elements are merely instructions to implement an abstract idea on a computer, or merely using a generic computer or computer components as a tool to perform the abstract idea (see MPEP 2106.05(f)). Accordingly, the additional elements recited in the claims do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, thus failing to integrate the abstract idea into a practical application. There are additional elements in the claim language such as: c) “sending the prompt to a Large Language Model (LLM) to generate a plurality of suggested computer code blocks;” – this limitation as drafted is an insignificant extra-solution activity (e.g., mere data gathering, See MPEP 2106.05(g)), a concept to receive data using a tool/agent, also lacking to mention technical details.
d) “receiving, from the LLM, the plurality of suggested computer code blocks;” – this limitation as drafted is an insignificant extra-solution activity (e.g., mere data gathering, See MPEP 2106.05(g)), a concept to receive data.
g) “causing display of a highest ranking valid suggested computer code block” - this limitation as drafted is mere instructions to apply an exception (See MPEP 2106.05(f), e.g., using a generic computer to display data). In addition the use of a “Large Language Model (LLM)” is also consider a field of use and technological environment. “the element amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use.” (See MPEP 2106.5(h)).
Therefore, “Do the claims recite additional elements that integrate the judicial exception into a practical application? No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
After having evaluating the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claims 1, 8 and 15 not only recites a judicial exception but that the claim is directed to the judicial exception as the judicial exception has not been integrated into practical application.
Step 2B:
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements “a system”, “at least one hardware processor”, “a computer-readable medium”, “a non-transitory machine-readable medium” and “one or more processors” are generic computer components used as tools to perform the abstract idea. Accordingly, the additional elements recited in the claims cannot provide an inventive concept. In addition, after further evaluation the claim as a whole doesn’t improve any function of a computer or to any other technology or technical field. Thus, the claims are not patent eligible.
Therefore, “Do the claims recite additional elements that amount to significantly more than the judicial exception? No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception.
Having concluded analysis within the provided framework, Claims 1, 8 and 15 do not recite patent eligible subject matter under 35 U.S.C. § 101. With regards to claim 2 (and similar for claims 9 and 16), it recites “wherein the request is received from an Integrated Development Environment (IDE) and the causing the display includes causing the IDE to display the highest ranking valid suggested computer code block.” as drafted, the claim recites mere instructions to apply an exception (See MPEP 2106.05(f)). Moreover, claim 2 does not recite any other additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claim 2 also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more Therefore, Claim 2 does not recite patent eligible subject matter under 35 U.S.C. § 101. With regards to claim 3 (and similar for claims 10 and 17), it recites “wherein the contextual information includes a location within existing source code at which the requested computer code is to be inserted.” as drafted, is an Insignificant Extra-Solution Activity (See MPEP 2106.05(g) – presenting data). Moreover, claim 3 does not recite any other additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claim 3 also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more Therefore, Claim 3 does not recite patent eligible subject matter under 35 U.S.C. § 101. With regards to claim 4 (and similar for claims 11 and 18), it recites “wherein the contextual information includes examples of input parameters of a function within the requested computer code.” as drafted, is an Insignificant Extra-Solution Activity (See MPEP 2106.05(g) – presenting data). Moreover, claim 4 does not recite any other additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claim 4 also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more Therefore, Claim 4 does not recite patent eligible subject matter under 35 U.S.C. § 101. With regards to claim 5 (and similar for claims 12 and 19), it recites “wherein the contextual information includes examples of output of a function within the requested computer code.” as drafted, is an Insignificant Extra-Solution Activity (See MPEP 2106.05(g) – presenting data). Moreover, claim 5 does not recite any other additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claim 5 also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more Therefore, Claim 5 does not recite patent eligible subject matter under 35 U.S.C. § 101.
With regards to claim 6 (and similar for claims 13 and 20), it recites “wherein the one or more quality metrics include a cohesion metric.” as drafted, is an Insignificant Extra-Solution Activity (See MPEP 2106.05(g) – presenting data). Moreover, claim 6 does not recite any other additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claim 6 also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more Therefore, Claim 6 does not recite patent eligible subject matter under 35 U.S.C. § 101.
