10381results 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 discloses a full-scene task autonomous execution method and system based on a large model, belongs to the technical field of computer application and artificial intelligence, and aims to solve the technical problems that an existing RPA technology is lack of autonomous thinking ability, and a browser use technology is limited in application range, is greatly influenced by factors such as webpage change and the like, and is poor in reliability. The Computer use technology is inaccurate in operation, high in development difficulty and has security risks, and the technical scheme comprises the following steps: establishing an MCP service, and compiling a task autonomous execution tool set containing various basic operation instructions including software opening, browser starting, precise clicking and information filling; the MCP service is deployed in the Windows virtual machine environment, and stable operation of the service is ensured; constructing a task autonomous execution workflow agent with task understanding, planning and scheduling capabilities; the user inputs a task target through a natural language, and the agent receives and records the specific demand of the user as a target guide for subsequent task generation and execution.

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 described herein relate to the use of generative artificial intelligence to facilitate rendering of user interface elements in a user interface associated with a digital assistant. A backend response is automatically generated in response to user input provided via the user interface associated with the digital assistant. Prompt data is generated. The prompt data includes an instruction to generate an intermediate representation of an output data structure supported by the digital assistant. The prompt data is provided to a generative machine learning model to obtain the intermediate representation. The intermediate representation is processed to obtain the output data structure. One or more user interface elements are rendered based on the output data structure. The one or more user interface elements present the response data via the user interface associated with the digital assistant.

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 a self-adaptive dynamic interface generation method and system based on a computing power host operation and maintenance platform, and relates to the technical field of operation and maintenance interface dynamic optimization. Aiming at the problem of static stiffness of an operation and maintenance interface in a complex scene, the scheme is adopted as follows: collecting user behaviors, a context environment and equipment data, and fusing into a multi-dimensional state vector; inputting the multi-dimensional state vector into a deep reinforcement learning model, and generating a dynamic interface strategy including layout component dynamic arrangement and function button priority ranking; designing a hierarchical multi-target reward function fusing operation efficiency reward, accuracy reward and cognitive load penalty, and performing strategy optimization; analyzing the optimized dynamic interface strategy through the cross-terminal rendering capability of the WebXR engine, and generating a front-end page adaptive to the current terminal; and continuously feeding back the collected data to the deep reinforcement learning model, and updating the network weight through a strategy gradient algorithm to realize user preference migration. According to the method and the device, cross-terminal self-adaptive dynamic interface generation can be realized.

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.

Systems and methods are described for providing multitenancy in an artificial intelligence (AI) platform. The platform can provide a user interface (UI) that allows for creation of an organization, tenants within that organization, and groups within tenants. This hierarchy can dictate resource permissions that impact which AI pipelines and AI pipeline objects are available to a user. The different tenants can have segregated data and utilize different pipeline objects, such as different prompts, datasets, or models. A marketplace UI can prioritize additional marketplace pipelines for approval and inclusion with the tenant.

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.

As disclosed herein, a computer-implemented method for travel planning is provided. The computer-implemented method may include receiving, via a conversational user interface (UI), a user input associated with a product offered by a booking application. The computer-implemented method may include retrieving, from a database associated with the booking application, at least one attribute of the product. The computer-implemented method may include determining, using a first machine learning (ML) model of a plurality of ML models, a user intent associated with the user input. The computer-implemented method may include generating, based on the at least one attribute and the user intent, a first response to the user input. The computer-implemented method may include providing, via the conversational UI, the first response to the user input. A system and a non-transitory computer-readable storage medium are also disclosed.

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 data processing system implements obtaining digital content as an output from the generative model; receiving a natural language prompt describing digital wrapping for the digital content, the digital wrapping to be presented to a recipient of the digital content; constructing a prompt based on the natural language prompt using the prompt construction unit; providing the second prompt to the generative model to cause the generative model to generate the digital wrapping; obtaining the digital wrapping as an output from the generative model; sending the digital content and the digital wrapping to a client device of a recipient; and causing the client device to present the digital wrapping on a second user interface of a client device and controls, which when activated, cause the client device to present an animation of the digital wrapping being removed and the digital content to be presented.

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.