45 results about "Dynamic user interface" patented technology

In an example embodiment, a solution is provided that introduces a dynamic user interface that automatically identifies payment methods available for a transaction. The dynamic user interface also may optionally present the available payment methods in an ordering, as determined by a machine learning model. More particularly, a payment processing system may determine a payment intents object, with all available payment methods for a transaction and optionally an ordering for those payment methods.

Approaches disclosed herein relate to dynamically determining and assigning user interface (UI) interaction modes across multiple multimodal devices based on real-time detection of input methods and screen sizes. Input methods are detected using web APIs. A screen size is determined using CSS pixel measurements and monitored for changes. CSS media queries are used to detect the effective resolution of the device's display. By applying breakpoints defined in CSS, the screen size can be categorized into predefined groups. The device state may be continuously monitored in real-time to identify any modifications, such as connecting or disconnecting input devices. Events triggered by these changes may be captured using the web APIs and event listeners. Such a system can listen for events like connecting or disconnecting devices to dynamically reassign the interaction mode. Such reassignment can be guided by an algorithm that prioritizes input methods based on the device's screen size and context.

Embodiments herein disclose a method of dynamically generating at least one widget / UI using an AI model for determining a user intent / context based on user input of speech or text, and identifying one or more associated devices with control / capabilities of the one or more associated devices that may be provided to the user to interact therewith. Embodiments herein disclose correlating user input with device information, and personalizing an AI model with user interaction, location, device information, which will be used to generate at least one widget / UI that is desired.

Systems and methods provide for integrating legacy systems into a unified platform. Embodiments include a system configured to ingest, normalize, and store data from multiple sources using a data layer, analyze and transform data using AI algorithms by a processing layer, and provide a dynamic user interface for data visualization and interaction by a presentation layer. Methods are provided for receiving, by a computing device, data such as email orders, vendor catalogs, CRM data, etc., extracting data from the content, processing the data using natural language processing techniques, analyzing the data using machine learning models, mapping the data to a unified platform's schema, managing the integration workflow using a workflow orchestration engine, providing real-time data insights, and resolving data errors or anomalies using an exception management system.

A computing system includes a processing circuit to receive a URL associated with a web page and a category type of a third party system; parse the web page to extract an audio or visual content item; determine the category type matches a category of a plurality of categories stored in a database; retrieve, from the database, at least one image or video associated with the category; receive a selection of a graphical representation of the third party system on a first graphical user interface (GUI) rendered by a user device; generate a second GUI including at least the extracted content item and the at least one image or video retrieved from the database; and transmit data corresponding to the second GUI for rendering on the user device.

Disclosed are systems and methods for dynamic user interfaces for authentication. A user interface comprising a plurality of user interface elements can be generated. The plurality of user interface elements can be generated according to a random placement. The each plurality of user interface elements can comprise a random plurality of attribute values.

A computer-implemented method including associating, at a resource having a plurality of task elements, a task element manifest with each task element from the plurality of task elements, thereby creating a resource map, receiving a request to complete a task; providing the request to complete the task and at least a portion of the resource map to a Large Language Model (LLM); and obtaining, from the LLM, a dynamic user interface to complete the task.

One embodiment sets forth a technique for generating prompts for generative artificial intelligence (AI) models. According to some embodiments, the technique can include the steps of receiving an initial prompt comprising natural language; generating a plurality of parameterized prompt attributes based on the initial prompt; generating a plurality of UI elements based on the parameterized prompt attributes; receiving user input modifying one or more of the UI elements; and generating an updated prompt based on the initial prompt and the user input.

A computer system includes memory hardware configured to store instructions and a database, and processor hardware configured to execute the instructions. The instructions include receiving input from a user. The instructions include querying the database to: obtain a label text associated with a specified national drug code (NDC), obtain a field associated with the label text, and obtain a set of field options associated with the field. The instructions include building a data structure including the label text, the field, and the set of field options. The instructions include converting the data structure to a dynamic web interface for displaying the label text, the field, and the set of field options. The instructions include receiving a user selection of a field option of the set of field options, updating the field to reflect the user selection, and generating a label by printing the label text including the updated field.

