8992results about "Execution for user interfaces" patented technology

A system and method is described for internationalization of web pages by extracting translatable content from extensible mark-up language (XML), or similar data-centric meta-language representations of web pages or data used to build web pages. The extracted translatable content is stored in a translation task repository (TTR) accessible by the web developer and the translator. The XML representation is then modified to include selection control logic to select the appropriate translations for insertion into the final web page. The translator accesses the TTR to translate the appropriate content and saves the translations back to the TTR associated with the original translatable data. The translations are obtained from the TTR as selection cases for the selection control logic of the XML representation. As the XML is converted into the web source code, the selection logic and translations are embedded therein facilitating building the web site in multiple different languages.

Systems and methods are described for building artificial intelligence (“AI”) agents. A server can provide a user interface (UI) that includes options to select and connect various agent objects. The agent objects can include prompt objects, dataset objects, model objects, and one or more code objects. A subset of the agent objects can be identified as available for selection based on evaluation of at least one management policy associated with an administrative user. A manifest file is generated based on the connected agent objects, and the manifest is validated against dependency rules to ensure that the stages of the agent meet prerequisites for the stages. Then, the server performs a simulated execution of an agent that corresponds to the validated manifest file, including an identification of at least one execution metric associated with the simulated execution.

Systems and methods for simulating multi-agent within a virtual world in response to generated hypotheses are disclosed herein. The system can generate a virtual world that includes a set of agents. The system can receive instructions that include a question, including a first query and a first input item, and a set of input traits, which can be used to instantiate a set of agents. The system can generate a first hypothesis representing the first input item and can execute a first simulation session to generate a first output set. The system can generate a second data schema associated with a second hypothesis and execute a second simulation session to generate, using the second hypothesis and the first query, a second output set. Accordingly, the system can generate a sentiment summary and display the sentiment (impression) summary at a graphical user interface (GUI) accessible to an experimenter.

The invention relates to the technical field of artificial intelligence, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a task processing method, device and equipment based on multi-agent cooperation and a medium. And constructing a sub-task dependency graph, sequentially scheduling and executing the sub-tasks, and performing parameter completion to obtain the completed sub-tasks. And tracking the execution progress and the historical record of the completion subtask through a preset progress agent, and generating an executable operation decision by using a preset decision agent. If the task execution does not reach the expected effect, feeding back difference information and a correction suggestion, adjusting an operation decision and generating an updating operation; if the task achieves the expected effect, task completion information is sent to the progress agent, and the state is updated to be task completion. According to the method, the perception accuracy is improved, a task dependence tracking and feedback correction mechanism is provided, and a cross-application automatic task is successfully realized.

Systems and methods are described for dynamic execution of artificial intelligence (“AI”) agents. A server can receive, from a client device, an input associated with an AI agent. Based on a manifest file or user profile, the server can identify a management policy that applies to the AI agent. The server then dynamically configures access to the agent objects based on applying the management policy. The management policy is applied to a device status of the client device, a user profile of a user of the client device, and / or a network configuration of the client device. The server then executes a modified workflow based on the dynamically configured access, wherein the modified workflow bypasses or changes operation of at least one of the agent objects. Based on the modified workflow, the server transmits an output to the client device.

Systems and methods are described for building artificial intelligence (“AI”) agents. A server can provide a user interface (UI) that includes options to select and connect various agent objects. The agent objects can include prompt objects, dataset objects, model objects, and one or more code objects. A subset of the agent objects can be identified as available for selection based on evaluation of at least one management policy associated with an administrative user. A manifest file is generated based on the connected agent objects, and the manifest is validated against dependency rules to ensure that the stages of the agent meet prerequisites for the stages. Then, the server performs a simulated execution of an agent that corresponds to the validated manifest file, including an identification of at least one execution metric associated with the simulated execution.

