717 results about "Message delivery" patented technology

A method for secure onboarding to a closed mesh network in which a camera-equipped device scans a QR code embedded in a durable physical carrier (e.g., adhesive bandages, photographs, ID cards) to extract an access credential, configuration profile, or secure URL that automatically configures the device for network access, authenticates the device to the mesh without manual credential entry or transmission of plaintext credentials over insecure channels, and establishes end-to-end encrypted communications between network nodes; the method supports cryptographically random, single-use or time-limited credentials with periodic refresh to prevent replay, encoding of VPN / WireGuard or proprietary configuration profiles, substantially real-time onboarding (e.g., under five seconds), optional multi-factor verification (biometric or PIN), immediate initiation of secure messaging, voice / video or data sessions upon onboarding, audit logging for compliance and revocation based on detected unauthorized activity, and covert or overt embedding of QR codes in environment-resistant carriers for emergency, disaster response, first responder, or covert deployment, with the QR code rendered unreadable or deactivated after successful onboarding to prevent credential reuse.

A system and method for privacy-preserving communication validation and multi-attribute trust scoring is disclosed. The system analyzes communication metadata to determine pattern legitimacy by comparing current communication patterns against relationship fingerprints without accessing communication content. The system validates relationship context between communicating parties using interaction graph analysis and historical communication data. Cross-network intelligence correlation compares current patterns against aggregated patterns across voice, email, and messaging services, creating a self-strengthening security framework that recognizes emerging threat patterns while validating legitimate communication behaviors. The system generates comprehensive multi-attribute trust assessments comprising individual trust attribute scores including engagement rate, reliability index, channel preference, temporal pattern, and behavioral pattern, combined into overall trust levels. Trust context is displayed through a user interface presenting simplified, intuitive, and actionable information with progressive disclosure capabilities, enabling informed user decisions while preserving privacy. Communication processing actions provide users with appropriate engagement options tailored to specific trust assessment results.

A core network server for defining authentication credentials and authenticating a wireless communication device according to WIFI communication protocols includes a central processing unit (CPU) and a non-transitory memory comprising executable instructions that when executed by the CPU, causes the core network server to receive an encrypted authentication request from a wireless communication device; send the encrypted authentication request to an authentication server based on one or more attributes in the encrypted authentication request; receive an indicator of a specialized network slice associated with the wireless communication device based on sending the encrypted authentication request; communicate authentication messages to the wireless communication device according to one or more network functions of the specialized network slice; and authenticate the wireless communication device according to the specialized network slice responsive to communicating the authentication messages.

This disclosure is generally directed to wireless communication systems and methods and relates particularly to a mechanism for implementing an artificial intelligence framework for adaptively configure the over-the-air communication interfaces of the wireless communication systems. AI network configuration functions may be provided as services for AI-assisted network configuration (AI configuration services, or AICS). Such AICS may be requested and configured via various messaging and signaling mechanisms. The AI model life cycle management including training, delivery, activation, inference, deactivation, switching, performance evaluation, and the like may be configured, triggered, and otherwise provisioned via control and data messages and signaling communicated between the various network elements in the wireless communication system.

A method, computer system, and computer program product are provided for dynamic user personalization using large language models. A conversation history is obtained comprising a plurality of messages associated with a user’s activity in a messaging system. The conversation history and a request to identify interests of the user, based on the conversation history, are provided to a large language model. A natural language output is received from the large language model comprising one or more identified interests of the user. A text sample, the natural language output, and a request to summarize the text sample based on the natural language output are provided to the large language model. A summary of the text sample that is personalized based on the one or more identified interests of the user is received from the large language model.