With regards to claim 7 (and similar for claim 14), it recites “wherein security validation is performed by using a static application security testing (SAST) tool.” as drafted, is mere instruction to apply an exception (See MPEP 216.05(f)). Moreover, claim 7 does not recite any other additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claim 7 also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more Therefore, Claim 7 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Therefore, Claims 1-20 do not recite patent eligible subject matter under 35 U.S.C. § 101.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 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.
Claims 1-3, 7-10 and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Carrara et al. (US Pub. No. 2025/0298585 – hereinafter Carrara) in view of Schaefer et al. (US Pub. No. 2025/0245122 – hereinafter Schaefer).
With respect to claim 1, Carrara teaches a system (See figure 2, a system (e.g., IDE system 202) comprising: at least one hardware processor (See figure 2, processor 212) and a computer-readable medium storing instructions that, when executed by the at least one hardware processor (See figure 2, memory 220), cause the at least one hardware processor to perform operations comprising: receiving a request to generate computer code for insertion into source code of a software application, the request including a description of the computer code (See figures 2-3 (and related text), IDE System 202, design input 312. See paragraphs [0061], [0068], “Additionally, the IDE system's development services can include a control code generation and analysis copilot that leverages generative AI to assist the user in creating, analyzing, and documenting control code for an industrial application, as well as to search for answers to specific questions relating to the control code or its development. The copilot can include a generative AI component 210 that responds to natural language prompts submitted by the user as part of design input 312.”. Furthermore, see figures 10-14 (and related text). Examiner notes: user input with instruction/description). generating a prompt based on the description of the computer code and contextual information regarding the computer code (See abstract, “An integrated development environment (IDE) leverages a generative AI model to generate industrial control code in accordance with specified functional requirements, which can be provided to the industrial IDE system as intuitive natural language spoken or written text. The industrial IDE can also analyze written code in response to natural language prompts submitted against the code, generate answers to user-submitted questions about the code, and offer recommendations for improving the code in response to specific questions or requests submitted by the user.”. See paragraphs [0058], [0061], [063], [0069]-[0071], “to address at least some of these or other issues, one or more embodiments described herein provide an integrated development environment (IDE) for designing, programming, and configuring aspects of an industrial automation system using generative artificial intelligence (AI) techniques. Embodiments of the industrial IDE can make use of a generative AI model and associated neural networks to generate portions of an industrial automation project—including control code, code commentary, data tags, I/O or device configurations, or other such project elements—in accordance with functional requirements provided to the IDE system as intuitive natural language inputs (e.g., spoken or written natural language text). The IDE system includes a specialized prompt engineering layer and associated custom models—trained using knowledge of various types of industrial control applications, knowledge of specific industrial verticals (e.g., automotive, pharmaceutical, food and drug, oil and gas, mining, textiles, power generation, etc.), vertical-specific industrial standards and best practices, and other such training data—that generates prompts or meta-prompts based on a user's natural language inputs for submission to generative AI models such as large language models (LLMs). The industrial IDE system can also leverage generative AI to analyze pre-written control code in response to natural language prompts submitted against the code, generate answers to user-submitted questions about the code, and offer recommendations for improving the code in response to specific questions or requests submitted by the user.”. Examiner notes: prompt generation). sending the prompt to a Large Language Model (LLM) to generate a plurality of suggested computer code blocks (See paragraph [0058], “The IDE system includes a specialized prompt engineering layer and associated custom models—trained using knowledge of various types of industrial control applications, knowledge of specific industrial verticals (e.g., automotive, pharmaceutical, food and drug, oil and gas, mining, textiles, power generation, etc.), vertical-specific industrial standards and best practices, and other such training data—that generates prompts or meta-prompts based on a user's natural language inputs for submission to generative AI models such as large language models (LLMs).”. See paragraph [0063], “The generative AI component 210 can generate and submit prompts or meta-prompts to one or more generative AI models and associated neural networks, where these prompts are generated based on natural language requests or queries submitted by the designer as well as domain-specific information contained in the custom models 222. Depending on the nature of the designer's request or query, the responses returned by the generative AI model in response to the prompts can be used by the project generation component 206 or the user interface component 204 to generate portions of the system project, to render answers to designer's questions about a portion of control code or about the design platform itself, to ascertain or to perform other IDE tasks.”