DETAILED ACTION
Continued Examination Under 37 CFR 1.114
1. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicants’ submission filed on 02/05/2026 has been entered.
Status of Claims
2. Claims 1, 8 and 15 have been amended. Claims 1-20 are pending in the application, of which claims 1, 8 and 15 are in independent form and these claims have been fully considered by the examiner. No canceled or newly added as of the final rejections dated 11/24/2025.
Response to the Amendments
3. Regarding art rejection: In regards to claims 1-20 Applicants arguments are not persuasive; further, Applicants' amendment necessitated new grounds of rejections presented in the following art rejection.
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 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.
4. Claims 1-5, 8-12 and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Goodman et al. (US Patent Application Publication No. 2025/0013438 A1 -herein after Goodman) in view of Yingqiao Liu et al. (US Patent Application Publication No. 2017/0168783 A1 -herein after Liu), and further in view of Pack, III et al. (US Patent Application Publication No. 2025/0086011 A1 herein after Pack).
Per claim 1:
Goodman discloses:
A system (At least see ¶[0021] - system leveraging a large language model to generate code script for an automated assistant routine from a natural language prompt ) comprising:
at least one hardware processor (At least see ¶[0006] - processing hardware to perform operations); and
a computer-readable medium storing instructions that, when executed by the at least one hardware processor (At least see ¶[0067] - Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices), cause the at least one hardware processor to perform operations comprising:
converting the command into a user portion of a natural language prompt (At least see ¶[0027] - initiated in response to user interaction with a virtual or hardware element at a client device, initiated in response to detecting a user gesture, and/or initiated in response to other abbreviated user interface input(s));
adding a predefined system portion to the natural language prompt (At least see ¶[0015] - issuing the user prompt to solicit the user to provide additional information that includes the fixed value for the slot in the code script that lacked the fixed value);
sending the natural language prompt to a Large Language Model (LLM) to generate a first piece of software code in a first programming language based on the natural language prompt (At least see ¶[0018] - processing the natural language prompt to generate the code script as the LLM output includes processing, by the LLM, the natural language prompt conditioned on the user prompt embedding for the user to generate the code script as the LLM output).
Goodman sufficiently discloses the system as set forth above, but Goodman does not explicitly disclose: scanning, by a scripting language interface, software programming code to identify a command written as executable code in the software programming code, the command defined in a scripting language interface library; validating the first piece of software code using a validation function of the first programming language; and inserting the first piece of software code into the software programming code.
However, Liu discloses:
scanning, by a scripting language interface, software programming code to identify a command written as executable code in the software programming code, the command defined in a scripting language interface library (At least see ¶[0007] - parse a plurality of executable code to define one or more generic processes for a plurality of software services, ¶[0036] - service is analyzed for execution and organized for order of execution, the scripting engine 114 may execute the service using a scripting language interface … execution may also include generating a function call to each portion of the service that includes business logic to be carried out by the processor-compiled architecture and generating a service call (command) to the service by retrieving and executing at least one code portion from a script repository [emphasis added]; also see ¶[0037]).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Liu into Goodman because Liu’s teaching provides a request to execute a service associated with the software application, determining portions of the service that include business logic to be carried out by the processor-compiled architecture and determining other portions of the service that include business logic to be carried out by scripted architecture, and executing the service using a scripting language interface, the executing including organizing the portions of the service and the other portions of the service according to the business process pattern, and generating a function call to each portion of the service that includes business logic to be carried out by the processor-compiled architecture (please see ¶[0003]).
Goodman modified by Liu sufficiently discloses the system as set forth above, but Goodman modified by Liu does not explicitly disclose: validating the first piece of software code using a validation function of the first programming language; and inserting the first piece of software code into the software programming code.
