Detailed Action
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Applicants have filed a formal response dated December 5th, 2025 in response to the October 1st, 2025 Office Action provided in the rejection of claims 1-20.
Status of Claims
2. Claims 1, 3-5, 8-10, 12, 14-16 and 18-20 have been amended. Claims 1-20 are pending in the application, of which claims 1, 10 and 16 are in independent form and these claims (1-20) are subject to following rejection(s) and/or objection(s) indicated under section and subsections of No. 3 below.
Response to the Amendments
(A). Regarding 35 U.S.C. § 112(d) rejection: Applicants reasoning and amendment to the claims 1, 10 and 16 lead to overcome the rejections under 35 U.S.C. § 112(d).
(B). Regarding art rejection: In regards to claims 1-20 Applicants arguments are not persuasive; further, Applicants' amendment necessitated same grounds; however, modified version of rejections presented in the following art rejection. Applicants’ current arguments are addressed through the following art rejections herewith by this office action.
As an initial mater Examiner likes to points out that the claims are interpreted in light of the specification; however, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Argument: Applicant contended that “Wang, whether considered singly or in combination with the other cited references, fails to describe, teach, or suggest at least ‘generating an experiment pre-runtime compatibility metric by comparing the one or more experiment process components and the analysis code validation representation’ and ‘providing, for display within a compatibility graphical user interface, a compatibility element indicating the experiment pre-runtime compatibility metric of the data analysis model relative to the experiment design representing whether the one or more experiment process components of the experiment design satisfy one or more conditions of the one or more analysis code validation components of the data analysis model,’” (please see Remarks page: 13/17).
Examiner respectfully disagree with the Applicants because combination of reference sufficiently discloses claim invention i.e. “generating an experiment pre-runtime compatibility metric by comparing the one or more experiment process components and the analysis code validation representation and providing, for display within a compatibility graphical user interface, a compatibility element indicating the experiment pre-runtime compatibility metric of the data analysis model relative to the experiment design representing whether the one or more experiment process components of the experiment design satisfy one or more conditions of the one or more analysis code validation components of the data analysis model”. For examples Wang discloses “a computer usable program code configured to provide a set of customized components … an automation process object interface that allows access to said available for use automation process data objects … (see page 4:17-23)”, on the other hands Rajagopalan disclose “applying the large language model to legacy application code that interfaces with the legacy database to extract one or more legacy application values-¶[0060], a chatbot program that is integrated with the large language model and configured to interface with the data store is executed. The chatbot can be integrated into the system to provide users with a natural language interface for interacting with the analyzed and stored data-¶[0093]”, wherein “generative AI component of the system generates target data model and code that will be compatible with modern, cloud-native platforms, speeding up the transformation process and Code Validation: The system leverages the generative AI component to generate needed test/mock data. The generated code is then tested for functional accuracy and performance using the test data to validate migration code (please see ¶[0020] -¶[0021]”). Moreover, Saini discloses “obtains a data analysis model that has been constructed with a user interface. The user interface, in the illustrative embodiment, is provided by the insight compute device 110. For example the user interface may be provided as executable instructions and data (e.g., hyper-text markup language, JavaScript, etc. and data, such as image data) to a web browser or other rendering engine executed on a corresponding user compute device 116, 118 for presentation to a user. As indicated in block 308, the insight compute device 110 may obtain the data analysis model from a user interface that enables users to identify one or more key performance indicators (please see ¶[0031]”; therefore, the combination of these cited reference adequately renders the inventive idea that is equal to Applicants’ claimed inventions such as; “generating an experiment pre-runtime compatibility metric by comparing the one or more experiment process components and the analysis code validation representation and providing, for display within a compatibility graphical user interface, a compatibility element indicating the experiment pre-runtime compatibility metric of the data analysis model relative to the experiment design representing whether the one or more experiment process components of the experiment design satisfy one or more conditions of the one or more analysis code validation components of the data analysis model”. Where the claimed and prior art products are identical or substantially identical in structure or composition, or are produced by identical or substantially identical processes, a primafacie case of either anticipation or obviousness has been established. In re Best, 562 F.2d 1252, 1255, 195 USPQ 430, 433 (CCPA 1977). "When the PTO shows a sound basis for believing that the products of the applicant and the prior art are the same, the applicant has the burden of showing that they are not." In re Spada, 911 F.2d 705, 709, 15 USPQ2d 1655, 1658 (Fed. Cir. 1990). Also see MPEP 2112.01.
