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 .
DETAILED ACTION
1. This initial office action is based on the Track One application filed on 12/04/2025, which claims 1-20 have been presented for examination.
Status of Claim
2. Claims 1-20 are pending in the application and have been examined below, of which, claims 1, 14 and 18 are presented in independent form.
Priority
3. This application is a CON of 18/926,599 filed on 10/25/2024
Information Disclosure Statement
4. No information disclosure statement (IDS) has been filed.
Examiner Notes
5. Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
6. Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 14 and 18 recite “with the web application system”, in lines 11, 11 and 10 respectively, appear to be unclear whether candidate components within or with the web application system. It renders the claim indefinite.
Claims 2-13, 15-17 and 19-20 are also rejected under 112(b) as being respectively dependent upon claims 1, 14 and 18.
Clarification and appropriate correction are 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.
7. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis specific to Claims 1, 14 and 18 are being presented below.
Claims 1, 14 and 18:
Step 1 Analysis:
Claims 1-13 of the instant application is direct to apparatus.
Claims 14-17 of the instant application is direct to method.
Claims 18-20 of the instant application is direct to product.
Thus, they are statutory categories.
Step 2 Analysis:
Claims 1, 14 and 18 recite:
(a) receive input data specifying data format requirements for one or more components of a web application system;
(b) generate, based on the data format requirements for the one or more components of the web application system, one or more component data ingestion parameters for the web application system, wherein the one or more component data ingestion parameters define acceptable data formats for the one or more components within the web application system;
(c) store the one or more component data ingestion parameters as configurable adherence parameters for determining compatibility of candidate components with the web application system.
Step 2A -- Prong 1:
The claims 1, 14 and 18 recites the limitations of:
(a) receive input data specifying data format requirements for one or more components of a web application system;
(b) generate, based on the data format requirements for the one or more components of the web application system, one or more component data ingestion parameters for the web application system, wherein the one or more component data ingestion parameters define acceptable data formats for the one or more components within the web application system;
Limitations (a)-(b) are limitations that, as drafted, are processes that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “receive”/collect and “generate”/create can be performed in the human mind with the aid of pen and paper. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Step 2A -- Prong 2:
The claim 1 recites the additional limitations of “An apparatus”, “one or more processors” and “a web application system”. The limitations of “An apparatus”, “one or more processors” and “a web application system” are recited at a high level of generality, i.e., merely instructions to implement the abstract idea on a generic computer or merely uses a computer as a tool to perform the abstract idea. Claim 15 recites the additional limitations of “one or more processors”; “a web application system”. The limitation of “one or more processors” and “a web application system” are recited at a high level of generality, i.e., merely instructions to implement the abstract idea on a generic computer or merely uses a computer as a tool to perform the abstract idea. Claim 18 recites the additional limitations of “One or more non-transitory computer-readable storage media”, “one or more processors, cause the one or more processors” and “a web application system”. The limitations of “One or more non-transitory computer-readable storage media”, “one or more processors, cause the one or more processors” and “a web application system” are recited at a high level of generality, i.e., merely instructions to implement the abstract idea on a generic computer or merely uses a computer as a tool to perform the abstract idea. Additionally, limitations (c) perform as well-understood, routine and conventional activity. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B:
As explained with respect to Step 2A Prong Two, the additional elements in the claim are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The same analysis applies here in 2B, i.e., simply adding extra-solution activity or well-understood, routine and conventional activity or generic computer components does not integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B since the courts have identified functions such as gathering, displaying, updating, transmitting/receiving and storing data as well- understood, routine, conventional activity. See MPEP 2106.05(d) and See MPEP 2106.05(g) . Therefore, claims are ineligible.
Dependent claims
Additionally, claims 2 and 20 recite “analyze existing components of the web application system to identify current data format usage patterns; and automatically recommend component data ingestion parameters based on the current data format usage patterns”. The addition limitation “analyze existing components of the web application system to identify current data format usage patterns” as drafted, is a process that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “analyze” can be performed in the human mind through observation, evaluation, judgment, opinion with the aid of pen and paper. As such, this limitation falls within the “Mental Processes” grouping of abstract idea. The addition limitation “automatically recommend component data ingestion parameters based on the current data format usage patterns” is merely insignificant extra solution activity of recommend/output data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 2 and 20 are ineligible.