However, Pack discloses:
validating the first piece of software code using a validation function of the first programming language (At least see ¶[0021] -Before outputting generated computer code, the code may be validated, i.e. by a sub-task may lint and test the computer code); and
inserting the first piece of software code into the software programming code (At least see ¶[0021] -Computer code generated by tasks selected by an agent LLM may be combined by another task selected by the agent LLM into the requested application or software component, which may then be tested by the agent LLM before being returned as output to the user).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Pack into Goodman modified by Liu’s invention because Pack’s teaching would enable automation with composable asynchronous tasks, which may allow for larger jobs to be decomposed into and accomplished with composable asynchronous tasks that use large language models (LLMs) and Generative Pretrained Transformers (GPTs), which performing by one or more LLM multiple asynchronous tasks result in outputting multiple validated output i.e. a request for the generation of computer code for a website, application, web application, or website component that includes certain features specified in the prompt, a request for the generation of data of a specific type, such as product descriptions for product in a database, or a request for the generation of any other suitable data type provide output using a previous output (please see ¶[0010] and ¶[0011]).
Per claim 2:
Pack also discloses:
command includes a natural language portion in which a desired piece of programming code is described in natural language (At least see ¶[0011] - prompt input by the user may be text written in plain, human understandable language, that includes a request for which responsive output may be generated using composable asynchronous tasks … a request for the generation of computer code for a website, application, web application, or website component that includes certain features specified in the prompt), and wherein command further includes one or more test functions (At least see ¶[0015] - a second sub-task may use an LLM to generate tests that can be run on the generated gallery container component).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Pack into Goodman’s invention because Pack’s teaching would enable automation with composable asynchronous tasks, which may allow for larger jobs to be decomposed into and accomplished with composable asynchronous tasks that use large language models (LLMs) and Generative Pretrained Transformers (GPTs), which performing by one or more LLM multiple asynchronous tasks result in outputting multiple validated output i.e. a request for the generation of computer code for a website, application, web application, or website component that includes certain features specified in the prompt, a request for the generation of data of a specific type, such as product descriptions for product in a database, or a request for the generation of any other suitable data type provide output using a previous output (please see ¶[0010] and ¶[0011]).
Per claim 3:
Pack also discloses:
executing the one or more test functions on the first piece of software code (At least see ¶[0034] - a composable asynchronous task called generateCode may use an LLM to generate computer code. A composable asynchronous task called testGenerateCode may be created in order to test generateCode); and
wherein the inserting is performed in response to a determination that the first piece of software code passes all of the one or more test functions (At least see ¶[0047] - composable asynchronous task 182 may output the computer code for the gallery container component back to the agent LLM 110 once the computer code has been linted and passed all tests executed on it).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Pack into Goodman’s invention because Pack’s teaching would enable automation with composable asynchronous tasks, which may allow for larger jobs to be decomposed into and accomplished with composable asynchronous tasks that use large language models (LLMs) and Generative Pretrained Transformers (GPTs), which performing by one or more LLM multiple asynchronous tasks result in outputting multiple validated output i.e. a request for the generation of computer code for a website, application, web application, or website component that includes certain features specified in the prompt, a request for the generation of data of a specific type, such as product descriptions for product in a database, or a request for the generation of any other suitable data type provide output using a previous output (please see ¶[0010] and ¶[0011]).
Per claim 4:
Pack also discloses:
in response to a determination that the first piece of software code fails the validation function, regenerating the first piece of software code by resending the natural language prompt to the LLM (At least see ¶[0023] - if computer code generated by a composable asynchronous task fails tests run against it in a sub-composable asynchronous task, the composable asynchronous task may regenerate the computer code …).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Pack into Goodman’s invention because Pack’s teaching would enable automation with composable asynchronous tasks, which may allow for larger jobs to be decomposed into and accomplished with composable asynchronous tasks that use large language models (LLMs) and Generative Pretrained Transformers (GPTs), which performing by one or more LLM multiple asynchronous tasks result in outputting multiple validated output i.e. a request for the generation of computer code for a website, application, web application, or website component that includes certain features specified in the prompt, a request for the generation of data of a specific type, such as product descriptions for product in a database, or a request for the generation of any other suitable data type provide output using a previous output (please see ¶[0010] and ¶[0011]).
