DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1, 11, and 20 are objected to because of the following informalities: The claims recite on line 20, "the available transpilation option of the with a highest output logit". Examiner suggests amending to "the available transpilation option with a highest output logit". Appropriate correction is required.
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 is directed to an abstract idea without significantly more.
Claim(s) 1, 11, and 20 recite(s):
A computer implemented system for automated transpilation of query source code by transforming an initial source code provided in an initial programmatic language into an output source code provided in a target programmatic language, the system comprising: a computer processor operating in conjunction with a non-transitory computer readable medium storing computer interpretable instruction sets, the computer processor configured to:
decompose the initial source code to generate a syntax tree (AST) data structure, the AST data structure representing programmatic constructs within the initial source code as nodes of a plurality of nodes of the AST data structure;
maintain a trained machine learning data model architecture, coupled to a transpiler library, trained for controlling transpilation between the initial programmatic language and the target programmatic language;
process the AST data structure using the trained machine learning data model architecture to automatically identify equivalent code for transpilation of each node of the AST data structure between the initial programmatic language and the target programmatic language;
and generate the output source code in the target programmatic language;
wherein upon identifying a plurality of available transpilation options in the transpiler library during the identification of equivalent code,
the trained machine learning data model is configured
to generate output logits corresponding to each available transpilation option,
the available transpilation option of the with a highest output logit being selected as the equivalent code for generation of the output source code in the target programmatic language.
Step 1: are the claims to a process, machine, manufacture, or a composition of matter?
Claim 1 is a machine
Claim 11 is a method
Claim 20 is a manufacture
Step 2A, Prong I; Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The limitation of "decompose", as drafted in #2 above, under its broadest reasonable interpretation, covers performance of the mind, but for generic computer parts. That is, other than reciting "computer processor", nothing in the claim element precludes the step from being performed by a person on paper.
The limitation of "identify", as drafted in #4 and #6 above, under its broadest reasonable interpretation, covers performance of the mind, but for generic computer parts. That is, other than reciting "computer processor", nothing in the claim element precludes the step from being performed by a person on paper.
The limitation of "generate", as drafted in #5 and #8 above, under its broadest reasonable interpretation, covers performance of the mind, but for generic computer parts. That is, other than reciting "computer processor", nothing in the claim element precludes the step from being performed by a person on paper.
Step 2A Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The "configured to" limitation in #1 and #7 above. As claimed and under BRI, is an additional element that is mere instructions to apply an exception. For example, "configured to" in the context of this claim encompasses merely configuring generic computer parts. See in the MPEP §§2106.05(f).
The "maintain" limitation in #3 above. As claimed and under BRI, is an additional element that is mere instructions to apply an exception. For example, "maintain" in the context of this claim encompasses merely training a machine learning model with gathered data. See in the MPEP §§2106.05(f).
The “available transpilation option” limitations in #9 above, as claimed and under BRI, is an additional element that is insignificant extra-solution activity. For example, "highest output selected" in the context of this claim encompasses calculating the highest output and selecting it. See in the MPEP §§ 2106.05(g).
Additionally, the claims recite the following additional element:
computer implemented system,
non-transitory computer readable medium,
computer processor
The element that is recited in the claims are stated at a high level of generality (i.e. as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using generic computer component. See the MPEP §§ 2106.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limitation on practicing the abstract idea(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using generic computer components cannot provide the inventive step.
Additionally, with regards to #9 above, per MPEP 2106.05(d)(ll), the courts have recognized the following computer function(s) as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity:
Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) (“The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.”);
Claim(s) 2 and 12 recite(s):
wherein the output source code in the target programmatic language is coupled with telemetry metadata representative of the plurality of available transpilation options and the selected available transpilation option used for the selection of the equivalent code;
and wherein the trained machine learning data model architecture is retrained using a combination of performance data and the telemetry metadata representative of the plurality of available transpilation options
Step 1: are the claims to a process, machine, manufacture, or a composition of matter?
Yes. Claim 2 is a machine
Yes. Claim 12 is a method
Step 2A, Prong I; Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
Step 2A Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The "retrained" limitation in #11 above. As claimed and under BRI, is an additional element that is mere instructions to apply an exception. For example, "retrained" in the context of this claim encompasses merely training a machine learning model with gathered data. See in the MPEP §§2106.05(f).
The “telemetry” limitations in #10 above, as claimed and under BRI, is an additional element that is insignificant extra-solution activity. For example, "coupled with telemetry" in the context of this claim encompasses updating a log with information on the execution of code. See in the MPEP §§ 2106.05(g).
Additionally, the claims recite the following additional element:
system
The element that is recited in the claims are stated at a high level of generality (i.e. as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using generic computer component. See the MPEP §§ 2106.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limitation on practicing the abstract idea(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using generic computer components cannot provide the inventive step.
Additionally, with regards to #10 above, per MPEP 2106.05(d)(ll), the courts have recognized the following computer function(s) as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity:
Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);
Claim(s) 3 and 13 recite(s):
wherein the performance data includes data sets extracted from daemon processes monitoring processing errors associated with downstream execution of the output source code
Step 1: are the claims to a process, machine, manufacture, or a composition of matter?
