6317 results about "Computing systems" patented technology

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

A computing system for adaptive electronic message classification employs a multi-agent architecture comprising a media feature analysis system, a user context refinement system, and a response synthesis system. The media feature analysis system generates pillar scores including message type, intent, and link risk scores with associated confidence values using trained classification models. When pillar scores and confidence values do not satisfy predetermined threshold conditions, the user context refinement system dynamically constructs contextual prompts using the pillar scores and confidence values as input parameters. User responses generate score modification data that refines the pillar scores and contextual response data for recommendation generation. The response synthesis system generates refined classifications and personalized recommendations using the refined pillar scores and contextual response data. An orchestration system coordinates agent interactions using learned uncertainty points and implements asymmetric influence algorithms with variable weighting based on content and URL analysis concordance.

A federated distributed computational system enables secure oncological therapy optimization through multi-expert integration and advanced uncertainty quantification. The system implements a multi-expert integration framework that coordinates domain-specific knowledge through token-space communication for precision oncological treatment, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration. Through a distributed graph architecture, the system enables advanced fluorescence imaging with wavelength-specific targeting, multi-level uncertainty estimation combining epistemic and aleatoric approaches, and multi-scale tensor-based integration with adaptive dimensionality control. The system implements light cone search and planning for adaptive treatment strategy optimization, enabling medical institutions and research organizations to collaborate on complex oncological therapy projects while maintaining strict data privacy controls.

The invention discloses a real-time computing system resource coordination and decision engine system based on a large language model. According to the system, a large language model is innovatively used as a central strategic decision engine, and a'decision-coordination-execution 'three-layer architecture is constructed. According to the system, macroscopic strategy generation and microscopic real-time control are decoupled by introducing a hierarchical decision-making mechanism (a strategic layer, a tactical layer and an execution layer), so that the core contradiction between LLM high reasoning delay and the microsecond / millisecond-level real-time requirement of the system is effectively solved, and the method is suitable for local computing equipment and a cloud data center. The system comprises a predictive strategy preloading system, and transient response can be achieved. Meanwhile, the system adopts an asynchronous event-driven decision-making mechanism for continuous intelligent optimization. According to the method, the top-down, semantic understanding-based and global collaborative intelligent management of the computing resources is realized, and the resource utilization efficiency, the system automation degree and the overall energy efficiency in a complex and dynamic computing environment are remarkably improved.

Systems and methods for protecting computing systems against ransomware attacks using AI-generated virtual file honeypots. Generative AI comprising a large language model generates virtual file honeypots automatically in response to attack vectors associated with suspect actors and ransomware families.

A computer-implemented method, computer program product and computing system for receiving a message concerning an event within a computer platform, wherein the message concerns a technology type and includes raw data; defining a cipher for the technology type, thus defining an associated cipher; processing the raw data included within the message using the associated cipher to define supplemental data for the technology type; and forming enriched data for the technology type based, at least in part, upon the raw data and the supplemental data.

Various examples, systems, and methods are disclosed relating to a model customization pipeline. A first computing system can receive at least one customization of at least one artificial intelligence (AI) model corresponding to a base instance. The first computing system can generate a customized instance of the at least one AI model by updating the base instance of the at least one AI model based on the at least one customization. The first computing system can generate a software component configured to perform at least one operation using the customized instance of the at least one AI model. The first computing system can package the software component and the customized instance of the at least one AI model into a first container instance. The first computing system can deploy the software component within a runtime environment.

The invention discloses an energy-efficient task scheduling method for an edge computing system, which comprises the following steps of: acquiring a task state, a computing node resource state, a link state and an energy consumption state according to a unified time slot under a computing power network control domain, and constructing a system state vector; performing priority evaluation on the to-be-scheduled task based on the residual delay budget, the candidate node energy efficiency coefficient and the queue position to obtain a target task set; inputting a system state vector and a target task set into an energy efficiency perception deep reinforcement learning scheduling model, outputting a task-node allocation decision under the constraint of computing node resources and task time delay, and introducing a system-level energy consumption ratio, self-adaptive energy consumption penalty and exploration bias facing high-energy-efficiency nodes into rewards; and scheduling tasks according to the allocation decision, recording state transition and instant rewards, updating a double-commentator and actor network, and performing iterative execution in continuous time slots. According to the method, task success rate, time delay, load balancing and energy-saving performance are considered, and system energy consumption is reduced.