Per claim 5:
Pack also discloses:
in response to a determination that the first piece of software code fails one or more of the one or more test functions, regenerating the first piece of software code by resending the natural language prompt to the LLM (At least see ¶[0023] - after computer code generated by the LLM of a composable asynchronous task fails a test, the prompt input to the composable asynchronous task may be modified to indicate which test the computer code failed, which may allow the LLM to generate computer code that does not fail that test).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Pack into Goodman’s invention because Pack’s teaching would enable automation with composable asynchronous tasks, which may allow for larger jobs to be decomposed into and accomplished with composable asynchronous tasks that use large language models (LLMs) and Generative Pretrained Transformers (GPTs), which performing by one or more LLM multiple asynchronous tasks result in outputting multiple validated output i.e. a request for the generation of computer code for a website, application, web application, or website component that includes certain features specified in the prompt, a request for the generation of data of a specific type, such as product descriptions for product in a database, or a request for the generation of any other suitable data type provide output using a previous output (please see ¶[0010] and ¶[0011]).
Per claim 8:
Goodman discloses:
A method (At least see ¶[0006] a computer-implemented method that when executed on data processing hardware causes the data processing hardware to perform operation) comprising:
converting the command into a user portion of a natural language prompt (At least see ¶[0027] - initiated in response to user interaction with a virtual or hardware element at a client device, initiated in response to detecting a user gesture, and/or initiated in response to other abbreviated user interface input(s));
adding a predefined system portion to the natural language prompt (At least see ¶[0015] - issuing the user prompt to solicit the user to provide additional information that includes the fixed value for the slot in the code script that lacked the fixed value);
sending the natural language prompt to a Large Language Model (LLM) to generate a first piece of software code in a first programming language based on the natural language prompt (At least see ¶[0018] - processing the natural language prompt to generate the code script as the LLM output includes processing, by the LLM, the natural language prompt conditioned on the user prompt embedding for the user to generate the code script as the LLM output).
Goodman sufficiently discloses the system as set forth above, but Goodman does not explicitly disclose: scanning, by a scripting language interface, software programming code to identify a command written as executable code in the software programming code, the command defined in a scripting language interface library; validating the first piece of software code using a validation function of the first programming language; and inserting the first piece of software code into the software programming code.
However, Liu discloses:
scanning, by a scripting language interface, software programming code to identify a command written as executable code in the software programming code, the command defined in a scripting language interface library (At least see ¶[0007] - parse a plurality of executable code to define one or more generic processes for a plurality of software services, ¶[0036] - service is analyzed for execution and organized for order of execution, the scripting engine 114 may execute the service using a scripting language interface … execution may also include generating a function call to each portion of the service that includes business logic to be carried out by the processor-compiled architecture and generating a service call (command) to the service by retrieving and executing at least one code portion from a script repository [emphasis added]; also see ¶[0037]).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Liu into Goodman because Liu’s teaching provides a request to execute a service associated with the software application, determining portions of the service that include business logic to be carried out by the processor-compiled architecture and determining other portions of the service that include business logic to be carried out by scripted architecture, and executing the service using a scripting language interface, the executing including organizing the portions of the service and the other portions of the service according to the business process pattern, and generating a function call to each portion of the service that includes business logic to be carried out by the processor-compiled architecture (please see ¶[0003]).
Goodman modified by Liu sufficiently discloses the system as set forth above, but Goodman modified by Liu does not explicitly disclose: validating the first piece of software code using a validation function of the first programming language; and inserting the first piece of software code into the software programming code.
However, Pack discloses:
validating the first piece of software code using a validation function of the first programming language (At least see ¶[0021] -Before outputting generated computer code, the code may be validated, i.e. by a sub-task may lint and test the computer code); and
inserting the first piece of software code into the software programming code (At least see ¶[0021] -Computer code generated by tasks selected by an agent LLM may be combined by another task selected by the agent LLM into the requested application or software component, which may then be tested by the agent LLM before being returned as output to the user).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Pack into Goodman modified by Liu’s invention because Pack’s teaching would enable automation with composable asynchronous tasks, which may allow for larger jobs to be decomposed into and accomplished with composable asynchronous tasks that use large language models (LLMs) and Generative Pretrained Transformers (GPTs), which performing by one or more LLM multiple asynchronous tasks result in outputting multiple validated output i.e. a request for the generation of computer code for a website, application, web application, or website component that includes certain features specified in the prompt, a request for the generation of data of a specific type, such as product descriptions for product in a database, or a request for the generation of any other suitable data type provide output using a previous output (please see ¶[0010] and ¶[0011]).
