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 .
Status of Claims
Claims 1-20 are presented for examination in this application. The application filing date on 10/222024. Claims 1, 8 and 15 are independent.
Examiner notes
(A). Examiner interpreted “specification” as information about a particular project , such as a service or application, which is associated with one or more lines of source code, describe the function of the source code using functional data extracted from the source code and update to additional contextual.0018 which have been recited in claims 1, 8, and 15.
(B). Drawings submitted on 10/22/2024 comply with the provisions of 37 CFR 1.121(d).
(C). Limitations have been provided with the Bold fonts in order to distinguish from the cited part of the reference (Italic).
(D). Examiner has cited particular columns, line numbers, references, or figures in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses to fully consider the reference in entirety, as potentially teaching all or part of the claimed invention. See MPEP § 2141.02 VI and 2123.
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111 (c).
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 non-statutory subject matter.
Step 1: Claims 1-7 and 15-20 are directed to a system and fall within the statutory category of machines and Claims 8-14 are directed to method and fall within the statutory category of processes. Therefore, “Are the claims to a process, machine, manufacture or composition of matter?” Yes.
In order to evaluate the Step 2A inquiry “Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?” we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Under Step 2A, Prong 1, the claim 8, recites limitation that recite an abstract idea. The limitation of “obtaining, …, one or more lines of code from …, the one or more lines of code corresponding to an application;” is a mental process (such as collecting data) can be done by human with pen and paper, the limitation of “extracting, …, functional data from the one or more lines of code;” is a mental process (such as evaluation) can be done by human with pen and paper, and the limitations of “generating, …, a specification for the application based at least in part on the functional data, the specification including at least a description of the code” is a mental process can be done by human with pen and paper.
Under Step 2A, Prong 2, the additional elements, “computing device”, “a code repository”, and “machine learning model” are recited are recited at a high level of generality. Thus, the claims as a whole do not integrate the exception into a practical application.
Under step 2B, the additional elements do not amount to significantly more than the abstract idea. As stated above, the claimed invention merely recites generic computer system for carrying out or applying the abstract idea. Furthermore, the courts have recognized that mere data gathering, such as those defined in the claim, are well-understood, routine, and convention computer functions which cannot serve as an inventive concept according to MPEP 21.06.05(d).
For the above reasons, the claim of this application are not patentable under 35 USC 101.
Under Step 2A, Prong 1, the claim 1, recites limitation that recite an abstract idea. The limitation of “obtain one or more lines of code from …, the one or more lines of code corresponding to an application;” is a mental process (such as collecting data) can be done by human with pen and paper, the limitation of “extract, …, functional data from the one or more lines of code;” is a mental process (such as evaluation) can be done by human with pen and paper, and the limitations of “generate, …, a specification for the application based at least in part on the functional data, the specification including at least a description of the code” is a mental process can be done by human with pen and paper.
Under Step 2A, Prong 2, the additional elements “a computing device comprising a processor and a memory;”, “machine-readable instructions”, “a code repository”, and “a machine learning model” functional data from the one or more lines of code;” are recited at a high level of generality. Thus, the claims as a whole do not integrate the exception into a practical application.
Under step 2B, the additional elements do not amount to significantly more than the abstract idea. As stated above, the claimed invention merely recites generic computer system for carrying out or applying the abstract idea. Furthermore, the courts have recognized that mere data gathering, such as those defined in the claim, are well-understood, routine, and convention computer functions which cannot serve as an inventive concept according to MPEP 21.06.05(d).
For the above reasons, the claim of this application are not patentable under 35 USC 101.
Under Step 2A, Prong 1, the claim 15, recites limitation that recite an abstract idea. The limitations of “extract, …, functional data from one or more lines of source code associated with a project”, “identify, …, one or more programming interfaces based at least in part on the functional data;”, and “extract, …, contextual data from the one or more programming interfaces” are mental processes such as collecting data, evaluations, or judgements can be done by human with a pen and paper. The limitation of “generate, …, a structured specification comprising at least the functional data and the contextual data” can be done by human with a pen and paper.
Under Step 2A, Prong 2, the additional elements, “a computing device comprising a processor and a memory;” , “a machine learning model” and “one or more programming interfaces”, are recited at a high level of generality. Thus, the claims as a whole do not integrate the exception into a practical application.