Also, Applicants must consider the cited prior art in view of one of ordinary skill in the art, not as a generalized literature. Examiner further respectfully points out that the specific statements in the references themselves which would spell out the claimed invention are not necessary to show obviousness, since questions of obviousness involves not only what references expressly teach, but what they would collectively suggest to one of ordinary skill in the art. See CTS Corp. v. Electro Materials Corp. of America 202 USPQ 22 (DC SNY ); and In re Burckel 201 USPQ 67 (CCPA). In re Burckel is cited in MPEP 716.02.
(C). Prior arts made of record are considered pertinent to applicant's disclosure. See MPEP § 707.05 For Examples:
Ke-Fei Zhang (CN 115080424 A) discloses inter alia “In one embodiment, after the performance monitoring data and log data of the test instance input the trained data analysis model, classifying the performance monitoring data and log data by the data analysis model, outputting the first code operation logic of the test instance in the normal state. at the same time, obtaining the second code operation logic of the test instance in the fault state, then the first code operation logic, comparing with the second code operation logic, from the first code operation logic, determining the code section different from the second code operation logic, so as to the position of the code section, A code fault location in a test instance is determined” (please see ¶[06] under Step 302).
3. Finality: Applicant's arguments filed December 5th, 2025 have been fully considered but they are not persuasive. THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Claim Rejections – 35 USC §103
4. 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.
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.
5. Claims 1, 8-10, 14-16 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (CN 102124445 A) in view of Rajagopalan et al. (US 2025/0156384 A1).
Per claim 1:
Wang discloses:
A computer-implemented method (At least see page 1:29-30 a method and system for developing for an automation process a screen viewable on a target computer that has user interface objects) comprising:
providing, for display via an experiment design user interface, one or more experiment design selection elements and one or more data analysis model selection elements (At least see page 4:17-19 - a common software development tool comprises a set of common design time components, the common design time component with data binding device);
in response to a user interaction with the one or more experiment design selection elements and the one or more data analysis model selection elements (At least see page 5:1-2 - provide a process object selector, which acts as a user interface to data binding device of to allow), identifying an experiment design comprising one or more experiment process components and a data analysis model (At least see page 11:10-11 - process object selector 309 acts as custom data binding of user interface so that the user can easily browse the automation process data objects can be used 301).
Wang sufficiently discloses the claimed limitations as set forth above, but Wang does not explicitly disclose: generating an analysis code validation representation by extracting one or more analysis code validation components from the data analysis model; generating a compatibility metric by comparing the one or more experiment process components and the analysis code validation representation; providing, for display within a compatibility graphical user interface, a compatibility element indicating the experiment pre-runtime compatibility metric of the data analysis model relative to the experiment design representing whether the one or more experiment process components of the experiment design satisfy one or more conditions of the one or more analysis code validation components of the data analysis model.
However, Rajagopalan discloses:
generating an analysis code validation representation by extracting one or more analysis code validation components from the data analysis model (At least see ¶[0020] - generative AI component of the system generates target data model and code that will be compatible with modern, cloud-native platforms, speeding up the transformation process and ¶[0021] Code Validation: The system leverages the generative AI component to generate needed test/mock data. The generated code is then tested for functional accuracy and performance using the test data to validate migration code);
generating an experiment pre-runtime compatibility metric by comparing the one or more experiment process components and the analysis code validation representation (At least see FIG. 1[Wingdings font/0xE0]104 with associated text (validation process is executed before the validated code is being executed at 105) [emphasis added]);
providing, for display within a compatibility graphical user interface (At least see [0155] - observability dashboard provides visibility into infrastructure performance by, for example, displaying error rates, [0168] - a dashboard is transmitted for display in a user interface, the dashboard indicating one or more metrics relating to a status …), a compatibility element indicating the experiment pre-runtime compatibility metric of the data analysis model relative to the experiment design representing whether the one or more experiment process components of the experiment design satisfy one or more conditions of the one or more analysis code validation components of the data analysis model (At least see FIG. 1[Wingdings font/0xE0]104 with associated text, and [0020] - generative AI component of the system generates target data model and code that will be compatible with modern, cloud-native platforms, speeding up the transformation process. [0021] Code Validation: The system leverages the generative AI component to generate needed test/mock data. The generated code is then tested for functional accuracy and performance using the test data to validate migration code and initiate data migration; also see [0171] - a large language model used for legacy system analysis and code generation, validation software, and a data store or repository).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Rajagopalan into Wang’s invention because Rajagopalan’s teaching provides Artificial Intelligence (AI) infused legacy application transformation system combines the power of Large Language Models (LLMs) and Generative AI (Gen-AI), and this cutting-edge approach enables the seamless modernization of outdated applications, ensuring they are optimized for current technologies and platforms while significantly minimizing the time and effort required (please see ¶[0182]).