Per claim 9:
Pack also discloses:
command includes a natural language portion in which a desired piece of programming code is described in natural language (At least see ¶[0011] - prompt input by the user may be text written in plain, human understandable language, that includes a request for which responsive output may be generated using composable asynchronous tasks … a request for the generation of computer code for a website, application, web application, or website component that includes certain features specified in the prompt), and wherein command further includes one or more test functions (At least see ¶[0015] - a second sub-task may use an LLM to generate tests that can be run on the generated gallery container component).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Pack into Goodman’s invention because Pack’s teaching would enable automation with composable asynchronous tasks, which may allow for larger jobs to be decomposed into and accomplished with composable asynchronous tasks that use large language models (LLMs) and Generative Pretrained Transformers (GPTs), which performing by one or more LLM multiple asynchronous tasks result in outputting multiple validated output i.e. a request for the generation of computer code for a website, application, web application, or website component that includes certain features specified in the prompt, a request for the generation of data of a specific type, such as product descriptions for product in a database, or a request for the generation of any other suitable data type provide output using a previous output (please see ¶[0010] and ¶[0011]).
Per claim 10:
Pack also discloses:
executing the one or more test functions on the first piece of software code (At least see ¶[0034] - a composable asynchronous task called generateCode may use an LLM to generate computer code. A composable asynchronous task called testGenerateCode may be created in order to test generateCode); and
wherein the inserting is performed in response to a determination that the first piece of software code passes all of the one or more test functions (At least see ¶[0047] - composable asynchronous task 182 may output the computer code for the gallery container component back to the agent LLM 110 once the computer code has been linted and passed all tests executed on it).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Pack into Goodman’s invention because Pack’s teaching would enable automation with composable asynchronous tasks, which may allow for larger jobs to be decomposed into and accomplished with composable asynchronous tasks that use large language models (LLMs) and Generative Pretrained Transformers (GPTs), which performing by one or more LLM multiple asynchronous tasks result in outputting multiple validated output i.e. a request for the generation of computer code for a website, application, web application, or website component that includes certain features specified in the prompt, a request for the generation of data of a specific type, such as product descriptions for product in a database, or a request for the generation of any other suitable data type provide output using a previous output (please see ¶[0010] and ¶[0011]).
Per claim 11:
Pack also discloses:
in response to a determination that the first piece of software code fails the validation function, regenerating the first piece of software code by resending the natural language prompt to the LLM (At least see ¶[0023] - if computer code generated by a composable asynchronous task fails tests run against it in a sub-composable asynchronous task, the composable asynchronous task may regenerate the computer code …).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Pack into Goodman’s invention because Pack’s teaching would enable automation with composable asynchronous tasks, which may allow for larger jobs to be decomposed into and accomplished with composable asynchronous tasks that use large language models (LLMs) and Generative Pretrained Transformers (GPTs), which performing by one or more LLM multiple asynchronous tasks result in outputting multiple validated output i.e. a request for the generation of computer code for a website, application, web application, or website component that includes certain features specified in the prompt, a request for the generation of data of a specific type, such as product descriptions for product in a database, or a request for the generation of any other suitable data type provide output using a previous output (please see ¶[0010] and ¶[0011]).
Per claim 12:
Pack also discloses:
in response to a determination that the first piece of software code fails one or more of the one or more test functions, regenerating the first piece of software code by resending the natural language prompt to the LLM (At least see ¶[0023] - after computer code generated by the LLM of a composable asynchronous task fails a test, the prompt input to the composable asynchronous task may be modified to indicate which test the computer code failed, which may allow the LLM to generate computer code that does not fail that test).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Pack into Goodman’s invention because Pack’s teaching would enable automation with composable asynchronous tasks, which may allow for larger jobs to be decomposed into and accomplished with composable asynchronous tasks that use large language models (LLMs) and Generative Pretrained Transformers (GPTs), which performing by one or more LLM multiple asynchronous tasks result in outputting multiple validated output i.e. a request for the generation of computer code for a website, application, web application, or website component that includes certain features specified in the prompt, a request for the generation of data of a specific type, such as product descriptions for product in a database, or a request for the generation of any other suitable data type provide output using a previous output (please see ¶[0010] and ¶[0011]).
