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 November 18th, 2025 in response to the September 9th, 2025 Office Action provided in the rejection of claims 1-20.
Status of Claims
2. Claims 1-20 have been amended. Claims 1-20 are pending in this application, of which claims 1, 9 and 17 are in independent form and these claims (1-20) are subject to following rejection(s) and/or objection(s) set forth in the following Office Action below.
Response to the Amendments
3. (A). 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.
(B). Finality: Applicant's arguments filed November 18th, 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
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
4. Claims 1-2, 5-10 and 13-18 are rejected under 35 U.S.C. 103 as being unpatentable over Shapiro et al. (US PAT. No. 11,922,143 B1 herein after Shapiro) in view of Hession et al. (US PAT. No. 10,445,683 B1 herein after Hession), and further in view of Sarkisian et al. (US Patent Application Publication No. 2020/0279364 A1 herein after Sarkisian).
Per claim 1:
Shapiro discloses:
An apparatus comprising: a processor that executes instructions in a memory to configure the processor to (At least see Col. 1:42-44 system may include electronic storage, one or more hardware processors configured by machine-readable instructions):
generate a query for display on a user device via a user interface of a user profile page in a software application hosted by the host platform (At least see Col. 9:39-42 -application development interface may include one or more user interface fields configured to receive selection by the developers of application template, also see Col. 3:62-63 -user interface of the client computing platform 102 may display one or more images and/or other content),
generate a prompt comprising the query and a response to the query received from the user device (At least see Col. 3:27-29 -response records may include a first response record for a first user. The first response record may include responses provided by the first user to prompts); and
display the design on the user interface (At least see Col. 3:62-66 -user interface of the client computing platform 102 may display one or more images and/or other content corresponding to the provided prompt (e.g., two or more images depicting different interior design styles), and/or other user interface elements).
Shapiro discloses the system as set forth above, but Shapiro does not explicitly discloses: ingest data from one or more websites via one or more application programming interfaces (APIs) and store the data within a data store of a host platform; generate a query for display on a user device via a user interface of a user profile page in a software application hosted by the host platform.
However, Hession disclose:
ingest data from a websites via an application programming interfaces (APIs); store the data in a data store of a host platform (At least see Col. 10:48-52 -scraping (harvesting or extracting) the requested data from websites or by interfacing with the delivery service computer via an application programming interface (API). Such information may be stored in a database as a database data structure).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hession into Shapiro’s invention because Hession’s teaching would provide data acquisition and processing module includes an extraction module configured to extract the source data from the plurality of delivery service computers as raw files, a mapping module configured to convert the raw files to a standardized format to provide formatted data, a linking module configured to perform record linkage on the formatted data according to identification data (see Col. 5:42-48).
Shapiro modified by Hession sufficiently discloses the system as set forth above, Shapiro modified by Hession does not explicitly disclose: generate a query for display on a user device via a user interface of a user profile page in a software application hosted by the host platform.
However, Sarkisian discloses:
generate a query for display on a user device via a user interface of a user profile page in a software application hosted by the host platform (At least see (At least see ¶[0013] -machine learning models and one or more post-processing algorithms, wherein, the one or more machine learning models are pre-trained to process the data in the database to evaluate performance or design of a structure from images, point cloud data).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Sarkisian into Shapiro modified by Hession because Sarkisian provides a new machine learning tool for assessing performance of structures, identifying entireties or portions of structures from images or drawings, assessing damage to structures including a user interface via which information can be output to a user and via which information and data can be input by the user to evaluate a design of the structure (see [0010] and [0023]).
Per claim 2:
Sarkisian also discloses:
wherein the AI model is trained on a plurality of images of different structures that are ingested from the website via the API (At least see ¶[0013] -one or more post-processing algorithms, wherein, the one or more machine learning models are pre-trained to process the data in the database to evaluate performance or design of a structure from images, point cloud data, or three-dimensional representations or drawings thereof).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Sarkisian into Shapiro modified by Hession because Sarkisian provides a new machine learning tool for assessing performance of structures, identifying entireties or portions of structures from images or drawings, assessing damage to structures including a user interface via which information can be output to a user and via which information and data can be input by the user to evaluate a design of the structure (see [0010] and [0023]).