Additionally, claim 3 recites “provide a graphical user interface that enables users to define the data format requirements through interactive selection of data types, data structures, and data validation rules” as drafted, is a process that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “provide” and “define” can be performed in the human mind through opinion with the aid of pen and paper. As such, this limitation falls within the “Mental Processes” grouping of abstract idea. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 3 is ineligible.
Additionally, claims 4 and 15 recite “wherein the one or more component data ingestion parameters specify one or more of: required data schemas, acceptable data encoding formats, mandatory metadata fields, data size limitations, and data transformation requirements” is merely insignificant extra solution activity of defining data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 4 and 15 are ineligible.
Additionally, claims 5 and 16 recite “generate one or more configuration element parameters based on one or more received component interaction requirements, wherein the one or more configuration element parameters define communication protocols and interface specifications for components within the web application system” is merely insignificant extra solution activity of outputting data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 5 and 16 are ineligible.
Additionally, claim 6 recites “validate the one or more component data ingestion parameters against existing system capabilities to ensure compatibility within the web application system” as drafted, is a process that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “validate” can be performed in the human mind through observation, evaluation, judgment, opinion with the aid of pen and paper. As such, this limitation falls within the “Mental Processes” grouping of abstract idea. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 6 is ineligible.
Additionally, claim 7 recites “monitor component interactions within the web application system and automatically update the configurable adherence parameters based on monitored interaction patterns” is merely insignificant extra solution activity of observing data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 7 is ineligible.
Additionally, claim 8 recites “generate parameter structures wherein component data ingestion parameters include mandatory parameters and optional parameters with different compliance requirements” is merely insignificant extra solution activity of outputting data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 8 is ineligible.
Additionally, claim 9 recites “generate parameter validation rules that automatically verify consistency and completeness of the one or more component data ingestion parameters and configuration element parameters” is merely insignificant extra solution activity of outputting data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 9 is ineligible.
Additionally, claim 10 recites “establish parameter dependency relationships that automatically update related parameters when base parameters are modified” is merely insignificant extra solution activity of gathering data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 10 is ineligible.
Additionally, claim 11 recites “generate parameter compliance reports that identify components within the web application system that do not meet current parameter requirements” is merely insignificant extra solution activity of gathering data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 11 is ineligible.
Additionally, claims 12, 17 and 19 recite “wherein the one or more component data ingestion parameters specify one or more of: required data schemas, acceptable data encoding formats, mandatory metadata fields, data size limitations, and data transformation requirements” is merely insignificant extra solution activity of defining data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 12, 17 and 19 are ineligible.
Additionally, claim 13 recites “apply the configurable adherence parameters to determine compatibility of candidate components with the web application system” is merely insignificant extra solution activity of processing data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 13 is ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
8. Claim(s) 1, 3-4, 6-11, 13-15 and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dangi et al. (US Pub. No. 2025/0377864 A1 – herein after Dangi) in view of Fedoruk et al. (US Patent No. 12,493,829 B1 – herein after Fedoruk).
Regarding claim 1.
