150 results about "Software agent" patented technology

A distributed indexing and resolution architecture is disclosed for decentralized systems requiring modular, trust-scoped mutation control and dynamic alias governance. The system comprises a plurality of index entries arranged in a parent-child hierarchy, each associated with a structured alias and governed by one or more anchor nodes. Anchors perform localized resolution, mutation validation, and restructuring operations under deterministic policy constraints, enabling semantic scope enforcement and entropy-sensitive adaptation without requiring global consensus or centralized control. The architecture supports scoped alias traversal, asynchronous mutation proposals, and elastic anchor registration based on system state metrics. The indexing substrate may be integrated into heterogeneous infrastructures, including systems comprising distributed software agents, semantic execution platforms, or pseudonymous identity frameworks. Anchors coordinate within defined trust domains to ensure lineage continuity, dynamic rekeying, and semantic integrity across independently governed segments of a decentralized namespace.

A user of an identity management system may configure a software agent with a set of parameters within one or more user interfaces of an agent management service. The set of parameters may include an identifier of an application programming interface (API) endpoint of a first service that the software agent is authorized to query and one or more permissions associated with queries to the API endpoint by the software agent. Further, the user may receive an authentication token for the software agent from the agent management service. The software agent may use the authentication token for accessing resources of the service via the API endpoint in accordance with the one or more permissions. Thus, the user may configure the first software agent with the authentication token to enable the software agent to perform the queries to the API endpoint of the first service.

Systems and methods of the present disclosure may receive, from a user computing device, a user-provided data record query including a natural language request for information associated with one or more data sources. User persona attributes of the user may be determined, such as a user role or security parameters or both. Based on the user persona attributes a context query may be generated to obtain context attributes associated with the user-provided query. The natural language request and the context attributes are input into the model orchestration large language model (LLM) to output instructions to machine learning (ML) agents based on the context attributes. The ML agents output responses associated with the user-provided data record query based on the instructions, and the responses are input into the model orchestration LLM to output to the user computing device a natural language response based on the context attributes.

Systems and methods of the present disclosure may receive, from a user computing device, a user-provided data record query including a natural language request for information associated with one or more data sources. User persona attributes of the user may be determined, such as a user role or security parameters or both. Based on the user persona attributes a context query may be generated to obtain context attributes associated with the user-provided query. The natural language request and the context attributes are input into the model orchestration large language model (LLM) to output instructions to machine learning (ML) agents based on the context attributes. The ML agents output responses associated with the user-provided data record query based on the instructions, and the responses are input into the model orchestration LLM to output to the user computing device a natural language response based on the context attributes.

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.

A multi-agent and human project management and collaboration tool can provide the ability to create and manage projects and software agents in a cohesive framework. The collaboration tool can provide a multi-agent, multi-player channel where agents and humans work together to achieve an outcome. The collaboration tool can initiate a new project, where the objective is defined, and a team is dynamically built to achieve this objective. The team roles are parsed, and an agent is created for each role. Each agent is equipped with a personalized skillset and a working memory, allowing them to effectively collaborate with humans and each other to progress autonomously through the project stages. The agents can complete tasks based on the project plan and the results are stored in the agent's working memory. The project status is updated, and a report can be generated after each stage, providing a record of the project's progress.

Network monitoring system (100) comprising: a communication network (NT) comprising a plurality of nodes (N1-NN) and a plurality of node connections (E1-EP); wherein each node comprises an asset (AS1-ASF) configured to receive / transmit packets; a plurality of software agents (AG1-AGN) each installed in at least part of said plurality of nodes; each software agent being configured to redirect packets from a respective asset; at least one monitoring computer (CS) provided with software resources configured to: receive the packets sent by each software agent (AG1-AGN); extract from said packets source addresses and destination addresses; identify the plurality of nodes (N1-NN) and the plurality of node connections (E1-EP) from said source and destination addresses and associate each node to a corresponding node identifier; constructing an abstract model (200) representing said communication network (NT), wherein each node is represented by the corresponding node identifier and each node connection is represented by a corresponding link between respective nodes.