Yes. Claim 3 is a system
Yes. Claim 13 is a method
Step 2A, Prong I; Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
Step 2A Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The limitation in #12 above. As claimed and under BRI, is an additional element that is mere instructions to apply an exception. For example, "data sets extracted from" in the context of this claim encompasses merely "gathering metrics from executed code". See in the MPEP §§2106.05(f).
Additionally, the claims recite the following additional element:
system
The element that is recited in the claims are stated at a high level of generality (i.e. as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using generic computer component. See the MPEP §§ 2106.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limitation on practicing the abstract idea(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using generic computer components cannot provide the inventive step.
Claim(s) 4 and 14 recite(s):
wherein the performance data includes data sets extracted from daemon processes monitoring processing speed associated with downstream execution of the output source code.
Step 1: are the claims to a process, machine, manufacture, or a composition of matter?
Yes. Claim 4 is a machine
Yes. Claim 14 is a method
Step 2A, Prong I; Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
Step 2A Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The limitation in #13 above. As claimed and under BRI, is an additional element that is mere instructions to apply an exception. For example, "data sets extracted from" in the context of this claim encompasses merely "gathering metrics from executed code". See in the MPEP §§2106.05(f).
Additionally, the claims recite the following additional element:
system
The element that is recited in the claims are stated at a high level of generality (i.e. as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using generic computer component. See the MPEP §§ 2106.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limitation on practicing the abstract idea(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using generic computer components cannot provide the inventive step.
Claim(s) 5 and 15 recite(s):
wherein the performance data includes data sets extracted from daemon processes monitoring a weighted average of processing speed and processing errors associated with downstream execution of the output source code, the weighted average representing an objective function being minimized during retraining of the trained machine learning data model architecture.
Step 1: are the claims to a process, machine, manufacture, or a composition of matter?
Yes . Claim 5 is a machine
Yes. Claim 15 is a method
Step 2A, Prong I; Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
Step 2A Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The limitation in #14 above. As claimed and under BRI, is an additional element that is mere instructions to apply an exception. For example, "data sets extracted from" in the context of this claim encompasses merely gathering metrics from executed code. See in the MPEP §§2106.05(f).
Additionally, the claims recite the following additional element:
system
The element that is recited in the claims are stated at a high level of generality (i.e. as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using generic computer component. See the MPEP §§ 2106.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limitation on practicing the abstract idea(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using generic computer components cannot provide the inventive step.
Claim(s) 6 and 16 recite(s):
wherein the retraining of the trained machine learning data model architecture is conducted in real or near-real time as errors or processing speed issues are identified during the downstream execution of the output source code, causing the trained machine learning data model architecture to be trained in accordance with a real-time feedback loop.
Step 1: are the claims to a process, machine, manufacture, or a composition of matter?
Yes. Claim 6 is a machine
Yes. Claim 16 is a method
Step 2A, Prong I; Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
Step 2A Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The limitation in #15 above. As claimed and under BRI, is an additional element that is mere instructions to apply an exception. For example, “retraining” in the context of this claim encompasses merely finetuning the machine learning algorithm. See in the MPEP §§2106.05(f).
Additionally, the claims recite the following additional element:
system
The element that is recited in the claims are stated at a high level of generality (i.e. as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using generic computer component. See the MPEP §§ 2106.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limitation on practicing the abstract idea(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using generic computer components cannot provide the inventive step.
Claim(s) 7 and 17 recite(s):
wherein the performance data includes data sets extracted from daemon processes monitoring processor load associated with downstream execution of the output source code.
Step 1: are the claims to a process, machine, manufacture, or a composition of matter?
Yes. Claim 7 is a machine
Yes. Claim 17 is a method
Step 2A, Prong I; Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
Step 2A Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The limitation in #16 above. As claimed and under BRI, is an additional element that is mere instructions to apply an exception. For example, "data sets extracted from" in the context of this claim encompasses merely gathering metrics from executed code. See in the MPEP §§2106.05(f).
Additionally, the claims recite the following additional element:
system
The element that is recited in the claims are stated at a high level of generality (i.e. as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using generic computer component. See the MPEP §§ 2106.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limitation on practicing the abstract idea(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using generic computer components cannot provide the inventive step.
Claim(s) 8 and 18 recite(s):
wherein the performance data includes data sets extracted from daemon processes monitoring memory usage associated with downstream execution of the output source code.
Step 1: are the claims to a process, machine, manufacture, or a composition of matter?
Yes. Claim 8 is a machine
Yes. Claim 18 is a method
Step 2A, Prong I; Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
Step 2A Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The limitation in #17 above. As claimed and under BRI, is an additional element that is mere instructions to apply an exception. For example, "data sets extracted from" in the context of this claim encompasses merely gathering metrics from executed code. See in the MPEP §§2106.05(f).
Additionally, the claims recite the following additional element:
system
The element that is recited in the claims are stated at a high level of generality (i.e. as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using generic computer component. See the MPEP §§ 2106.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limitation on practicing the abstract idea(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using generic computer components cannot provide the inventive step.