A virtual environment is provided that dynamically adapts user interface (UI) elements presented / displayed within the virtual environment based on a user profile of a user navigating the virtual environment. A user profile associated with a user includes a set of UI elements that the user is authorized to view / access and / or is relevant to the user. As the user navigates the virtual environment, a processor renders only those UI elements for display to a user device of the user that are associated with the user profile of the user and does not render UI elements that are not associated with the user profile of the user.

The present technology relates to methods and systems for streamlining the process of diagnostic imaging; in particular, that introduce parallel, rather than sequential, reading, accomplished by generative artificial intelligence (A.I) including large language models (LLMs). The methods and systems herein can also include additional features, such as a dynamic user interface, facilitation of real-time editing, text formatting, gamification elements or features, and an interoperability module that is configured to adopt a variety of healthcare standards. The technology constitutes a significant overhaul on traditional radiology and healthcare workflow processes.

A computing system includes a processing circuit configured to: receive information associated with a web page and a category type of a third party system; extract a content item; determine the category type matches a category of a plurality of categories stored in a database; retrieve, from the database, at least one image or video associated with the category; receive a selection of a graphical representation of the third party system on a first graphical user interface (GUI) rendered by a user device; generate a second GUI including at least the extracted content item and the at least one image or video retrieved from the database, the at least one image or video to reflect a difference between a first value and a second value associated with the third party system; and, transmit data corresponding to the second GUI for rendering on the user device.

1. The name of the design product: the main body of the thumbnail preview dynamic graphical user interface of electronic equipment. 2. The use of the design product: electronic equipment. 3. The design points of the design product: the part of the graphical user interface claimed. 4. The picture or photo that best indicates the design points: change state figure 3. 5. The use of the graphical user interface: the interface as a whole is used to preview thumbnails in the display space of electronic equipment (such as XR equipment), and the main part of the interface claimed shows the change process of the user selecting the thumbnail being previewed. The front view shows that the user is browsing multiple thumbnails in the display area of the electronic equipment, and when the user selects the rightmost thumbnail for preview, the interface changes in the order of change state figure 1 to change state figure 3, first the selected thumbnail is marked with a double frame, then the picture is enlarged in size and displayed in the middle of the interface with a light color, while the other unselected thumbnails fade in color, and finally the enlarged thumbnail is displayed in the middle of the interface in a clear manner for the user to view. 6. Other circumstances that need to be explained: the dashed part in the figure is not claimed.

Systems and methods are provided for integrating legacy systems into a unified platform. Implementations include a system configured to: ingest, normalize, and store data from multiple sources using a data layer; analyze and transform the data using AI algorithms by a processing layer; and provide a data visualization and interactive, dynamic user interface by a presentation layer. Methods are provided for receiving data (such as email orders, vendor catalogs, CRM data, etc.) from computing devices; extracting data from the content; processing the data using natural language processing techniques; analyzing the data using machine learning models; mapping the data to patterns on a unified platform; managing integration workflows using a workflow orchestration engine; providing real-time data insights; and resolving data errors or anomalies using an exception management system.

A computing device comprises a communications module; a processor coupled with the communications module; and a memory coupled to the processor and storing processor-executable instructions which, when executed by the processor, configure the processor to initiate an automated teller machine session; engage an artificial intelligence engine to determine at least one dynamic user interface to be presented during the automated teller machine session; and present the at least one dynamic user interface on a display of an automated teller machine.

Certain aspects of the disclosure provide an adaptive content management system configured to generate dynamic user interfaces. In certain aspects, methods for adaptive content management include caching user interface data, including user interface responses to user interaction with the user interface, and other user interface data and providing such cached user interface data to a large language model (LLM) to generate a dynamic user interface screen based on a plurality of key-value pairs indicating user interface objects for display on the dynamic user interface screen.

The present disclosure generally relates to systems, software, and computer-implemented methods for action determination for a multiple-category model. One example method includes establishing secure communication channels with a plurality of systems where data for a user is stored. User data for one or more categories is received via the secure communication channels from the plurality of systems. For each category, a set of rules is applied to the user data for the category to generate a resiliency score for the category for the user. A set of recommended actions is generated for the user based on a comparison of the resiliency score for a category to a threshold score for the category. A dynamic user interface is provided that uses the resiliency scores and the sets of recommended actions to display an interactive resiliency visual with which the user can interact. Machine learning may be applied to generate resiliency scores.