Under step 2B, the additional elements do not amount to significantly more than the abstract idea. As stated above, the claimed invention merely recites generic computer system for carrying out or applying the abstract idea. Furthermore, the courts have recognized that mere data gathering, such as those defined in the claim, are well-understood, routine, and convention computer functions which cannot serve as an inventive concept according to MPEP 21.06.05(d).
For the above reasons, the claim of this application are not patentable under 35 USC 101.
Dependent Claims 2-3, 9-10 and 16 are not patent eligible for the same reasons given for claim 1, wherein the “… receive a trigger associated … ” , “obtain the one or more lines …” and “extract, with the machine learning … .” is merely a generic computer component for applying the abstract idea, thus fails to integrate the judicial exception into a practical application, nor an inventive concept.
Further Dependent Claims 4 and 11 is rejected for similar reasons as articulated for claim 1 as the additional limitations wherein “the machine-readable instructions further cause the computing device to at least save the specification in association with the application in a data store” is a generic computing apparatus that does not allude to a practical application of the abstract idea or amount to significantly more.
Further Dependent Claims 5-7, 12-14 and 17019 are rejected for similar reasons as articulated for claim 1 as “generate one or more artifacts …” , “modify the specification …” , “… publish the one or more artifacts …” and “… generate a flow diagram based …”. Under Prong 1, the limitations in red are functions that can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, opinion, thus it is reasonable to identify these limitation as reciting a mental process.
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.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claims 1, 3-6, 8 and 10-13 are rejected under 35 U.S.C. 103 as being obvious over Parisa et al (US 11150880 B1, hereinafter Parisa) in view of Gabel et al (US 12498920 B2, hereinafter Gabel).
As to claim 1. Parisa discloses system, comprising:
a computing device comprising a processor and a memory (Fig. 4, element 402, 404); and
machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least (col. 1, ll. 58-62, Computer readable program code is collectively stored on the one or more computer readable storage media. The computer readable program code is executed by a central processing unit (CPU) of a computer system):
obtain one or more lines of code from a code repository, the one or more lines of code corresponding to an application (col. 3, ll. 54-56, Cloud service adoption system 104 receives a source code 114 of a software application. Source code 114 is also referred to herein as application source code. Further, col. 5. ll. 42-44, the application configuration to extract the services and derive the context of source code for the source code files in the source code repository);
extract, with a machine learning model, functional data from the one or more lines of code (col. 3, ll. 63-65, extracted service catalog 116 (i.e., a catalog of services extracted from source code of the multiple applications)); and
generate, with the machine learning model (col. 1, ll. 28-30, The method includes developing, by the one or more processors, a machine learning model to learn application source code accessed from a source code repository),
Parisa does not explicitly disclose the following limitations but,
Gabel discloses a specification for the application based at least in part on the functional data, the specification including at least a description of the code (col. 2, ll. 31-34, FIG. 1A and FIG. 1B illustrate example lines of source code that may be used to assist with performing software engineering tasks, under an embodiment. Lines of source code 102. Further col. 2, ll. 59-63, An issue report 106 describes a software engineering task, which the system can read automatically from the issue tracking software that a software developer is using for editing issue reports, requirements, and specifications, as depicted by FIG. 1A. Further, col. 6, ll.24-26, data format used to train a machine-learning model to assist with performing software engineering tasks).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Parisa to include a specification for the application based at least in part on the functional data, the specification including at least a description of the code, as disclosed by Gabel, for the purpose of with a machine-learning model that learned to assist with software engineering tasks and the machine-learning model uses the transformed context data to predict completions of the software engineering task (see Abstract of Gabel).
As to claim 3, Parisa discloses the system wherein the machine-readable instructions further cause the computing device to at least:
extract, with the machine learning model, contextual data from an application environment (col. 1, ll. 34-38, The method further includes based on the training data, extracting, by the one or more processors, services from the application source code and deriving, by the one or more processors, a context of the application source code. The extracting); and
Gable discloses modify, with the machine learning model, the specification based at least in part on the contextual data (col. 4, ll. 47-52, the system retrieves context data which establishes the software engineering task's context, such as Sofia's source code changes. Next, the system transforms the context data which includes Sofia's source code changes to be compatible with the data format used to train a machine-learning model).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Parisa to include modify, with the machine learning model, the specification based at least in part on the contextual data, as disclosed by Gabel, for the purpose of transforming the context data to be compatible with the data format (see col. 6, ll. 3-4 of Gabel).