Dangi discloses
An apparatus comprising one or more processors and at least one non-transitory memory comprising instructions that, when executed by the one or more processors, cause the one or more processors to:
receive input data specifying data format requirements for one or more components of a web application system (retrieve text representative of one or more requirements for a software product – See paragraph [0008]. Receive an input representative of one or more requirements (e.g., in text or other formats)… retrieve text from the input representative of the requirements (and criteria, query, and/or at least a portion of the software product), and to provide the text to a prompting layer, module, or component for the prompting layer to generate a prompt including the text and an instruction corresponding to one or more criteria (e.g., standards) for processing of the requirements – See paragraphs [0032-0034]. The at least the portion of the software product can be an architectural component such as “user authentication module” or “database management subsystem.” – See paragraph [0043]. The software 832 or application(s) 842 can respectively include web-based service software or applications – See paragraph [0126]);
generate, based on the data format requirements for the one or more components of the web application system (generate feedback to a query in view of the one or more requirements, the one or more criteria, the query, and/or at least the portion of the software product. The API layer, using the user interface layer can at least one of present the feedback or present a modification of the requirements and associated criteria generated using the feedback – See paragraph [0034]), one or more component data ingestion parameters for the web application system (a user interface layer, module, or component to receive an input representative of one or more requirements (e.g., in text or other formats), and to provide responses regarding the requirements. The system can include an application programming interface (API) layer, module, or component to retrieve text from the input representative of the requirements (and criteria, query, and/or at least a portion of the software product), and to provide the text to a prompting layer, module, or component for the prompting layer to generate a prompt including the text and an instruction corresponding to one or more criteria (e.g., standards) for processing of the requirements – See paragraph [0034]. The parameters can include details like the type of requirements (functional, non-functional, specifications, user stories) – See paragraph [0046]. Adapt the large language model to the specific task of reviewing software requirements. This can include updating/training one or more parameters known as prompts, which can be prepended to input text to guide the LLM towards generating outputs that are specific to the requirements – See paragraph [0087]), wherein the one or more component data ingestion parameters define acceptable data formats for the one or more components within the web application system (the configuration of the neural network using the training data can include a prompt tuning of the neural network, wherein prompt tuning includes updating a set of parameters of the neural network based on one or more annotations of the plurality of examples of requirements or the plurality of examples of feedback – See paragraph [0005]. The presentation can be in various formats such as textual summaries, annotated documents, visual highlights, and/or interactive dashboards. For example, method can include outputting the feedback regarding the one or more requirements – See paragraph [0104]); and
Dang discloses the commands can include instructions to retrieve, store, and/or process data related to software requirements. For example, commands might instruct the collection system 112 to fetch some or all existing user stories related to a specific software module or compile feedback on these requirements – See paragraph [0046]).
Dang does not disclose
store the one or more component data ingestion parameters as configurable adherence parameters for determining compatibility of candidate components with the web application system.
Fedoruk discloses
store the one or more component data ingestion parameters as configurable adherence parameters (generate a manifest file that stores the arrangement of agent objects in the AI agent. When the manifest is validated, the AI agent is displayed as an execution flow within the UI – See col. 2, lines 64-67 and col. 3, line 1) for determining compatibility of candidate components with the web application system (Validating the manifest can include checking the agent objects against dependency rules. Dependency rules dictate events that must occur before at least one of the selected agent objects can execute. The UI can display a validation of the manifest file. A validation service can perform the validation – See col. 3, lines 1-7. The AI platform can validate that the output of the first block being connected matches the expected input format of the block it is being connected to. If it does not, the AI platform can indicate the format mismatch on the UI and either prevent the connection of the two blocks in the UI or allow the connection but flagged it as an error condition. In one example, the UI suggests an available format conversion code block as an intermediate step between the two blocks. This can be the case when a code object exists for reformatting the output of the first block into a usable format for the second block – See col. 36, lines 35-46).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Fedoruk’s teaching into Dangi’s invention because incorporating Fedoruk’s teaching would enhance Dangi to enable suggests an available format conversion code block as suggested by Fedoruk (See col. 36, lines 35-46).
Regarding claim 3, the apparatus of claim 1,
Dangi discloses
wherein the at least one non-transitory memory further comprises instructions that, when executed by the one or more processors, cause the one or more processors to:
provide a graphical user interface that enables users to define the data format requirements through interactive selection of data types, data structures, and data validation rules (The text can be processed audio or speech, written text, images, structured data, or any other form of input data – See paragraph [0037]. receive and process 3D models or CAD files as part of the input. For instance, a user can submit a CAD model of a user interface component, such as a dashboard, into the GUI 400. The large language model 116 can receive the CAD model and the requirement “The system must provide a user-friendly dashboard for monitoring system health.” The large language model 116 can provide feedback on the usability and compatibility of the design, suggesting modifications if necessary – See paragraph [0083]. Inputs data types – See paragraph [0138]. Rule – See paragraph [0076]).
Regarding claim 4, the apparatus of claim 1,
Dangi discloses
wherein the one or more component data ingestion parameters specify one or more of: required data schemas, acceptable data encoding formats, mandatory metadata fields, data size limitations, and data transformation requirements (At the model layer process 220, the large language model 116 can receive the input+prompt 212 as input, model the received data, and output a response 130. In some implementations, the large language model 116 can be a neural network such as a transformer architecture. The transformer architecture can transform the prompt representative of the one or more requirements and/or criteria into the feedback in a human-readable format-response 130 – See paragraph [0074]. The input 901 includes image data, the input processor 901 may resize the image data to a standard size compatible with format of a corresponding input channel and/or may normalize pixel values to a common range (e.g., 0 to 1) to ensure a consistent representation – See paragraph [0148]).
