44 results about "Intelligence system" patented technology

Systems, methods, and apparatus are disclosed for omnimodal sensing, data fusion, and autonomous decision-making across physical and digital domains and further integrates a Multimodal Diagnostic System (MDS) and Impairment Recognition and Intervention System (IRIS) with defense architecture or a system architecture that can be compliant with the Modular Open Systems Approach (MOSA) and Sensor Open Systems Architecture (SOSA) to ensure interoperability. The system can utilize real-time multisensory fusion, cryptographic provenance via blockchain, and resilient magnetoelectric communication to support mission-critical decision-making across manned and unmanned platforms in denied or contested environments.

Methods, systems, and computer storage media for providing a modular cybersecurity platform are described. The modular cybersecurity platform is implemented using a modular cybersecurity engine that operates based on an analytical framework for dynamic data analysis and data management in a data intelligence system. In particular, the analytical framework is based on complementary modular components that are designed to interoperate in the modular cybersecurity engine. The modular cybersecurity engine includes a modular distributed system, a credential detection system, and a credential semantic graph system. The modular cybersecurity engine supports cybersecurity and sensitive data management scenarios that can empower investigators in various investigations, and provide automated flows that are highly scalable and support different types of functionality (e.g., priority embedding pipeline, credential scanning, and credential semantic graph analysis). The utility of the modular cybersecurity engine is demonstrated by its wide-ranging application in addressing complex cybersecurity challenges and sensitive data management tasks.

An anthropomorphic AI control system including a plurality of sensors configured to collect industrial input data. The control system also includes an artificial intelligence-enabled edge-deployed computing device configured to analyze and fuse multimodal input data and generate an output through anthropomorphic computing. The computing device includes a cognitive module performing real-time decision-making at the sensor edge and a controller to manage industrial process actuators across operational domains. In management of industrial fluid flow, the control system may include AI-enabled fluid characterization modules to calculate the Reynolds number and incorporate compliance with AGA3 and AGA8 standards.

A system and method for implementing experiential manifold cognition that extends persistent cognitive machines beyond discrete thought caching to continuous geometric representation of experience. The system maintains an experiential manifold comprising a differentiable manifold with Riemannian metric tensor encoding semantic relationships, compression pressure field governing memory consolidation, and potential field encoding goals and attention. Input data is projected onto the manifold through adaptive geometric diffusion preserving semantic structure. The system executes geometric transformations including metric evolution, geodesic computation, and curvature estimation. During non-interactive periods, autonomous evolution occurs through trajectory recombination and selective pruning. A user interface enables visualization and direct manipulation of manifold geometry, translating navigation into geodesic traversal and edits into metric modifications. The system maintains persistence across sessions and enables controlled federation between multiple manifolds through consent-bounded synchronization. Applications include persistent narrative worlds, collaborative cognitive spaces, and experiential intelligence systems that learn through geometric evolution.

Various techniques for providing a DNS automated intelligence solution are disclosed. In some embodiments, a DNS Automated Intelligence System (DAISy) is disclosed that includes a system designed to create threat intelligence for use in protective DNS, or DNS Detection and Response systems which control access to internet resources at a DNS resolver. The disclosed DAISy solution includes the ingestion of raw source data, the curation and refinement of this source data into specialized data sets used for identifying threats, active processes to increase visibility into internet domain names, and can also include human-in-the-loop acceleration that allows for rapid automation, and a modular incorporation of DNS-specific signatures for identification of suspicious domain names. The disclosed DAISy solution is self-sustaining, automated, and incorporates human guidance. Moreover, it is effective for scaling the detection of malicious and suspicious domains, which is not possible with existing traditional approaches to DNS security.

Systems and methods for utilizing nursing intuition to support early clinical intervention are provided. The systems and methods may include a vector database stored on one or more memory devices, the vector database comprising one or more vector embeddings positioned in a vector field, wherein each of the one or more vector embeddings is indicative of medical data of one or more individuals, and wherein the positioning of the one or more vector embeddings in the vector field is based on the similarity of the one or more vector embeddings to each other. The systems and methods may also include at least one machine learning model. The machine learning model may be trained to establish a likelihood of clinical deterioration of one or more individuals based on the vector database.