Per claim 5:
Hession also disclose:
a numerical value associated with an objective identified by the user profile page and the structure (At least see Col. 16:58-62 - website's database 42 for a restaurant's full information and current menu (step 124) based on the user's established location, the selected restaurant, and the time of day, and it retrieves full restaurant information results).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hession into Shapiro’s invention because Hession’s teaching would provide data acquisition and processing module includes an extraction module configured to extract the source data from the plurality of delivery service computers as raw files, a mapping module configured to convert the raw files to a standardized format to provide formatted data, a linking module configured to perform record linkage on the formatted data according to identification data (see Col. 5:42-48).
Per claim 6:
Hession also disclose:
simultaneously display the design and numerical value via the user interface (At least see Col. 9:41-42 - combined data can be viewed on the page simultaneously).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hession into Shapiro’s invention because Hession’s teaching would provide data acquisition and processing module includes an extraction module configured to extract the source data from the plurality of delivery service computers as raw files, a mapping module configured to convert the raw files to a standardized format to provide formatted data, a linking module configured to perform record linkage on the formatted data according to identification data (see Col. 5:42-48).
Per claim 7:
Hession also disclose:
using the AI model, determine a timeline for obtaining the numerical value (At least see Col. 17:51-54 - delivery service's API (step 156) (e.g., for determining the delivery fee and/or tax on the fly). A delivery time can be estimated, for instance, based on historical data specific to a particular delivery service).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hession into Shapiro’s invention because Hession’s teaching would provide data acquisition and processing module includes an extraction module configured to extract the source data from the plurality of delivery service computers as raw files, a mapping module configured to convert the raw files to a standardized format to provide formatted data, a linking module configured to perform record linkage on the formatted data according to identification data (see Col. 5:42-48).
Per claim 8:
Sarkisian discloses:
wherein the design is a three-dimensional (3D) image, and wherein the processor is further configured to: move a field of view of the 3D image (At least see ¶[0021] - a three-dimensional digital model of the identified elements by grouping the pixels or points for each class identified by the one or more machine learning models and converting them into two-dimensional lines or three-dimensional components with the lines being created by reducing the groups of pixels or points down to lines or polylines running through a center of that pixel or point group).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Sarkisian into Shapiro modified by Hession because Sarkisian provides a new machine learning tool for assessing performance of structures, identifying entireties or portions of structures from images or drawings, assessing damage to structures including a user interface via which information can be output to a user and via which information and data can be input by the user to evaluate a design of the structure (see [0010] and [0023]).
Per claim 9:
Shapiro discloses:
A method comprising (At least see Col. 1:7-8 - systems and methods for providing a user interface that facilitates application development), the processor configured to:
generating a query for display on a user device via a user interface of a user profile page in a software application hosted by the host platform (At least see Col. 9:39-42 -application development interface may include one or more user interface fields configured to receive selection by the developers of application template, also see Col. 3:62-63 -user interface of the client computing platform 102 may display one or more images and/or other content),
generating a prompt comprising the query and a response to the query received from the user device (At least see Col. 3:27-29 -response records may include a first response record for a first user. The first response record may include responses provided by the first user to prompts); and
displaying the design on the user interface (At least see Col. 3:62-66 -user interface of the client computing platform 102 may display one or more images and/or other content corresponding to the provided prompt (e.g., two or more images depicting different interior design styles), and/or other user interface elements).
Shapiro discloses the system as set forth above, but Shapiro does not explicitly disclose: ingesting data from one or more websites via one or more application programming interfaces (APIs) and store the data within a data store of a host platform; generatjing a query for display on a user device via a user interface of a user profile page in a software application hosted by the host platform.
However, Hession disclose:
ingesting data from a websites via an application programming interfaces (APIs); store the data in a data store of a host platform (At least see Col. 10:48-52 -scraping (harvesting or extracting) the requested data from websites or by interfacing with the delivery service computer via an application programming interface (API). Such information may be stored in a database as a database data structure).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hession into Shapiro’s invention because Hession’s teaching would provide data acquisition and processing module includes an extraction module configured to extract the source data from the plurality of delivery service computers as raw files, a mapping module configured to convert the raw files to a standardized format to provide formatted data, a linking module configured to perform record linkage on the formatted data according to identification data (see Col. 5:42-48).
Shapiro modified by Hession sufficiently discloses the system as set forth above, Shapiro modified by Hession does not explicitly disclose: generating a query for display on a user device via a user interface of a user profile page in a software application hosted by the host platform.