The invention discloses an Android system user interface interaction method and device based on a large language model, and relates to the technical field of intelligent man-machine interaction, and the method comprises the steps: receiving a natural language instruction of a user, and synchronously capturing a real-time interface state of a current Android screen to generate a structured context containing semantic information of interactive elements; inputting the instruction and the context into a large language model, and generating a hierarchical task plan formed by a plurality of atomic operations with a logic dependency relationship; based on a dynamic adaptation mechanism, converting the atomic operation sequence into an operation instruction which can be executed by the Android system; receiving interface change information after instruction execution, and feeding back the interface change information as a new round of context to the large language model to determine a task execution state; and finally generating natural language feedback information to the user according to the execution state. By means of the mode, complex cross-application tasks can be understood and executed, and interaction intelligence of the Android system is remarkably improved through real-time interface perception and dynamic planning adjustment.

The invention discloses a multi-platform-oriented intelligent interface adaptation and rendering optimization method and system, and belongs to the technical field of interface rendering. The method comprises the following steps: firstly, acquiring and analyzing interface description information, and extracting hierarchical structures and constraint relationships of interface elements to obtain abstract description features; performing adaptive layout mapping based on the environment characteristic parameters of the target platform, and dynamically adjusting space occupation and display priorities under the constraint of keeping the relative position relationship of interface elements unchanged to obtain a layout scheme after platform adaptation; determining resource consumption nodes according to interface element distribution characteristics, and determining a layered rendering strategy by analyzing visibility states and updating frequency characteristics; applying the layout scheme and the rendering strategy to a rendering execution process to generate output; and collecting performance feedback data to adaptively update a space occupation and rendering strategy. According to the method and the device, automatic adaptation and rendering performance optimization of the cross-platform interface are realized, and the multi-platform interface rendering efficiency and the user experience are improved.

A platform for multi-stage simulations in a multi-agent simulator computing system utilizes context sharing across simulation sessions. The platform generates agents based on input traits, executes simulation sessions to produce outputs, and persists outputs and agent configuration data (e.g., metadata associated with simulation sessions). A graphical user interface displays simulation outputs. In response to user interaction with a particular output item (e.g., a user follow-up query or inquiry into simulation traceability), the platform identifies the associated agent using persisted configuration data and performs additional operations, which can include generation and execution of additional queries for follow-up simulations.

The invention discloses a lightweight display interface rendering optimization system, which relates to the technical field of image processing, and comprises an acquisition module, an interface analysis module, a behavior analysis module and an attention analysis and rendering module, dynamically calculating an effective view radius by utilizing a visual tunneling effect; the interface analysis module performs character string fuzzy matching in combination with the context input by the user, identifies the search intention of the user and improves the weight of a corresponding component; the attention analysis module performs multi-modal weighted fusion on the physiological fixation data and the sparse content saliency thermodynamic diagram; according to the method, by recognizing the semantic intention and the physiological fixation point of the user, on the premise that core visual experience continuity is guaranteed, GPU load and video memory bandwidth occupation are remarkably reduced, and balance of high-performance display and low-power-consumption operation is achieved.

Systems and methods are described for dynamic execution of artificial intelligence (“AI”) agents. A server can receive, from a client device, an input associated with an AI agent. Based on a manifest file or user profile, the server can identify a management policy that applies to the AI agent. The server then dynamically configures access to the agent objects based on applying the management policy. The management policy is applied to a device status of the client device, a user profile of a user of the client device, and / or a network configuration of the client device. The server then executes a modified workflow based on the dynamically configured access, wherein the modified workflow bypasses or changes operation of at least one of the agent objects. Based on the modified workflow, the server transmits an output to the client device.