. Furthermore, see paragraphs [0069], [0081], [0083]. Examiner notes: prompt sent to LLM). receiving, from the LLM, the plurality of suggested computer code blocks (See figures 11, 14 (and related text) and paragraph [0092], “When displaying a control code recommendation in response to a user's request, the copilot window 802 can display the user's original prompt in a prompt window 1102, the recommended control code in code window 1104, and natural language implementation details 1106 that provide additional information or context about the recommended control code. The recommended control code can be rendered in code window 1104 in any control code format, including but not limited to structured text, industrial DSL, ladder logic, Python, C#, or another format. In some embodiments, the copilot window 802 can allow the user to switch the view of the proposed control code between two or more formats.”. Examiner notes: code suggestion). Carrara is silent to disclose, however in an analogous art, Schaefer teaches: validating each of the plurality of suggested computer code blocks for syntax, security, and functionality (See abstract, “The prompt is sent to the LLM, and a response is obtained. The response includes a proposed fix comprising a set of edits to be applied to the source code and/or configuration files. The response is processed and validated, and a fix suggestion is assembled and provided based on the response. The fix suggestion includes a natural language explanation of the proposed fix, suggested source-code changes, and selectable options for accepting, rejecting, and editing the fix suggestion.”. See paragraph [0036], “Once each prompt response from the LLM has been obtained, it is processed and validated. The initial processing of the prompt may include identifying syntactic and semantic errors in the response. The errors can be identified based on rules and trained logic that is applied by the system to identify the error. In some instances, upon identifying errors in the response, the system fixes the errors. By way of example, the system may change referenced line numbers in the set of edits in order to promote syntactic correctness of the suggested source-code changes. In such an example, the system may identify two duplicated numbers for lines of code (e.g., two lines numbered line 15, for instance). In this example, the system can renumber the lines of code so the lines are correctly recited as sequential numbers.”. See paragraphs [0038]-[0039], [0042], [0070], “The validation can also, in some cases be performed in combination with the code analysis tool that produced the alert (e.g., a Static Application Security Testing (SAST) tool or a Dynamic Application Security Testing (DAST) tool) that can analyze the source-code modified with the proposed fixes to determine whether the proposed fixes will resolve the alert issues. When a proposed fix that is identified in the prompt response(s) passes the validation checks, a corresponding fix suggestion is assembled and provided to a user interface. In some instances, the fix suggestion includes the natural language explanation of the proposed fix, the suggested source-code changes, and selectable options for accepting, rejecting, and editing the fix suggestion. When a user selects a selectable option to accept the proposed fix, the system will automatically modify the source-code file and or included dependencies based on the proposed fix that is being accepted.”. Furthermore, see figures 3-5 (and related text). Examiner note: validation of the fix/response/output/suggestion). ranking any valid suggested computer code blocks based on one or more quality metrics (See figures 1, 3-5 (and related text) and paragraph [0048], “In some cases, the LLM may generate a plurality of proposed fixes in response to the prompt. In such cases, the method may involve assembling and providing a plurality of different fix suggestions, each comprising different suggested changes to the source code. This allows the user to choose from a variety of potential fixes for the alert, increasing the likelihood of finding a fix that is suitable for their specific situation and preferences. The plurality of different fix suggestions can be presented separately and/or combined into a single composite fix suggestion. The system may also apply logic and rules to rank the different fix suggestions and present the fix suggestion that is ranked highest. The ranking of the different fix suggestions may, for instance, be based on an evaluation of which fix suggestion matches a predetermined set of user preferences and historical interactions when addressing similar types of alerts.”. Examiner note: ranking) and causing display of a highest ranking valid suggested computer code block (See figures 1, 3-5 (and related text) and paragraph [0048], “In some cases, the LLM may generate a plurality of proposed fixes in response to the prompt. In such cases, the method may involve assembling and providing a plurality of different fix suggestions, each comprising different suggested changes to the source code. This allows the user to choose from a variety of potential fixes for the alert, increasing the likelihood of finding a fix that is suitable for their specific situation and preferences. The plurality of different fix suggestions can be presented separately and/or combined into a single composite fix suggestion. The system may also apply logic and rules to rank the different fix suggestions and present the fix suggestion that is ranked highest. The ranking of the different fix suggestions may, for instance, be based on an evaluation of which fix suggestion matches a predetermined set of user preferences and historical interactions when addressing similar types of alerts.”