However, Sarkisian discloses:
generating a query for display on a user device via a user interface of a user profile page in a software application hosted by the host platform (At least see (At least see ¶[0013] -machine learning models and one or more post-processing algorithms, wherein, the one or more machine learning models are pre-trained to process the data in the database to evaluate performance or design of a structure from images, point cloud data).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Sarkisian into Shapiro modified by Hession because Sarkisian provides a new machine learning tool for assessing performance of structures, identifying entireties or portions of structures from images or drawings, assessing damage to structures including a user interface via which information can be output to a user and via which information and data can be input by the user to evaluate a design of the structure (see [0010] and [0023]).
Per claim 10:
Sarkisian also discloses:
wherein the AI model is trained on a plurality of images of different structures that are ingested from the website via the API (At least see ¶[0013] -one or more post-processing algorithms, wherein, the one or more machine learning models are pre-trained to process the data in the database to evaluate performance or design of a structure from images, point cloud data, or three-dimensional representations or drawings thereof).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Sarkisian into Shapiro modified by Hession because Sarkisian provides a new machine learning tool for assessing performance of structures, identifying entireties or portions of structures from images or drawings, assessing damage to structures including a user interface via which information can be output to a user and via which information and data can be input by the user to evaluate a design of the structure (see [0010] and [0023]).
Per claim 13:
Shapiro disclose:
generating images of an interior of the structure based on execution of the AI model on images of interiors of other structures which are ingested from the one or more websites, and display the images of the interior as thumbnails within the user profile page of the software application (At least see Col. 3:62-67 - user interface of the client computing platform 102 may display one or more images and/or other content corresponding to the provided prompt (e.g., two or more images depicting different interior design styles), and/or other user interface elements. In some implementations, the one or more images and/or other content may be displayed simultaneously with the prompt).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Shapiro into Sarkisian modified by Hession because machine learning provides developers with aa powerful tool but also add an additional layer of complexity to application development; as such, Shapiro’s teaching provides solutions to simplify the development of applications with underlying machine-learning models through a no-code or minimal-code application development interface, and resulting applications utilize machine-learning models to perform application functions, and the machine-learning models provide a mechanism to customize outputs for each individual user of the application through a refining process (see Col. 1:22-52).
Per claim 14:
Shapiro disclose:
generating images of an exterior of the structure based on execution of the AI model on images of exteriors of other structures which are ingested from the one or more websites, and display the images of the exterior as thumbnails within the user profile page of the software application (At least see Col. 4:1-12 - response by the first user to the prompt may include selection of an image. The selected image may be stored in the first response record. In some implementations, the selected image may be stored in conjunction with the prompt. Responses within individual response records may be stored in an unstructured format, semi-structured format, and/or other data formats. By way of non-limiting illustration, a response record having an unstructured format may store responses in one or more files without an organizational schema. A response record having a semi-structured format may store responses in a tree-like structure, graph structure, and/or other data structures).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Shapiro into Sarkisian modified by Hession because machine learning provides developers with aa powerful tool but also add an additional layer of complexity to application development; as such, Shapiro’s teaching provides solutions to simplify the development of applications with underlying machine-learning models through a no-code or minimal-code application development interface, and resulting applications utilize machine-learning models to perform application functions, and the machine-learning models provide a mechanism to customize outputs for each individual user of the application through a refining process (see Col. 1:22-52).
Per claim 15:
Hession also disclose:
using the AI model, determine a timeline for obtaining the numerical value (At least see Col. 17:51-54 - delivery service's API (step 156) (e.g., for determining the delivery fee and/or tax on the fly). A delivery time can be estimated, for instance, based on historical data specific to a particular delivery service).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hession into Shapiro’s invention because Hession’s teaching would provide data acquisition and processing module includes an extraction module configured to extract the source data from the plurality of delivery service computers as raw files, a mapping module configured to convert the raw files to a standardized format to provide formatted data, a linking module configured to perform record linkage on the formatted data according to identification data (see Col. 5:42-48).
Per claim 16:
Sarkisian discloses:
wherein the design is a three-dimensional (3D) image, and wherein the processor is further configured to: moving a field of view of the 3D image (At least see ¶[0021] - a three-dimensional digital model of the identified elements by grouping the pixels or points for each class identified by the one or more machine learning models and converting them into two-dimensional lines or three-dimensional components with the lines being created by reducing the groups of pixels or points down to lines or polylines running through a center of that pixel or point group).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Sarkisian into Shapiro modified by Hession because Sarkisian provides a new machine learning tool for assessing performance of structures, identifying entireties or portions of structures from images or drawings, assessing damage to structures including a user interface via which information can be output to a user and via which information and data can be input by the user to evaluate a design of the structure (see [0010] and [0023]).
