151 results about "Virtual agent" patented technology

In described techniques, a request may be received at a virtual agent. The request may be processed at a router prompt to determine, from a language model, a topic prompt of a plurality of topic prompts associated with the request. The topic prompt and the request may be provided to the language model. A response to the request may be received from the language model and in response to the topic prompt and the request. Accordingly, the response may be provided using the virtual agent.

A system to protect an industrial asset includes a plurality of monitoring nodes each generating a data stream of current monitoring node values in time-domain, and a virtual agent associated with each of the plurality of monitoring nodes. the virtual agent being configured to detect anomalous performance of the corresponding monitoring node and configured to communicate with one or more other virtual agents via a network.

In various examples, a language model may be trained and used as part of an interface for a simulation system. For instance, user inputs may be applied to the language model and the language model may be trained to generate code, make API calls, or perform any other operations to interact with and / or control various aspects of the simulation. In some examples, the language model may generate code for, among other things, creating and / or customizing a virtual environment associated with the simulation. For instance, the generated code may include, but is not limited to, code for rendering the virtual environment, code for rendering and simulating behaviors of virtual agents (e.g., pedestrians, vehicles, animals, etc.) and / or any other objects (e.g., road signs, buildings, trees, etc.) within the virtual environment, code for recreating and simulating real-world events from recorded sensor data, etc.

In various examples, systems and methods are disclosed relating to displaying images in chatbot / NPC / virtual agent / digital avatar / etc. responses. A system can identify text corresponding to an image in an electronic document and can store a representation of the text in association with an identifier of the image. The system can receive an input prompt for a machine-learning model. The system can generate a response to the input prompt using the machine-learning model. The response can include the image responsive to identifying the representation of the text using a searching function and an output of the machine-learning model.

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.

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.

A system is described for training a virtual agent of a listing network platform using a machine learning model. The system establishes a communication session with a virtual agent of a listing network platform and generates, by a first machine learning model, conversation data representing a customer support issue associated with the listing network platform. The system transmits, by the first machine learning model, at least a portion of the conversation data to the virtual agent via the communication session. The system receives one or more responses to the at least the portion of the conversation data from the virtual agent in the communication session and stores a simulated conversation comprising the at least the portion of the conversation data generated by the first machine learning model and the one or more responses received from the virtual agent.

The invention relates to an unmanned cluster virtual-real interactive simulation method based on three-dimensional motion capture, which comprises the following steps of: operating autonomous navigation and autonomous exploration functions of a single real intelligent agent in a real environment, acquiring posture information of the real intelligent agent through a motion capture system, and acquiring image, point cloud and IMU (Inertial Measurement Unit) posture information through a sensor; the autonomous navigation and autonomous exploration functions of a single virtual intelligent body are operated in a simulation environment, and posture information, images, point clouds and IMU posture information of the virtual intelligent body are obtained; sending data stored in a real environment to a simulation platform in real time, comparing and calculating a dynamic error, a kinematics error, a sensor error and an environment error, and correcting a virtual agent model and a simulation environment model; in a real environment and a simulation environment, multi-machine topology flexible formation and multi-machine cluster autonomous navigation are carried out at the same time, whether various errors of each virtual agent and a real agent exceed a set threshold value or not is calculated through comparison, and virtual-real interaction iteration training is carried out until the errors converge.

Systems and techniques for facilitating unified multichannel communication are provided. The described systems and techniques improve communication technology through an encompassing, channel-agnostic approach which unifies disparate communication modes into a singular coherent thread. A unified multichannel communication (“UMC”) service of a UMC platform can initialize a UMC thread for a UMC session, where the UMC thread can be used to facilitate unified multichannel communication. The UMC session can involve multiple participants, including human users and software agents (e.g., conversational bots, virtual agents, digital assistants, and other dialog interfaces). The UMC platform can facilitate creating and interacting with a digital assistant providing unified multichannel communication.