Per claim 15:
Goodman discloses:
A non-transitory machine-readable medium storing instructions which, when executed by one or more processors (At least see ¶[0067] - Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices), cause the one or more processors to perform operations comprising:
converting the command into a user portion of a natural language prompt (At least see ¶[0027] - initiated in response to user interaction with a virtual or hardware element at a client device, initiated in response to detecting a user gesture, and/or initiated in response to other abbreviated user interface input(s));
adding a predefined system portion to the natural language prompt (At least see ¶[0015] - issuing the user prompt to solicit the user to provide additional information that includes the fixed value for the slot in the code script that lacked the fixed value);
sending the natural language prompt to a Large Language Model (LLM) to generate a first piece of software code in a first programming language based on the natural language prompt (At least see ¶[0018] - processing the natural language prompt to generate the code script as the LLM output includes processing, by the LLM, the natural language prompt conditioned on the user prompt embedding for the user to generate the code script as the LLM output).
Goodman sufficiently discloses the system as set forth above, but Goodman does not explicitly disclose: scanning, by a scripting language interface, software programming code to identify a command written as executable code in the software programming code, the command defined in a scripting language interface library; validating the first piece of software code using a validation function of the first programming language; and inserting the first piece of software code into the software programming code.
However, Liu discloses:
scanning, by a scripting language interface, software programming code to identify a command written as executable code in the software programming code, the command defined in a scripting language interface library (At least see ¶[0007] - parse a plurality of executable code to define one or more generic processes for a plurality of software services, ¶[0036] - service is analyzed for execution and organized for order of execution, the scripting engine 114 may execute the service using a scripting language interface … execution may also include generating a function call to each portion of the service that includes business logic to be carried out by the processor-compiled architecture and generating a service call (command) to the service by retrieving and executing at least one code portion from a script repository [emphasis added]; also see ¶[0037]).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Liu into Goodman because Liu’s teaching provides a request to execute a service associated with the software application, determining portions of the service that include business logic to be carried out by the processor-compiled architecture and determining other portions of the service that include business logic to be carried out by scripted architecture, and executing the service using a scripting language interface, the executing including organizing the portions of the service and the other portions of the service according to the business process pattern, and generating a function call to each portion of the service that includes business logic to be carried out by the processor-compiled architecture (please see ¶[0003]).
Goodman modified by Liu sufficiently discloses the system as set forth above, but Goodman modified by Liu does not explicitly disclose: validating the first piece of software code using a validation function of the first programming language; and inserting the first piece of software code into the software programming code.
However, Pack discloses:
validating the first piece of software code using a validation function of the first programming language (At least see ¶[0021] -Before outputting generated computer code, the code may be validated, i.e. by a sub-task may lint and test the computer code); and
inserting the first piece of software code into the software programming code (At least see ¶[0021] -Computer code generated by tasks selected by an agent LLM may be combined by another task selected by the agent LLM into the requested application or software component, which may then be tested by the agent LLM before being returned as output to the user).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Pack into Goodman modified by Liu’s invention because Pack’s teaching would enable automation with composable asynchronous tasks, which may allow for larger jobs to be decomposed into and accomplished with composable asynchronous tasks that use large language models (LLMs) and Generative Pretrained Transformers (GPTs), which performing by one or more LLM multiple asynchronous tasks result in outputting multiple validated output i.e. a request for the generation of computer code for a website, application, web application, or website component that includes certain features specified in the prompt, a request for the generation of data of a specific type, such as product descriptions for product in a database, or a request for the generation of any other suitable data type provide output using a previous output (please see ¶[0010] and ¶[0011]).
Per claim 16:
Pack also discloses:
command includes a natural language portion in which a desired piece of programming code is described in natural language (At least see ¶[0011] - prompt input by the user may be text written in plain, human understandable language, that includes a request for which responsive output may be generated using composable asynchronous tasks … a request for the generation of computer code for a website, application, web application, or website component that includes certain features specified in the prompt), and wherein command further includes one or more test functions (At least see ¶[0015] - a second sub-task may use an LLM to generate tests that can be run on the generated gallery container component).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Pack into Goodman’s invention because Pack’s teaching would enable automation with composable asynchronous tasks, which may allow for larger jobs to be decomposed into and accomplished with composable asynchronous tasks that use large language models (LLMs) and Generative Pretrained Transformers (GPTs), which performing by one or more LLM multiple asynchronous tasks result in outputting multiple validated output i.e. a request for the generation of computer code for a website, application, web application, or website component that includes certain features specified in the prompt, a request for the generation of data of a specific type, such as product descriptions for product in a database, or a request for the generation of any other suitable data type provide output using a previous output (please see ¶[0010] and ¶[0011]).