The invention relates to a real-time interaction violation detection method, system and device and a medium. The method comprises the following steps: acquiring an online interaction session original information flow containing a user text sequence, a voice signal, an image file and an interaction behavior timestamp; extracting text semantic vectors, voice acoustic features and image visual content description, and integrating to generate an initial feature vector set; based on the interaction behavior timestamps, constructing an interaction time sequence diagram by taking the initial vectors as nodes, calculating multi-modal association weights among the nodes and updating connection edges to obtain a multi-modal fusion diagram; and inputting the fused graph into a graph neural network, outputting a global graph embedded vector through message passing and node aggregation, matching a preset violation mode vector library to calculate a similarity score, determining a violation type, and generating a risk assessment conclusion containing the violation type and confidence. According to the method, cross-statement and cross-modal context violation association is effectively captured, violation judgment accuracy is improved, and an intervention basis is provided for a platform.

A system and method for contextual three-dimensional messaging in augmented reality (AR) environments is disclosed. The system receives chat messages with specified real-world destinations and stores them associated with those locations. When a user wearing an AR device enters a destination location, the system detects their presence using techniques like GPS, Wi-Fi positioning, or computer vision. It then generates a 3D visual representation of the message and determines an appropriate spatial position within the physical environment based on environmental analysis and object detection. The 3D message is displayed at the determined position in the AR view. The system can analyze message content to identify topics and match them to detected real-world objects for contextual placement. Users can interact with displayed messages through gestures or voice commands to reply, forward, delete, or reposition messages. This enables immersive, location-aware messaging experiences that seamlessly blend digital content with the physical world.

The invention discloses a reconfigurable intelligent surface (RIS)-assisted multiple-input-multiple-output (MIMO) implicit channel estimation method based on a graph attention network, which is used for efficient downlink transmission in a multi-user scene. Firstly, a graph attention network is designed, user nodes and RIS nodes are modeled in a unified mode, received pilot signals serve as initial features, spatial feature expression is enhanced in combination with user three-dimensional position information, and therefore interference between users and a spatial correlation structure are accurately represented; secondly, end-to-end feature aggregation is achieved based on a message passing mechanism, a base station beam forming matrix and an RIS reflection coefficient are directly predicted under the condition that explicit channel estimation is not needed, and the total transmitting power constraint and the unit mode constraint are met through normalization processing so as to complete joint optimization; according to the method, the users and the speed of the system can be remarkably improved under limited pilot frequency overhead, and the method has excellent generalization performance and robustness in a multi-user complex propagation environment.

Operating effectively in an increasingly prolific Internet of Things environment, one needs the ability to message, whether device status, actions taken, recommendations for actions or other information to those with a need to know and support responses to actionable messages. Texts and e-mail are limited; they can neither provide other outputs, be targeted in intelligent fashion nor selectively relayed or contain execution instructions for target devices. The present invention is an Extended-Capability Messaging Appliance (ECMA) that communicates with other IoT devices that provides these capabilities and can include AI. ECMA and non-ECMA AI elements can constitute a Hybrid AI Backbone. The Hybrid AI Integrator receives results from multiple A elements and develops one or more conclusions. An ECMA may be configured as a Body-Area Network for individuals or a Business-Area Network for industry and other commercial applications with an AI Agent called BANDIT that processes requests from a user.

The invention discloses a computing power network resource scheduling method and system, and the method comprises the steps: responding to a service request, carrying out the coding of a network state through an edge attention graph neural network, and enabling the network to integrate an edge feature vector into attention weight calculation in message transmission, so as to sense a link state; based on a coding result, a scheduling decision of joint optimization calculation and network resources is generated through a self-adaptive multi-target reinforcement learning strategy network, and the reward function weight can be dynamically adjusted according to the network state; decisions are executed based on a distributed multi-agent architecture, and all agents realize global coordination through a message coordination mechanism. According to the invention, efficient, adaptive and extensible scheduling of multiple targets in the dynamic computing power network is realized.