As to claim 4, Gabel discloses the system wherein the machine-readable instructions further cause the computing device to at least save the specification in association with the application in a data store (col. 1, ll. 33-39, Software tools for requirements and specifications management and design/architecture are a broad category of products, which include process-focused management systems that facilitate communication and cataloging of requirements and specifications, modeling tools that allow visualization of potential software designs with various degrees of fidelity, and project management systems that are often used to store requirements and specifications).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Parisa to include modify, with the machine learning model, the specification based at least in part on the contextual data, as disclosed by Gabel, for the purpose of transforming the context data to be compatible with the data format (see col. 6, ll. 3-4 of Gabel).
As to claim 5, Gabel discloses the system wherein the machine-readable instructions further cause the computing device to at least:
generate one or more artifacts based at least in part on the functional data (col. 2, ll. 31-37, FIG. 1A and FIG. 1B illustrate example lines of source code [i.e. artifacts] that may be used to assist with performing software engineering tasks, under an embodiment. Lines of source code 102 illustrate example source code for performing a standard Artificial Intelligence (AI)-driven code completion that interprets a comment to generate a full line of source code. Further, col. 31, ll. 5-8, A source code file can be an object in a computer system for storing a text listing of commands to be compiled or assembled into an executable computer program ); and
modify the specification based at least in part on the one or more artifacts (col. 2, ll. 49-58, The system differs significantly from software issue trackers which generate issue reports by being able to specialize its behavior through text descriptions of the requirements and specifications of software engineering tasks. Recognizing that change is the center of modern software development, the system has extended the standard code completion idiom with a machine-learning model that can propose changes in issue reports and source code and a toolchain that can present these changes in a streamlined way).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Parisa to include generate one or more artifacts based at least in part on the functional and modify the specification based at least in part on the one or more artifacts, as disclosed by Gabel, for the purpose of transforming the context data to be compatible with the data format (see col. 6, ll. 3-4 of Gabel).
As to claim 6, Gabel discloses the system wherein the machine-readable instructions further cause the computing device to at least publish the one or more artifacts in an architecture playbook associated with the application (col. 4, ll. 1-6, The system links the retrieved data by linking commits of source code by a version control system to the issue reports [i.e. publish] in the issue trackers that the commits resolve, and linking the issue reports to the more general documents that the issue reports reference [i.e. playbook]. Note: examiner interpreted playbook as reference per specification par. 0024).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Parisa to include the system wherein the machine-readable instructions further cause the computing device to at least publish the one or more artifacts in an architecture playbook associated with the application, as disclosed by Gabel, for the purpose of implementing the interpreted issue reports, requirements, and specifications as source code. (see col. 3, ll. 39-41 of Gabel).
As to claim 8, it is a method claim, having similar limitations of claim 1. Thus, claim 8 is also rejected under the same rationale as cited in the rejection of claim 1.
As to claim 10, it is the method claim, having similar limitations of claim 3. Thus, claim 10 is also rejected under the same rationale as cited in the rejection of claim 3.
As to claim 11, it is the method claim, having similar limitations of claim 4. Thus, claim 11 is also rejected under the same rationale as cited in the rejection of claim 4.
As to claim 12, it is the method claim, having similar limitations of claim 5. Thus, claim 12 is also rejected under the same rationale as cited in the rejection of claim 5.
Claims 2 and 9 are rejected under 35 U.S.C. 103 as being obvious over Parisa et al and Gabel et al as applied to the claims 1, 8 and further in view of Batni et al (US 9,229,693 B1).
As to claim 2, Parisa discloses the system wherein the machine-readable instructions further cause the computing device to at least:
obtain the one or more lines of code in response to the trigger (col. 3, ll. 54-55, Cloud service adoption system 104 receives a source code 114 of a software application).
Parisa as modified by Gabel does not explicitly disclose the following limitation, but
Batni discloses receive a trigger associated with the application (col. 2, ll. 60-63, Developers may also trigger and/or cause software builds to be initiated automatically once changes to the software code in a particular repository have been pushed to the particular repository);
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Parisa to include receive a trigger associated with the application, that can be used for selection of communication links, as disclosed by Batni, for the purpose of software builds to be initiated automatically once changes to the software code (see col. 2, ll. 62-63 of Batni).
As to claim 9, it is the method claim, having similar limitations of claim 2. Thus, claim 9 is also rejected under the same rationale as cited in the rejection of claim 2.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being obvious over Parisa et al and Gabel et al as applied to the claims 1, 8 and further in view of McClory et al (US 10,691,514 B2).