Aspects of the present disclosure relate to dynamic interaction with an automatically created electronic document. Embodiments include displaying, in a user interface associated with a computing application, a representation of an electronic document that was automatically created based on a source electronic document. Embodiments include displaying, in the user interface, a representation of the source electronic document in a same screen as the representation of the electronic document. Embodiments include detecting, via the user interface, a user action with respect to an item in the electronic document. Embodiments include displaying, in the user interface, based on the user action, in the same screen as the representation of the electronic document and the representation of the source electronic document, a visual indication that the item was automatically determined based on a particular portion of the source electronic document.

The invention provides a dynamic user interface generation method and system based on generative AI and related equipment. The method comprises the following steps: acquiring a natural language demand, and analyzing the natural language demand into a user intention graph containing a task target node, an object node and an operation node; inputting the user intention graph into a constraint solver, introducing a layout constraint, an interaction constraint and an accessibility constraint into the constraint solver, and generating a corresponding user interface syntax tree based on nodes and edges of the user intention graph; generating a front-end code by utilizing a code generator based on the user interface syntax tree, and replacing the front-end code into a front-end system in a running state; and collecting time data and error data of task execution in the running process of the front-end system, and feeding back the time data and the error data to the constraint solver to update weight parameters of the layout constraint, the interaction constraint and the accessibility constraint.

The present technology relates to methods and systems for streamlining a diagnostic imaging process; in particular, methods and systems for streamlining diagnostic imaging processes that introduce parallel rather than sequential radiography implemented by generative artificial intelligence (A.I), including large language models (LLMs). The methods and systems herein may also include additional functionality, such as a dynamic user interface, real-time editing convenience, text formatting, gamification elements or functionality, and interoperability modules configured to employ a variety of healthcare standards. The technology forms a major innovation for the traditional radiology and medical care workflow process.

A unified platform may comprise a combination of independent frameworks that have been integrated and configured to collaboratively operate seamlessly. In some aspects, the unified platform may comprise one or more of an authentication and authorization framework, a dynamic user interface framework, a workflow state management framework, a notification and active data loss and prevention (DLP) engine framework, and an orchestration engine framework. Each of the frameworks included in the unified platform may comprise one or more of the plurality of computing devices executing computer-readable program instructions.

Embodiments described herein are generally directed to client / server software application development frameworks, and are particularly directed to systems and methods for supporting portable and extensible dynamic user interfaces and data validation. An application development framework is provided, wherein a rules engine component and rules are provided at both the client-side and the server-side, for use with a particular application. The rules engine and rules can be different implementations at each end. The client and server can communicate to exchange the latest versions of the rules; however the rules themselves are agnostic as to which (client-side or server-side) engine they are run on. The system allows users to configure policies associated with a client-side user interface and a backend server, that operate together to provide application portability and extensibility, for example to control the presentation, logic, defaulting, and operation of a data set associated with a dynamic user interface.

An interactive system is presented for interacting with a user through biosignals and sensor data. The system includes a user input system, an interaction framework, a dynamic user interface (UI), a processor, and a memory. The interaction framework processes biosignal inputs, sensor data, and current context data to provide a context estimation, which may be used to classify user attention and determine the state of selection targets. The dynamic UI receives the UI configuration and LLM / GenAI suggestions or instructions, and provides output to the user. The system can also include additional features, such as presenting selection targets in the idle state, analyzing biosignal inputs and context data to determine user attention, and performing actions based on selection states.

Embodiments herein provide a method and a system for determining a cardio respiratory fitness of the user with an interactive solution. The solution includes establishing a connection between mobile device and wearable sensor device using short-range communication and receiving and storing Movement Intensity (MI) and a Heart Rate (HR) of user wearing wearable sensor device while performing a physical activity at a predefined place. The mobile device displays dynamic user interface based on MI and HR, which includes user avatar, at least two antagonist characters and performance indicator (PI). The mobile device controls position and movements of the user avatar across the deviation factor spectrum. Also, mobile device increases performance level of the user based on MI and HR and determines the cardio respiratory fitness of the user based on the at least one of HR, the MI and the combinations of HR and MI.