The invention provides a simulation scene multi-target calculation method and device based on parallel scheduling and a server, and relates to the technical field of industrial simulation application, and the method comprises the steps: obtaining a parameter set of a plurality of simulation tasks, and carrying out the task clustering processing of the parameter set according to the parameter similarity, and obtaining a plurality of task clusters; all the task clusters are distributed to different computing nodes, parallel solving processing of simulation tasks is carried out on the task clusters in all the computing nodes on the basis of a self-adaptive tuning model of a message passing interface, and local simulation results corresponding to all the computing nodes are obtained; and carrying out convergence and recombination processing on the local simulation results to obtain a complete simulation result data set, carrying out compression and indexed storage processing on the simulation result data set to generate a lightweight simulation result, and carrying out streaming visualization processing on the lightweight simulation result to obtain a target interactive simulation visualization result. According to the method, the simulation efficiency of simulation scene multi-target calculation can be remarkably improved.

Disclosed are systems and methods that provide a decision-intelligence (DI)-based, computerized framework for determining and implementing the mechanisms for compiling and / or displaying a summary of an electronic message within an inbox. The disclosed framework provides novel mechanisms for triaging messaging, in that mechanism for how they are pre-, post- and / or in real-time processed by the server, sender and / or recipient devices pursuant to providing summarizations for an improved user consumption experience. The framework operates to determine which messages are candidates for specific forms of triaging, then queues such messages for summarization according to the mechanism for which it will be delivered. Such determination and summarization can effectuate an improved messaging experience as it relates to how messages are handled by inboxes and / or displayed for interaction by recipient users.

The invention belongs to the technical field of block chains, and particularly relates to an alliance chain efficient atomicity cross-chain interaction method and system based on distributed digital identities. The method comprises the following steps: deploying a cross-chain contract and a cross-chain gateway for alliance chains, and establishing a cross-chain channel between the alliance chains based on the cross-chain contract and the cross-chain gateway; and carrying out cross-chain asset transfer, cross-chain information transmission and cross-chain smart contract calling based on the established cross-chain channel. According to the alliance chain cross-chain interaction method based on the decentralized identity identification, interaction tasks such as cross-chain asset transfer and message passing can be safely and efficiently processed, and cross-chain intelligent contract asynchronous calling with atomicity guarantee can be executed. The historical consensus nodes are tracked through the DID technology, the dependence on a third party is reduced in the cross-chain operation, the decentralization level of the system is further improved, the safety and reliability of the system are remarkably enhanced, and meanwhile the cross-chain interaction efficiency is improved.

The invention provides an abnormal user detection method and system based on topology awareness and hub node guidance. The method comprises the steps of obtaining graph structure data; fusing the topological relation in the graph structure data and the initial features of the nodes by using a topological adaptive label evolution mechanism, and predicting and optimizing labels of unlabeled nodes to generate an optimized label distribution matrix; based on the node initial features, taking the optimized label distribution matrix as a supervision signal, and generating semantic hub node features through a double-layer multi-scale contrast learning mechanism; dynamically screening neighbors of each target node by adopting a strategy gradient driven neighbor aggregation mechanism based on semantic hub node features; performing message passing through a multilayer graph neural network based on the screened neighbor set, and fusing node representations of different layers by using an attention mechanism to obtain a final node representation; and inputting the final node representation into a classifier, and outputting an abnormal user detection result.

The invention relates to the technical field of computers, and discloses a visual operation and maintenance supervision platform based on a digital twinning technology, and the platform comprises a data preprocessing module which is used for preprocessing a multi-source heterogeneous monitoring data flow; the data fusion module is used for constructing an equipment topological relation graph, extracting an initial feature vector set of equipment nodes and carrying out space-time fusion; the digital twin modeling module is used for performing parametric modeling and semantic annotation; the real-time synchronization module is used for extracting a model updating instruction set and carrying out asynchronous message transmission and parallel updating; the anomaly detection module is used for training an anomaly detection model and analyzing anomaly types and anomaly reasons; the root cause tracing module is used for constructing an associated sub-graph of abnormal equipment, predicting a root cause candidate equipment list and carrying out hybrid reasoning and evidence fusion; the visual presentation module is used for visualizing a fault influence range, dynamically displaying network traffic and performing multi-view roaming and information drilling; according to the invention, the fault positioning efficiency and the operation and maintenance management level are improved.