Per claim 17:
Shapiro discloses:
A computer-readable storage medium comprising instructions stored therein which when executed by a processor cause the processor to (At least see Col. 1:45-47 - machine-readable instructions may cause the one or more hardware processors to facilitate providing a user interface that facilitates application development) perform: generating a query for display on a user device via a user interface of a user profile page in a software application hosted by the host platform (At least see Col. 9:39-42 -application development interface may include one or more user interface fields configured to receive selection by the developers of application template, also see Col. 3:62-63 -user interface of the client computing platform 102 may display one or more images and/or other content),
generating a prompt comprising the query and a response to the query received from the user device (At least see Col. 3:27-29 -response records may include a first response record for a first user. The first response record may include responses provided by the first user to prompts); and
displaying the design on the user interface (At least see Col. 3:62-66 -user interface of the client computing platform 102 may display one or more images and/or other content corresponding to the provided prompt (e.g., two or more images depicting different interior design styles), and/or other user interface elements).
Shapiro discloses the system as set forth above, but Shapiro does not explicitly disclose: ingesting data from one or more websites via one or more application programming interfaces (APIs) and store the data within a data store of a host platform; generatjing a query for display on a user device via a user interface of a user profile page in a software application hosted by the host platform.
However, Hession disclose:
ingesting data from a websites via an application programming interfaces (APIs); store the data in a data store of a host platform (At least see Col. 10:48-52 -scraping (harvesting or extracting) the requested data from websites or by interfacing with the delivery service computer via an application programming interface (API). Such information may be stored in a database as a database data structure).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hession into Shapiro’s invention because Hession’s teaching would provide data acquisition and processing module includes an extraction module configured to extract the source data from the plurality of delivery service computers as raw files, a mapping module configured to convert the raw files to a standardized format to provide formatted data, a linking module configured to perform record linkage on the formatted data according to identification data (see Col. 5:42-48).
Shapiro modified by Hession sufficiently discloses the system as set forth above, Shapiro modified by Hession does not explicitly disclose: generating a query for display on a user device via a user interface of a user profile page in a software application hosted by the host platform.
However, Sarkisian discloses:
generating a query for display on a user device via a user interface of a user profile page in a software application hosted by the host platform (At least see (At least see ¶[0013] -machine learning models and one or more post-processing algorithms, wherein, the one or more machine learning models are pre-trained to process the data in the database to evaluate performance or design of a structure from images, point cloud data).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Sarkisian into Shapiro modified by Hession because Sarkisian provides a new machine learning tool for assessing performance of structures, identifying entireties or portions of structures from images or drawings, assessing damage to structures including a user interface via which information can be output to a user and via which information and data can be input by the user to evaluate a design of the structure (see [0010] and [0023]).
Per claim 18:
Sarkisian also discloses:
wherein the AI model is trained on a plurality of images of different structures that are ingested from the website via the API (At least see ¶[0013] -one or more post-processing algorithms, wherein, the one or more machine learning models are pre-trained to process the data in the database to evaluate performance or design of a structure from images, point cloud data, or three-dimensional representations or drawings thereof).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Sarkisian into Shapiro modified by Hession because Sarkisian provides a new machine learning tool for assessing performance of structures, identifying entireties or portions of structures from images or drawings, assessing damage to structures including a user interface via which information can be output to a user and via which information and data can be input by the user to evaluate a design of the structure (see [0010] and [0023]).
5. Claims 3-4, 11-12 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sarkisian et al. (US Patent Application Publication No. 2020/0279364 A1 herein after Sarkisian) in view of Hession et al. (US PAT. No. 10,445,683 B1 herein after Hession), further in view of Hariri et al. (US Patent Application Publication No 2024/0330580 A1 herein after Hariri).
Per claim 3:
Shapiro modified by Hession and Sarkisian sufficiently discloses the system as set forth above, but Shapiro modified by Hession and Sarkisian does not explicitly disclose: receive feedback about the design from the user interface; generate additional query using the AI model on the feedback; and display the additional query on the user interface.