The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a dynamic user interface and machine learning tools to generate data-driven digital content and multivariate testing recommendations for distributing digital content across computer networks. In particular, in one or more embodiments, the disclosed systems utilize machine learning models to generate digital recommendations at multiple development stages of digital communications that are targeted on particular performance metrics. For example, the disclosed systems utilize historical information and recipient profile data to generate recommendations for digital communication templates, fragment variants of content fragments, and content variants of digital content items. Ultimately, the disclosed systems generate multivariate testing recommendations incorporating selected fragment variants to intelligently narrow multivariate testing candidates and generate more meaningful and statistically significant multivariate testing results.

The aim of the invention is to define a method that provides a simple and efficient way for creating and / or customizing a user interface on the client side, in particular that the options so made immediately during the production run will be highly desirable. According to the present invention, this object is achieved by a method for generating a user interface (MMOM UI) in at least one MOM system that controls a manufacturing environment of various manufacturing resources existing in a plurality of business domains, the method comprises the following steps: a) providing a business domain model for each business domain, said business domain model comprising, in addition to other information, all related information of resources and objects and attributes thereof in a particular business domain, the attributes of said resources and objects potentially being themes of UI tags (multiple UI tags); b) generating a set of metadata (MOM metadata) from all business domain models, the set of metadata comprising information related to resources and objects and their related attributes; c) enhancing the metadata set to include additional UI-related information regarding how these business domain models should be presented; d) utilizing, by a dynamic rendering engine executing in the manufacturing environment on the client side, the metadata set about the business domain model, including enrichment using UI-related information; e) selecting information to be displayed in a user interface (MMOM UI) via an interface provided by a web framework (SWF), which provides the metadata set to the user in a selectable manner, according to the available metadata set; and f) using the selected information by the dynamic rendering engine to generate a user interface (MOM UI) showing the selected information from the rendering data in the metadata set and the enhanced UI-related information. Thus, the present method eliminates the need for an intermediate design stage that requires a running application to design or develop a UI page. With the present invention, a client user can design and configure a UI in one stage along with the rest of the business domain model, without having to have a running software development system. Both the business domain model and the corresponding UI are developed / deployed / published together at one time.

Systems and methods provide for integrating legacy systems into a unified platform. Embodiments include a system configured to ingest, normalize, and store data from multiple sources using a data layer, analyze and transform data using AI algorithms by a processing layer, and provide a dynamic user interface for data visualization and interaction by a presentation layer. Methods are provided for receiving, by a computing device, data such as email orders, vendor catalogs, CRM data, etc., extracting data from the content, processing the data using natural language processing techniques, analyzing the data using machine learning models, mapping the data to a unified platform's schema, managing the integration workflow using a workflow orchestration engine, providing real-time data insights, and resolving data errors or anomalies using an exception management system.

Features for adaptation and control of a graphical user interface (GUI) and GUI functionality are disclosed herein. In an example, a user interface control is provided in a GUI to receive interaction from a user, which is provided in an enabled state to offer functionality for the user to attempt an initial commerce action with a constrained item. The user interface control is changed from the enabled state to a disabled state, in response to receipt of the interaction from the user, and a delay time period is calculated for the disabled state (with the delay time period being uniquely calculated for the user). The user interface control is changed from the disabled state to the enabled state, upon expiration of the delay time period, and the enabled state of the user interface control provides functionality for the user to perform a commerce action with the constrained item.

A system for dynamic user interface interactions comprising a computing device configured to receive behavior data, generate one or more educational modules and a selectable event graphic, configure a first remote device to display an event handler graphic corresponding to a data-reception event handler, receive from the first remote device a plurality of interactive data generated by at least the data-reception event handler, configure the first remote device to generate a graphical view, wherein the graphical view includes at least a display element generated as a function of the one or more educational modules and the selectable event graphic corresponding to a selectable event handler, wherein the selectable event handler is configured to receive interaction of the selectable event graphic and trigger an event action based on a comparison.

Systems / techniques that facilitate dynamic user-interface comparison between machine learning output and training data are provided. In various embodiments, a system can access a data candidate on which a machine learning classifier is to be executed. In various aspects, the system can generate, via execution of the machine learning classifier, a predicted classification based on the data candidate. In various instances, the system can render, on an electronic display, a confidence graph that visually depicts how well or how poorly the data candidate and the predicted classification fit into an annotated training dataset on which the machine learning classifier has been trained.