In some implementations, a network device associated with a non-terrestrial network (NTN) may establish a connection with a user equipment (UE). The network device may receive, from a server, a message. The network device may attempt to deliver the message to the UE, wherein an attempt to deliver the message is unsuccessful based on the connection being lost between the network device and the UE. The network device may transmit, to the server, an indication that the attempt to deliver the message to the UE is unsuccessful. The network device may reestablish the connection with the UE. The network device may receive, from the UE, a keep alive message. The network device may transmit, to the server, the keep alive message. The network device may receive, from the server, the message in response to the keep alive message. The network device may transmit, to the UE, the message.

Methods, systems, and bitstream syntax are described for determining the processing order of metadata messaging, such as supplemental enhancement information (SEI) messaging in MPEG video coding.

Systems and methods for integrating generative artificial intelligence (AI) capabilities within Software as a Service (SaaS) platforms. One of the computer-implemented methods are for querying a generative AI model about structured data in a SaaS environment, enabling users to interact with and manipulate data through AI-assisted interfaces. One of the systems maintains a generative AI agent configured to interact with SaaS platform data as a virtual team member, capable of understanding context and nuances of project data. Also described are methods for color-context aware data analysis, generation of interactive elements in messaging sessions, and cross-application generative AI agent interactions triggered by user mentions. Also described is facilitating the creation of custom SaaS platform products by combining functionalities from existing products using generative AI. The systems and methods represent advancement in AI-driven SaaS customization and data analysis.

The invention relates to the technical field of servo control, in particular to a servo driving current harmonic compensation method and system for electromagnetic interference suppression. The method comprises the following steps: extracting common-mode and differential-mode interference by using current and voltage sampling and combining differential measurement, and configuring a multi-stage passive filter according to spectrum characteristics to construct a suppression channel; establishing a digital interference cancellation mechanism, generating a reverse compensation signal by adopting self-adaptive trapped wave and a DSP (Digital Signal Processor), and injecting the reverse compensation signal into a control loop to form a software-defined multi-channel suppression architecture; after interference suppression, a high-order tensor model is constructed for the purified three-phase current, and an alternating least square method is adopted for decomposition so as to separate multi-band harmonic waves; a graph neural network is introduced, harmonic coupling is represented, and dominant and residual harmonic decoupling is realized through message passing; according to a grading progressive strategy, dominant harmonic waves are firstly subjected to coarse compensation, then residual harmonic waves are finely adjusted, and a compensation sequence is adaptively optimized through iterative feedback, so that high-robustness closed-loop control of servo driving is realized.

A system and method for performing rolling updates of distributed applications in a software-defined network environment. An application manager coordinates deployment of updated versions of fxDeviceApp and fxCloudApp components to isolated execution environments on distinct network elements, such as edge devices and cloud-based containers or virtual machines. During the update process, the system establishes secure communication tunnels between the updated components using a virtual messaging fabric, verifies the integrity of the updated components before activation, and maintains service continuity by activating the new versions only after successful validation. The original application instances are decommissioned after the updated components are fully operational. The system supports dynamic scaling of execution environments based on load conditions, policy-governed update concurrency and ordering, coordinated deployment across multiple zones, automatic rollback on failure, and audit logging of deployment events. These mechanisms enable secure, resilient, and minimally disruptive application lifecycle management in distributed and programmable networking environments.