Regarding claim 6, the apparatus of claim 1,
Dangi discloses
wherein the at least one non-transitory memory further comprises instructions that, when executed by the one or more processors, cause the one or more processors to:
validate the one or more component data ingestion parameters against existing system capabilities to ensure compatibility within the web application system (prompt tuning can be used to update/train the neural network. For example, the configuration of the neural network can include performing prompt tuning of the neural network. Prompt tuning can include adjusting the model's attention mechanisms to better focus on the nuances of software requirement language. In some implementations, the one or more parameters can be the weights and biases of the neural network layers. The one or more parameters can be updated based on performance metrics derived from test results – See paragraph [0091]. The output can include suggestions for improving the clarity or verifiability of the requirement, adjustments to the technical descriptions, or additional test cases to further improve compliance – See paragraph [0078]).
Fedoruk also discloses
wherein the at least one non-transitory memory further comprises instructions that, when executed by the one or more processors, cause the one or more processors to:
validate the one or more component data ingestion parameters against existing system capabilities to ensure compatibility within the web application system (The system can generate a manifest file that stores the arrangement of agent objects in the AI agent. When the manifest is validated, the AI agent is displayed as an execution flow within the UI. Validating the manifest can include checking the agent objects against dependency rules. Dependency rules dictate events that must occur before at least one of the selected agent objects can execute. The UI can display a validation of the manifest file. A validation service can perform the validation – See col. 2, lines 65-67 and col. 3, lines 1-7).
Regarding claim 7, the apparatus of claim 1,
Dangi discloses
wherein the at least one non-transitory memory further comprises instructions that, when executed by the one or more processors, cause the one or more processors to:
monitor component interactions within the web application system and automatically update the configurable adherence parameters based on monitored interaction patterns (the configuration of the neural network using the training data can include a prompt tuning of the neural network, wherein prompt tuning includes updating a set of parameters of the neural network based on one or more annotations of the plurality of examples of requirements or the plurality of examples of feedback – See paragraph [0005]. Method 500 can incorporate user prompt tuning techniques to adapt the large language model to the specific task of reviewing software requirements. This can include updating/training one or more parameters known as prompts, which can be prepended to input text to guide the LLM towards generating outputs that are specific to the requirements – See paragraph [0087]).
Regarding claim 8, the apparatus of claim 1,
Dangi discloses
wherein the at least one non-transitory memory further comprises instructions that, when executed by the one or more processors, cause the one or more processors to:
generate parameter structures wherein component data ingestion parameters include mandatory parameters and optional parameters with different compliance requirements (The language model can be updated/trained on examples of requirements, associated criteria, and corresponding feedback and/or suggestions provided for the requirements and associated criteria. The examples of requirements and associated criteria can be selected to relate to a diverse range of requirements to prevent overfitting. The language model can be updated/trained using a p-tuning technique in which a prompting layer is configured to generate prompts to be combined with (e.g., prepended to) the input text from a user at runtime – See paragraph [0033-0034]).
Regarding claim 9, the apparatus of claim 1,
Fedoruk discloses
wherein the at least one non-transitory memory further comprises instructions that, when executed by the one or more processors, cause the one or more processors to:
generate parameter validation rules that automatically verify consistency and completeness of the one or more component data ingestion parameters and configuration element parameters (The management rules can relate to data validation and testing. These rules can be used for automated quality checks, including constraints, data consistency, completeness, and type validation – See col. 24, lines 29-32).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Fedoruk’s teaching into Dangi’s invention because incorporating Fedoruk’s teaching would enhance Dangi to enable manage rules that can relate to data validation and testing as suggested by Fedoruk (See col. 24, lines 29-32).