A system is disclosed herein for attributing user affective responses to discrete elements within an experience. Software agents extract token instances from sensor data, while a response decomposition module allocates portions of measured affective response to tokens based on user attention. A token library stores token-response associations across users and domains, enabling prediction of responses in new contexts. An orchestration module coordinates agents and library updates, with privacy managers ensuring local processing of raw data. The system distinguishes tokens of interest from background elements, supporting cross-domain learning and overcoming limitations of static, siloed affective measurement systems.

A system and method for privilege assurance protection of computer networks that remedies the deficiencies of the current directory service structure. The system uses a software agent to collect and store snapshots of all network resources on a computer network by identifying network domains, searching the directory service of each domain for network resources, and periodically querying the network resources for changes. The software agent communicates with a backend server which provides searching, querying, storage, administrative and other functionality to the agent via remote procedure calls.

The governed interaction interface maintains an identity-preserving semantic-state for a knowledge entity and updates it only through validated semantic evidence generated autonomously by execution mechanisms. Semantic intentions describe semantic needs and are transformed into capability-requests containing no operational commands. Execution mechanisms—including environmental devices, perception models, symbolic analyzers, virtual actors, and software agents—interpret capability-requests independently through tool-side semantic interpreters and may act or decline to act. The system does not observe or evaluate tool behavior and receives only semantic evidence describing semantic meaning of any resulting effect. The governance engine evaluates each evidence fragment independently under identity, coherence, lineage, evidentiary sufficiency, deviation constraints, and contextual compatibility. Semantic-state transitions occur only when validated evidence satisfies governance constraints. The system performs no prediction, optimization, control computation, multimodal fusion, or supervisory coordination, remaining fully separated from device-level behavior.

A user of an identity management system may configure a software agent with a set of parameters within one or more user interfaces of an agent management service. The set of parameters may include an identifier of an application programming interface (API) endpoint of a first service that the software agent is authorized to query and one or more permissions associated with queries to the API endpoint by the software agent. Further, the user may receive an authentication token for the software agent from the agent management service. The software agent may use the authentication token for accessing resources of the service via the API endpoint in accordance with the one or more permissions. Thus, the user may configure the first software agent with the authentication token to enable the software agent to perform the queries to the API endpoint of the first service.

To assist users in artificial intelligence driven software development, techniques for agentic software development assistance leveraging application behavioral models are disclosed. A software agent receives a codebase issue and orchestrates an iteration of a refining process. To orchestrate the process, the software agent feeds the codebase issue and instructions to generate a plan into a development assistant, which automatically returns a plan to the agent. The agent feeds the plan and instructions to generate a solution to the codebase issue back to the development assistant, which automatically returns a solution to the agent. The agent feeds the solution back to the development assistant, which tests the solution in order to verify the solution's quality in responding to the codebase issue. The solution is awarded a quality score, and the quality score is returned to the agent. Based on the quality score, another iteration of the refining process can be performed.

The Universal Transportation Operating System (UniTOS) is an autonomous cyber-physical operating system that coordinates all movable Assets—machines, containers, vehicles, and software agents—across every Domain of human and machine activity. UniTOS integrates three core services: the Process Layout Asset Navigation Tool (PLANT) for PLAN authoring and systemic learning, the PLAN Execution Tool (PLANET) for Asset-level execution and variance reporting, and the Monitor-Manage-Maintain (M3) service for Domain-level coordination and variance reconciliation.

A server accesses, during a contact center engagement performed by a software agent configured to generate and output automated responses to prompts, a prompt provided by a contact center user device. The server determines, using a natural language processing engine of the software agent, to use an application programming interface call based on the prompt, the natural language processing engine comprising a generative pre-trained transformer trained to generate natural language responses to natural language prompts. The server transmits an access request representing the application programming interface call to a governance engine configured to generate data structures responsive to application programming interface calls in a format readable by the software agent. The server receives a data structure responsive to the access request from the governance engine. The server generates, using the natural language processing engine, a natural language response based on the data structure.

A vehicle artifact (e.g., software, firmware, content, etc.) distribution system uses direct vehicle-to-vehicle communications to extend a range of physical infrastructure and to improve speed and cost of distributing artifacts to vehicles. An artifact to be distributed is assigned a vehicle-to-vehicle distribution policy that defines gateway vehicle selection, wherein gateway vehicles are used to distribute the artifact to other recipient vehicles. A local software agent executing on the vehicles distributes the artifact in accordance with the vehicle-to-vehicle distribution policy using locally installed antennas of the vehicles.