The invention discloses an intelligent lock recognition method based on a time sequence diagram neural network, and the method comprises the following steps: collecting multi-source event data of an intelligent lock, unifying the format, embedding codes, and generating an event feature vector set; constructing a time-varying multi-relation graph, and executing message passing and feature aggregation to obtain a neighborhood aggregation result; constructing a dual-frequency memory time sequence diagram neural network, and extracting long-term and short-term behavior characteristics in slow-frequency and fast-frequency memory units; inputting the slow frequency memory state and the fast frequency memory state into a gating fusion layer, and combining time and scene information to generate a full-time-domain embedded vector; calculating identity matching, abnormal risk and forgery credibility, and outputting a comprehensive identification result; and executing instant response and security processing according to an identification result, and carrying out network updating. According to the method, the sequence diagram neural network and the double-frequency memory mechanism are utilized, multi-element dynamic recognition of the intelligent lock is achieved, and the method has the advantages of being high in self-learning, high in recognition accuracy and excellent in safety robustness.

A fault detection system for detecting a fault condition in a direct current (DC) system. The system may include a DC power transmitter to energize a cable and a DC power receiver connected to the cable. A signal generator at the receiver employs orthogonal frequency division multiplexing (OFDM) and combines symbols for messaging or data communications with redundant symbols dedicated to fault detection. At a signal receiver, the transmitted OFDM signal is used to measure changes in the channel transfer function by monitoring changes in a parameter of the received redundant symbols. The redundant symbols need not be decoded to detect a greater-than-threshold change in the transfer function that is indicative of a fault on the cable.

The invention discloses a patent clustering method and system based on a pre-trained heterogeneous graph neural network, and relates to the technical field of data analysis. In the pre-training stage, a patent heterogeneous graph network is constructed based on target domain patent data; performing message transmission through the meta-path based on the current heterogeneous graph network to obtain patent meta-path representation; calculating a Transform attention score based on the representation; and pre-training the patent network based on the attention aggregation meta-path representation. In the downstream fine tuning stage, on the basis of patents in the other field, patent meta-path representations are obtained in accordance with the pre-training stage, downstream meta-path representations are aggregated on the basis of attention scores of pre-training, and the sensitivity of downstream clustering tasks is enhanced by using prompt vectors; the patents are clustered based on the aggregated characterization; and sending the patent clustering data to the target terminal equipment according to the sequence. According to the method, pre-training and prompt learning are combined to reinforce low-resource adaptability, patents can be accurately clustered, and the user field technology retrieval and analysis efficiency is improved.

The invention relates to a practical consensus algorithm for file resource sharing, which comprises reputation-driven task allocation: according to reputation values calculated in real time, nodes with high reputation values are preferentially selected by the algorithm to form a core consensus group to execute the most critical consensus task, and the number of nodes participating in core communication is remarkably reduced. And protocol flow optimization: fundamentally reducing the communication complexity to O (n) level by limiting the scale of the core consensus group and reconstructing a message transmission path. And node state dynamic autonomy: the algorithm has real-time evaluation and dynamic adjustment capabilities on node types. The reputation model continuously monitors node behaviors, can automatically perform degradation processing on low-reputation or fault nodes, and can timely discover and safely isolate or delete bad nodes through a specific mechanism without restarting the system. Through test verification, the reliability, safety, higher throughput and lower delay of the consensus algorithm are verified.

Methods and apparatuses directed to die solutions that more efficiently detect, and recover from, data transfer failures. In one example, a die includes a register and host controller. The host controller receives a request for a data transfer, such as a read from, or write to, a memory device. In response to the request, the host controller reads a value from the register, and determines a timeout value based on the read value. The host controller also generates a data transfer command that includes the timeout value, and transmits the data transfer command to the memory device. The data transfer command causes the memory device to initiate a timer based on the timeout value, and to initiate the data transfer to memory. If the timer expires before the data is successfully transferred, the memory device transmits a response to the request indicating that the data transfer failed.