Claim(s) 9 and 19 recite(s):
wherein during a pre-training duration after instantiation of an untrained machine learning data model architecture, the downstream execution is conducted on a non-production environment simulating real-world usage and the untrained machine learning data model architecture is first trained during execution in the non-production environment to establish the trained machine learning data model architecture for usage in a production environment.
Step 1: are the claims to a process, machine, manufacture, or a composition of matter?
Yes. Claim 9 is a machine
Yes. Claim 19 is a method
Step 2A, Prong I; Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
Step 2A Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The limitation in #18 above. As claimed and under BRI, is an additional element that is mere instructions to apply an exception. For example, "execution is conducted on a non-production environment" in the context of this claim encompasses merely executing software in an environment other than production. See in the MPEP §§2106.05(f).
Additionally, the claims recite the following additional element:
system
The element that is recited in the claims are stated at a high level of generality (i.e. as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using generic computer component. See the MPEP §§ 2106.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limitation on practicing the abstract idea(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using generic computer components cannot provide the inventive step.
Claim(s) 10 recite(s):
wherein the computer implemented system is a special purpose computing machine operating in a data center coupled to a message bus having an application programming interface for receiving data sets representative of the initial source code and for providing the output source code as output data sets to a distributed data processing or query system.
Step 1: are the claims to a process, machine, manufacture, or a composition of matter?
Yes. Claim 10 is a machine
Step 2A, Prong I; Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
Step 2A Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The limitations in #19 above, as claimed and under BRI, is an additional element that is insignificant extra-solution activity. For example, providing and receiving in the context of this claim encompasses data transmission. See in the MPEP §§ 2106.05(g).
Additionally, the claims recite the following additional element:
system
The element that is recited in the claims are stated at a high level of generality (i.e. as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using generic computer component. See the MPEP §§ 2106.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limitation on practicing the abstract idea(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using generic computer components cannot provide the inventive step.
Additionally, with regards to #19 above, per MPEP 2106.05(d)(ll), the courts have recognized the following computer function(s) as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
Claim(s) 1-3, 11-13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable in view of US 20250306882 A1 (Hereinafter referred to as Abdella), US 20250342017 A1 (Hereinafter referred to as Morales), US 20240345894 A1 (Hereinafter referred to as Ribard), US 20230040412 A1 (Hereinafter referred to as Ramsl), and US 20250252338 A1 (Hereinafter referred to as Cohen).
Regarding claim 1, Abdella teaches:
A computer implemented system for automated transpilation of query source code by transforming an initial source code provided in an initial programmatic language into an output source code provided in a target programmatic language, the system comprising: a computer processor operating in conjunction with a non-transitory computer readable medium storing computer interpretable instruction sets, the computer processor configured to:
decompose the initial source code to generate a syntax tree (AST) data structure, the AST data structure representing programmatic constructs within the initial source code as nodes of a plurality of nodes of the AST data structure (Para. [29], Abdella shows "The AST engine 122 of the server 102 is configured to determine an abstract syntax tree from the source code (e.g., source code received from the client device 104 or stored in the repository 106). An abstract syntax tree is a data structure used in compilers to represent structure of a program code. The abstract syntax tree abstracts away the syntactic details of the program code, focusing on its syntactic structure. Each node of the tree denotes a construct occurring in the program code. The AST engine 122 generates the abstract syntax tree by parsing the source code and organizing syntactical structures of the source code into a tree-like format. Each node of the tree-like format represents a different “abstract” syntactic structure of the program." Para. [72], Abdella shows "a standout feature is the intelligent translation approach, provided in the present disclosure, that respects the architectural nuances and idiomatic patterns of the target platform rather than merely converting code on a line-by-line basis. For instance, when translating from SQL to PySpark" Examiner notes the above citations show translating one source code language to a target language. Also shown is generating an abstract syntax tree from the initial source code);
process the AST data structure using the trained machine learning data model architecture to automatically identify equivalent code for transpilation of each node of the AST data structure between the initial programmatic language and the target programmatic language (Para. [44-45], Abdella shows "In some implementations, the desired programming constructs or nodes are identified a priori based on the specific code chunking strategy employed. This allows the chunks engine 128 to efficiently traverse the abstract syntax tree and extract the relevant nodes and subtrees corresponding to the pre-defined constructs of interest, such as functions, classes, loops, or files. By specifying these constructs beforehand, the chunking process can be optimized to focus on the most meaningful and logical code segments, ensuring consistency and maintainability of the generated target code. The translation engine 130 of the server 102 is configured to use the large language model 110 to convert the logical chunks and/or other elements derived from the abstract syntax tree, program specifications, and dependency graph into corresponding code segments in the target language or format. Large language models, with their advanced understanding and generation capabilities, can produce human-like text based on input provided to them. The logical chunks and/or the other elements are provided as inputs to large language model 110 to obtain the code segments in the target language or format. These code segments are also referred to herein as converted chunks." Examiner notes the above citations shows traversing an abstract syntax tree and identifying nodes representing program structures to convert using a LLM.);
and generate the output source code in the target programmatic language (Para. [46], Abdella shows "The post processing engine 132 of the server 102 is configured to combine the code segments (i.e., converted chunks) into cohesive code blocks. The post processing engine 132 deduplicates and ensures that the combined code maintains the integrity and functionality of the original source code. In the present disclosure, the post processing engine 132 provides as output intermediate code. The intermediate code is in the target language.");
Abdella does not disclose:
maintain a trained machine learning data model architecture, trained for controlling transpilation between the initial programmatic language and the target programmatic language;
a trained machine learning data model architecture, coupled to a transpiler library
wherein upon identifying a plurality of available transpilation options in the transpiler library during the identification of equivalent code,
the trained machine learning data model is configured to generate output logits corresponding to each available transpilation option, the available transpilation option of the with a highest output logit being selected as the equivalent code for generation of the output source code in the target programmatic language.