As to claim 7, Parisa as modified by Gabel does not explicitly disclose the following limitation but,
McClory discloses the system wherein the machine-readable instructions further cause the computing device to at least generate a flow diagram based at least in part on the specification, the flow diagram corresponding to the application (col. 21, ll. 35-41, execute the generated test application to provide randomized input that simulates peak access patterns the application may experience via its APIs when the application is executing in a production environment; (3) monitor the responses of the container application to determine compliance with the API specifications information for the application; and (4) generate testing result information based on the determined compliance with the API specifications information for the application. Further, col. 22, ll. 44-52, the infrastructure management component 318-1 may be generally configured to execute or otherwise interpret infrastructure creation workflow information to create an application infrastructure for an application to be created within a designated infrastructure services provider system. As previously discussed, infrastructure creation information may define an infrastructure creation workflow. The infrastructure creation workflow may be configured to create. Further, col. 34, ll. 56-58, a computer usable or readable storage device having stored thereon computer software (control logic) and/or data. ).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Parisa to include receive a trigger associated with the application, that can be used for selection of communication links, as disclosed by McClory, for the purpose of creating workflow based on the determined application creation configuration information for the application (see col. 30, ll. 50-52 of McClory).
As to claim 14, it is the method claim, having similar limitations of claim 7. Thus, claim 14 is also rejected under the same rationale as cited in the rejection of claim 7.
Claim 15 is rejected under 35 U.S.C. 103 as being obvious over Parisa et al (US 11150880 B1, hereinafter Parisa) in view of Fozdar et al (US 20190243622 A1, hereinafter Fozdar).
As to claim 15, Parisa discloses a system, comprising:
a computing device comprising a processor and a memory (Fig. 4, element 402, 404);
a machine learning model stored in the memory (col. 1, ll. 58-62); and
machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least (col. 1, ll. 58-62):
extract, with the machine learning model, functional data from one or more lines of source code associated with a project (abstract, A machine learning model is devAppliceloped to learn application source code accessed from a source code repository. The machine learning model is trained by building training data that includes the application source code and technical labels. Services are extracted from the application source code and a context of the application source code is derived. Using a decision tree model, a mapping of the extracted services to respective cloud services is determined. Further, col. 3, ll. 63-65, extracted service catalog 116 (i.e., a catalog of services extracted from source code of the multiple applications));
generate, with the machine learning model, a structured specification comprising at least the functional data and the contextual data (col. 1, ll. 28-50, , a machine learning model to learn application source code accessed from a source code repository. The method further includes training, by the one or more processors, the machine learning model by building [i.e. generating] training data that includes the application source code and technical labels. The method further includes based on the training data, extracting, by the one or more processors, services from the application source code and deriving, by the one or more processors, a context of the application source code [i.e. contextual data]. The extracting and the deriving are performed by applying the machine learning model to the application source code. The method further includes using a natural language processing module and based on the context of the application source code, determining, by the one or more processors, functionalities of the extracted services. The method further includes using a decision tree model and based on the functionalities of the extracted services, determining, by the one or more processors, a mapping of the extracted services to respective cloud services, so that a functionality of one or more cloud services mapped to a given extracted service by the mapping matches a functionality of the given extracted service .Further, col. 3, ll. 36-37, Embodiments of the present invention enable an organization to move towards composable architecture [i.e. structure specification]).
Parisa does not explicitly disclose the following limitations but,
Fozdar discloses identify, with the machine learning model, one or more programming interfaces based at least in part on the functional data (par. 0017, … s. As used herein, the term “large language model (LLM)” is inclusive of multimodal models in addition to text-only models and, more broadly speaking, is intended to encompass any type of machine learning model that is capable of processing a natural language query to perform the activity planning and API selection tasks… .Further, par. 0075, … causing execution of a data integration application on a remote device via a network; causing surfacing of a GUI corresponding to the data integration [i.e. functional data] application on a display of the remote device; receiving, via the GUI, a natural language input representing a data integration task; generating, via an LLM, a set of ordered activities corresponding to the data integration task represented by the natural language input; selecting, via the LLM, at least one API for performing each activity within the set of ordered activities …);
extract, with the machine learning model, contextual data from the one or more programming interfaces (par. 0086, … receive, via the GUI, a natural language input representing a data integration task; generate, via an LLM, a set of ordered activities corresponding to the data integration task represented by the natural language input; select, via the LLM, at least one API for performing each activity within the set of ordered activities; execute the at least one API for performing each activity to generate a context for each activity; generate a data pipeline based on the set of ordered activities, the at least one API for performing each activity, and the corresponding context for each activity);
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Parisa to include identify, with the machine learning model, one or more programming interfaces based at least in part on the functional data and extract, with the machine learning model, contextual data from the one or more programming interfaces, as disclosed by Fozdar, for the purpose of performing each activity, and the corresponding context for each activity (par. 0086 of Fozdar).