However, Hariri discloses:
receive feedback about the design from the user interface; generate an additional query using the AI model on the feedback; and display the additional query on the user interface (At least see ¶[0078] - a user interface (e.g., the user interface 142) may be utilized to receive refinement feedback 210 from the user regarding editing (e.g., refining) one or more parts of the generative output 208 or generating a new generative output 208 (e.g., re-submitting the prompt 202 to the generative model 204, with the generative model 204 providing non-deterministic outputs)).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hariri into Shapiro modified by Hession and Sarkisian because it is beneficial for users of conventional generative models, separate of development environments often must spend substantial quantities of time and effort navigating between the generative models and the development environment to generate generative content using the generative models, inserting the generative content into the development environment, personalizing the pasted generative content, and formatting the generative content; however, optimizing interactions between users, machine-learned large language models, and development environments, implementations of the present disclosure can substantially reduce the time required by users. In turn, this eliminates the expenditure of substantial quantities of computer resources that a user would otherwise use (see ¶[0035]).
Per claim 4:
Hariri also discloses:
receive an additional response to the additional query via the user interface; based on the additional responses, modify the design displayed on the user interface (At least see ¶[0006] - generative model may be a machine-learned model trained to process language input prompts to generate a language output. The operations may further include receiving a generative output generated by the generative model in response to the prompt, and generating a modified output by modifying the generative output based at least in part on historical user data for a user associated with the prompt).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hariri into Shapiro modified by Hession and Sarkisian because it is beneficial for users of conventional generative models, separate of development environments often must spend substantial quantities of time and effort navigating between the generative models and the development environment to generate generative content using the generative models, inserting the generative content into the development environment, personalizing the pasted generative content, and formatting the generative content; however, optimizing interactions between users, machine-learned large language models, and development environments, implementations of the present disclosure can substantially reduce the time required by users. In turn, this eliminates the expenditure of substantial quantities of computer resources that a user would otherwise use (see ¶[0035]).
Per claim 11:
Shapiro modified by Hession and Sarkisian sufficiently discloses the system as set forth above, but Shapiro modified by Hession and Sarkisian does not explicitly disclose: receiving feedback about the design from the user interface; generating additional query using the AI model on the feedback; and displaying the additional query on the user interface.
However, Hariri discloses:
receiving feedback about the design from the user interface; generating additional query using the AI model on the feedback; and displaying the additional query on the user interface (At least see ¶[0078] - a user interface (e.g., the user interface 142) may be utilized to receive refinement feedback 210 from the user regarding editing (e.g., refining) one or more parts of the generative output 208 or generating a new generative output 208 (e.g., re-submitting the prompt 202 to the generative model 204, with the generative model 204 providing non-deterministic outputs)).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hariri into Shapiro modified by Hession and Sarkisian because it is beneficial for users of conventional generative models, separate of development environments often must spend substantial quantities of time and effort navigating between the generative models and the development environment to generate generative content using the generative models, inserting the generative content into the development environment, personalizing the pasted generative content, and formatting the generative content; however, optimizing interactions between users, machine-learned large language models, and development environments, implementations of the present disclosure can substantially reduce the time required by users. In turn, this eliminates the expenditure of substantial quantities of computer resources that a user would otherwise use (see ¶[0035]).
Per claim 12:
Hariri also discloses:
receiving an additional response to the additional query via the user interface; based on the additional responses, modifying the design displayed on the user interface (At least see ¶[0006] - generative model may be a machine-learned model trained to process language input prompts to generate a language output. The operations may further include receiving a generative output generated by the generative model in response to the prompt, and generating a modified output by modifying the generative output based at least in part on historical user data for a user associated with the prompt).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hariri into Shapiro modified by Hession and Sarkisian because it is beneficial for users of conventional generative models, separate of development environments often must spend substantial quantities of time and effort navigating between the generative models and the development environment to generate generative content using the generative models, inserting the generative content into the development environment, personalizing the pasted generative content, and formatting the generative content; however, optimizing interactions between users, machine-learned large language models, and development environments, implementations of the present disclosure can substantially reduce the time required by users. In turn, this eliminates the expenditure of substantial quantities of computer resources that a user would otherwise use (see ¶[0035]).
Per claim 19:
Shapiro modified by Hession and Sarkisian sufficiently discloses the system as set forth above, but Shapiro modified by Hession and Sarkisian does not explicitly disclose: receiving feedback about the design from the user interface; generating additional query using the AI model on the feedback; and displaying the additional query on the user interface.
