31927 results about "Graphical user interface" patented technology

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.

Techniques and systems for the dynamic generation of a map interface include generating temporal activity models by representing individual social media postings as having respective density distributions in time. Each posting's temporal density distribution spans multiple sequential time windows centered on the posting's timestamp, with density contributions decreasing in value for time windows further from the timestamp. The temporal models may be combined with spatial density distributions to generate comprehensive geo-temporal representations of social media activity. A graphical user interface displays an interactive map with overlay elements determined based on calculated activity attributes, including detected temporal patterns and anomalies identified by comparing current activity models against historical baselines. The modeling approach enables improved visualization of activity patterns while providing inherent privacy protection through probabilistic representation of individual posts.

Systems and methods for generating contextualized data visualizations. A first graphical user interface (GUI) object based on first data is provided, along with a second GUI object based on the second data. A user may select a scenario to be analyzed and, in response to the single selection of the scenario, the system models a first impact of the scenario on the first data to generate predicted first data and separately models a second impact of the scenario on the second data to generate predicted second data. The predicted data may be generated by an artificial intelligence system including a prediction processor. The first and second GUI objects are updated using the predicted first and second data, respectively.

An example operation includes one or more of establishing a communication session between a first user device and a second user device from among the plurality of user devices, training an artificial intelligence (AI) model to learn user interface preferences of a plurality of user devices during the communication session, receiving a description associated with the communication session, generating a plurality of windows of content and displaying the plurality of windows of content on a user interface of the first user device during the communication session based on execution of the AI model on the description associated with the communication session, and generating a second plurality of windows of content and displaying the second plurality of windows of content on a user interface of the second user device based on an execution of the AI model on the description associated with the communication session.

In some embodiments, an electronic device generates virtual lighting effects while presenting a content item. In some embodiments, an electronic device enhances navigation to a respective playback position of a content item. In some embodiments, an electronic device displays media content in a three-dimensional environment. In some embodiments, an electronic device presents media content in different modes of presentation.

An example operation includes one or more of ingesting profile data of a user from an external data source, identifying a plurality of features of a profile hosted by the external data source based on an execution of an artificial intelligence (AI) model on the ingested profile data of the user from the external data source, identifying a feature from among the plurality of features of the profile hosted by the external data source that is not connected to a local user profile of the user, and displaying a connection request on a user interface, wherein the connection request includes a link to a page associated with the identified feature hosted by the external data source.

An example operation includes one or more of rendering a graphical user interface within a software application including a plurality of elements, modifying locations of the plurality of elements within the graphical user interface based on user inputs on the graphical user interface, generating a dynamic mapping of the graphical user interface including the modified locations of the plurality of elements based on an execution of an artificial intelligence (AI) model on the rendered graphical user interface, and storing the dynamic mapping of the graphical user interface within a storage.

A system for monitoring hemodynamic status of a patient can include a transducer, an adapter and one or more monitor devices. The adapter may be in communication with the transducer and the one or more monitor devices. The adapter can be configured to receive and process data from the transducer such as unprocessed physiological data. The adapter can be configured to transmit data to the monitor device(s) such as processed and / or unprocessed physiological data. The adapter can be configured to generate, and transmit to the monitor devices(s), user interface data for rendering interactive graphical user interfaces to display information such as physiological information relating to a hemodynamic status of the patient. The adapter can be configured to receive and process, from the monitor device(s) user commands or instructions to control an operation of the system or its components.

A system receives vehicle metric data from a gateway device connected to a vehicle. The vehicle gateway device gathers data related to operation of the vehicle and / or location data. The system receives data from multiple vehicles in a fleet. The vehicle gateway device gathers vehicle metric data at a high frequency. Instead of transmitting a large amount of vehicle metric data at a fine level of granularity, the vehicle gateway device aggregates and buckets the vehicle metric data over a period of time (such as, every five minutes). The system uses the bucketed data for fleet management analysis.

A three-dimensional preview of content can be generated and presented at an electronic device in a three-dimensional environment. The three-dimensional preview of content can be presented concurrently with a two-dimensional representation of the content in a content generation environment presented in the three-dimensional environment. While the three-dimensional preview of content is presented in the three-dimensional environment, one or more affordances can be provided for interacting with the one or more computer-generated virtual objects of the three-dimensional preview. The one or more affordances may be displayed with the three-dimensional preview of content in the three-dimensional environment. The three-dimensional preview of content may be presented on a three-dimensional tray and the one or more affordances may be presented in a control bar or other grouping of controls outside the perimeter of the tray and / or along the perimeter of the tray.

A system, method, and computer-program product includes identifying a plurality of code synthesis items for a target programming language, generating a code synthesis prompt based on a first sampling of the plurality of code synthesis items, synthesizing, via a large language model, a plurality of raw code segments using the code synthesis prompt, executing the plurality of raw code segments with a code interpreter associated with the target programming language, determining one or more valid code segments of the plurality of raw code segments that the code interpreter successfully executed, aggregating, via a second sampling, the one or more valid code segments into one or more validated code synthesis training samples, and training a code generation model using the one or more validated code synthesis training samples. User interfaces may be provided to allow target coding tasks to be specified via text or speech.

Verifying large language model responses involves obtaining a query and its corresponding answer from a large language model. This conversational text is then fed into a second large language model, which translates the answer into first-order logic. The verification process uses an automated theorem prover. It checks the validity of this logic translation by determining the unsatisfiability of two scenarios: one where the negation of the logic translation and domain-specific logic formulas are combined, and another where the logic translation itself is combined with these formulas. Based on this analysis, the theorem prover ascertains whether the translated answer is valid, invalid, or neither. The final step is communicating this verification status through an appropriate output medium, such as a graphical user interface, a database, or a report, providing a structured and methodical approach to assessing the accuracy and reliability of language model responses.

Methods, computer systems, computer-storage media, and graphical user interfaces are provided for efficiently generating video insights based on text representations of videos. In embodiments, text data associated with a video is obtained. Thereafter, a model prompt to be input into a large language model is generated. The model prompt includes the text data associated with the video. As output from the large language model, a text representation that represents the video in natural language based on the text data is obtained. The text representation is provided as input into a machine learning model to generate a video insight that indicates context of the video.