Per claim 8:
Rajagopalan also discloses:
generate experiment pre-runtime the compatibility metric by generating a positive experiment pre-runtime compatibility metric based on determining the one or more experiment process components satisfy the one or more analysis code validation components from the analysis code validation representation (At least see [0117] - examining the existing interfaces based on the analysis of the code artifacts and mapping them to the identified domains. This ensures that the correct schema design is generated based on the characteristics and data elements associated with the identified domain).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Rajagopalan into Wang’s invention because Rajagopalan’s teaching provides Artificial Intelligence (AI) infused legacy application transformation system combines the power of Large Language Models (LLMs) and Generative AI (Gen-AI), and this cutting-edge approach enables the seamless modernization of outdated applications, ensuring they are optimized for current technologies and platforms while significantly minimizing the time and effort required (please see ¶[0182]).
Per claim 9:
Rajagopalan discloses:
in response to a negative experiment pre-runtime compatibility metric, identifying a modified experiment design comprising one or more modified experiment process components based on additional user interactions with one or more additional experiment design elements (At least see [0139] - Users can further add custom logic and can correct any errors or incomplete aspects of the code to ensure the generated code aligns with a desired functionality); and
providing, for display within the compatibility graphical user interface, an additional compatibility element indicating an additional experiment pre-runtime compatibility metric of the data analysis model relative to the modified experiment design (At least see [0117] - examining the existing interfaces based on the analysis of the code artifacts and mapping them to the identified domains. This ensures that the correct schema design is generated based on the characteristics and data elements associated with the identified domain).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Rajagopalan into Wang’s invention because Rajagopalan’s teaching provides Artificial Intelligence (AI) infused legacy application transformation system combines the power of Large Language Models (LLMs) and Generative AI (Gen-AI), and this cutting-edge approach enables the seamless modernization of outdated applications, ensuring they are optimized for current technologies and platforms while significantly minimizing the time and effort required (please see ¶[0182]).
Per claim 10:
Wang discloses:
A system (At least see page 1:29-30 a method and system for developing for an automation process a screen viewable on a target computer that has user interface objects) comprising:
at least one processor; and
at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:
provide, for display via an experiment design user interface, one or more experiment design selection elements and one or more data analysis model selection elements (At least see page 4:17-19 - a common software development tool comprises a set of common design time components, the common design time component with data binding device);
in response to a user interaction with the one or more experiment design selection elements and the one or more data analysis model selection elements (At least see page 5:1-2 - provide a process object selector, which acts as a user interface to data binding device of to allow), identify an experiment design comprising one or more experiment process components and a data analysis model (At least see page 11:10-11 - process object selector 309 acts as custom data binding of user interface so that the user can easily browse the automation process data objects can be used 301).
Wang sufficiently discloses the claimed limitations as set forth above, but Wang does not explicitly disclose: generate an analysis code validation representation by extracting one or more analysis code validation components from the data analysis model; generate a compatibility metric by comparing the one or more experiment process components and the analysis code validation representation; provide, for display within a compatibility graphical user interface, a compatibility element indicating the experiment pre-runtime compatibility metric of the data analysis model relative to the experiment design representing whether the one or more experiment process components of the experiment design satisfy one or more conditions of the one or more analysis code validation components of the data analysis model.
However, Rajagopalan discloses:
generate an analysis code validation representation by extracting one or more analysis code validation components from the data analysis model (At least see ¶[0020] - generative AI component of the system generates target data model and code that will be compatible with modern, cloud-native platforms, speeding up the transformation process and ¶[0021] Code Validation: The system leverages the generative AI component to generate needed test/mock data. The generated code is then tested for functional accuracy and performance using the test data to validate migration code);
generate an experiment pre-runtime compatibility metric by comparing the one or more experiment process components and the analysis code validation representation (At least see FIG. 1[Wingdings font/0xE0]104 with associated text (validation process is executed before the validated code is being executed at 105) [emphasis added]);
provide, for display within a compatibility graphical user interface (At least see [0155] -AI to build target Infrastructure as a Code (IaC) scripts by interpreting high-level requirements into specific configurations and to generate and transmit an observability dashboard with interfaces to a client/user dashboard/interface. The observability dashboard provides visibility into infrastructure performance by, for example, displaying error rates), a compatibility element indicating the experiment pre-runtime compatibility metric of the data analysis model relative to the experiment design representing whether the one or more experiment process components of the experiment design satisfy one or more conditions of the one or more analysis code validation components of the data analysis model (At least see FIG. 1[Wingdings font/0xE0]104 with associated text, and [0020] - generative AI component of the system generates target data model and code that will be compatible with modern, cloud-native platforms, speeding up the transformation process. [0021] Code Validation: The system leverages the generative AI component to generate needed test/mock data. The generated code is then tested for functional accuracy and performance using the test data to validate migration code and initiate data migration; also see [0171] - a large language model used for legacy system analysis and code generation, validation software, and a data store or repository).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Rajagopalan into Wang’s invention because Rajagopalan’s teaching provides Artificial Intelligence (AI) infused legacy application transformation system combines the power of Large Language Models (LLMs) and Generative AI (Gen-AI), and this cutting-edge approach enables the seamless modernization of outdated applications, ensuring they are optimized for current technologies and platforms while significantly minimizing the time and effort required (please see ¶[0182]).