However, in the analogous art of code translation and program synthesis, Morales teaches:
maintain a trained machine learning data model architecture, trained for controlling transpilation between the initial programmatic language and the target programmatic language (Para. [11], Morales shows "the LLM is trained on a multi-language data corpus for code-to-code translation" Para. [52]. Morales shows "the computing device 400 and translation and synthesis manager 422 include API(s) that provides programmatic access to add, remove, or change one or more functions of the computing device 400. In some embodiments, components/modules of the computing device 400 and translation and synthesis manager 422 are implemented using standard programming techniques. For example, the translation and synthesis manager 422 may be implemented as an executable running on the CPU 403, along with one or more static or dynamic libraries. In other embodiments, the computing device 400 and translation and synthesis manager 422 may be implemented as instructions processed by a virtual machine that executes as one of the other programs 430. In general, a range of programming languages known in the art may be employed for implementing such example embodiments, including representative embodiments of various programming language paradigms, including but not limited to, object-oriented (e.g., Java, C++, C #, Visual Basic.NET, Smalltalk, and the like), functional (e.g., ML, Lisp, Scheme, and the like), procedural (e.g., C, Pascal, Ada, Modula, and the like), scripting (e.g., Perl, Ruby, Python, JavaScript, VBScript, and the like), or declarative (e.g., SQL, Prolog, and the like)." Para. [3], Morales shows "there are pre-defined libraries that facilitate conversion from Python to C or between other high-level languages" Examiner notes the above citation shows);
In addition, in the analogous art of c, Ribard teaches:
a trained machine learning data model architecture, coupled to a transpiler library (Para. [27], Ribard shows "the model execution system imports a model stored using an Open Neural Network Exchange (ONNX) format, or other formats corresponding to a computational graph representation. The model execution system converts the imported model to an internal model and/or associated representation such as an internal computational graph or abstract syntax tree (AST) structure. The model execution system addresses the problem of computational graph optimization via a transpiler module that analyzes the internal computational graph or AST and/or performs optimization passes to improve execution speed and flexibility. The model execution system can automatically convert code corresponding to the executable internal computational graph (or AST) to code that uses a native API of a target platform." Examiner notes the above citation shows a neural network model using a transpiler module (library) to convert code.)
In addition, in the analogous art of multi-language source code search engine, Ramsl teaches:
wherein upon identifying a plurality of available transpilation options during the identification of equivalent code (Para. [120-121], Ramsl shows "Example 1 is a method comprising: automatically translating, by one or more processors, first source code from a first programming language to a second programming language, the first source code comprising a first plurality of functions; generating, based on each function in the translated first source code, an embedding vector for each function of the first plurality of functions; generating, based on each function of a second plurality of functions in second source code in the second programming language, an embedding vector for the function; determining, based on the embedding vectors, a similarity measure between the first source code and the second source code; and causing a user interface to be presented that includes, a similarity measure between the first source code and the second source code. In Example 2, the subject matter of Example 1 includes, accessing third source code in the first programming language, the third source code comprising a third plurality of functions; accessing fourth source code in the second programming language, the fourth source code comprising a fourth plurality of functions, each function of the fourth plurality of functions annotated with an identifier of a corresponding function of the third plurality of functions; and training, using the third source code and the fourth source code, a machine learning model to translate from the first programming language to the second programming language; wherein the automatically translating of the first source code from the first programming language to the second programming language is performed using the trained machine learning model." Para. [123-124], Ramsl shows "In Example 4, the subject matter of Examples 1-3 includes, storing, in a database, the embedding vectors for each function of the first plurality of functions and the second plurality of functions; receiving, via a second user interface, a search string; converting the search string to a vector; searching the database for functions having similar vectors to the vector for the search string; based on results of the search, selecting either the first source code or the second source code; and causing a third user interface to be presented, the third user interface identifying the selected source code. In Example 5, the subject matter of Example 4 includes, generating a complexity score for each function of the first plurality of functions and the second plurality of functions based on documentation for the function; wherein the selecting of either the first source code or the second source code is further based on the complexity scores." Examiner notes the above citations shows multiple translations of source codes and based on a calculated score using the trained machine learning model, selecting either the first or second source code),
In addition, in the analogous art of self-improving artificial intelligence, Cohen teaches:
the trained machine learning data model is configured to generate output logits corresponding to each available transpilation option, the available transpilation option of the with a highest output logit being selected as the equivalent code for generation of the output source code in the target programmatic language (Para. [29], Cohen shows "the prior probability for the next action (as predicted by the machine learning model 115), and/or the current value estimation. Such a selection process may favor actions with high prior probability that lead to children with high value, but also accounts for uncertainty in those estimates by increasing the weight of actions that have few or zero visits. For example, the search system 120 may use Equation 2 below to determine which child of the current node to select (e.g., by selecting the child having the highest score), where π′(a)=softmax(logits+π(completedQ)), logits refers to the unnormalized probabilities of each of the children nodes, completed is the action value of the child (e.g., the current value estimation of the child), N(a) is the number of times the given child a has been visited, and Σ.sub.bN(b) is the sum of the number of times each other child of the same parent has been visited." Examiner notes the above citation shows logits are unnormalized probabilities that machine learning models use to handle selections and probabilities. This would be used in any selection, score, and ranking process done by machine learning models).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Morales into the teachings of Abdella to implement “maintain a trained machine learning data model architecture, trained for controlling transpilation between the initial programmatic language and the target programmatic language“. The modification would have been obvious as one of ordinary skill in the art would be motivated to prevent incorrect code generation (Morales, Para. [4]).
Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Ribard into the teachings of Abdella to implement “a trained machine learning data model architecture, coupled to a transpiler library”. The modification would have been obvious as one of ordinary skill in the art would be motivated improve execution speed and flexibility (Ribard, Para. [27]).
Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Ramsl into the teachings of Abdella to implement “wherein upon identifying a plurality of available transpilation options during the identification of equivalent code”. The modification would have been obvious as one of ordinary skill in the art would be motivated to reduce the level of effort expended in searching for and identifying existing source code projects with desired functionality, reducing the probability that equivalent source code will be developed again (Ramsl, Para. [24]).
Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Cohen into the teachings of Abdella to implement “the trained machine learning data model is configured to generate output logits corresponding to each available transpilation option, the available transpilation option of the with a highest output logit being selected as the equivalent code for generation of the output source code in the target programmatic language”. The modification would have been obvious as one of ordinary skill in the art would be motivated to use logits to allow the machine learning model to select the option with a higher probability of success.
Regarding claim 2, Abdella as modified in claim 1 teaches:
wherein the output source code in the target programmatic language is coupled with telemetry metadata representative of the plurality of available transpilation options and the selected available transpilation option used for the selection of the equivalent code; and wherein the trained machine learning data model architecture is retrained using a combination of performance data and the telemetry metadata representative of the plurality of available transpilation options (Para. [47], Abdella shows "The verification engine 134 is configured to obtain feedback and provide the feedback to the translation engine 130 for updating the large language model 110" Examiner notes the citation above shows using the feedback which contains metadata containing performance data of the intermediate code used to update (retrain) the machine learning model. Where the data is also used to determine if the code has reached a satisfactory state or continue the feedback loop and modify the translation of the code until it has reached the expected output).
Regarding claim 3, Abdella as modified in claim 2 teaches:
wherein the performance data includes data sets extracted from daemon processes monitoring processing errors associated with downstream execution of the output source code (Para. [47], Abdella shows "The verification engine 134 is configured to obtain feedback and provide the feedback to the translation engine 130 for updating the large language model 110 and/or updating a future prompt provided to the large language model 110. The feedback provided includes any compiler errors, any compiler warnings, any artifacts observed in the output, any run-time errors, any deviation of the output from the expected output (e.g., different numerical results printed, different variable states present in both outputs, etc.)." Examiner notes the above citation shows obtaining feedback (performance data with expected output data) from executed output code).
With regards to claim 11, it is a method claim having similar limitations as cited in claim 1 above.
Thus, claim 11 is also rejected under the same rationale as cited in the rejection of claim 1 above.
With regards to claim 20, it is a computer readable medium claim having similar limitations as cited in claim 1 above.
Thus, claim 20 is also rejected under the same rationale as cited in the rejection of claim 1 above.
With regards to claim 12, it is a method claim having similar limitations as cited in claim 2 above. Thus, claim 12 is also rejected under the same rationale as cited in the rejection of claim 2 above.
With regards to claim 13, it is a method claim having similar limitations as cited in claim 3 above. Thus, claim 13 is also rejected under the same rationale as cited in the rejection of claim 3 above.
Claim(s) 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable in view of US 20250306882 A1 (Hereinafter referred to as Abdella), US 20250342017 A1 (Hereinafter referred to as Morales), US 20240345894 A1 (Hereinafter referred to as Ribard), US 20230040412 A1 (Hereinafter referred to as Ramsl), and US 20250252338 A1 (Hereinafter referred to as Cohen) in further view of US 20240411542 A1 (Hereinafter referred to as Goyal).
Regarding claim 4, Abdella as modified above teaches claim 4, but does not disclose:
wherein the performance data includes data sets extracted from daemon processes monitoring processing speed associated with downstream execution of the output source code.