. Examiner note: displaying). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Carrara’s teaching, which uses an integrated development environment (IDE) that leverage a generative AI model to generate industrial control code in accordance with specified functional requirements, which can be provided to the industrial IDE system as intuitive natural language spoken or written text, by validating each of the plurality of suggested computer code blocks for syntax, security, and functionality and ranking any valid suggested computer code blocks based on quality metrics as suggested by Schaefer, as Schaefer would provide techniques and systems for generating fixes for security vulnerabilities and other errors that are identified in the alerts provided by code analysis tools and, even more particularly, for utilizing artificial intelligence (AI) to assist in the automated generation of these fixes to facilitate improvements in efficient processing and accuracy of the fixes (see paragraph [0005]). With respect to claim 2, Carrara is silent to disclose, however in an analogous art, Schaefer teaches wherein the request is received from an Integrated Development Environment (IDE) and the causing the display includes causing the IDE to display the highest ranking valid suggested computer code block (See paragraph [0070], “After validating and processing the prompt response, the system will assemble a fix suggestion that includes relevant information from the prompt response and corresponding dependencies and metadata information, as well as the natural language description of the proposed fixes identified within the fix suggestion. The assembly process may also include generating a file that is presentable with the fix suggestion information, such as, for example, a file that is displayable with the fix suggestions within an Integrated Development Environment (IDE). In some alternative embodiments, the system may also include selectable links to access referenced dependencies and code, as well as links that are selectable to cause the system to automatically apply or reject the proposed fixes identified in the presented fix suggestion. Links can also be presented to enable a user to select the link to edit a proposed fix prior to applying the fix.”). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Carrara’s teaching, which uses an integrated development environment (IDE) that leverage a generative AI model to generate industrial control code in accordance with specified functional requirements, which can be provided to the industrial IDE system as intuitive natural language spoken or written text, by using an Integrated Development Environment (IDE) to display the highest ranking valid suggested computer code block as suggested by Schaefer, as Schaefer would provide techniques and systems for generating fixes for security vulnerabilities and other errors that are identified in the alerts provided by code analysis tools and, even more particularly, for utilizing artificial intelligence (AI) to assist in the automated generation of these fixes to facilitate improvements in efficient processing and accuracy of the fixes (see paragraph [0005]). With respect to claim 3, Carrara teaches wherein the contextual information includes a location within existing source code at which the requested computer code is to be inserted (See figure 14 (and related text). Examiner notes: insertion point/area in code).
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With respect to claim 7, Carrara is silent to disclose, however in an analogous art, Schaefer teaches wherein security validation is performed by using a static application security testing (SAST) tool (See paragraph [0042], “The validation can also, in some cases be performed in combination with the code analysis tool that produced the alert (e.g., a Static Application Security Testing (SAST) tool or a Dynamic Application Security Testing (DAST) tool) that can analyze the source-code modified with the proposed fixes to determine whether the proposed fixes will resolve the alert issues. When a proposed fix that is identified in the prompt response(s) passes the validation checks, a corresponding fix suggestion is assembled and provided to a user interface. In some instances, the fix suggestion includes the natural language explanation of the proposed fix, the suggested source-code changes, and selectable options for accepting, rejecting, and editing the fix suggestion. When a user selects a selectable option to accept the proposed fix, the system will automatically modify the source-code file and or included dependencies based on the proposed fix that is being accepted.”). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Carrara’s teaching, which uses an integrated development environment (IDE) that leverage a generative AI model to generate industrial control code in accordance with specified functional requirements, which can be provided to the industrial IDE system as intuitive natural language spoken or written text, by using a static application security testing (SAST) tool for validation purposes as suggested by Schaefer, as Schaefer would provide mechanism for analyzing the source-code modified with the proposed fixes to determine whether the proposed fixes will resolve the alert issues (see paragraph [0042]).