The invention discloses a self-adaptive low-delay motion scene live broadcast method and system, and particularly relates to the technical field of scene live broadcast. Through unified mapping and exception suppression of multi-source time sequence data, a multi-scale sliding window predictor and a short-time autoregression and long-time trend sensing algorithm are combined; a more accurate bandwidth prediction result with interval confidence description is generated, characteristics such as offset cumulant, fluctuation intensity and error residence time are extracted by using a residual trajectory, threshold crossing frequency, switching amplitude, direction alternation rate and critical zone residence duration are analyzed synchronously with a parameter switching log, critical oscillation characteristics are formed, and the bandwidth prediction accuracy is improved. The risk identification is more accurate, the high-frequency oscillation risk score of the system is calculated through a normalization and time sequence risk identifier, the risk assessment result is mapped into an executable stable intervention strategy, the system state observation sequence after adjustment execution is subjected to short-time assessment, and a feedback packet is formed to write back a closed loop. And online optimization of the weight, the decision threshold and the cooling time of the bandwidth predictor is realized.

Techniques for determining a presence of an object, especially an object such as animal or debris, in a path of a vehicle, are discussed herein. For example, sensors of various modalities, which may include multispectral sensors, may capture data representing an environment the vehicle is traversing. In examples, one or more trained machine learned (ML) models, operating on a vehicle computing system, may detect and / or classify objects in the environment, based on input data of one or more modalities or spectral bands. The ML models may be pre-trained using training data including real sensor data, synthetic data, and / or augmented data, along with auto-generated annotations. In some examples, hyperspectral data may be used to identify materials associated with detected objects. A confidence score associated with the detection of the object may also be computed. The vehicle may be controlled based on detection of the object and its classification.

The invention discloses a distributed computing task non-perception migration method and system under optical fiber network interruption. The distributed computing task non-perception migration method comprises the following steps: constructing a distributed task migration system and an output system architecture; collecting network state indexes, constructing a network topological graph, and analyzing the state of an optical fiber link for fault prediction; obtaining a calculation task operation state, establishing a sub-task mapping relation, constructing a directed dependency graph, and generating a state snapshot; analyzing resource requirements, reserving standby resources, and deploying a distributed cache system; analyzing a fault influence range, extracting an influenced calculation sub-graph, and selecting a migration target node; in a target node preloading environment, reconstructing an execution context, and redirecting a communication path; and setting a data change capture mechanism, synchronizing incremental data and executing consistency verification. According to the method, non-perception migration of the computing tasks is realized, task continuity and data consistency are guaranteed, and the reliability of the distributed computing system in an optical fiber network fault scene is improved.

Action and / or process determination and recommendations for Robotic Process Automation (RPA) using semantic action graphs is disclosed. Semantic action graphs are graphs that store individual actions, and potentially graphical elements and / or text associated with the actions, as nodes, as well as the relationships between nodes as edges. Metadata to develop the semantic action graphs may be derived from task mining applications that can monitor the interactions of users with computing systems, workforce intelligence, etc. The semantic action graphs may be for a user, an organization, an industry, product-wide, etc. At their lowest level of granularity, the recommendations may be for mouse clicks, key presses, Application Programming Interface (API) calls, system events, etc. At higher levels of granularity, the recommendations may be for opening an order, creating a lead, approving a work item, etc.

A data processing system implements performing a root cause analysis that includes identifying a first anomalous signal data predictive of a root cause of a first anomaly in signal data received from a computing system, analyzing the sub-signals of the first anomalous signal data to generate labeled training data, training a gradient boosted tree model using the labeled training data, generating a decision tree based approximating a predictive performance of the gradient boosted tree model, determining insights data predictive of the root cause of the first anomaly based on the gradient boosted tree model and the decision tree, aggregating the insights and analyzing the aggregated insights data to determine a predicted root cause for the first anomaly, determining a confidence level associated with the predicted root cause, and categorizing the predicted root cause into one of a plurality of categories based on the confidence level.

A method of question answering using enhanced retrieval-augmented generation according to an embodiment includes receiving, by a computing system, a user query, pre-processing, by the computing system, the user query to determine whether the user query is associated with malicious intent, retrieving, by the computing system, relevant data from a knowledge base by using a keyword index and a semantic index in response to determining that the user query is not associated with malicious intent, prompting, by the computing system, a large language model to generate an answer to the user query based on only the relevant data retrieved from the knowledge base, and receiving, by the computing system, the answer to the user query from the large language model in response to the prompt.

Access to secured items in a computing system is requested instead of being persistent. Access requests may be granted on a just-in-time basis. Anomalous access requests are detected using machine learning models based on historic patterns. Models utilizing conditional probability or collaborative filtering also facilitate the creation of human-understandable explanations of threat assessments. Individual machine learning models are based on historic data of users, peers, cohorts, services, or resources. Models may be weighted, and then aggregated in a subsystem to produce an access request risk score. Scoring principles and conditions utilized in the scoring subsystem may include probabilities, distribution entropies, and data item counts. A feedback loop allows incremental refinement of the subsystem. Anomalous requests that would be automatically approved under a policy may instead face human review, and low threat requests that would have been delayed by human review may instead be approved automatically.