Aspects of the disclosure relate to software systems of a tracking system (100) configured to process data from tracking tags ( 102, 104) affixed to assets to be tracked. The software systems may be used to process data such as payloads, interact with various systems including client facing systems, and make predictions regarding tracked assets. The predictions may provide information regarding the status of the tracked, assets during trips. Tire predictions regarding the trips may include destinations of the assets during the trips, events occurring during the trips, conditions of the assets during the trip, estimated, time of arrival at the one or more destinations. etc. The software systems may be robust such that these predictions may be made with minimal or no information pertaining to the trips beyond payloads from tracking tags (102, 104) affixed to the assets to be tracked.

A medical intelligence system generates medical assistance data determined to be relevant to an ongoing medical procedure. A state inference engine obtains medical video data and / or sensor data from sensors during the medical procedure and generates recognition data indicative of a state of the medical procedure. A medical procedure intelligence engine accesses a knowledge graph populated from multiple disparate sources and determines, based on a medical intelligence model, the medical assistance data based on the recognition data and the information in the knowledge graph. The knowledge graph and the medical intelligence model may be updated as additional knowledge and medical data is learned. The medical intelligence system may generate information such as, for example, video clips or instructions relating to a next step in the medical procedure, billing codes or descriptions of the medical procedure for medical records, and / or other data characterizing performance of the medical procedure.

A recursive symbolic intelligence system is disclosed that employs continuously evolving symbolic nodes represented as multi-dimensional vectors with physical, cultural, and optionally functional sub-components. The system implements a mathematically defined recursive update function s(i)(t+1)=α·s(i)(t)+β·f(adj)({s(j)(t)})+γ·f(input)(v(i)), wherein α, β, and γ are tunable weighting factors; f(adj), aggregates contributions from semantically and topologically adjacent nodes; and f(input), processes incoming multi-modal input including text, audio, video, and sensor data. A tamper-evident ledger configured with a cryptographic hashing function such as SHA-256 records each symbolic update, and a scheduling module employing a multi-armed bandit algorithm together with a meta-learning engine utilizing covariance matrix adaptation evolution strategy dynamically optimizes processing resources and hyper-parameters. This system provides a continuous, adaptive, and auditable framework for dynamic knowledge representation applicable to domains such as autonomous systems, adaptive content generation, and symbolic legacy encoding.

A computer-implemented whole-health intelligence system and method providing personalized clinical and integrative health guidance based on multimodal data including medical history, lifestyle behaviors, psychosocial factors, environmental inputs, and biological metrics. The system constructs a hybrid digital health twin combining graph-based physiological relationships and vectorized embeddings, then generates personalized recommendations via generative AI models and clinical rule-based logic. Safety gating logic evaluates risk patterns and escalates medically significant findings to clinician review. The system supports longitudinal health optimization, annual precision-health evaluations, home-care coordination, and clinician co-pilot review workflows.

The present application describes systems and methods for populating a DNS cache of a recursive DNS server using information gathered by a threat intelligence system. The threat intelligence system may collect some or all DNS responses from one or more recursive DNS servers as the one or more DNS servers process various received requests. Since the threat intelligence engine has access to this DNS data, the DNS data may be used to seed a DNS cache of a recursive DNS server.

A surgical intelligence system can configure positioning and orientation of a surgical instrument during a surgical procedure by considering a current surgical step and a subsequent surgical step. The system can include a processor configured to identify a current acting surgical step and a subsequent surgical step associated with a surgical instrument. The system can determine a plurality of possible position configurations for the current acting step, evaluate an orientation of the instrument and an orientation of the patient, and calculate a corresponding downstream configuration for the subsequent step. Based on the determination, the system can adjust a current configuration and select a suitable configuration for the current acting step.