With respect to claims 8-10 and 14, the claims are directed to a method that corresponds to the system recited in claims 1-3 and 7, respectively (see the rejection of claims 1-3 and 7 above). With respect to claims 15-17, the claims are directed to a medium that corresponds to the method recited in claims 1-3, respectively (see the rejection of claims 1-3 above; wherein Carrara also teaches such a medium in figure 2 (e.g., memory 220).
Claims 4-5, 11-12 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Carrara et al. (US Pub. No. 2025/0298585 – hereinafter Carrara) in view of Schaefer et al. (US Pub. No. 2025/0245122 – hereinafter Schaefer) and further in view of Liguori et al. (US Pat. No. 12,524,214 – hereinafter Liguori). With respect to claim 4, Carrara in view of Schaefer is silent to disclose, however in an analogous art, Liguori teaches wherein the contextual information includes examples of input parameters of a function within the requested computer code (See figure 4 (and related text) and column 16 lines 27-58, “FIG. 4 depicts example templates and response definitions according to some examples. Example prompt templates 412 include a troubleshooting prompt template and a project design prompt template. Example prompt templates 412 provide additional cues to an LLM about the scope and/or performance of various tasks. Prompt templates can include placeholders that can be populated with custom text, typically offset from the prompt text by special characters and identified by a variable name (indicated by braces surrounding a variable name in this and subsequent figures). In using a prompt template, agents 122 populate the placeholders prior to submitting the resulting prompt to an LLM. For example, the troubleshooting prompt template includes a placeholder 402 for a user prompt, a placeholder 404 for an identification of data sources that the LLM has at its disposal, and a placeholder 406 for a response definition that describes the form of the expected response. Prompt templates can also be used for messages sent to a user (e.g., to wrap additional context around a response or responses from an LLM).”). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the combination of Carrara and Schaefer, by including examples of input parameters of a function within the requested computer code as suggested by Liguori, as Liguori would enhance the process by making it easier to deliver highly-available software to production quickly and reliably. With respect to claim 5, Carrara in view of Schaefer is silent to disclose, however in an analogous art, Liguori teaches wherein the contextual information includes examples of output of a function within the requested computer code (See figure 4 (and related text) and column 16 lines 27-58, “FIG. 4 depicts example templates and response definitions according to some examples. Example prompt templates 412 include a troubleshooting prompt template and a project design prompt template. Example prompt templates 412 provide additional cues to an LLM about the scope and/or performance of various tasks. Prompt templates can include placeholders that can be populated with custom text, typically offset from the prompt text by special characters and identified by a variable name (indicated by braces surrounding a variable name in this and subsequent figures). In using a prompt template, agents 122 populate the placeholders prior to submitting the resulting prompt to an LLM. For example, the troubleshooting prompt template includes a placeholder 402 for a user prompt, a placeholder 404 for an identification of data sources that the LLM has at its disposal, and a placeholder 406 for a response definition that describes the form of the expected response. Prompt templates can also be used for messages sent to a user (e.g., to wrap additional context around a response or responses from an LLM).”). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the combination of Carrara and Schaefer, by including examples of output of a function within the requested computer code as suggested by Liguori, as Liguori would enhance the process by making it easier to deliver highly-available software to production quickly and reliably.