Claim 16 is rejected under 35 U.S.C. 103 as being obvious over Parisa et al and Fozdar et al as applied to claim 15 in the above and further in view of Batni et al.
As to claim 16, Parisa discloses the system of wherein the machine-readable instructions, when executed by the processor, further cause the computing device to at least:
obtain the one or more lines of code in response to the trigger (col. 3, ll. 54-55, Cloud service adoption system 104 receives a source code 114 of a software application).
Parisa as modified by Fozdar does not explicitly disclose the following limitation, but
Batni discloses receive a trigger associated with the project (col. 2, ll. 60-63, Developers may also trigger and/or cause software builds to be initiated automatically once changes to the software code in a particular repository have been pushed to the particular repository);
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Parisa to include receive a trigger associated with the project, as disclosed by Batni, for the purpose of software builds to be initiated automatically once changes to the software code (see col. 2, ll. 62-63 of Batni).
Claims 17-18 are rejected under 35 U.S.C. 103 as being obvious over Parisa et al and Fozdar et al as applied to claim 15 in the above and further in view of Gabel et al.
As to claim 17, Parisa as modified by Fozdar does not explicitly disclose the following limitation, but
Gabel discloses the system wherein the machine-readable instructions, when executed by the processor, further cause the computing device to at least:
generate one or more artifacts based at least in part on the functional data (col. 2, ll. 31-37, FIG. 1A and FIG. 1B illustrate example lines of source code [i.e. artifacts] that may be used to assist with performing software engineering tasks, under an embodiment. Lines of source code 102 illustrate example source code for performing a standard Artificial Intelligence (AI)-driven code completion that interprets a comment to generate a full line of source code. Further, col. 31, ll. 5-8, A source code file can be an object in a computer system for storing a text listing of commands to be compiled or assembled into an executable computer program ); and
modify the structured specification based at least in part on the one or more artifacts (col. 2, ll. 49-58, The system differs significantly from software issue trackers which generate issue reports by being able to specialize its behavior through text descriptions of the requirements and specifications of software engineering tasks. Recognizing that change is the center of modern software development, the system has extended the standard code completion idiom with a machine-learning model that can propose changes in issue reports and source code and a toolchain that can present these changes in a streamlined way).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Parisa to include generate one or more artifacts based at least in part on the functional and modify the specification based at least in part on the one or more artifacts, as disclosed by Gabel, for the purpose of transforming the context data to be compatible with the data format (see col. 6, ll. 3-4 of Gabel).
As to claim 18, Gabel discloses the system wherein the machine-readable instructions, when executed by the processor, further cause the computing device to at least publish, with the machine learning model, the one or more artifacts in an architecture playbook associated with the project (col. 4, ll. 1-6, The system links the retrieved data by linking commits of source code by a version control system to the issue reports [i.e. publish] in the issue trackers that the commits resolve, and linking the issue reports to the more general documents that the issue reports reference [i.e. playbook]. Note: examiner interpreted playbook as reference per specification par. 0024).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Parisa to include the system wherein the machine-readable instructions, when executed by the processor, further cause the computing device to at least publish, with the machine learning model, the one or more artifacts in an architecture playbook associated with the project, as disclosed by Gabel, for the purpose of implementing the interpreted issue reports, requirements, and specifications as source code. (see col. 3, ll. 39-41 of Gabel).
Claim 19 is rejected under 35 U.S.C. 103 as being obvious over Parisa et al and Fozdar et al as applied to claim 15 in the above and further in view of McClory et al.