Per claim 17:
Pack also discloses:
executing the one or more test functions on the first piece of software code (At least see ¶[0034] - a composable asynchronous task called generateCode may use an LLM to generate computer code. A composable asynchronous task called testGenerateCode may be created in order to test generateCode); and
wherein the inserting is performed in response to a determination that the first piece of software code passes all of the one or more test functions (At least see ¶[0047] - composable asynchronous task 182 may output the computer code for the gallery container component back to the agent LLM 110 once the computer code has been linted and passed all tests executed on it).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Pack into Goodman’s invention because Pack’s teaching would enable automation with composable asynchronous tasks, which may allow for larger jobs to be decomposed into and accomplished with composable asynchronous tasks that use large language models (LLMs) and Generative Pretrained Transformers (GPTs), which performing by one or more LLM multiple asynchronous tasks result in outputting multiple validated output i.e. a request for the generation of computer code for a website, application, web application, or website component that includes certain features specified in the prompt, a request for the generation of data of a specific type, such as product descriptions for product in a database, or a request for the generation of any other suitable data type provide output using a previous output (please see ¶[0010] and ¶[0011]).
Per claim 18:
Pack also discloses:
in response to a determination that the first piece of software code fails the validation function, regenerating the first piece of software code by resending the natural language prompt to the LLM (At least see ¶[0023] - if computer code generated by a composable asynchronous task fails tests run against it in a sub-composable asynchronous task, the composable asynchronous task may regenerate the computer code …).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Pack into Goodman’s invention because Pack’s teaching would enable automation with composable asynchronous tasks, which may allow for larger jobs to be decomposed into and accomplished with composable asynchronous tasks that use large language models (LLMs) and Generative Pretrained Transformers (GPTs), which performing by one or more LLM multiple asynchronous tasks result in outputting multiple validated output i.e. a request for the generation of computer code for a website, application, web application, or website component that includes certain features specified in the prompt, a request for the generation of data of a specific type, such as product descriptions for product in a database, or a request for the generation of any other suitable data type provide output using a previous output (please see ¶[0010] and ¶[0011]).
Per claim 19:
Pack also discloses:
in response to a determination that the first piece of software code fails one or more of the one or more test functions, regenerating the first piece of software code by resending the natural language prompt to the LLM (At least see ¶[0023] - after computer code generated by the LLM of a composable asynchronous task fails a test, the prompt input to the composable asynchronous task may be modified to indicate which test the computer code failed, which may allow the LLM to generate computer code that does not fail that test).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Pack into Goodman’s invention because Pack’s teaching would enable automation with composable asynchronous tasks, which may allow for larger jobs to be decomposed into and accomplished with composable asynchronous tasks that use large language models (LLMs) and Generative Pretrained Transformers (GPTs), which performing by one or more LLM multiple asynchronous tasks result in outputting multiple validated output i.e. a request for the generation of computer code for a website, application, web application, or website component that includes certain features specified in the prompt, a request for the generation of data of a specific type, such as product descriptions for product in a database, or a request for the generation of any other suitable data type provide output using a previous output (please see ¶[0010] and ¶[0011]).
5. Claims 6-7, 13-14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Goodman et al. in view of Yingqiao Liu and Pack, III et al., and further in view of Niemi et al. (US Patent Application Publication No. 2024/0370534 A1 herein after Niemi).
Per claim 6:
Goodman modified by Liu and Pack sufficiently discloses the system as set forth above, but Goodman modified by Liu and Pack does not explicitly disclose: generating a hash for the command by applying a hash function to the command; and saving the hash and the first piece of software code in a file system cache.