Regarding claim 10, the apparatus of claim 1,
Fedoruk discloses
wherein the at least one non-transitory memory further comprises instructions that, when executed by the one or more processors, cause the one or more processors to:
establish parameter dependency relationships that automatically update related parameters when base parameters are modified (the UI module 208 generates one or more UIs that permit a user, such as an information technology (IT) administrator, to define AI agents that each include one or more objects having associated parameters, as well as relationships between the object(s). Each AI agent can include a directed graph that includes multiple objects and indicates how the outputs of one or more objects are input into, or otherwise depend on, other object(s) – See col. 14, lines 35-41).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Fedoruk’s teaching into Dangi’s invention because incorporating Fedoruk’s teaching would enhance Dangi to enable to provide a directed graph that includes multiple objects and indicates how the outputs of one or more objects are input into, or otherwise depend on, other object(s) as suggested by Fedoruk (See col. 14, lines 35-41).
Regarding claim 11, the apparatus of claim 1,
Fedoruk discloses
wherein the at least one non-transitory memory further comprises instructions that, when executed by the one or more processors, cause the one or more processors to:
generate parameter compliance reports that identify components within the web application system that do not meet current parameter requirements (Tools for time and expense tracking allow agents to record and manage time entries, expenses, timesheets, and related categories. These tools may support approval and rejection workflows, tagging, receipt management, and the generation of reports. Configuration options may include user assignments, rate setting, template usage, data import/export, and the creation of custom fields, providing comprehensive control over personnel and operational cost tracking – See col. 53, lines 28-35).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Fedoruk’s teaching into Dangi’s invention because incorporating Fedoruk’s teaching would enhance Dangi to enable to support approval and rejection workflows, tagging, receipt management, and the generation of reports as suggested by Fedoruk (See col. 53, lines 28-35).
Regarding claim 13, the apparatus of claim 1,
Fedoruk discloses
wherein the at least one non-transitory memory further comprises instructions that, when executed by the one or more processors, cause the one or more processors to:
apply the configurable adherence parameters to determine compatibility of candidate components with the web application system (the platform user can then select and connect agent objects within the UI. Doing so can cause the server or another device to generate a manifest file based on selected agent objects that are connected on the UI. The manifest file can keep track of specific versions of the agent objects and their position coordinates on the screen. The manifest all tracks dependencies, which include perquisite events and resources that are needed prior to executing one or more stages of the agent (e.g., prior to executing one or more agent objects) – See col. 2, lines 37-47).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Fedoruk’s teaching into Dangi’s invention because incorporating Fedoruk’s teaching would enhance Dangi to enable to generate a manifest file based on selected agent objects that are connected on the UI as suggested by Fedoruk (See col. 2, lines 37-47).
Regarding claim 14.
A computer-implemented method comprising:
Regarding claim 1, recites the same limitations as rejected claim 1 above.
Regarding claim 15, recites the same limitations as rejected claim 4 above.
Regarding claim 17, recites the same limitations as rejected claim 12 above.
Regarding claim 18.
One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to:
Regarding claim 18, recites the same limitations as rejected claim 1 above.
9. Claim(s) 2, 5, 12, 16 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dangi and Fedoruk as applied to claims 1, 14 and 18 respectively above, and further in view of Brechtel et al. (US Pub. No. 2025/0298979 A1 – herein after Brechtel).
Regarding claim 2, the apparatus of claim 1,
Brechtel discloses
wherein the at least one non-transitory memory further comprises instructions that, when executed by the one or more processors, cause the one or more processors to:
analyze existing components of the web application system to identify current data format usage patterns (the project server 120 may utilize the language model to check structured formats or to check for consistency between unstructured and structured data – See paragraph [0080] and Fig. 12 and Fig. 13); and
automatically recommend component data ingestion parameters based on the current data format usage patterns (generate different views of the systems modeling information based on user-selected filters or parameters – See paragraph [0429]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Brechtel’s teaching into Dangi’s and Fedoruk’s inventions because incorporating Brechtel’s teaching would enhance Dangi and Fedoruk to enable generate different views of the systems modeling information based on user-selected filters or parameters as suggested by Brechtel (paragraph [0429]).