However, Hariri discloses:
receiving feedback about the design from the user interface; generating additional query using the AI model on the feedback; and displaying the additional query on the user interface (At least see ¶[0078] - a user interface (e.g., the user interface 142) may be utilized to receive refinement feedback 210 from the user regarding editing (e.g., refining) one or more parts of the generative output 208 or generating a new generative output 208 (e.g., re-submitting the prompt 202 to the generative model 204, with the generative model 204 providing non-deterministic outputs)).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hariri into Shapiro modified by Hession and Sarkisian because it is beneficial for users of conventional generative models, separate of development environments often must spend substantial quantities of time and effort navigating between the generative models and the development environment to generate generative content using the generative models, inserting the generative content into the development environment, personalizing the pasted generative content, and formatting the generative content; however, optimizing interactions between users, machine-learned large language models, and development environments, implementations of the present disclosure can substantially reduce the time required by users. In turn, this eliminates the expenditure of substantial quantities of computer resources that a user would otherwise use (see ¶[0035]).
Per claim 20:
Hariri also discloses:
receiving an additional response to the additional query via the user interface; based on the additional responses, modifying the design displayed on the user interface (At least see ¶[0006] - generative model may be a machine-learned model trained to process language input prompts to generate a language output. The operations may further include receiving a generative output generated by the generative model in response to the prompt, and generating a modified output by modifying the generative output based at least in part on historical user data for a user associated with the prompt).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hariri into Shapiro modified by Hession and Sarkisian because it is beneficial for users of conventional generative models, separate of development environments often must spend substantial quantities of time and effort navigating between the generative models and the development environment to generate generative content using the generative models, inserting the generative content into the development environment, personalizing the pasted generative content, and formatting the generative content; however, optimizing interactions between users, machine-learned large language models, and development environments, implementations of the present disclosure can substantially reduce the time required by users. In turn, this eliminates the expenditure of substantial quantities of computer resources that a user would otherwise use (see ¶[0035]).
CONCLUSION
6. The prior art made of record and have yet relied upon is considered pertinent to applicant's disclosure, and will be frequently referred as to support the responses to the Applicant’s arguments, for example:
I. Utkarsh Saxena (US 20240330579 A1) discloses “website development system automatically generates text for a webpage. The system obtains a prompt template associated with a section of the webpage, where the prompt template includes one or more parameters. Based on the webpage, the prompt template determines a first value for a first one of the one or more parameters. A request to provide input for a second value of a second parameter is sent for display to a user. Using the prompt template, the first value, and the second value, the system generates a prompt to a large language model to generate text for the section of the webpage”(please see Abstract of the reference).
II. Tuna et al. (US 20240385809 A1) teaches “development environment, the development of machine learning and data analysis applications can be performed on a client by first retrieving the relevant dataset from a remote data store. For example, a cloud-based machine learning service can provide a user interface for performing client-side machine learning development. Using the provided user interface, a data query can be provided that retrieves a remotely stored dataset. For example, a list of available and accessible data resources, such as databases and corresponding tables and columns, can be displayed in the client development environment and a data query to retrieve the desired entries can be provided. The provided data query is executed by the machine learning service and the resulting data results are returned to the client. In various embodiments, the client-side environment converts the data results from a web-based programming language used to implement the client environment to a second programming language used for performing machine learning analysis. The client development environment can include a user interface view for viewing the retrieved dataset such as a user interface view for displaying a portion of the retrieved data in table form with the appropriate column header names and the ability to navigate through the retrieved data entries” (please see [0013]).
III. Liu et al. (US 20240078092 A1) teaches “technology provides assistance to a user for the discovery of program features, including detecting a selection of a data structure within a user interface, determining a contextual parameter based on the selected data structure, the contextual parameter associated with a modifiable feature of the selected data structure, determining options for generating program code configured to modify the modifiable feature are available based on the contextual parameter and a predefined inferential relationship between the contextual parameter and the modifiable feature of the selected data structure, and prompting the user in the user interface with information indicating that the determined options for generating the program code are accessible in the user interface” (please see [0003]).
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hyung S. Sough can be reached on 571-272-6799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Status information for published applications may be obtained from Patent Public Search tool (for all users) – A link to the Patent Public Search Tool is available at www. Uspto.gov/PatentPublicSearch. To find a U.S. patent or U.S. patent application publication, open the Patent Public Search tool by selecting “Start search”. Type the U.S. patent or U.S. patent application publication number in the “Search” panel without any punctuation and followed by an”.pn.”.
Should you have questions on access to the system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ZIAUL A CHOWDHURY/ Primary Examiner, Art Unit 2192
02/16/2026