Described are an intelligence system and process for material blending in the gasification. The system includes a subsystem for material blending in the gasification, wherein the subsystem for material blending in the gasification includes a raw material property rapid analysis module, for obtaining raw material property parameters of raw materials according to characteristic spectral line intensities of in-furnace raw materials; a blended material property prediction module, for establishing a prediction model, and predicting blended material property parameters by means of the prediction model according to raw material property parameters and raw material proportions; a blending scheme optimization module, for establishing an optimization model, and obtaining an optimized blending scheme by means of the optimization model according to blended material property parameters; a blending scheme economy evaluation module, for outputting a blending scheme with the optimum technical economy.

The invention discloses a situation intelligent system for artificial intelligence agency and an implementation method. The system comprises a situation data acquisition layer, a multi-modal feature extraction layer, a deep fusion understanding layer, an intelligent reasoning decision-making layer and a personalized adaptation layer. According to the AI Agent situation intelligent implementation method based on the layered architecture, through organic combination of key technologies such as deep multi-modal fusion, intelligent reasoning decision making and personalized adaptation, the situation understanding and intelligent decision making capacity of AI Agents in a complex dynamic environment is remarkably improved.

Provided herein are systems and methods comprising one or more virtual machines and a compiler to enable automatic differentiation across a stack. Further provided herein are technologies, integrations, and workflows to allow for automatic differentiation.

This disclosure describes techniques to distinguish between attack traffic and legitimate user traffic at a site and take action on those findings. A system monitors traffic volumes to the site to determine whether it falls within predicted levels based on historical patterns. If traffic volume deviates from predicted levels, then traffic is analyzed by client signatures (such as a tuple of operating system and user agent) to determine whether the deviation is due to a subset of client signatures rather than factors that are more broadly applicable across clients. A subset of client types driving increased traffic levels may indicate an attack. Based on this intelligence the system can take action to mitigate the traffic or, if legitimate traffic, adjust the general forecasting model.

Example systems and methods permit threat intelligence to be determined and used at a local, regional, and / or global level in a communications network. A threat intelligence system may collect traffic information from local computing systems and analyze it for malicious traffic. If a measure of malicious traffic in a local computing system is reached, mitigation actions may be taken in that local computing system. In addition, threat measures may be amplified in other local computing systems, other regions, or globally in the network, in order to more quickly react to a known threat as it may spread in a network.

Systems and methods for analyzing computer-readable image data to determine deviations from planogram data are disclosed. Image data of at least one physical display unit is obtained. The image data is annotated by an image processing engine to identify at least one item within the image data. Planogram data of a planogram corresponding to the at least one physical display unit is obtained and the at least one item within the image data is compared to an expected aspect of the at least one item within the planogram data. A user interface is generated including the image data and at least one interface indicator at least partially overlapping with the at least one item in the image data. At least one graphical aspect of the interface indicator is selected based on the comparison between the item within the image data and the item within the planogram data.

The invention belongs to the technical field of domain name detection, discloses a DGA domain name detection method fusing BERT and multi-scale deep learning, and provides a brand new DGA domain name detection thought by combining semantic modeling ability in natural language processing and a multi-scale feature extraction mechanism in deep learning. According to the method, global semantic information and local structure features of domain name characters are extracted in parallel through a double-branch structure, and a cross-attention mechanism (Cross-Attention) is introduced in a fusion stage to realize dynamic interaction of the two types of features, so that the aspects of detection precision, robustness and generalization performance are remarkably improved. According to the method, a detection framework of semantic-structure double-branch joint modeling and attention fusion is put forward for the first time, and hidden or unknown DGA domain names which are difficult to find by a traditional algorithm can be effectively recognized. The method is particularly suitable for intelligent security detection of enterprise DNS traffic, real-time analysis of malicious domain names and automatic alarm tasks of a threat intelligence system.

Aspects of the disclosure relate to software systems of a tracking system configured to process data from tracking tags affixed to assets to be tracked. The software systems may be used to process data such as payloads, interact with various systems including client facing systems, and make predictions regarding tracked assets. The predictions may provide information regarding the status of the tracked assets during trips. The predictions regarding the trips may include destinations of the assets during the trips, events occurring during the trips, conditions of the assets during the trip, estimated time of arrival at the one or more destinations, etc. The software systems may be robust such that these predictions may be made with minimal or no information pertaining to the trips beyond payloads from tracking tags affixed to the assets to be tracked.