However, in the analogous art of improved automation of software change, Goyal teaches:
wherein the performance data includes data sets extracted from daemon processes monitoring processing speed associated with downstream execution of the output source code (Para. [107], Goyal shows "the test performance data 313 includes an input and/or intermediary data constructs based upon which the modified software program generated the output. Additionally, or alternatively, in some embodiments, the test performance data 313 includes one or more metrics that define a performance level of the modified software program. For example, in some contexts, the test performance data 313 includes a processing time, latency metric, throughput metric, average response time, average que time, error rate, request rate, central processing unit (CPU) usage, memory usage, virtual users per unit of time, peak response time, peak concurrent virtual users, and/or the like.").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Goyal into the teachings of Abdella as modified to implement “wherein the performance data includes data sets extracted from daemon processes monitoring processing speed associated with downstream execution of the output source code”. The modification would have been obvious as one of ordinary skill in the art would be motivated to verify that the software is able to perform at the expected performance (Goyal, Para. [107]).
With regards to claim 14, it is a method claim having similar limitations as cited in claim 4 above. Thus, claim 14 is also rejected under the same rationale as cited in the rejection of claim 4 above.
Claim(s) 5-6 and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable in view of US 20250306882 A1 (Hereinafter referred to as Abdella), US 20250342017 A1 (Hereinafter referred to as Morales), US 20240345894 A1 (Hereinafter referred to as Ribard), US 20230040412 A1 (Hereinafter referred to as Ramsl), and US 20250252338 A1 (Hereinafter referred to as Cohen) in further view of US 20240411542 A1 (Hereinafter referred to as Goyal) and US 20240427900 A1 (Hereinafter referred to as Aloraini).
Regarding claim 5, Abdella as modified teaches claim 2 as cited above, but does not disclose:
wherein the performance data includes data sets extracted from daemon processes monitoring processing speed and processing errors associated with downstream execution of the output source code,
the weighted average representing an objective function being minimized during retraining of the trained machine learning data model architecture.
However, in the analogous art of improved automation of software changes, Goyal teaches:
wherein the performance data includes data sets extracted from daemon processes monitoring processing speed and processing errors associated with downstream execution of the output source code (Para. [107], Goyal shows "the test performance data 313 includes an input and/or intermediary data constructs based upon which the modified software program generated the output. Additionally, or alternatively, in some embodiments, the test performance data 313 includes one or more metrics that define a performance level of the modified software program. For example, in some contexts, the test performance data 313 includes a processing time, latency metric, throughput metric, average response time, average que time, error rate, request rate, central processing unit (CPU) usage, memory usage, virtual users per unit of time, peak response time, peak concurrent virtual users, and/or the like." Examiner notes the above citation shows monitoring programs executed during testing to retrieve metrics related to processing speeds and errors.),
Additionally, in the analogous art of evaluation of privacy incident risk in computer code, Aloraini teaches:
the weighted average representing an objective function being minimized during retraining of the trained machine learning data model architecture (Para. [99-100], Aloraini shows "a first set of historical score is generated corresponding to features associated with the author of code changeset 106, a second set of historical score is generated for a reviewer of the code changeset, a third set of historical score is generated based on features obtained from previous incident history 804, a fourth set of historical score is generated based on additional features 808, and so on. In some implementations, each score is then combined or aggregated (e.g., based on a summation, weighted average, etc.) to generate a combined historical score. In various embodiments, predictive model 806 comprises a ML model, neural network (e.g., a deep neural network or an artificial neural network), or other artificial intelligence (AI) model. In examples, predictive model 806 is trained using information observed from previous privacy incidents, such features associated with pull requests (e.g., bug introducing pull requests) that resulted in past privacy incidents. Example algorithms that are used to select features for use in generating predictive model 806 and/or train predictive model 806 include, but are not limited to SelectKBest, ExtraTreesClassifier, Recursive Feature Elimination, Random Forest, Support Vector Machine (SVM), Logistic Regression, Naïve Bayes, linear classifiers (LCs), or any other supervised and/or unsupervised learning algorithms. In some further implementations, one or more feedback loops are provided such that risk score 108 (and associated information, such as privacy tokens present in the corresponding code changeset, historical features described herein, privacy incident occurrences associated with the changeset, etc.) may be provided to a suitable algorithm to further refine and/or train predictive model 806 to improve its accuracy." Examiner notes the above citation shows receiving a metric related to code and further using the metric as a weighted average to refine the machine learning model.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Goyal into the teachings of Abdella as modified to implement “wherein the performance data includes data sets extracted from daemon processes monitoring processing speed and processing errors associated with downstream execution of the output source code”. The modification would have been obvious as one of ordinary skill in the art would be motivated to verify that the software is able to perform at the expected performance (Goyal, Para. [107]).
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Aloraini into the teachings of Abdella as modified to implement “the weighted average representing an objective function being minimized during retraining of the trained machine learning data model architecture”. The modification would have been obvious as one of ordinary skill in the art would be motivated to improve the accuracy of the machine learning model (Aloraini, Para. [100]).