A trained model of an online system is used to generate action recommendations by predicting future demands. The online system gathers in-store data by receiving, from a device of a picker and / or a computing system of an in-store physical receptacle, data with information about an inventory of an item. The online system estimates, based on conversion data for the item, a level of inventory for the item. The trained model is then applied to predict, based on the in-store data and the estimated level of inventory, a demand prediction score indicative of a future demand for the item. The online system generates, based on the estimated level of inventory and the demand prediction score, a depletion metric indicative of a time period until the inventory of the item is depleted. Based on the depletion metric, the online system triggers an action in relation to the inventory of the item.

A method, computer program product, and computing system for generating a plurality of chunks for a plurality of text portions of a document, wherein the document includes the plurality of text portions and a plurality of images. Each chunk is indexed using a word embedding. Each of the plurality of images is indexed based upon, at least in part, a position of a respective image relative to a corresponding chunk. An image placeholder is generated for each of the plurality of images. A plurality of image-enhanced embeddings is generated by inserting the image placeholder for each of the plurality of images into a respective word embedding for the corresponding chunk. The plurality of image-enhanced embeddings are provided for processing a query using a generative artificial intelligence (AI) model.

A method for operating a first computer system is provided. The method includes: building a database comprising entries, each entry indicating a failure in one or more second computer systems and an action pattern to remedy the failure, the action pattern indicating log files to be accessed to remedy the failure. In response to detecting a failure in the first computer system, one or more entries of the database that match the detected failure may be identified. The one or more action patterns associated with the identified one or more entries may be used for extracting debugging data representing the detected failure. At least part of the debugging data may be sent to an external system. In response to the sending, instructions to remedy the detected failure may be received from the external system.

Various systems and methods are described implementing a multi-access edge computing (MEC) based system to realize MEC federation management and broker functions for MEC frameworks. In an example, performing edge federation management functions of edge computing systems, to establish a partnership among multiple edge federation managers as a federation, include: using system data attributes to establish the partnership; using authentication data attributes to enable the edge federation managers to securely authenticate; using authorization data attributes to enable the edge federation managers to perform authorization; using availability zone data attributes to define zones in the federation; and using management and settlement information data attributes to enable management of resources in the federation. Further operations include communicating the data attributes via respective connections with the edge federation managers, and the use of defined interfaces and operations.

An adaptive modular multimodal communication and computing panel includes a multilayer stack with a protective layer transmissive in selected bands, a reconfigurable communication layer operable in phased array, reflectarray, hybrid phased reflector, free space optical, or quantum modes, and electronics with heterogeneous processors. A multi scale interconnect and input and output fabric couples electrical, radio frequency, guided optical, and free space optical domains through interfaces including electro optic transduction and RF or baseband conversion. The fabric may implement programmable true time delay, resonators, and comb referenced timing. A management system coordinates beamforming, sensing, routing, calibration, workload placement, and security. Panels tessellate and connect by electrical, radio frequency, and fiber optic interfaces, supporting hot swappable modules, blind mate connectors, robotic servicing, and anti tamper features. Power and thermal subsystems harvest, store, regulate, and dissipate energy. The architecture scales from chip level modules to vehicle, airborne, maritime, orbital, and deployable systems.

There are provided systems and methods for a domain-knowledge guided agent framework for automated system analysis. An online transaction processor or other service provider may provide computing services and platforms to entities, which may require compliance enforcement for different policies, regulations, and the like. To provide compliance review, investigations, and enforcement in a computing system of a service provider, the service provider may implement an intelligent and automated agent and framework that may utilize different large language models for processing compliance investigation requests and queries. The agent may utilize the models to plan and execute tasks using an available toolkit of computing operations and capabilities for compliance investigation. A data guard module may also be used to ensure data privacy and security is maintained. Within a main task, sub-tasks may be executed by models with specific domain knowledge.

A computing system is disclosed for generating and optimizing schema-conformant object models. The system obtains input data from multiple sources in structured, semi-structured, or unstructured formats and processes the data using parsing logic to derive a structured rule set defining spatial relationships and constraint conditions. The system generates schema-conformant object representations comprising parametric assemblies, applies the constraint conditions using machine-executed resolution logic, and produces candidate object models through iterative modification of spatial components, dimensional parameters, and adjacency relationships. Performance values are computed for the candidate models using quantitative evaluation of spatial efficiency, material usage, and constraint compliance. An optimized object model is selected based on the computed performance values and may be used to generate construction project plans including technical drawings, material specifications, and scheduling instructions.