Per claim 14:
Rajagopalan also discloses:
generate experiment pre-runtime the compatibility metric by generating a positive experiment pre-runtime compatibility metric based on determining the one or more experiment process components satisfy the one or more analysis code validation components from the analysis code validation representation (At least see [0117] - examining the existing interfaces based on the analysis of the code artifacts and mapping them to the identified domains. This ensures that the correct schema design is generated based on the characteristics and data elements associated with the identified domain).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Rajagopalan into Wang’s invention because Rajagopalan’s teaching provides Artificial Intelligence (AI) infused legacy application transformation system combines the power of Large Language Models (LLMs) and Generative AI (Gen-AI), and this cutting-edge approach enables the seamless modernization of outdated applications, ensuring they are optimized for current technologies and platforms while significantly minimizing the time and effort required (please see ¶[0182]).
Per claim 15:
Rajagopalan discloses:
in response to a negative experiment pre-runtime compatibility metric, identifying a modified experiment design comprising one or more modified experiment process components based on additional user interactions with one or more additional experiment design elements (At least see [0139] - Users can further add custom logic and can correct any errors or incomplete aspects of the code to ensure the generated code aligns with a desired functionality); and
providing, for display within the compatibility graphical user interface, an additional compatibility element indicating an additional experiment pre-runtime compatibility metric of the data analysis model relative to the modified experiment design (At least see [0117] - examining the existing interfaces based on the analysis of the code artifacts and mapping them to the identified domains. This ensures that the correct schema design is generated based on the characteristics and data elements associated with the identified domain).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Rajagopalan into Wang’s invention because Rajagopalan’s teaching provides Artificial Intelligence (AI) infused legacy application transformation system combines the power of Large Language Models (LLMs) and Generative AI (Gen-AI), and this cutting-edge approach enables the seamless modernization of outdated applications, ensuring they are optimized for current technologies and platforms while significantly minimizing the time and effort required (please see ¶[0182]).
Per claim 16:
Wang discloses:
A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computing device to:
provide, for display via an experiment design user interface, one or more experiment design selection elements and one or more data analysis model selection elements (At least see page 4:17-19 - a common software development tool comprises a set of common design time components, the common design time component with data binding device);
in response to a user interaction with the one or more experiment design selection elements and the one or more data analysis model selection elements (At least see page 5:1-2 - provide a process object selector, which acts as a user interface to data binding device of to allow), identify an experiment design comprising one or more experiment process components and a data analysis model (At least see page 11:10-11 - process object selector 309 acts as custom data binding of user interface so that the user can easily browse the automation process data objects can be used 301).
Wang sufficiently discloses the claimed limitations as set forth above, but Wang does not explicitly disclose: generate an analysis code validation representation by extracting one or more analysis code validation components from the data analysis model; generate a compatibility metric by comparing the one or more experiment process components and the analysis code validation representation; provide, for display within a compatibility graphical user interface, a compatibility element indicating the experiment pre-runtime compatibility metric of the data analysis model relative to the experiment design representing whether the one or more experiment process components of the experiment design satisfy one or more conditions of the one or more analysis code validation components of the data analysis model.