The invention relates to the technical field of industrial automation process control, and discloses a data source adaptation and control binding method, device, equipment and medium, comprising: establishing a variable model to generate a variable directory, constructing a data source adapter to generate a data source mapping table and a read-write interface set, generating a control tree in a visual control designer, establishing a binding relationship between control attributes and variables, loading a variable binding engine, analyzing a binding path, generating an execution plan, registering change monitoring, accessing a read-write interface set of a target data source adapter based on the execution plan to complete variable read-write, and generating an execution record, and responding to a variable change event or a control input event to trigger control value write-back and interface refreshing, and finally collecting a variable directory, a binding relationship, a control tree and an execution record to generate a package configuration file. Through a unified variable model and control binding mechanism, cross-data-source analysis and bidirectional linkage are realized, and the interaction efficiency and the system maintainability are improved.

In some examples, there may be provided systems, devices, and methods for using data from disparate databases to determine characteristics of a set of users within an organizational unit and generate customized user interface elements for display within a user interface.

Systems and methods are described for tool discovery and ingestion for artificial intelligence (“AI”) agents. An AI platform can discover a first tool specification that includes an action and a description. The first tool specification is ingested to create a first tool object. The first tool object includes the action and an endpoint. Tool labels are determined from the specification and applied to the first tool object. A user interface displays the tool object, and it is added to an AI agent. The AI agent includes a manifest file that is used to execute the AI agent. This includes determining whether the AI agent is authorized to perform the action, and providing the AI agent with access to a tool credential, wherein the tool credential is sent to the endpoint.

The invention discloses a home page multi-component parallel prefetching and rendering first screen acceleration method and device and a storage medium, and the method comprises the following steps: constructing a global dependency relationship model based on the metadata and dependency relationship of each component of a home page, and calculating the priority for each component according to a predefined priority evaluation strategy, generating a component scheduling sequence constrained by the priority and the dependency relationship; controlling a concurrency control strategy of the component; preferentially rendering a visual placeholder structure, and smoothly replacing the real content to a user visible area through a progressive rendering replacement strategy; when the variable quantity of the component data exceeds a preset threshold value, triggering an increment updating strategy based on the difference content; generating a cache identifier according to component granularity, reading and writing data in a multi-level cache structure based on the cache identifier, and ensuring data consistency by adopting a background asynchronous verification strategy; and collecting performance indexes in a page rendering process, and adjusting the priority evaluation strategy, the concurrency control strategy and the increment updating strategy according to the performance indexes.

A computing platform can generate a virtual world and a set of virtual agents having agent traits and interaction rules. For example, by automatically analyzing experimenter queries, the platform can bind the agents to machine learning models that perform tasks responsive to the questions. The platform can generate additional hypotheticals to enable chains of simulations. The platform can execute a first simulation session by causing the set of agents to generate a first output set. Using an additional data source to generate new world characteristics, such as environmental and / or temporal conditions, the platform can generate and apply agent evolution logic for subsequent simulations, such as by performing mutation of agent traits, selection of agents, crossover of agent characteristics, and / or deletion of agents that do not meet a fitness threshold. The platform can persist agent states and outputs to generate user-interactive visualizations, aggregations, and training data sets for training agent models.

Techniques for simulating multi-agent interactions within a virtual world in response to generated hypotheses using dynamically retrieved external data are disclosed herein. A computing system can receive instructions including a user input with a seed phrase and provide the user input to a configuration generation model to generate agent traits associated with agents in a virtual world and / or data-source identifiers associated with external data sources. The system can retrieve external data from the external data sources and provide the agent traits and external data to a hypothesis generation model to generate a hypothesis that includes a constraint associated with the external data. The system can instantiate agents that have the agent traits and execute a simulation session consistent with the constraint (e.g., by causing the agents to generate an output set using the hypothesis and question). The system can generate a representation of the output set for analysis.

The embodiment of the invention provides a mobile banking multi-mode interaction method and system based on a language user interface, and relates to the field of financial science and technology, the method comprises the following steps: obtaining input information of multiple modes input by a user, and fusing the input information of multiple modes to obtain a unified natural language instruction; inputting the unified natural language instruction into a large language model for intention classification and parameter extraction to obtain an operation intention corresponding to the unified natural language instruction and a corresponding service parameter; and executing the operation intention according to the unified natural language instruction and the service parameters. The problem that in the prior art, multi-mode input information in mobile phone banking application is mutually separated, and user intentions cannot be analyzed and understood in a collaborative mode is solved.