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 sewage treatment whole-process operation regulation and control calculation system based on deep learning, and relates to the technical field of intelligent control, and the system comprises a strategy optimization module which optimizes a future pollution trend prediction sequence and a regulation and control factor weight table by using a reinforcement learning strategy network in combination with a genetic algorithm optimizer, dynamically generating an aeration, dosing and backflow operation parameter combination aiming at a regulation target to form a dynamic regulation instruction set; the instruction execution module is used for issuing a dynamic regulation and control instruction set to the programmable logic controller by utilizing an industrial control interface, and collecting response information and real-time effluent quality data as execution feedback information; and the model updating module carries out error comparison on execution feedback information and a future pollution trend prediction sequence, constructs a weighted error sequence, and dynamically corrects deep network parameters through an online fine tuning strategy to form a prediction control model. According to the method, the generalization ability and robustness of the predictive control model are remarkably improved.

A method, computer program product, and computing system for processing a query provided to a generative AI model. A content portion retrieved by a Retrieval Augmented Generation system for the query is processed. User context information associated with a user providing the query is determined. Hyperparameters are generated for processing the prompt with the generative AI model by processing the query, the content portion, and the user context information using run-time surrogate model inversion optimization.

The present invention relates to systems and methods for proactive real-time anomaly detection in cross-environment RPC (Remote Procedure Call) communications within computing systems. Utilizing an Intelligent GraphRPC Method, this invention integrates advanced graph analysis techniques to enhance fault detection and workflow management. The method features a dual-graph approach, employing both real-time and aggregated dependency graphs, which allows for continuous monitoring and analysis of RPC interactions to detect and prevent unauthorized or misconfigured RPC calls between staging and production environments. An ingestion pipeline further supports the system by aggregating and archiving call graph data, providing beneficial insights into service dependencies and potential security risks. This proactive anomaly detection system is designed to seamlessly integrate into existing monitoring and alerting frameworks, providing a robust solution to safeguard data integrity and operational stability, thereby minimizing losses and reputational damage due to data breaches and system disruptions.

The invention relates to the technical field of transformer substation intelligent inspection, in particular to a meta-learning-based small-sample transformer substation equipment state adaptive inspection system, which comprises a state acquisition module for acquiring the current feature vector and environmental parameter data of a target node; the drift detection module is used for comparing environment parameters to judge data drift and dynamically adjusting a confidence coefficient threshold value; the risk assessment module inputs the feature data into a meta-learning model to output an initial risk probability, and generates an effective risk probability based on threshold filtering; the blind area measurement module is used for acquiring unobserved nodes and calculating system state blind area entropy; the scheduling decision-making module is used for comparing the blind area entropy with a threshold value and generating an entropy reduction bottom instruction or a self-adaptive routing inspection distribution instruction; the strategy updating module is used for extracting an actual inspection result and feeding back to the model for parameter updating; according to the invention, the scheduling difficulty when the resources are limited is solved, and the self-adaptive capability of the system under different environment interferences is improved.

A method of operation of a compute system includes: generating a body mask based on the patient image; generating an overlaid synthetic ailment image for a targeted skin ailment and based on a patient image; generating a refined synthetic image based on the overlaid synthetic ailment image; generating a fused image based on the refined synthetic image by smoothing a visual imperfection and guided by the body mask; and communicating the fused image for displaying on a device.

A federated distributed computational system enables secure drug discovery and resistance tracking through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates molecular dynamics simulations with machine learning models for drug discovery analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for molecular dynamics simulation and resistance pattern detection. Through a distributed graph architecture, the system enables real-world clinical data integration, resistance evolution tracking, and multi-scale tensor-based analysis with adaptive dimensionality control. The system implements real-time drug response prediction through multi-modal data analysis, enabling pharmaceutical companies and research institutions to collaborate on complex drug discovery projects while maintaining strict data privacy controls.

The invention relates to the technical field of power dispatching management and control, and discloses a power distribution network power dispatching method based on a virtual power plant AI large model and demand response, and the method comprises the steps: collecting the operation data of a power distribution network, and constructing a feature vector; an AI large model is adopted to calculate and predict load output, and joint uncertainty information is output; calculating a system power unbalance amount, and generating a scheduling strategy; issuing a scheduling instruction corresponding to the scheduling strategy and executing the scheduling instruction; comprehensive performance evaluation indexes are calculated, and whether a performance reduction reason diagnosis mechanism is started or not is judged; according to the method, the AI large model is adopted, the load power, the photovoltaic output and the wind power output are predicted at the same time through a multi-task learning strategy, and the correlation among multiple variables is fully utilized; by constructing a multi-objective optimization model, comprehensively considering economy, safety and reliability and adopting an improved particle swarm optimization algorithm for solving, coordinated optimization configuration of demand response resources is realized, power grid fluctuation is effectively reduced, and power supply reliability is improved.