As to claim 19, Parisa as modified by Fozdar does not explicitly disclose the following limitation but,
McClory discloses the system wherein the machine-readable instructions, when executed by the processor, further cause the computing device to at least generate a flow diagram based at least in part on the structured specification, the flow diagram corresponding to the project (col. 21, ll. 35-41, execute the generated test application to provide randomized input that simulates peak access patterns the application may experience via its APIs when the application is executing in a production environment; (3) monitor the responses of the container application to determine compliance with the API specifications information for the application; and (4) generate testing result information based on the determined compliance with the API specifications information for the application. Further, col. 22, ll. 44-52, the infrastructure management component 318-1 may be generally configured to execute or otherwise interpret infrastructure creation workflow information to create an application infrastructure for an application to be created within a designated infrastructure services provider system. As previously discussed, infrastructure creation information may define an infrastructure creation workflow. The infrastructure creation workflow may be configured to create. Further, col. 34, ll. 56-58, a computer usable or readable storage device having stored thereon computer software (control logic) and/or data. ).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Parisa to include receive a trigger associated with the application, that can be used for selection of communication links, as disclosed by McClory, for the purpose of creating workflow based on the determined application creation configuration information for the application (see col. 30, ll. 50-52 of McClory).
Claim 20 is rejected under 35 U.S.C. 103 as being obvious over Parisa et al and Fozdar et al as applied to the claim 15 in the above and further in view of Allamanis et al. (US 20190243622 A1, hereinafter Allamanis).
As to claim 20, Parisa as modified by Fozdar does not explicitly disclose the following but,
Allamanis discloses the system wherein the machine-readable instructions, when executed by the processor, further cause the computing device to at least:
identify, with the machine learning model, one or more data flows based at least in part on the functional data (par. 0052, … By applying machine learning on the corpus of model data, the service may then construct a “machine learning model. … . Further, par. 0132, … For example, the learnable function is able to learn from the derived data flows and control flows, which were embodied in the graph in the form of links between the nodes. Thus, the learnable function learns characteristics about the variables not only from elements that are associated with those variables (e.g., declarations, names, operators, etc.) but also from relational aspects associated with those variables (e.g., how the variables interrelate to one another, how the data flows between the variables, how the control flow executes in relation to the variables, etc.). …);
extract, with the machine learning model, contextual data from the one or more data flows (par. 0052, … By applying machine learning on the corpus of model data, the service may then construct a “machine learning model. … . Further, par. 0126, … relevant context information (e.g., the elements, data flows, control flows, etc.) is extracted, derived, or otherwise distilled from the source code (e.g., a compiler is able to naturally extract the context)); and
modify the structured specification based at least in part on the one or more data flows (par. 0068, … As used herein, elements may be associated with source code tokens, particular portions of a syntax tree, or even different source code types. These elements may be embodied in the graph in the form of graph nodes. Furthermore, the links between these nodes beneficially demonstrate the various relationships that are present between the nodes and may be associated with the data flows and/or control flows that occur between those nodes).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Parisa to include identify, with the machine learning model, one or more data flows based at least in part on the functional data extract, with the machine learning model, contextual data from the one or more data flows and modify the structured specification based at least in part on the one or more data flows, as disclosed by Allamanis, for the purpose of solving any disadvantages or that operate only in environment. (par. 0005 of Allamanis).
Conclusion
Prior arts made of record are considered pertinent to applicant's disclosure. See MPEP § 707.05 (C) For Examples:
I. Lin et al. (US 20240427566 A1) discloses: “Systems and methods for software development using machine learning models are disclosed. In one embodiment, a method for programmatically generating source code using a machine learning model includes obtaining a set of project configuration information that defines a programming task, indexing original source code in a code repository, advancing the programming task by constructing a prompt to a machine learning model to obtain new source code as an output, writing the new source code to the code repository, identifying an intervention request that requires human input to complete the programming task, obtaining human input for the intervention request from a human using digital communications as an outcome, and writing the intervention request and outcome to an intervention database.” (please see abstract).
II. Ongarato et al. (US 20240152356 A1) discloses: “Disclosed are various embodiments for an automated process for configuring a client device for software development in a particular development environment defined by an organization. In one example, a computing device is configured to configure a development environment associated with an organization on the computing device. A developer tool application associated with the organization can be downloaded. The computing device can modify a targeted application associated with the developer tool application and generate documentation for the targeted application based at least in part on the modification of the targeted application. An error assessment of a software upload can be performed. The software upload can be transmitted to a code repository based at least in part on the error assessment indicating that the software upload is compliant with an error configuration file.” (please see [xxx]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD H KABIR whose telephone number is (571)270-1341. The examiner can normally be reached M-F, 8:00 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sam Sough can be reached at 571-272-6799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Mohammad Kabir/
Examiner, Art Unit 2192
/S. Sough/SPE, Art Unit 2192