Systems / techniques that facilitate automatic product support systems and methods via generative artificial intelligence (GAI) and customer interactions are provided. In various embodiments, a system can access an electronic interaction record pertaining to a software product. In various aspects, the system can synthesize, via execution of GAI on the electronic interaction record, first text that describes a problem afflicting the software product. In various instances, the system can determine, based on executing the GAI on the first text, whether there is an available software feature in an available software feature repository that addresses or solves the problem. In various cases, the system can, in response to a determination that there is no available software feature that addresses or solves the problem, synthesize, via execution of the GAI on the first text, a recommended design alteration to the software product that would address or solve the problem.

The invention relates to the technical field of artificial intelligence testing, discloses a method for automatically extracting UI elements of a Web page based on an AI large model, realizes automatic extraction of positioning information of the UI elements of the Web page of an embedded product, directly provides the positioning information to a Web testing framework for use, and solves the problems of low UI element extraction efficiency and insufficient adaptability in a traditional scheme. Playwright is used for obtaining page screenshots and HTML DOM information, after intelligent cleaning, the page screenshots and the HTML DOM information are transmitted to an AI model to analyze page structures, interactive elements such as buttons and input boxes are automatically recognized, and an optimal selector is generated; furthermore, page navigation and multi-page element collection are realized by combining an AI-driven automatic tool, and finally, an operation sequence is organized through a GUI (Graphical User Interface), so that automatic testing is quickly completed. According to the method, the problems of low efficiency and poor adaptability of a traditional method are solved, the accuracy and maintenance efficiency of Web testing are remarkably improved, and the method is suitable for dynamic and complex modern Web pages.

The invention relates to a visual component management method and system based on support of multi-user collaboration, and belongs to the technical field of computer software collaborative design. Wherein multi-party editing requests are received and verified through a cooperative access mechanism, structured component data are constructed by using a component modeling model, concurrent control and a difference buffer are adopted to coordinate parallel modification, write conflicts are identified and processed based on a conflict detection algorithm, and traceable management of component versions is realized through a snapshot generation model. According to the method, efficient, consistent and conflict-controllable unified management of the visual components in a multi-person online cooperation scene is realized, and the cooperation efficiency of component development and the system reliability are effectively improved.

The system provides a permissions engine derives permissions for responses from artificial intelligence (AI) agents. The permissions are derived based on the access permissions of chunks used to generate the response, and the response is cached for reuse with users meeting the permissions level. The system can also derive an access permission level for a user attempting to access the cached result or other content. This can be based on a managed access profile that includes user behavior criteria and device criteria. Likewise, the system can validate and derive sensitivity levels of resources that are ingested for use in the artificial intelligence (AI) agents and discriminate between chunks to use in synthesizing results based on the derived permissions of the chunks and their relationship to the user.

This application describes, amongst other things, methods and systems for building and deploying agents. An example method includes obtaining orchestration data about a set of task-specific components selected to provide a response to the prompt, where each respective task-specific components in the set of task-specific components is configured to assist with a respective clinical task of the one or more clinical tasks. The method further includes, determining an order in which each respective task-specific components of the set of task-specific components should be utilized to prepare a complete response to the prompt that address the one or more clinical tasks based on the obtained orchestration data about the set of task-specific components. The method also includes, in accordance with the determined order, providing first data related to the prompt to a first task-specific component and receiving a first response from the first task-specific component.

A computing platform can generate a virtual world and a set of virtual agents that have agent traits (attributes). A trait distribution for a set of agents Y can be generated by processing seed data to generate trait values T and a trait distribution algorithm. The algorithm assigns a particular trait value T′ to an agent Y′, conforming to the trait distribution algorithm applied to the set of agents Y. The platform uses the agents in a simulation session, prompting an AI model with the particular trait value T′ to generate an output set. The output set can be displayed and navigated to provide further information about the trait value T′, which enables analysis of agent behaviors and outputs based on agent traits.