Regarding claim 5, the apparatus of claim 1,
Brechetel discloses
wherein the at least one non-transitory memory further comprises instructions that, when executed by the one or more processors, cause the one or more processors to:
generate one or more configuration element parameters based on one or more received component interaction requirements (perform a semantic analysis of the significance of changes made to the model. This analysis may involve examining the nature and impact of the changes, such as the addition or removal of components, alterations in system relationships, or modifications to system parameters – See paragraph [0567]), wherein the one or more configuration element parameters define communication protocols (a commit request operation involves sending a request to the distributed version control system to update the stored snapshots with the modified version of the first snapshot. The system may use various techniques to submit the commit request, such as network communication protocols – See paragraph [0671]) and interface specifications for components within the web application system (By using a transport layer, the thread system 1302 may carry engineering parameters to connectors that correspond to the inputs and outputs of the relevant analysis or design files. By operating as a transport layer, and routing data back to the original model, the thread system 1302 may offload significant complexity in designing systems – See paragraph [0173]. The interface 1212 may be an interface for the language graph representation, and the interface 1212 may have meta-methods that enhance the language graph representation but are not contained in the literal in memory (e.g., NetworkX) directed graph data structure used to interact with the language graph representation. The directed graph data structures may not be natively exported to the target language model (e.g., SysML) textual notation, but the interface 1212 and object model (e.g. classes) may enable export to such language model textual notation – See paragraphs [0158-0159]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Brechtel’s teaching into Dangi’s and Fedoruk’s inventions because incorporating Brechtel’s teaching would enhance Dangi and Fedoruk to enable perform a semantic analysis of the significance of changes made to the model. This analysis may involve examining the nature and impact of the changes, such as the addition or removal of components, alterations in system relationships, or modifications to system parameters as suggested by Brechtel (paragraph [0567]).
Regarding claim 12, the apparatus of claim 5,
Fedoruk discloses
wherein the one or more component data ingestion parameters specify one or more of: required data schemas, acceptable data encoding formats, mandatory metadata fields, data size limitations, and data transformation requirements (Data post-processing object parameters define operations applied to processed or analyzed data to validate results, transform outputs – See col. 25, lines 8-10).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Fedoruk’s teaching into Dangi’s invention because incorporating Fedoruk’s teaching would enhance Dangi to enable to provide data post-processing object parameters define operations applied to processed or analyzed data to validate results, transform outputs as suggested by Fedoruk (See col. 25, lines 8-10).
Regarding claim 16, recites the same limitations as rejected claim 5 above.
Regarding claim 19, recites the same limitations as rejected claim 12 above.
Regarding claim 20, recites the same limitations as rejected claim 2 above.
Conclusion
10. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mirmiran (US Pub. No. 2026/0072934) discloses executing the AI model on the formatted data structure and the prompt to generate a modified formatted data structure, executing the filter on the modified formatted data structure and the conditions associated with the formatted data structure to determine that the modified formatted data structure matches the conditions, and in response, deploying a software system via a host platform and executing the modified formatted data structure as part of the software system – See Abstract and specification for more details.
Vattikutti (US Pub. No. 2025/0265076 A1) discloses the policy enforcement engine communicates the set of provisioning scripts to a set of technology-specific interpreters of the plurality of technology-specific interpreters. The set of technology-specific interpreters communicate with the set of technologies to provision the set of resources – See Abstract and specification for more details.
Schmidt et al. (US Pub. No. 2025/0021309 A1) discloses applying a large language model (LLM) transformer to transform the generated human-readable front-end code into a machine-readable format and build the web or mobile application – See Abstract and specification for more details.
Lim et al. (US Pub. No. 2023/0086308 A1) discloses retrieving, by the library component, the list of one or more application component definitions from a manifest; receiving a request to load an application component of the one or more application components; binding, by the library component, attributes and events for each of the one or more application components from the web application; appending the application component into the web page element; and rendering the application component and the web page element using the web browser so that the application component is displayed in the web browser – See Abstract and specification for more details.
Subramanian et al. (US Pub. No. 2025/0265013 A1) discloses use dynamically generated validation rules. These validation rules comprise a first validation rule portion that is generated using a standardized validation process (e.g., corresponding to a standardized schema) and a second validation rule portion that is generated using a validation process selected based on a non-standardized schema that is specific to a respective asset type of the plurality of respective asset types – See Abstract and specification for more details.
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/MONGBAO NGUYEN/ Examiner, Art Unit 2192
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