Techniques for a container orchestration system are disclosed. A virtual agent may be executed in a first service tenancy of a cloud network manager. The virtual agent may be executed on a virtual node of a container orchestration system. A container orchestration API server may be executed in a second service tenancy of the cloud network manager. A request from the container orchestration API server to the virtual agent may be routed through a customer tenancy of a customer of the cloud network manager.

Embodiments of the present disclosure provide a computer-implemented method. The computer-implemented method performed by a controller includes generating a plurality of virtual agents with Artificial Intelligence (AI) capabilities based at least on organizational data. Furthermore, the computer-implemented method includes delegating at least a portion of operational workflows to one or more of the plurality of virtual agents. The computer-implemented method further includes generating a plurality of employee digital twins associated with a plurality of respective employees, based at least on employee-specific data. Furthermore, the computer-implemented method includes generating at least one workplace digital twin representing at least one respective workplace environment. The computer-implemented method also includes enabling communication amongst a plurality of disparate groupings generated by selecting a plurality of entities from one or more of a plurality of employee devices, the plurality of virtual agents, the plurality of employee digital twins, or the at least one workplace digital twin.

Methods and systems for creating and using a knowledge base in a contact center. Templates, including responses to specific issues, are made available to agents in dynamic manner to allow agents to be more efficient while performing their daily job. A centralized database of templates is created in a novel manner to be used for multiple digital channels. The templates can be used and reused by multiple systems such as an agent assistant system or a virtual agent system, providing an integrated and consistent experience for responding to communications in a contact center.

The invention discloses an evolved memory system and interaction method for a universal virtual agent in a GUI (Graphical User Interface) environment, and the system integrates sub-task nodes after decomposition of various tasks and the dependency relationship thereof by constructing a global memory graph, forms a memory graph, and generates a feasible GUI operation sequence by adopting a search algorithm based on the graph. And abstracting the sequence into a transferable guide rule by means of a large language model. When the task is executed, the matching rule and the subtask nodes are retrieved through semantic and structure mixing, the agent decision is assisted, the memory graph is updated after successful execution, and continuous evolution is achieved. When the system is practically applied, the intelligent agent can be driven to execute a GUI task through a natural language instruction, the intelligent agent generates an operation action by utilizing a rule and a retrieval mechanism in the memory graph, interacts with an interface to complete the task, and feeds back an interaction track which is successfully executed to the system, so that the structure of the memory graph is continuously optimized.

A method and system for training a virtual agent is provided herein. The method and system comprises storing conversations between the virtual agent and a user in logs. The method and system further comprises mining the logs to retrieve utterances. The method and system further comprises computing regression for each of the plurality of the charging time segments. The method and system further comprises providing a score to the utterances. Further, the method ranking the utterances based on the score.

An execution method and an execution system for a virtual meeting are provided. The execution method includes the following steps. A discussion item is received. The discussion item is compiled into a plurality of tasks. The tasks are distributed to a plurality of virtual agents. At least one analysis information is obtained by at least one of the virtual agents using an industrial data database. The industrial data database is built via an analytic AI model. At least one guidance information is obtained by at least one of the virtual agents using an industrial knowledge database. The industrial knowledge database is built via a generative AI model. If the analysis information and the guidance information meet the predetermined condition, the virtual expert compiles the analysis information and the guidance information into a recommendation report.

Embodiments of the present disclosure may include a dual-layered artificial intelligence system including a leading virtual agent and a set of other virtual agents: the leading agent, equipped with vast general knowledge, interfaces with the user and enforces guidelines in the overarching goal and progress, branding, guiderails, regulatory compliance, and the system's voice, while the other agents contain vast knowledge in a specific domain. These other agents only communicate with the leading agent and are called upon by the leading agent when their respective expertise is needed to solve the goal.

Metadata associated with program code documentation is identified, wherein the program code documentation is associated with corresponding program code. A natural language question regarding the corresponding program code is obtained via a virtual agent. A response to the natural language question is determined based on the metadata using one or more trained machine learning models. The response to the natural language question is provided to the virtual agent.