Regarding claim 6, Abdella as modified in claim 5 teaches:
wherein the retraining of the trained machine learning data model architecture is conducted in real or near-real time as errors or processing speed issues are identified during the downstream execution of the output source code, causing the trained machine learning data model architecture to be trained in accordance with a real-time feedback loop (Para. [47-48], Abdella shows "The verification engine 134 is configured to obtain feedback and provide the feedback to the translation engine 130 for updating the large language model 110 and/or updating a future prompt provided to the large language model 110. The feedback provided includes any compiler errors, any compiler warnings, any artifacts observed in the output, any run-time errors, any deviation of the output from the expected output (e.g., different numerical results printed, different variable states present in both outputs, etc.). A feedback loop involving the translation engine 130, the post processing engine 132, and the verification engine 134 can be used to fine-tune the intermediate code to eliminate negative results in the output of the verification engine 134. The feedback loop is an automated loop fine-tuning the intermediate code such that the expected output and the output match or some iteration or loop threshold is reached. The feedback loop and iterating over this feedback loop enhances the quality and/or accuracy of the intermediate code over time. In some implementations, if the iteration or loop threshold is reached, then a copilot mode is activated. In the copilot mode, the client device 104 provides input on how to change the intermediate code. The input can be incorporated in a next prompt provided to the translation engine 130." Examiner notes the above citation shows a feedback loop, wherein feedback is provided to update (finetuning) the large language model and also updating the outputted code to eliminate negative results).
With regards to claim 15, it is a method claim having similar limitations as cited in claim 6 above. Thus, claim 15 is also rejected under the same rationale as cited in the rejection of claim 5 above.
With regards to claim 16, it is a method claim having similar limitations as cited in claim 6 above. Thus, claim 16 is also rejected under the same rationale as cited in the rejection of claim 6 above.
Claim(s) 7-8 and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable in view of US 20250306882 A1 (Hereinafter referred to as Abdella), US 20250342017 A1 (Hereinafter referred to as Morales), US 20240345894 A1 (Hereinafter referred to as Ribard), US 20230040412 A1 (Hereinafter referred to as Ramsl), and US 20250252338 A1 (Hereinafter referred to as Cohen) in further view of US 20230119536 A1 (Hereinafter referred to as Cosentino).
Regarding claim 7, Abdella as modified teaches claim 2 as cited above, but does not disclose:
wherein the performance data includes data sets extracted from daemon processes monitoring processor load associated with downstream execution of the output source code.
However, in the analogous art of application profiling, Cosentino teaches:
wherein the performance data includes data sets extracted from daemon processes monitoring processor load associated with downstream execution of the output source code (Para. [14-15], Cosentino shows "BPF tools may be used to observe operating systems and allows users to run small pieces of code quickly and safely inside the operating system. The BPF system may monitor various processor resource consumption data (e.g., CPU load, CPU idle time, etc.) ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Cosentino into the teachings of Abdella as modified to implement "wherein the performance data includes data sets extracted from daemon processes monitoring processor load associated with downstream execution of the output source code. The modification would have been obvious as one of ordinary skill in the art would be motivated to monitor the systems state and determine if the system is under heavy load (Cosentino, Para. [15]).
Regarding claim 8, Abdella as modified teaches claim 2 as cited above, but does not disclose:
wherein the performance data includes data sets extracted from daemon processes monitoring memory usage associated with downstream execution of the output source code.
However, in the analogous art of application profiling, Cosentino teaches:
wherein the performance data includes data sets extracted from daemon processes monitoring memory usage associated with downstream execution of the output source code (Para. [14-15], Cosentino shows "BPF tools may be used to observe operating systems and allows users to run small pieces of code quickly and safely inside the operating system. one or more BPF tools (e.g., the Biolatency BPF tool) may be used to analyze the latency of input/output (I/O) operations (e.g., by determining I/O usage in terms of memory amount per unit of time)" Examiner notes the above citation shows observing and analyzing memory usage in terms of input output operations. A program that that receives source code and outputs source code would fall in this definition).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Cosentino into the teachings of Abdella as modified to implement “wherein the performance data includes data sets extracted from daemon processes monitoring memory usage associated with downstream execution of the output source code.”. The modification would have been obvious as one of ordinary skill in the art would be motivated to monitor the systems state and determine if the system is under heavy load (Cosentino, Para. [15]).
With regards to claim 17, it is a method claim having similar limitations as cited in claim 7 above. Thus, claim 17 is also rejected under the same rationale as cited in the rejection of claim 7 above.
With regards to claim 18, it is a method claim having similar limitations as cited in claim 8 above. Thus, claim 18 is also rejected under the same rationale as cited in the rejection of claim 8 above.
Claim(s) 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable in view of US 20250306882 A1 (Hereinafter referred to as Abdella), US 20250342017 A1 (Hereinafter referred to as Morales), US 20240345894 A1 (Hereinafter referred to as Ribard), US 20230040412 A1 (Hereinafter referred to as Ramsl), and US 20250252338 A1 (Hereinafter referred to as Cohen) in further view of US 20230066501 A1 (Hereinafter referred to as Jonietz).