However, Niemi discloses:
generating a hash for the command by applying a hash function to the command (At least see ¶[0034] - value, number, string, and/or other data generated using the hash function on the data 102 may be appended to the data 102 by the watermark generation module 104); and
saving the hash and the first piece of software code in a file system cache (At least see ¶[0035] -hash function value, serial number, frame identification value, stream identification value, etc.). In these and other embodiments, the data 102 may be stored in combination with the watermark 106 in the data storage location, also refer to ¶[0121]).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Niemi into Goodman modified by Liu and Pack’s teaching would provide an ability to identify and compensate for one or more errors introduced by one or more elements of a computing system, such that one or more watermarks is/are appended to corresponding data, which the watermark may be compared to information corresponding to a data capture request that may be stored by the CPU in a central memory location of the system that allows for identification of errors introduced internally, and storage location may be verified based at least on an analysis with respect to the watermark and information corresponding to the data capture request (please see ¶[0005] through ¶[0007]).
Per claim 7:
Niemi also discloses:
identifying, by a scripting language interface, a second command in software programming code (At least see ¶[0034] - value, number, string, and/or other data generated using the hash function on the data 102 may be appended to the data 102 by the watermark generation module 104);
generating a hash for the second command by applying the hash function to the command (At least see ¶[0035] -serial number may be appended to the data 102 as part of the watermark 106. In some embodiments, the watermark 106 that may be generated and/or appended to the data 102 may include any combination of the foregoing (e.g., timestamp, MAC value, CRC value, hash function value, serial number, frame identification value, stream identification value. also refer to ¶[0121]);
determining whether the hash for the second command is contained in the file system cache; and in response to a determination that the hash for the second command is contained in the file system cache, retrieving the first piece of software code from the file system cache and inserting the first piece of software code into the software programming code in lieu of converting the command into a user portion of a second natural language prompt and sending the second natural language prompt to the LLM (At least see ¶[0037] -data 102 may be generated using one or more sensors and may be appended with a watermark 106 that may be unique to the data 102. Continuing the example, during data processing, the data 102 and the watermark 106 may be stored together in a first data storage location, and the first data storage location may be verified by comparing the watermark 106 to information stored in a central memory location of the system).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Niemi into Goodman modified by Liu and Pack because Niemi’s teaching would provide an ability to identify and compensate for one or more errors introduced by one or more elements of a computing system, such that one or more watermarks is/are appended to corresponding data, which the watermark may be compared to information corresponding to a data capture request that may be stored by the CPU in a central memory location of the system that allows for identification of errors introduced internally, and storage location may be verified based at least on an analysis with respect to the watermark and information corresponding to the data capture request (please see ¶[0005] through ¶[0007]).
Per claim 13:
Goodman modified by Liu and Pack sufficiently discloses the system as set forth above, but Goodman modified by Liu and Pack does not explicitly disclose: generating a hash for the command by applying a hash function to the command; and saving the hash and the first piece of software code in a file system cache.
However, Niemi discloses:
generating a hash for the command by applying a hash function to the command (At least see ¶[0034] - value, number, string, and/or other data generated using the hash function on the data 102 may be appended to the data 102 by the watermark generation module 104); and
saving the hash and the first piece of software code in a file system cache (At least see ¶[0035] -hash function value, serial number, frame identification value, stream identification value, etc.). In these and other embodiments, the data 102 may be stored in combination with the watermark 106 in the data storage location, also refer to ¶[0121]).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Niemi into Goodman modified by Liu and Pack because Niemi’s teaching would provide an ability to identify and compensate for one or more errors introduced by one or more elements of a computing system, such that one or more watermarks is/are appended to corresponding data, which the watermark may be compared to information corresponding to a data capture request that may be stored by the CPU in a central memory location of the system that allows for identification of errors introduced internally, and storage location may be verified based at least on an analysis with respect to the watermark and information corresponding to the data capture request (please see ¶[0005] through ¶[0007]).