A system for automated healthcare management of a patient including a virtual agent system that includes multiple specialized personas, each configured to perform specific tasks within a healthcare management process. The personas include a schedule builder persona configured to generate a personalized care plan based on patient data, integrating input from one or more expert resources to tailor the care plan to the patient's needs; and a message builder persona configured to generate and schedule patient communications based on the generated personalized care plan, ensuring that each message is compliant with healthcare regulations.

A system adaptively generates a virtual experience inclusive of a virtual agent. The system receives speech text from a constrained machine-learned language model configured to provide adaptive speech for the agent. The system parses the speech text into a plurality of speech units, wherein a speech unit is an atomic unit representative of natural breaks in human speech. The system applies a hashing function to each speech unit to determine a corresponding hash. The system, for each hash, queries a cache database to identify whether the cache database includes a cached hash that matches the queried hash. Responsive to identifying a matching hash to a first queried hash, the system retrieves a first audio byte stored with the matching hash. The system generates a voiceover track for the virtual agent with the first audio byte for presentation to a user.

Techniques are disclosed that relate to a computer system implementing a conversational artificial intelligence platform. The computer system may receive a request that includes text data specifying an utterance of a user. The computer system determines, form the utterance, an intent of the user. The computer system selects a first virtual agent to handle the request based on the determined intent. The computer system routes the text data and subsequent text data of the request to the first virtual agent for handling. The computer system receives, from the first virtual agent, a message that the determined intent is incorrect, the message including state information indicative of actions taken by the first virtual agent relative to the request. The computer system selects a second virtual agent to handle the request based on the state information. The computer system may receive input from an interactive voice response system or another channel.

A system for executing actions based on user input is provided. The system comprises a virtual agent for a software application, wherein the virtual agent is configured to store a correlation between actions available in the software application. Further, the system associates one or more of the actions with one or more tags. The system receives at least an audio input from a user of the software application and uses the input to identify an action desired by the user to be performed among the actions. Further, the system executes one or more actions based on the desired action and the correlation between the actions available in the software application.

Techniques for a container orchestration system are disclosed. A container orchestration API server receives a request from a virtual agent in a container orchestration cluster that includes a cloud provider authentication token. The request is generated by the virtual agent using a resource principal that corresponds to the virtual agent. The container orchestration API server attempts authentication of the virtual agent using a container orchestration native authentication. Upon failure of the container orchestration native authentication, the container orchestration API server authenticates the virtual agent using a cloud provider authentication unit via an authentication webhook using the cloud provider authentication token.

In various examples, virtual participant-based microservices for video conferencing applications and systems are provided. A virtual participant service provides a subject matter expert to participants of a conference session. A virtual participant may be presented within a video conferencing environment as a simulated meeting participant that other meeting participants may interact with using natural conversational language. The virtual participant service may include a virtual participant controller frontend service that interfaces with the video conferencing platform, an avatar manager to generate an avatar representing the virtual participant, and an LLM services gateway that functions as a microservices server for one or more LLM-based services that may be accessed through the virtual participant. The virtual participant service may use natural language processing to evaluate spoken requests for information and provide a response back to the human user participants of the conference channel using an animated avatar.

A virtual learning system is introduced herein that provides a virtual reality classroom environment having a virtual agent. The virtual agent plays the role of an active student and promotes classroom participation through verbal and nonverbal interactions with the teacher and other students. The virtual agent, which is embodied as a 3D virtual avatar in virtual reality, interacts with an actual teacher and actual students with both spoken language and body gestures. The behaviors of the virtual agent avatar help to encourage other students to participate more actively in the virtual classroom environment.

A method and system for generating a response through a virtual agent is provided herein. The method comprises receiving information associated with a plurality of themes and topics. The method further comprises creating a knowledgebase based on the information received. The method further comprises analyzing the knowledgebase based on an intent identified, using an Artificial Intelligence (AI) model. Further, the method comprises generating a response corresponding to the intent through the virtual agent based on analyzation.