However, Rajagopalan discloses:
generate an analysis code validation representation by extracting one or more analysis code validation components from the data analysis model (At least see ¶[0020] - generative AI component of the system generates target data model and code that will be compatible with modern, cloud-native platforms, speeding up the transformation process and ¶[0021] Code Validation: The system leverages the generative AI component to generate needed test/mock data. The generated code is then tested for functional accuracy and performance using the test data to validate migration code);
generate an experiment pre-runtime compatibility metric by comparing the one or more experiment process components and the analysis code validation representation (At least see FIG. 1[Wingdings font/0xE0]104 with associated text (validation process is executed before the validated code is being executed at 105) [emphasis added]);
provide, for display within a compatibility graphical user interface (At least see [0155] -AI to build target Infrastructure as a Code (IaC) scripts by interpreting high-level requirements into specific configurations and to generate and transmit an observability dashboard with interfaces to a client/user dashboard/interface. The observability dashboard provides visibility into infrastructure performance by, for example, displaying error rates), a compatibility element indicating the experiment pre-runtime compatibility metric of the data analysis model relative to the experiment design representing whether the one or more experiment process components of the experiment design satisfy one or more conditions of the one or more analysis code validation components of the data analysis model (At least see FIG. 1[Wingdings font/0xE0]104 with associated text, and [0020] - generative AI component of the system generates target data model and code that will be compatible with modern, cloud-native platforms, speeding up the transformation process. [0021] Code Validation: The system leverages the generative AI component to generate needed test/mock data. The generated code is then tested for functional accuracy and performance using the test data to validate migration code and initiate data migration; also see [0171] - a large language model used for legacy system analysis and code generation, validation software, and a data store or repository).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Rajagopalan into Wang’s invention because Rajagopalan’s teaching provides Artificial Intelligence (AI) infused legacy application transformation system combines the power of Large Language Models (LLMs) and Generative AI (Gen-AI), and this cutting-edge approach enables the seamless modernization of outdated applications, ensuring they are optimized for current technologies and platforms while significantly minimizing the time and effort required (please see ¶[0182]).
Per claim 19:
Rajagopalan discloses:
in response to a negative experiment pre-runtime compatibility metric, identifying a modified experiment design comprising one or more modified experiment process components based on additional user interactions with one or more additional experiment design elements (At least see [0139] - Users can further add custom logic and can correct any errors or incomplete aspects of the code to ensure the generated code aligns with a desired functionality); and
providing, for display within the compatibility graphical user interface, an additional compatibility element indicating an additional experiment pre-runtime compatibility metric of the data analysis model relative to the modified experiment design (At least see [0117] - examining the existing interfaces based on the analysis of the code artifacts and mapping them to the identified domains. This ensures that the correct schema design is generated based on the characteristics and data elements associated with the identified domain).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Rajagopalan into Wang’s invention because Rajagopalan’s teaching provides Artificial Intelligence (AI) infused legacy application transformation system combines the power of Large Language Models (LLMs) and Generative AI (Gen-AI), and this cutting-edge approach enables the seamless modernization of outdated applications, ensuring they are optimized for current technologies and platforms while significantly minimizing the time and effort required (please see ¶[0182]).
Per claim 20:
Rajagopalan also discloses:
generate experiment pre-runtime the compatibility metric by generating a positive experiment pre-runtime compatibility metric based on determining the one or more experiment process components satisfy the one or more analysis code validation components from the analysis code validation representation (At least see [0117] - examining the existing interfaces based on the analysis of the code artifacts and mapping them to the identified domains. This ensures that the correct schema design is generated based on the characteristics and data elements associated with the identified domain).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Rajagopalan into Wang’s invention because Rajagopalan’s teaching provides Artificial Intelligence (AI) infused legacy application transformation system combines the power of Large Language Models (LLMs) and Generative AI (Gen-AI), and this cutting-edge approach enables the seamless modernization of outdated applications, ensuring they are optimized for current technologies and platforms while significantly minimizing the time and effort required (please see ¶[0182]).
6. Claims 2-7, , 11-13 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (CN 102124445 A) in view of Rajagopalan et al. (US 2025/0156384 A1), and further in view of Saini (US 20250190983 A1).
Per claim 2:
Wang modified by Rajagopalan discloses the method as set forth above, but Wang modified by Rajagopalan does not explicitly disclose: generating the analysis code validation representation by generating a directed acyclic graph for the data analysis model comprising at least one analysis model task node, at least one analysis code validation component node from the one or more analysis code validation components, and at least one dependent analysis model task node with an edge from the at least one analysis model task node.
However, Saini discloses:
generating the analysis code validation representation by generating a directed acyclic graph for the data analysis model comprising at least one analysis model task node, at least one analysis code validation component node from the one or more analysis code validation components, and at least one dependent analysis model task node with an edge from the at least one analysis model task node (At least see ¶[0037] - in a user interface 700 that may be produced by the insight compute device 110, a view of the data analysis model from FIG. 6 has been shifted such that further nodes 720, 722 are visible in section 710. Nodes 720, 722 feed into node 622, which is also shown in FIG. 6. In the user interface 700, the node 722 has been selected and contributing cohort information and anomaly detection information pertaining to the population associated with the selected node 722 is displayed in corresponding sections 712, 714. The user interface 800 of FIG. 8 represents another data analysis model that may be managed by a user).
It would have been obvious to one ordinary skill in the art before the affective filing date of the claimed invention to incorporate Saini into Wang modified by Rajagopalan because Saini’s invention would provide a user interface that may be provided by the insight compute device includes a section in which an analysis tree may be constructed and edited, wherein the analysis tree includes multiple nodes (e.g., segments), and segments of a population associated each of the nodes are aggregated into a total population represented by the node; as such the sections details regarding the selected node, information regarding contributing cohorts are displayed, which the contributing cohorts is displayed in a table format, with one or more sortable columns (please see ¶[0036]).