Per claim 14:
Niemi also discloses:
identifying, by a scripting language interface, a second command in software programming code (At least see ¶[0034] - value, number, string, and/or other data generated using the hash function on the data 102 may be appended to the data 102 by the watermark generation module 104);
generating a hash for the second command by applying the hash function to the command (At least see ¶[0035] -serial number may be appended to the data 102 as part of the watermark 106. In some embodiments, the watermark 106 that may be generated and/or appended to the data 102 may include any combination of the foregoing (e.g., timestamp, MAC value, CRC value, hash function value, serial number, frame identification value, stream identification value. also refer to ¶[0121]);
determining whether the hash for the second command is contained in the file system cache; and in response to a determination that the hash for the second command is contained in the file system cache, retrieving the first piece of software code from the file system cache and inserting the first piece of software code into the software programming code in lieu of converting the command into a user portion of a second natural language prompt and sending the second natural language prompt to the LLM (At least see ¶[0037] -data 102 may be generated using one or more sensors and may be appended with a watermark 106 that may be unique to the data 102. Continuing the example, during data processing, the data 102 and the watermark 106 may be stored together in a first data storage location, and the first data storage location may be verified by comparing the watermark 106 to information stored in a central memory location of the system).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Niemi into Goodman modified by Liu and Pack because Niemi’s teaching would provide an ability to identify and compensate for one or more errors introduced by one or more elements of a computing system, such that one or more watermarks is/are appended to corresponding data, which the watermark may be compared to information corresponding to a data capture request that may be stored by the CPU in a central memory location of the system that allows for identification of errors introduced internally, and storage location may be verified based at least on an analysis with respect to the watermark and information corresponding to the data capture request (please see ¶[0005] through ¶[0007]).
Per claim 20:
Goodman modified by Liu and Pack sufficiently discloses the system as set forth above, but Goodman modified by Liu and Pack does not explicitly disclose: generating a hash for the command by applying a hash function to the command; and saving the hash and the first piece of software code in a file system cache.
However, Niemi discloses:
generating a hash for the command by applying a hash function to the command (At least see ¶[0034] - value, number, string, and/or other data generated using the hash function on the data 102 may be appended to the data 102 by the watermark generation module 104); and
saving the hash and the first piece of software code in a file system cache (At least see ¶[0035] -hash function value, serial number, frame identification value, stream identification value, etc.). In these and other embodiments, the data 102 may be stored in combination with the watermark 106 in the data storage location, also refer to ¶[0121]).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Niemi into Goodman modified by Liu and Pack because Niemi’s teaching would provide an ability to identify and compensate for one or more errors introduced by one or more elements of a computing system, such that one or more watermarks is/are appended to corresponding data, which the watermark may be compared to information corresponding to a data capture request that may be stored by the CPU in a central memory location of the system that allows for identification of errors introduced internally, and storage location may be verified based at least on an analysis with respect to the watermark and information corresponding to the data capture request (please see ¶[0005] through ¶[0007]).
CONCLUSION
6. Prior arts made of record most closely related to the current application, and considered pertinent to applicant's disclosure. See MPEP § 707.05
Harald Winroth (NPL: A Scripting Language Interface to C++ Libraries) discloses “Many scripting language interpreters are embeddable, which means that they can be integrated into
any application program. The standard command repertoire can be extended with new, application-specific commands, implemented by external functions written in C. For example, when using a word processor, it is often convenient to express complicated editing operations in a built-in macro language. When the word processor is launched, it can instantiate a new scripting language interpreter and add application-specific commands such as basic text editing operations, see Figure 1. The possibility of extending the core scripting language with new commands implemented as external functions makes an embeddable interpreter a very powerful tool. However, it is usually assumed that extensions will be written in C, and most scripting languages have very little support for other languages. In this paper, we discuss how C++ libraries can be accessed from a scripting language. The use of object-oriented libraries means that classes and objects must be represented in the scripting language and that polymorphism must be supported. Furthermore, we will
assume that the C++ libraries have been written independently of the scripting language and thus have not been designed to cooperate with an interpreter. This will introduce additional problems that have to do with object identity and memory management, buton the other hand, it will enable us to use any existing C++ library without modifying its source code.” (please refer to page 1-2).
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZIAUL A. CHOWDHURY whose telephone number is (571)270-7750. The examiner can normally be reached on 9:30PM 6:30PM Monday -Friday.
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/ZIAUL A CHOWDHURY/ Primary Examiner, Art Unit 2192
03/07/2026