44041 results about "User interface" patented technology

A computing system may include non-transitory computer-readable media storing instructions that when executed by the one or more computer processors, causes the computing system to perform operations comprising: causing presentation of a graphical user interface, the graphical user interface being associated with patient health data, wherein the graphical user interface is configured to: display one or more user interface elements, receive information identifying a logging action and at least one classification associated with the logging action, generate, by the processor, one or more models based on the received information, based on the generated one or more models, automatically associate the one or more user interface elements with one or more logging actions and update the graphical user interface with at least a portion of the one or more user interface elements.

An example operation may include one or more of receiving, via a user interface, a request to test a software program, reading source code of the software program and identifying a vulnerability in the source code based on the reading, generating repair code for fixing the identified vulnerability based on execution of a generative artificial intelligence (GenAI) model on the source code and a repository of repair code used to repair previous vulnerabilities, and displaying information about the repair code via the user interface.

An example operation may include one or more of executing tests on a software application via a test environment of a test platform and logging results of the tests in a log file, identifying a new feature to be added to the software application based on execution of a machine learning model on logged results of the tests stored in the log file, generating source code for the new feature to be added to the software application based on execution of a generative artificial intelligence (GenAI) model on the new feature and a repository of source code, and displaying the generated source code via a user interface of the software application.

A computer-implemented method is disclosed. The method includes: obtaining web content of a webpage; determining whether the web content complies with defined first requirements for user accessibility; in response to determining that the web content does not comply with the first requirements: identifying non-compliant web content elements of the webpage; and presenting, via a user interface of a source code editor: indications of first portions of source code for the webpage corresponding to the non-compliant web content elements; and a user interface element representing an action for altering one or more of the non-compliant web content elements.

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support generative artificial intelligence (AI)-assisted analytics of structured data sets. For example, a system may receive a prompt that includes information associated with a structured data set which includes at least some numerical data. The system may provide the prompt as input to an agent orchestrator to select one or more generative AI agents to perform analytics tasks corresponding to the information. The agent orchestrator includes a trained AI classifier configured to select the one or more generative AI agents from a plurality of generative AI agents. The system may execute an ensemble model to generate a response to the prompt based on the structured data set. The ensemble model includes the one or more generative AI agents. The system may output a graphical user interface (GUI) that includes one or more elements based on the response.

Systems and methods for assessing machine learning model fairness are provided. A graphical user interface (GUI) is rendered in a web browser. Input data from the web browser is obtained. A first input produced by a first GUI control is received to identify a prediction column in the input data. A second input produced by a second GUI control is received to identify a ground truth column. A third input produced by a third GUI control is received to identify a sensitive attribute column. A plurality of groups within a plurality of sensitive attribute values in the sensitive attribute column are automatically displayed. The system processes the input data based on the prediction column, the ground truth column and the sensitive attribute column to generate one or more algorithmic fairness analysis, which are used to display a visual report.

In some embodiments, a computer system changes a level of detail with which a respective environment is being displayed. In some embodiments, a computer system applies a neutralization adjustment to generate a representation of the physical environment. In some embodiments, a computer system displays a user interface object in a three-dimensional environment with a three-dimensional stereoscopic effect. In some embodiments, a computer system facilitates light blending. In some embodiments, a computer system transitions between three-dimensional environments using visual effects. In some embodiments, a computer system detects a movement in a viewpoint of a user while displaying a portal to a virtual environment, and either maintains or ceases display of the portal based on the amount of movement and / or a direction in which the portal opens. In some embodiments, a computer system outputs a different sound effect when initiating display of different virtual three-dimensional environments.

In an aspect, a computer-implemented method may include: performing word tokenization on identifiers in a first identifier set to obtain a first set of tokens; performing word tokenization on identifiers in a second identifier set to obtain a second set of tokens; obtaining a weighting value for each of a plurality of tokens in the first set of tokens and the second set of tokens based on a frequency of occurrence; for each identifier of a plurality of identifiers in the first identifier set and of a plurality of identifiers in the second identifier set, obtaining embeddings for the identifier based on the embeddings of tokens obtained by performing word tokenization on that identifier and based on the weighting value for such tokens; obtaining similarity metrics. The similarity metrics may be used for rapid query handling to generate user interface screens in real time.

An example operation may include one or more of storing a generative artificial intelligence (GenAI) model configured to create images, displaying a plurality of prompts on a user interface of a software application, receiving an identifier of a goal of the user and attributes of the goal via the plurality of prompts on the user interface, executing the GenAI model on the identifier of the goal of the user and the attributes of the goal to generate a custom image of the goal for the user, and displaying the custom image of the goal via the user interface of the software application.