Per claim 3:
Saini also discloses:
comparing the one or more experiment process components and the analysis code validation representation by comparing the at least one analysis code validation component node, of the at least one analysis model task node, to the one or more experiment process components to generate the compatibility metric (At least see ¶[0039] -in response to user selection of an icon or other user interface element to edit a node of the analysis tree. In the window 1010, the user may edit the name of the node and define aggregation logic. The aggregation logic, in the illustrative embodiment, defines the nodes that feed into the selected node. Referring to FIG. 11, a user interface 1100 that may be provided by the insight compute device 110 includes a window 1110 that enables a user to edit the analysis settings associated with a data analysis model).
It would have been obvious to one ordinary skill in the art before the affective filing date of the claimed invention to incorporate Saini into Wang modified by Rajagopalan because Saini’s invention would provide a user interface that may be provided by the insight compute device includes a section in which an analysis tree may be constructed and edited, wherein the analysis tree includes multiple nodes (e.g., segments), and segments of a population associated each of the nodes are aggregated into a total population represented by the node; as such the sections details regarding the selected node, information regarding contributing cohorts are displayed, which the contributing cohorts is displayed in a table format, with one or more sortable columns (please see ¶[0036]).
Per claim 4:
Saini also discloses:
upon determining the one or more experiment process components do not satisfy one or more conditions of the at least one analysis code validation component node, generating a negative compatibility metric for the at least one analysis model task node (At least see ¶[0039] - user interface elements to enable the user to specify an analysis ID, a name, a goal, a product family to which the data analysis model pertains, one or more data sources utilized by the data analysis model, metrics represented in the data analysis model, and cohorts (e.g., attributes of interest) utilized by the data analysis model for clustering and cohort analysis).
It would have been obvious to one ordinary skill in the art before the affective filing date of the claimed invention to incorporate Saini into Wang modified by Rajagopalan because Saini’s invention would provide a user interface that may be provided by the insight compute device includes a section in which an analysis tree may be constructed and edited, wherein the analysis tree includes multiple nodes (e.g., segments), and segments of a population associated each of the nodes are aggregated into a total population represented by the node; as such the sections details regarding the selected node, information regarding contributing cohorts are displayed, which the contributing cohorts is displayed in a table format, with one or more sortable columns (please see ¶[0036]).
Per claim 5:
Saini also discloses:
disabling the at least one analysis model task node based on the negative compatibility metric (At least see ¶[0039] - data analysis model pertains, one or more data sources utilized by the data analysis model, metrics represented in the data analysis model).
It would have been obvious to one ordinary skill in the art before the affective filing date of the claimed invention to incorporate Saini into Wang modified by Rajagopalan because Saini’s invention would provide a user interface that may be provided by the insight compute device includes a section in which an analysis tree may be constructed and edited, wherein the analysis tree includes multiple nodes (e.g., segments), and segments of a population associated each of the nodes are aggregated into a total population represented by the node; as such the sections details regarding the selected node, information regarding contributing cohorts are displayed, which the contributing cohorts is displayed in a table format, with one or more sortable columns (please see ¶[0036]).
Per claim 6:
Saini also discloses:
executing the experiment design with the at least one analysis model task node disabled and an additional analysis model task node from the analysis code validation representation enabled (At least see ¶[0035] - User interface element 532 enables a user to enter a term to search for within the set of data analysis models 510, 512, 514, 516, 518, 520. In response to detecting a selection of the user interface element 540, the insight compute device 110 provides a user interface to enable a user to design a new data analysis model).
It would have been obvious to one ordinary skill in the art before the affective filing date of the claimed invention to incorporate Saini into Wang modified by Rajagopalan because Saini’s invention would provide a user interface that may be provided by the insight compute device includes a section in which an analysis tree may be constructed and edited, wherein the analysis tree includes multiple nodes (e.g., segments), and segments of a population associated each of the nodes are aggregated into a total population represented by the node; as such the sections details regarding the selected node, information regarding contributing cohorts are displayed, which the contributing cohorts is displayed in a table format, with one or more sortable columns (please see ¶[0036]).