A computing system is provided, which receives a molecular graph at a message passing graph neural network (MPGNN), and produces scalar embeddings representing features of nodes and edges of the graph and vector embeddings representing geometric relationships of the graph. The system processes the scalar embeddings via a vector scalar interactive message passing mechanism of a message passing sub-block of theMPGNN to generate and pass scalar information from the scalar embeddings to an embedding space containing the vector embeddings. The system updates the vector embeddings based on the embedding space containing the scalar information and the vector embeddings. The system updates the scalar embeddings based on run-time geometry calculations of the geometric relationships encoded in the vector embeddings. The system computes an updated molecular graph based on the updated scalar and vector embeddings and outputs a target molecular property value based on the updated molecular graph.

The invention discloses a remote sensing image classification method of a convolutional neural network (CNN) based on regional relationship modeling of a graph neural network (GNN). According to the method, a region graph structure is constructed after CNN feature extraction: a feature graph is divided into a plurality of region blocks (nodes), edges (adjacent matrixes) between the nodes are constructed based on spatial position proximity and feature similarity, initial features of the nodes are input into a graph neural network, semantic association between the regions is captured through multiple rounds of message passing, and graph enhanced region representation is obtained. And fusing the region-level representation output by the GNN with the global / local features of the CNN, and outputting a final category through a classifier. In order to adapt to the multi-scale and complex background characteristics of the remote sensing image, the invention provides (1) a self-adaptive region division strategy; (2) a space-semantic mixed composition rule is established; (3) a graph message transmission unit with a learnable attention weight; and (4) carrying out multilevel feature fusion and loss discrimination. Experiments prove that the method is obviously superior to a comparison baseline method on standard remote sensing data sets such as NWPU-RESISC45, UC Merceed, GID, DOTA and the like, and the scene classification precision, the inter-class separability, the robustness to shielding and noise and the like are obviously improved. The method has high engineering realizability and is suitable for preprocessing modules for remote sensing scene classification, ground feature recognition, downstream semantic segmentation and target detection.

The application discloses a kind of de-server systems, comprising: message management device can receive first message, first message is used to indicate scheduling first stateful function operation first state instance;First message is stored in the first message queue corresponding to first state instance, first message queue is also used to store multiple messages, each message in multiple messages is used to indicate one stateful function operation first state instance;Second message is transmitted to the second stateful function corresponding to second message, and the second stateful function corresponding to second message is operated to idle first state instance, wherein, second message is the message located at the front end of first message queue.The technical scheme of the application controls the time-sharing operation of different stateful functions to state instance by the way of message management, and the stateful function only operates the state instance after obtaining the message of the state instance, so as to avoid resource waste.

The invention relates to the field of robot path planning, in particular to a multi-robot path planning method based on group control, which comprises the following steps: on the basis of a visibility graph, generating a communication undirected weighted graph, introducing a Laplacian matrix, and reflecting the connectivity of the graph according to a second small feature value of the Laplacian matrix; the arrival time of the robots is controlled by adjusting the side weights, space-time conflicts are avoided, the robot path planning sequence is determined according to the contribution value of the second small feature value, and the overall performance of multi-robot cooperation is optimized; according to the method, data in robot path planning are integrated through the Transform model, the comprehensiveness of environmental understanding is improved, spatial constraints of the path are optimized through a message passing mechanism of the graph neural network, the robot dynamically adapts to environmental changes, the path is generated through the generative adversarial network, diversity and high efficiency are achieved, local optimum is avoided, and the method is suitable for being popularized and applied. And dynamic obstacle avoidance and real-time path optimization are realized through cooperation of multiple models.

A service layer may be configured to offload requests and responses on behalf of IoT applications in order to reduce congestion and / or overhead on underlying networks, applications and devices. The service layer may be enabled with the capability to script and generate IoT application requests such that the requests can be initiated by the service layer without the IoT application having to re-issue the same request repeatedly (e.g., to periodically retrieve a sensor reading). This scripting may be initiated by request originators. The service layer may support the capability to monitor and detect repetitive request patterns and initiate the scripting itself. This scripting functionality may enable the service layer to perform operations on behalf of IoT applications. As a result, the messaging overhead on IoT applications and devices as well as underlying networks can be minimized.