When a network element attempts to establish a session with another network element, a security verification agent may be activated in one or both network elements. The security verification agents, such as front-end processors, virtual network functions, or other software agents, may reside in each of the network elements.

Systems and methods of the present disclosure may receive, from a user computing device, a user-provided data record query including a natural language request for information associated with one or more data sources. User persona attributes of the user may be determined, such as a user role or security parameters or both. Based on the user persona attributes a context query may be generated to obtain context attributes associated with the user-provided query. The natural language request and the context attributes are input into the model orchestration large language model (LLM) to output instructions to machine learning (ML) agents based on the context attributes. The ML agents output responses associated with the user-provided data record query based on the instructions, and the responses are input into the model orchestration LLM to output to the user computing device a natural language response based on the context attributes.

Systems and methods of the present disclosure may receive, from a user computing device, a user- provided data record query including a natural language request for information associated with one or more data sources. User persona attributes of the user may be determined, such as a user role or security parameters or both. Based on the user persona attributes a context query may be generated to obtain context attributes associated with the user-provided query. The natural language request and the context attributes are input into the model orchestration large language model (LLM) to output instructions to machine learning (ML) agents based on the context attributes. The ML agents output responses associated with the user-provided data record query based on the instructions, and the responses are input into the model orchestration LLM to output to the user computing device a natural language response based on the context attributes.

A method of managing a decentralised repository of stored digital units of information, the decentralised repository comprised of a plurality of inter-acting peer-to-peer computing systems, comprising steps of: (a) loading a software agent on each of a plurality of peer-to-peer computing systems, the software agents making up a network of distributed software agents; (b) using the network of distributed software agents to manage the decentralized repository of stored digital units of information; wherein at least one of (i) software agent code for at least one of the software agents, and (ii) a cryptographic digest of such software agent code, is stored in at least one block in the decentralised repository.

This operation mode selection device selects an operation mode of a ship power system including one or a plurality of generators and batteries, the operation mode is determined through at least the number of generators in operation, the selection of a generator to be operated, a load sharing ratio of the generator and a battery, the selection of charging and discharging of the battery, and the device comprises a reinforcement learning unit which selects the operation mode; and performing reinforcement learning on a software agent.

A network storage system comprises a first application node, storage nodes, and a switch fabric comprising switches providing data paths. Each data path connects the first application node to a storage node of the plurality of storage nodes. The system further comprises a control plane software agent. The control plane software agent configures the network storage system to receive, via the first application node, a request for storing a file, select a first storage node, initialize a file identifier for the file based on a network topology of the switch fabric and the storage nodes, and store the file on the first storage node. The file identifier describes the first storage node, a storage location in the first storage node, and a first data path of the data paths. The first data path includes a first set of switches connecting the first application node to the first storage node.

The disclosed systems, methods and computer readable media relate to managing Non-Volatile Memory Express (NVMe) over Transmission Control Protocol (TCP) (NVMeOTCP) connections between a smart network interface card (smartNIC) and a block storage data plane (BSDP) of a cloud computing environment. A software agent (“agent”) executing at the smartNIC may manage a number of network paths (active and, in some cases, passive network paths). The agent may monitor the network traffic (e.g., input / output operations (IOPS)) through the paths (e.g., using established NVMeOTCP connections corresponding to the paths). If a condition is met relating to a performance threshold associated with the monitored paths, the agent may increase or decrease the number established NVMeOTCP connections to match real time network conditions.

A computing system may be used to support techniques to automate tasks using large language models (LLMs) and software agents. A user device may provide a data set to the computing system, and the computing system may process the data set to identify the one or more tasks using an LLM. The computing system may select one or more software agents to execute each of the identified tasks. For example, the computing system may identify a respective type for each task, and the computing system may select a respective software agent of a set of supported software agents configured to execute the respective type of task. Each software agent may execute a respective task to produce an output, such as by generating a summary of a transcript, transmitting one or more communications to users associated with the organization, or providing responses to inquiries, among other examples.