The implementations herein disclose advanced systems and methods for integrating, analyzing, and reasoning over heterogeneous data at scale. In some implementations, the system comprises a synergistic data processing infrastructure featuring: a graph database core for unified data representation; specialized loaders for concurrent ingestion and processing of structured, unstructured, and time series data; a natural language reasoning engine leveraging large language models; and a multi-modal user interface.

An example operation may include one or more of receiving a first code base and a second code base from a software repository, wherein each of the first and second code bases comprise source code of a plurality of different software programs, identifying a source code within the first code base that is interdependent with a source code within the second code base based on execution of a generative artificial intelligence (GenAI) model on the first and second code bases, determining a software component to connect the source code within the first code base to the source code within the second code base, and displaying an identifier of the software component via a user interface.

An example operation may include one or more of ingesting data from one or more websites via one or more application programming interfaces (APIs) and storing the data within a data store of a host platform, displaying one or more prompts on a user interface of a user profile page within a software application hosted by the host platform, receiving one or more responses to the one or more prompts, generating a travel itinerary based on execution of an artificial intelligence (AI) model on the data from the one or more websites, the one or more prompts, and the one or more responses, and displaying the travel itinerary via the user interface of the user profile page of the software application.

An example operation may include one or more of receiving a plurality of architecture documents of a plurality of different domains of a software architecture, identifying a missing component that is missing from within the software architecture between a first domain and a second domain among the plurality of domains based on execution of a generative artificial intelligence (GenAI) model based on the plurality of architecture documents of the software architecture, generating a recommended modification to the software architecture based on the identified missing component, and displaying the recommended modification via a user interface.

A computer-implemented method is disclosed. The method includes: identifying, in a graphical user interface, a first user interface element and a second user interface element, the first and second user interface elements being of different types and associated with a first group of user interface elements; receiving, via a keyboard, input of a command to trigger a first interaction with both the first and second user interface elements; determining that the first interaction is not defined for one of the first and second user interface elements; and performing a related action associated with the first interaction to the one of the first and second user interface elements.

An example operation may include one or more of training a generative artificial intelligence (GenAI) model based on execution of the GenAI model on software libraries and descriptions of intent of the software libraries, receiving a first set of software libraries and a second set of software libraries, identifying a first software library within the first set of software libraries that includes redundant functionality with a second software library within the second set of software libraries based on execution of a generative artificial intelligence (GenAI) model on the first and second sets of libraries, and displaying an identifier of the first and second software libraries via a user interface.

In some embodiments, an electronic device changes the immersion level of a virtual environment and / or spatial effect in a three-dimensional environment based on the geometry of the physical environment around the device. In some embodiments, an electronic device modifies the virtual environment and / or spatial effect in response to detecting a movement of the device. In some embodiments, an electronic device moves a user interface of an application into and / or out of a virtual environment. In some embodiments, an electronic device selectively changes the display of a simulated environment and / or atmospheric effect in a three-dimensional environment based on movement of an object associated with a viewpoint of a user. In some embodiments, an electronic device provides feedback to a user in response to a user moving a virtual object to and / or into a simulated environment.

Some examples of the disclosure are directed to systems and methods for displaying and interacting with a representation of a user interface of an electronic device in a three-dimensional environment. While presenting a first user interface, a first electronic device presents a representation of a second user interface of a second electronic device based on display data representing the second user interface. In response to detecting a respective event corresponding to user input, if a gaze of a user of the first electronic device is directed to the representation of the second user interface, the first electronic device causes the second electronic device to perform a first operation directed to the second user interface based on the respective event. Alternatively, if the gaze is directed to the first user interface, the first electronic device performs a second operation directed to the first user interface based on the respective event.

In some embodiments, an electronic device enhances interactions with virtual objects in a three-dimensional environment. In some embodiments, an electronic device enhances interactions with selectable user interface elements. In some embodiments, an electronic device enhances interactions with slider user interface elements. In some embodiments, an electronic device moves virtual objects in a three-dimensional environment and facilitates accessing actions associated with virtual objects.

An example operation may include one or more of ingesting profile data from one or more websites via one or more application programming interfaces (APIs) and storing the profile data within a data store, displaying one or more prompts on a user interface of a user profile page within a software application hosted by a host platform, receiving one or more responses to the one or more prompts, determining a goal for a user of the user profile page based on execution of an artificial intelligence (AI) model on the profile data, the one or more prompts, and the one or more responses to the one or more prompts, and displaying an identifier of the goal via the user interface of the user profile page of the software application.

An example operation may include one or more of storing a current portfolio of a user in memory, receiving contextual data of the user from a user device of the user, identifying an asset of interest of the user based on execution of a generative artificial intelligence (GenAI) model based on the received contextual data of the user and the current portfolio of the user stored in memory, predicting a performance of the current portfolio with the identified asset of interest included therein at a future point in time, and displaying the predicted performance of the current portfolio with the identified asset of interest included therein on a user interface.

