Abstract: Methods, system, and non-transitory processor-readable storage medium for UI iconography validation system are provided herein. An example method includes receiving, by a user interface (UI) iconography validation system, a plurality of icons under test. The UI iconography validation system creates tuples by pairing each icon under test with each valid icon from an icon database and processes the created tuples through a trained Siamese neural network model to generate matching scores. The UI iconography validation system determines a validation status of each icon under test based on the matching scores, where an icon is validated if any of its tuples generates a positive matching score and flags icons that fail to generate any positive matching scores as invalid icons.

Abstract: Methods, system, and non-transitory processor-readable storage medium for email targeting system are provided herein. An example method includes receiving by an email server system, from an email client system, an email campaign intended for a plurality of recipients. The email targeting system categorizes each of the plurality of recipients into recipient behavior classifications based on previous recipient behavior responding to previous email campaigns. The email targeting system generates a personalized templated email communication for each of recipient behavior classifications, to replace the email campaign, where the email targeting system uses the email campaign, a large language model, and the recipient behavior classifications to generate the personalized templated email.

Abstract: Methods, apparatus, and processor-readable storage media for machine learning-based user interface element processing systems are provided herein. An example computer-implemented method includes processing, into one or more data structures, data pertaining to user activity with respect to at least one web-based application user interface, and metadata pertaining to one or more user devices associated with one or more portions of the user activity; determining, by processing at least a portion of the data structure(s) using one or more machine learning techniques, one or more user activity-related commonalities across at least a portion of the data and at least a portion of the metadata; generating, based on the one or more determined user activity-related commonalities, one or more testing recommendations with respect to one or more elements of the at least one web-based application user interface; and performing one or more automated actions based on the generated testing recommendation(s).

Abstract: Methods, system, and non-transitory processor-readable storage medium for UI design kit approval system are provided herein. An example method includes receiving, by a Continuous Integration/Continuous Delivery (CI/CD) pipeline runner system, a configuration interface defining User Interface (UI) design standards validation parameters for source code. The UI design kit approval system scans the source code to validate UI design standards associated with the source code using the configuration interface. The configuration interface is received by the UI design kit approval system via a UI design approval integration interface that integrates with the CI/CD pipeline runner system. The UI design kit approval system generates a UI design standards validation report indicating a compliance assessment of UI design standards associated with the source code.

Abstract: Methods, apparatus, and processor-readable storage media for software development documentation generation systems with artificial intelligence-based processing of data structures are provided herein. An example computer-implemented method includes compiling, from one or more data sources, information pertaining to at least one set of software code in one or more data structures; generating documentation associated with development of the at least one set of software code by processing at least a portion of the information in the one or more data structures using one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on one or more portions of the generated documentation associated with the development of the at least one set of software code.

Abstract: Methods, apparatus, and processor-readable storage media for automated user-centric server management are provided herein. An example computer-implemented method includes obtaining requests for server-related resource(s) access in connection with at least one time period; identifying at least a first portion of the requests as corresponding to a first category of user type and at least a second portion of the requests as corresponding to a second category of user type by processing user credentials from the requests; automatically processing the first portion of the requests; prioritizing, in a request queue data structure, the second portion of the requests based on request volume parameters related to the time period(s) and at least a subset of the corresponding user credentials; and automatically processing, during the time period(s), the second portion of the requests from the request queue data structure in accordance with the prioritizing.

Abstract: Methods, system, and non-transitory processor-readable storage medium for a simulation analysis system are provided herein. An example method includes monitoring, by a simulation analysis system, user interactions with User Interface (UI) elements on a user interface to obtain click patterns associated with the UI elements. The simulation analysis system generates simulated user profiles based on the user interactions. The simulation analysis system calculates a User Interface/User Experience (UI/UX) impact using the click patterns associated with the simulated user profiles, and identifies a set of high impact UI elements associated with the UI.

Abstract: Methods, apparatus, and processor-readable storage media for enhancing web page loading using machine learning techniques are provided herein. An example computer-implemented method includes obtaining activity-related data associated with at least one user device and at least one web application during a web browsing session; generating one or more predictions of one or more web pages, associated with the at least one web application, to be sought in connection with the web browsing session by processing at least a portion of the activity-related data using one or more statistical algorithms and one or more machine learning techniques; and automatically preloading, in connection with the at least one web application, at least one of the one or more web pages for use in the web browsing session by the at least one user device.

Abstract: Methods, apparatus, and processor-readable storage media for generating context-based and user-related predictions using artificial intelligence techniques are provided herein. An example computer-implemented method includes determining one or more user parameters by processing information related to a user in association with at least one web application; determining context information associated with the user accessing one or more portions of the at least one web application; generating one or more predictions associated with future use of the at least one web application by the user by processing at least a portion of the one or more user parameters and at least a portion of the context information using one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the one or more predictions associated with future use of the at least one web application by the user.