The present invention belongs to the technical field related to robot teleoperation, and discloses a robot teleoperation safety control method and system that uses operation intention recognition and adaptive control obstacle functions. The method includes: establishing a virtual agent between the teleoperated master robot and slave robot; calculating the nominal control input of the virtual agent, converting the nominal control input into a safety control input using an adaptive control obstacle function as a constraint, and using the safety control input to control the virtual agent to track the master robot according to a preset desired tracking trajectory; setting the slack variables in the adaptive control obstacle function according to the operator's interaction intention with the target; calculating the expected control input of the slave robot, and using this expected control to control the slave robot to track the virtual agent, thereby achieving teleoperation control of the slave robot by the master robot. The present invention solves the problem of being unable to achieve safety control functions for different operator interaction intentions with the target within a unified control framework.

An artificial intelligence, AI, controller network entity (120) is disclosed for controlling a plurality of AI agents (110a-n) of a distributed AI, DAI, group of AI agents (110a-n) for jointly performing an AI task in a mobile network (100). The AI controller network entity (120) is configured to determine for each of the plurality of AI agents (110a-n) a current AI agent status of a plurality of possible AI agent statuses, wherein the plurality of possible AI agent statuses comprise an active status, an idle status, and a dummy status, wherein in the active status an AI agent is active in the AI task of the DAI group of agents (110a- n) and wherein in the idle status an AI agent is temporally not active in the AI task of the DAI group of agents (110a-n), wherein the DAI group of AI agents (110a-n) comprises a dummy AI agent (110a) and wherein the dummy AI agent (110a) is not capable of being active in the AI task of the DAI group. Moreover, the controller network entity (120) is configured to control for one or more of the plurality of AI agents (110a-n) a transition from the current AI agent status of the AI agent to a different AI agent status of the plurality of possible AI agent statuses during the AI task.

Persistent storage contains a state machine for a request routing process and a plurality of requests related to the request routing process. One or more processors are configured to apply the state machine through operations including: obtaining a request from the plurality of requests; providing, to a criticality detection application and to an intent detection application, a representation of the request, wherein the criticality detection application is configured to detect a criticality of the request, and wherein the intent detection application is configured to determine a semantic intent of the request; receiving, from the criticality detection application and the intent detection application, respective indications of a detected criticality of the request and a detected intent of the request; determining whether to route the request to a channel that is one of a live agent, a virtual agent, or a search-based application; and routing the request to the channel as determined.

The invention relates to a UE5-based ship fire evacuation simulation method. The method comprises the following steps: S1, constructing a mobile virtual agent in a virtual ship cabin scene; s2, setting an evacuation area matrix and an evacuation area connection condition matrix of a virtual ship cabin; s3, constructing a mobile virtual agent fire evacuation model based on the evacuation area matrix and the evacuation area connection condition matrix; and S4, solving the evacuation model to obtain an evacuation path, and controlling the mobile virtual agent to perform evacuation simulation according to the evacuation path. Compared with the prior art, the method has the advantages of optimizing a fire evacuation route and the like.

A request processing method, executed by a load balancing node in a request processing system, wherein the request processing system comprises a reverse proxy node, a virtual proxy node, and the load balancing node, and request forwarding is performed between the reverse proxy node and the virtual proxy node by means of a plurality of socket nodes. The method comprises: upon receipt of a plurality of traffic requests by the reverse proxy node, determining, from among the plurality of socket nodes, socket nodes available for processing (302); for each socket node available for processing, determining, from the virtual proxy node, a processing instance having a binding relationship with the socket node available for processing, so as to obtain a plurality of processing instances (304); determining, from among the plurality of processing instances, selected processing instances matching socket nodes available for processing (306); and on the basis of multiple pieces of instance running information respectively corresponding to the selected processing instances, respectively distributing the plurality of traffic requests to the selected processing instances, so that the selected processing instances process the distributed traffic requests (308).