An example operation may include one or more of storing a portfolio of assets of a user in memory, receiving text from a conversation between the user on a first device and a second user on a second device, identifying an upcoming life event of the user based on execution of a generative artificial intelligence (GenAI) model on the received text from the conversation, determining a change to the portfolio of assets of the user based on the upcoming life event and existing assets within the portfolio of assets, and displaying the change to the portfolio of assets of the user via a user interface.

Some examples of the disclosure are directed to systems and methods for scrolling a user interface element. A user interface element including a plurality of selectable options scrollable along a first axis can be presented in an environment by a device. In some examples, the device detects an input corresponding to a request to scroll the user interface element. In accordance with a determination that the input satisfies one or more first criteria, including a criterion that is satisfied when the input includes movement of the device that corresponds to movement of a head of a user that exceeds a threshold amount of movement over a period of time, the device scrolls the user interface element in a first mode of operation. In accordance with a determination that the input satisfies one or more second criteria, the device scrolls the user interface element in a second mode of operation.

A generative AI-supported system (100) for real-time demand forecasting and inventory optimization in supply chain management, which includes the following: a data collection and integration module configured to aggregate and preprocess structured and unstructured data from multiple sources, including historical sales records, IoT sensors, external market trends, and environmental factors; a generative AI-based forecasting module configured to process input data, recognize complex patterns, and generate highly accurate real-time demand forecasts using deep learning techniques; an inventory optimization module configured to dynamically calculate and adjust inventory levels based on demand forecasts, replenishment plans, lead times, and safety stock requirements; a decision support and analysis module configured to generate actionable insights, provide simulations of multiple scenarios, and recommend cost-effective strategies for supply chain operations; a real-time warning and notification module configured to monitor supply chain activities and inform stakeholders of potential disruptions, anomalies, or demand spikes; a user interface and integration module configured to provide outputs via a user-friendly dashboard and seamlessly integrate with existing enterprise resource planning (ERP), warehouse management systems (WMS), and logistics platforms.

An example operation may include one or more of displaying a report on a user interface of a software application on a user device, listening to a call between a user on the user device and a different user on a second user device that is connected to the user device via a network, executing a generative artificial intelligence (GenAI) model based on content that is heard during the call and content within the report displayed on the user interface to identify content within the displayed report that is discussed during the call, and modifying the displayed report to emphasize the identified content within the displayed report on the user interface.

An example operation may include one or more of training a generative artificial intelligence (GenAI) model via execution of the GenAI model on descriptions and diagrams of a software architecture, receiving a plurality of different architecture views from a plurality of different domains of the software architecture, generating an architecture diagram of the plurality of domains of the software architecture in combination based on execution of the GenAI model on the plurality of different architecture views from the plurality of different domains, and displaying the architecture diagram of the plurality of domains of the software architecture via a user interface.

In some embodiments, a computer system displays a caption for a media item at different depths depending on the depth of the portion of the media item over which the caption is displayed. In some embodiments, a computer system displays a user interface element that includes information associated with the media item at different locations relative to the media item depending on attention of the user. In some embodiments, a computer system displays a user interface element that includes information associated with the media item with different visual appearances depending on visual characteristics of the portion of the media item over which the user interface element is displayed.

An example operation may include one or more of receiving a current status of a portfolio of a user and previous actions taken on the portfolio of the user over a predetermined period of time, determining a goal for the user based on execution of a generative artificial intelligence (GenAI) model on the current status of the portfolio of the user and the previous actions taken, receiving a request for the portfolio of the user via a user device, and in response to the request, generating a text-based description of the goal and displaying the text-based description of the goal with portfolio content from the portfolio of the user on a user interface of the user device.

An example operation may include one or more of establishing a network connection between a computing system and one or more external sources over a computer network, receiving a request from a user via a software application on a user device, collecting data about the user from the one or more external sources via the established network connection, executing a machine learning model on the collected data about the user to determine a goal of the user, and displaying an image of the goal via a user interface of the software application.

An example operation may include one or more of training a generative artificial intelligence (GenAI) model based on architecture diagrams of a software architecture and descriptions of the architecture diagrams, displaying one or more prompts on a user interface, receiving one or more natural language responses associated with the software architecture in response to the one or more prompts, generating a text-based response to the natural language query submitted via the user interface based on execution of the GenAI model on the one or more prompts and the one or more natural language responses associated with the software architecture, and displaying the text-based response via the user interface.

An example operation may include one or more of training a generative artificial intelligence (GenAI) model to generate images based on user data using a dataset of images, executing the GenAI model based on input data from a user interface of a software application to generate an image corresponding to the input data, and displaying the image via a user interface of a software application, receiving feedback about the image via the user interface; andretraining the GenAI model based on the generated image and the received feedback about the image via the user interface.