Techniques for simulating multi-agent interactions within a virtual world in response to generated hypotheses are disclosed herein. A computing system can receive an input dataset of a first format and process the dataset, in connection with an input configuration, to generate a virtual world populated with a set of agents. The system can receive input (e.g., a query, an API message, an AI model output) specifying attributes of a particular item or scenario to test within the virtual world. Based on the input, the system can generate a hypothesis, execute a simulation session according to the hypothesis, and generate an associated output set for display on a graphical user interface. The disclosed techniques enable flexible, tunable simulations of various within the virtual world based on multimodal inputs and / or contextual information.

The invention particularly relates to a training reasoning method based on voice-text-image multi-mode comparative learning, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing a voice-text-GUI screenshot triple, and processing element masks; generating positive and negative alignment pairs of the instruction clauses and the GUI elements; the multiple encoders process corresponding modes, and the Rewriter converts a voice transcription text into a structured sequence; through global / local double fusion and double contrast learning, coordinate loss optimization alignment is carried out. According to the invention, through global and local double-layer fusion and a comparative learning mechanism, fine-grained alignment of voice, text and GUI elements is realized; element masks are generated by means of SAM, post-processing optimization is carried out, accurate element-level visual features are extracted in combination with RoIAlign, and a foundation is laid for alignment; all modal dimensions are unified through linear projection, coarse-grained semantic association is realized through a global fusion layer, and a local fusion layer fuses voice rhythm information to enhance association through cross-modal attention and fine correspondence of a gating mechanism capture sub-step and elements.

A method is performed at a computer system that is in communication with a display generation component and one or more input devices. A first window and a second window are concurrently displayed. An input that changes a spatial arrangement of the first window is received at the computer system. In response, in accordance with a determination that the change in the spatial arrangement of the first window would cause the first window to occlude the second window such that less than a predetermined amount of the second window would be visible, moving the second window so that, after the second window is moved, at least the predetermined amount of the second window is visible.

The invention discloses an end-side assistant construction method based on multi-mode multi-agent driving. The method comprises the following steps: defining an instruction mode and constructing a template library; an end-to-end reasoning framework based on a multi-modal large model is introduced to serve as a core base of a GUI agent; and based on a long-range task execution scheme of reinforcement learning and hierarchical planning, constructing a double-layer solution of reinforcement learning training and hierarchical planning execution. According to the method, the generalization ability, the single-step operation accuracy and the long-range task planning execution ability can be improved.

Techniques for URL handling in Retrieval-Augmented Generation (RAG) systems are disclosed. A RAG system uses a generative AI response in the selection of links to include in a modified output from a RAG system. The links are based on URLs that are extracted from ingested documents. In response to a query, the system generates a response and then performs a matching operation to match a portion of the generated response to a URL in a URL-keyword mapping. The system finds a match between a portion of the generated response and a keyword that is associated with a URL, generates a hyperlink using the matched URL, and adds the hyperlink to the response.

The invention discloses a low-code platform design system based on modularization and a dynamic extension component, and relates to the technical field of low-code development, and the system comprises a form module which is used for standardly defining a data model and dynamically rendering a user interface, realizing unification of form configuration through a structured metadata specification, and establishing a form module; bidirectional binding of the view and the data model is realized; the process module is used for realizing modeling, execution and state management of a business process, supporting automatic task scheduling and multi-instance management based on standardized process elements, and keeping consistency of a process state through an increment synchronization mechanism; and the data processing module is used for cooperatively managing platform data resources, supporting dynamic expansion and decoupling dependence between the form module and the process module through an event-driven mechanism. According to the method, the strong binding relationship between the decoupling form and the process is established, the reusability of the process module is improved, and the process module is independent of the form and can independently provide API service to the outside, so that the method is adaptive to more business scenes.