Per claim 7:
Saini also discloses:
providing, for display within the compatibility graphical user interface, the compatibility element indicating an analysis task corresponding to the at least one analysis model task node as incompatible relative to the experiment design with a description representing the one or more conditions of the at least one analysis code validation component node (At least see ¶[0037] - in a user interface 700 that may be produced by the insight compute device 110, a view of the data analysis model from FIG. 6 has been shifted such that further nodes 720, 722 are visible in section 710. Nodes 720, 722 feed into node 622, which is also shown in FIG. 6. In the user interface 700, the node 722 has been selected and contributing cohort information and anomaly detection information pertaining to the population associated with the selected node 722 is displayed in corresponding sections 712, 714. The user interface 800 of FIG. 8 represents another data analysis model that may be managed by a user. As shown, in the section 812, cohorts (e.g., attributes of interest) may be identified by the insight compute device 110 through machine learning (e.g., through the execution of one or more machine learning algorithms). As such, the user may rely on the insight compute device 110 to identify attributes of interest).
It would have been obvious to one ordinary skill in the art before the affective filing date of the claimed invention to incorporate Saini into Wang modified by Rajagopalan because Saini’s invention would provide a user interface that may be provided by the insight compute device includes a section in which an analysis tree may be constructed and edited, wherein the analysis tree includes multiple nodes (e.g., segments), and segments of a population associated each of the nodes are aggregated into a total population represented by the node; as such the sections details regarding the selected node, information regarding contributing cohorts are displayed, which the contributing cohorts is displayed in a table format, with one or more sortable columns (please see ¶[0036]).
Per claim 11:
Wang modified by Rajagopalan discloses the system as set forth above, but Wang modified by Rajagopalan does not explicitly disclose: generate the analysis code validation representation by generating a directed acyclic graph for the data analysis model comprising at least one analysis model task node, at least one analysis code validation component node from the one or more analysis code validation components, and at least one dependent analysis model task node with an edge from the at least one analysis model task node.
However, Saini discloses:
generate the analysis code validation representation by generating a directed acyclic graph for the data analysis model comprising at least one analysis model task node, at least one analysis code validation component node from the one or more analysis code validation components, and at least one dependent analysis model task node with an edge from the at least one analysis model task node (At least see ¶[0037] - in a user interface 700 that may be produced by the insight compute device 110, a view of the data analysis model from FIG. 6 has been shifted such that further nodes 720, 722 are visible in section 710. Nodes 720, 722 feed into node 622, which is also shown in FIG. 6. In the user interface 700, the node 722 has been selected and contributing cohort information and anomaly detection information pertaining to the population associated with the selected node 722 is displayed in corresponding sections 712, 714. The user interface 800 of FIG. 8 represents another data analysis model that may be managed by a user).
It would have been obvious to one ordinary skill in the art before the affective filing date of the claimed invention to incorporate Saini into Wang modified by Rajagopalan because Saini’s invention would provide a user interface that may be provided by the insight compute device includes a section in which an analysis tree may be constructed and edited, wherein the analysis tree includes multiple nodes (e.g., segments), and segments of a population associated each of the nodes are aggregated into a total population represented by the node; as such the sections details regarding the selected node, information regarding contributing cohorts are displayed, which the contributing cohorts is displayed in a table format, with one or more sortable columns (please see ¶[0036]).
Per claim 12:
Saini also discloses:
comparing the one or more experiment process components and the analysis code validation representation by comparing the at least one analysis code validation component node, of the at least one analysis model task node, to the one or more experiment process components to generate the compatibility metric (At least see ¶[0039] -in response to user selection of an icon or other user interface element to edit a node of the analysis tree. In the window 1010, the user may edit the name of the node and define aggregation logic. The aggregation logic, in the illustrative embodiment, defines the nodes that feed into the selected node. Referring to FIG. 11, a user interface 1100 that may be provided by the insight compute device 110 includes a window 1110 that enables a user to edit the analysis settings associated with a data analysis model);
upon determining the one or more experiment process components do not satisfy one or more conditions of the at least one analysis code validation component node, generating a negative compatibility metric for the at least one analysis model task node (At least see ¶[0039] - user interface elements to enable the user to specify an analysis ID, a name, a goal, a product family to which the data analysis model pertains, one or more data sources utilized by the data analysis model, metrics represented in the data analysis model, and cohorts (e.g., attributes of interest) utilized by the data analysis model for clustering and cohort analysis).
It would have been obvious to one ordinary skill in the art before the affective filing date of the claimed invention to incorporate Saini into Wang modified by Rajagopalan because Saini’s invention would provide a user interface that may be provided by the insight compute device includes a section in which an analysis tree may be constructed and edited, wherein the analysis tree includes multiple nodes (e.g., segments), and segments of a population associated each of the nodes are aggregated into a total population represented by the node; as such the sections details regarding the selected node, information regarding contributing cohorts are displayed, which the contributing cohorts is displayed in a table format, with one or more sortable columns (please see ¶[0036]).