Regarding claim 19, Abdella teaches claim 2 as cited above, but does not disclose:
wherein during a pre-training duration after instantiation of an untrained machine learning data model architecture, the downstream execution is conducted on a non-production environment simulating real-world usage and the untrained machine learning data model architecture is first trained during execution in the non-production environment to establish the trained machine learning data model architecture for usage in a production environment.
However, in the analogous art of anomaly detection, Jonietz teaches:
wherein during a pre-training duration after instantiation of an untrained machine learning data model architecture, the downstream execution is conducted on a non-production environment simulating real-world usage and the untrained machine learning data model architecture is first trained during execution in the non-production environment to establish the trained machine learning data model architecture for usage in a production environment. (Para. [64], Jonietz shows "the network or machine learning model 129 is trained offline (e.g., in a non-production or real-time environment), using historical probe data 101 as input and the outputs of a traditional probe data processing pipeline (e.g., such as the conventional traffic processing pipeline 201) which includes all or some combination of the processing steps 207-217 as mentioned before to label the historical probe data 101 for training. In the online production mode (e.g., predictive mode as opposed to training mode of the machine learning model 129)" Examiner notes the above citation shows pretraining a machine learning model with a simulated environment of the machine learning models usage)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Jonietz into the teachings of Abdella as modified to implement “wherein during a pre-training duration after instantiation of an untrained machine learning data model architecture, the downstream execution is conducted on a non-production environment simulating real-world usage and the untrained machine learning data model architecture is first trained during execution in the non-production environment to establish the trained machine learning data model architecture for usage in a production environment”. The modification would have been obvious as one of ordinary skill in the art would be motivated to train the machine learning model for quicker and cheaper, as well as having less chance to make mistakes in production (Jonietz, Para. [64]).
With regards to claim 19, it is a method claim having similar limitations as cited in claim 9 above. Thus, claim 19 is also rejected under the same rationale as cited in the rejection of claim 9 above.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable in view of US 20250306882 A1 (Hereinafter referred to as Abdella), US 20250342017 A1 (Hereinafter referred to as Morales), US 20240345894 A1 (Hereinafter referred to as Ribard), US 20230040412 A1 (Hereinafter referred to as Ramsl), and US 20250252338 A1 (Hereinafter referred to as Cohen) in further view of US 20090235283 A1 (Hereinafter referred to as Kim).
Regarding claim 10, Abdella as modified teaches claim 1 as cited above, but does not disclose:
wherein the computer implemented system is a special purpose computing machine operating in a data center coupled to a message bus having an application programming interface for receiving data sets representative of the initial source code and for providing the output source code as output data sets to a distributed data processing or query system.
However, in the analogous art of global API deployment and routing, Kim teaches:
wherein the computer implemented system is a special purpose computing machine operating in a data center coupled to a message bus having an application programming interface for receiving data sets representative of the initial source code and for providing the output source code as output data sets to a distributed data processing or query system (Para. [33], Kim shows "FIG. 3 is a functional block diagram of an exemplary processing device 300, which may be used to implement originating processing device 104 .. data center server 204, and/or servers within server cluster 208 in embodiments consistent with the subject matter of this disclosure. Processing device 300 may be a desktop personal computer (PC), a laptop PC, a handheld processing device, or other processing device. Processing device 300 may include a bus 310, an input device 320, a memory 330, a read only memory (ROM) 340, an output device 350, a processor 360, a storage device 370, and a communication interface 380. Bus 310 may permit communication among components of processing device 300." Para. [20], Kim shows "particular server cluster at the data center may receive the API call, may obtain the corresponding data, and may return the data to the server via the private backend network. The server may ensure that the returned data is in a proper format and may send the data to an originator of the API call in the proper format. Examples of formats, which may be used in embodiments consistent with the subject matter of this disclosure, may include Really Simple Syndication (RSS) feed, Atom Syndication Format (an XML language used for Web feeds), hypertext transfer protocol (HTTP), JavaScript Object Notation (JSON), Atom Publishing Protocol (APP), as well as other formats" Examiner notes the above citation shows a system with a processing device in a server cluster located at a data center coupled with a message bus capable of communication with other systems. The device in the data center capable of receiving API call with data the format which can be Json or other code and also sending back code from the data center as well in a valid format.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kim into the teachings of Abdella as modified to implement “wherein the computer implemented system is a special purpose computing machine operating in a data center coupled to a message bus having an application programming interface for receiving data sets representative of the initial source code and for providing the output source code as output data sets to a distributed data processing or query system”. The modification would have been obvious as one of ordinary skill in the art would be motivated to make scaling more cost effective by allowing data centers to accessible to different service architectures (Kim, Para. [1]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20250110853 A1 – This prior art teaches translating source code to other languages
US 20250199787 A1 -This prior art teaches use context and AST to translate code
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/Z.A.M./Examiner, Art Unit 2193
/Chat C Do/Supervisory Patent Examiner, Art Unit 2193
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