A method may include collecting, by a software agent connected to a target application, a set of observations of executing the target application while in a logging mode. The set of observations identifies instances of operations of the target application. The method may also include transmitting the set of observations to a security service, and receiving an allow list and a confidence estimator model from the security service. The security service generalizes the operations into multiple general operations in the allow list and trains the confidence estimator model based on the set of observations. The method may in addition include transitioning, by the software agent, to a blocking mode, and controlling, according to the allow list and the confidence estimator model, performance by the target application of second instances of second operations while in blocking mode.

A method for controlling an actuator based on control parameters generated by reinforcement learning, as well as an associated device and a related training method, are disclosed. The method for controlling an actuator based on control parameters generated by reinforcement learning comprises the steps of determining (S1) a set of control parameters with a plurality of discrete control parameters for the respective actuator, receiving (S2) a control parameter generated by a software agent, mapping (S3) the control parameter to one of the control parameters of the control parameter set, and controlling (S4) the actuator based on the control parameter. Furthermore, the invention relates to a method for improved training of a software agent for controlling an actuator and a device for carrying out this method.

Systems and methods described herein provide a computer-implemented method for process simulation using a multi-agent system. A software agent is trained using historical process data associated with a process. Each software agent represents a single task in the process. Based on the candidate process model, the process is simulated by executing the multi-agent system to generate simulation results. The software agent operates autonomously within the multi-agent system based on the trained behavior. The process is also performed using a runtime configuration based on the candidate process model. Execution of the process generates runtime results for comparison with the simulation results. In some examples, the candidate process model is adjusted based on the comparison, and simulation and execution of the process are repeated using the adjusted multi-agent system and the adjusted runtime configuration, respectively. A deployment process model is generated based on the adjusted candidate process model.

Techniques for reinforcement learning software agents augmented with external data are provided. A reinforcement learning model supporting the software agent can be trained based on information obtained from one or more knowledge bases, such as online forums. The trained reinforcement learning model can be tested in an environment with limited connectivity to the external environment to meet performance criteria. The reinforcement learning software agent can be deployed within the environment along with the tested and trained reinforcement learning model to autonomously perform actions to process requests.

A system and method for bi-directional quality testing of web real-time communications (WebRTC) sessions. In an embodiment, the system and method comprise an operator network, cloud contact center, cloud contact center agent application, and a synthetic software agent comprised of agent automation software, injected API shim code, virtual audio devices, audio processing applications, and media servers, capable of performing automated and to end communication testing. In order to provide end to end testing, especially with respect to voice quality, the synthetic agent software may control and monitor the audio channels (both send and receive) of the browser communication session.

The present disclosure is directed to systems and methods directed to improving the functions of a vehicle. Systems and methods are provided that provide a custom tool that autogenerates a set of software agents that allows a system to separate processing, transmission and receiving of messages to achieve better synchronization. The disclosure herein also provides a simplified method of key provisioning by designating one client as a server and assigning a symmetric key to every other client permanently provisioned between that client and the server. Systems and method are further provided that predict faults in a vehicle. Systems and methods are also provided that preserve data in the event of a system crash. Systems and methods are also provided in which an operating system of a vehicle detects the presence of a new peripheral and pulls the related interface file for that new peripheral. Further, a data synchronization solution is provided herein which provides optimized levels of synchronization.

Disclosed herein is an agent-first application architecture that facilitates intelligent, metadata-driven interactions across distributed computing environments. The system comprises a Metadata Repository, which defines communication policies, interaction rules, and contextual parameters. An Agent Orchestration Layer governs autonomous software agents, dynamically managing their lifecycle, task delegation, and execution logic. A Self-Learning Engine continuously refines agent decision-making models through real-time metadata updates and adaptive learning mechanisms. The system further incorporates an Event-Driven Middleware, which enables real-time, event-triggered execution of agent actions, ensuring adaptive, scalable, and autonomous decision-making across complex multi-system environments. By eliminating rigid API dependencies and leveraging metadata-centric intelligence, the architecture enhances cross-system interoperability, resilience, and adaptability. The invention is particularly suited for dynamic, multi-agent ecosystems requiring real-time adaptability, self-optimization, and intelligent orchestration of system interactions.