A system and method to identify and track one or more targets of interest. The system may include a plurality of collection systems to capture information related to visual identifiers and / or electronic signatures associated with targets in selected locations The system may further include an intelligence system to determine a target of interest based on the information related to visual identifiers and / or electronic signatures and to track the target of interest.

A method may include obtaining, from a user device, an electronic document to be processed by a model. The method may also include obtaining an optical character recognition output of the electronic document. The method may further include obtaining, from the user device, an output configuration. The method may also include generating a first output and a second output based on the electronic document and the optical character recognition output. The first output and the second output may be defined by the output configuration. The method may further include transmitting the first output and the second output to the user device. The method may also include obtaining a feedback from the user device. The method may further include updating the model using the feedback from the user device.

A system and method to identify and track one or more targets of interest. The system may include a plurality of collection systems to capture information related to visual identifiers and / or electronic signatures associated with targets in selected locations. The system may further include an intelligence system to determine a target of interest based on the information related to visual identifiers and / or electronic signatures and to track the target of interest.

Example systems and methods permit threat intelligence to be determined and used at a local, regional, and / or global level in a communications network. A threat intelligence system may collect traffic information from local computing systems and analyze it for malicious traffic. If a measure of malicious traffic in a local computing system is reached, mitigation actions may be taken in that local computing system. In addition, threat measures may be amplified in other local computing systems, other regions, or globally in the network, in order to more quickly react to a known threat as it may spread in a network.

An autonomous operational health intelligence system for mission-critical SAP cloud workloads and method thereof is disclosed. The invention relates to a device-based structure configured to continuously monitor, analyze, predict, and regulate operational conditions of enterprise workloads deployed across distributed cloud infrastructure environments. The system comprises at least one processing unit coupled with a non-transitory memory unit, a telemetry acquisition unit for receiving real-time operational data from application execution systems, database systems, and infrastructure resources, a data harmonization processor for transforming heterogeneous telemetry signals into structured operational datasets, an analytical processing unit for deriving workload health indicators, a predictive assessment processor for generating forward-looking stability indicators based on historical and real-time execution patterns, a control determination unit for identifying deviations and producing corrective directives, and a response execution unit configured to transmit operational control signals to remote computing elements.

The invention discloses a network security wisdom assistant and system based on an RAG and a large model, specifically, the network security wisdom system comprises a large model module, a model service module and a micro-service application system, and the large model module specifically comprises an LLM large language model, an Embedding model and a Re-ranker model; the model service module specifically comprises a model management sub-module and an RAG framework sub-module; the micro-service application system adopts a front end and rear end separation framework, the rear end adopts a SpringCloud technology stack, and the front end is constructed by Vue, Element and Admin. The method comprises the following steps: configuring an RAG knowledge base by using a Langchain-Chatchat component, and configuring an RAG knowledge base by using a Langchain-Chatchat component; configuring a large model by using the large model module and the model service module; and starting and operating the micro-service application system.

Systems, computer program products, and methods for an automated mesh service-based deployment intelligence system are provided. This system is designed to streamline the deployment of software by integrating a processing device and a non-transitory storage device. The storage device contains instructions that, when executed by the processing device, enable the ingestion of data from various sources such as monitoring systems, databases, and application performance management tools. Once ingested, the data is stored and processed to discern deployment patterns and detect any anomalies. Utilizing a machine learning model, the system anticipates potential deployment issues by analyzing this data. It then orchestrates the deployment of software artifacts accordingly, taking into account the insights gained from the machine learning model. Furthermore, the system is capable of real-time optimization of the deployment process to preemptively resolve any predicted issues, thereby enhancing the efficiency and reliability of software deployment operations.

A device may include a barrel in fluid communication with a needle. A device may include a piston comprising a plunger. A device may include a microprocessor. A device may include a wireless communication module. A device may include a transaction intelligence system configured to: document a healthcare transaction relating to the intelligent injection device, document a billing item relating to the intelligent injection device, detect an administrative error relating to the intelligent injection device, track a reimbursement process, wherein a machine learning system: analyzes medication administration data; and optimizes a billing workflow.