Per claim 13:
Saini also discloses:
provide, for display within the compatibility graphical user interface, the compatibility element indicating an analysis task corresponding to the at least one analysis model task node as incompatible relative to the experiment design with a description representing the one or more conditions of the at least one analysis code validation component node (At least see ¶[0037] - in a user interface 700 that may be produced by the insight compute device 110, a view of the data analysis model from FIG. 6 has been shifted such that further nodes 720, 722 are visible in section 710. Nodes 720, 722 feed into node 622, which is also shown in FIG. 6. In the user interface 700, the node 722 has been selected and contributing cohort information and anomaly detection information pertaining to the population associated with the selected node 722 is displayed in corresponding sections 712, 714. The user interface 800 of FIG. 8 represents another data analysis model that may be managed by a user. As shown, in the section 812, cohorts (e.g., attributes of interest) may be identified by the insight compute device 110 through machine learning (e.g., through the execution of one or more machine learning algorithms). As such, the user may rely on the insight compute device 110 to identify attributes of interest).
It would have been obvious to one ordinary skill in the art before the affective filing date of the claimed invention to incorporate Saini into Wang modified by Rajagopalan because Saini’s invention would provide a user interface that may be provided by the insight compute device includes a section in which an analysis tree may be constructed and edited, wherein the analysis tree includes multiple nodes (e.g., segments), and segments of a population associated each of the nodes are aggregated into a total population represented by the node; as such the sections details regarding the selected node, information regarding contributing cohorts are displayed, which the contributing cohorts is displayed in a table format, with one or more sortable columns (please see ¶[0036]).
Per claim 17:
Wang modified by Rajagopalan discloses the system as set forth above, but Wang modified by Rajagopalan does not explicitly disclose: generate the analysis code validation representation by generating a directed acyclic graph for the data analysis model comprising at least one analysis model task node, at least one analysis code validation component node from the one or more analysis code validation components, and at least one dependent analysis model task node with an edge from the at least one analysis model task node.
However, Saini discloses:
generate the analysis code validation representation by generating a directed acyclic graph for the data analysis model comprising at least one analysis model task node, at least one analysis code validation component node from the one or more analysis code validation components, and at least one dependent analysis model task node with an edge from the at least one analysis model task node (At least see ¶[0037] - in a user interface 700 that may be produced by the insight compute device 110, a view of the data analysis model from FIG. 6 has been shifted such that further nodes 720, 722 are visible in section 710. Nodes 720, 722 feed into node 622, which is also shown in FIG. 6. In the user interface 700, the node 722 has been selected and contributing cohort information and anomaly detection information pertaining to the population associated with the selected node 722 is displayed in corresponding sections 712, 714. The user interface 800 of FIG. 8 represents another data analysis model that may be managed by a user).
It would have been obvious to one ordinary skill in the art before the affective filing date of the claimed invention to incorporate Saini into Wang modified by Rajagopalan because Saini’s invention would provide a user interface that may be provided by the insight compute device includes a section in which an analysis tree may be constructed and edited, wherein the analysis tree includes multiple nodes (e.g., segments), and segments of a population associated each of the nodes are aggregated into a total population represented by the node; as such the sections details regarding the selected node, information regarding contributing cohorts are displayed, which the contributing cohorts is displayed in a table format, with one or more sortable columns (please see ¶[0036]).
Per claim 18:
Saini also discloses:
generate the compatibility metric by generating a negative compatibility metric based on determining the one or more experiment process components do not satisfy the one or more analysis code validation components from the analysis code validation representation (At least see ¶[0039] - user interface elements to enable the user to specify an analysis ID, a name, a goal, a product family to which the data analysis model pertains, one or more data sources utilized by the data analysis model, metrics represented in the data analysis model, and cohorts (e.g., attributes of interest) utilized by the data analysis model for clustering and cohort analysis).
It would have been obvious to one ordinary skill in the art before the affective filing date of the claimed invention to incorporate Saini into Wang modified by Rajagopalan because Saini’s invention would provide a user interface that may be provided by the insight compute device includes a section in which an analysis tree may be constructed and edited, wherein the analysis tree includes multiple nodes (e.g., segments), and segments of a population associated each of the nodes are aggregated into a total population represented by the node; as such the sections details regarding the selected node, information regarding contributing cohorts are displayed, which the contributing cohorts is displayed in a table format, with one or more sortable columns (please see ¶[0036]).
CONCLUSION
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZIAUL A. CHOWDHURY whose telephone number is (571)270-7750. The examiner can normally be reached on 9:30PM 6:30PM Monday -Friday.
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/ZIAUL A CHOWDHURY/ Primary Examiner, Art Unit 2192
01/08/2026