With respect to claims 11-12, the claims are directed to a method that corresponds to the system recited in claims 4-5, respectively (see the rejection of claims 4-5 above). With respect to claims 18-19, the claims are directed to a medium that corresponds to the method recited in claims 4-5, respectively (see the rejection of claims 4-5 above).
Claims 6, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Carrara et al. (US Pub. No. 2025/0298585 – hereinafter Carrara) in view of Schaefer et al. (US Pub. No. 2025/0245122 – hereinafter Schaefer) and further in view of Ray et al. (US Pub. No. 2021/0406004 – hereinafter Ray). With respect to claim 6, Carrara in view of Schaefer is silent to disclose, however in an analogous art, Ray teaches wherein the one or more quality metrics include a cohesion metric (See abstract, figure 1 and paragraph [0037], “Dependency Mappings 128 may include: Resource Dependencies, Coupling/Cohesion Metrics (e.g., Cyclic Dependencies), etc.”). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the combination of Carrara and Schaefer, by managing quality metrics include a cohesion metric as suggested by Ray, as Ray would provide implementation for code quality (see paragraph [0007]). With respect to claim 13, the claim is directed to a method that corresponds to the system recited in claim 6, respectively (see the rejection of claim 6 above). With respect to claim 20, the claim is directed to a medium that corresponds to the method recited in claim 6, respectively (see the rejection of claim 6 above).
Additional Claim Rejections - 35 USC § 103
Claims 4-5, 11-12 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Carrara et al. (US Pub. No. 2025/0298585 – hereinafter Carrara) in view of Schaefer et al. (US Pub. No. 2025/0245122 – hereinafter Schaefer) and further in view of Blum et al. (US Pat. No. 12,499,239 – hereinafter Blum). With respect to claim 4, Carrara in view of Schaefer is silent to disclose, however in an analogous art, Blum teaches wherein the contextual information includes examples of input parameters of a function within the requested computer code (See figure 1B (and related text)) Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the combination of Carrara and Schaefer, by including examples of input parameters of a function within the requested computer code as suggested by Blum, as Blum would enhance the process by making it easier to deliver supplemental access, analytic or enrichment function (see abstract). With respect to claim 5, Carrara in view of Schaefer is silent to disclose, however in an analogous art, Blum teaches wherein the contextual information includes examples of output of a function within the requested computer code (See figure B (and related text)). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the combination of Carrara and Schaefer, by including examples of output of a function within the requested computer code as suggested by Blum, as Blum would enhance the process by making it easier to deliver supplemental access, analytic or enrichment function (see abstract).
With respect to claims 11-12, the claims are directed to a method that corresponds to the system recited in claims 4-5, respectively (see the rejection of claims 4-5 above). With respect to claims 18-19, the claims are directed to a medium that corresponds to the method recited in claims 4-5, respectively (see the rejection of claims 4-5 above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Finkman et al. (US Pub. No. 2025/0225212) discloses Aa method for real-time evaluating code leakage during software code development when the developer is using a code assistant tool, comprising performing code leakage estimation by identifying and processing, using an LLM-based model, the most updated code segments as the segments evolve; and evaluating, using the LLM-based model, the extent to which a written code has been inadvertently revealed to one or more code assistant servers by reconstructing the original code from the requests sent to each code assistant server. (see abstract).
McMorran et al. (US Pub. No. 2025/0123814) engineers a prompt for submission to a language model, such as a software development large language model. Some embodiments ascertain a relationship between code development information and potential context. Code development information includes static analysis results, project settings, development tool history or status data, and other software development data which augments training data previously embedded in the language model. Some embodiments compute a prompt inclusion score of the potential context, based on at least the relationship, and use the inclusion score to determine whether to include the potential context in the language model prompt. In some scenarios, an embodiment determines where to place the context in the prompt. Scoring is performed by a formula, statistical scoring model, or machine learning scoring model. Some embodiments reduce context inclusion false positives and false negatives that were based on the use of embedding similarity scores alone. (see abstract).
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/ANIBAL RIVERACRUZ/Primary Examiner, Art Unit 2192