112 results about "Intelligence system" patented technology

The invention relates to the technical field of management and control systems, in particular to a digital intelligence system applied to collaborative management and control of water-power engineering construction participating units, which comprises a project data management module covering water-power engineering construction full life cycle management, extracting multi-source information data from various subsystems, and constructing a project view and a project management information base; the project risk analysis module is used for constructing an intelligent risk analysis model for risk analysis and generating a dynamic evaluation result, a graded early warning notification and an auxiliary decision scheme; the project collaborative management module is used for processing graded early warning notification and auxiliary decision-making schemes by using a multi-layer perceptron model, and generating finalizing service achievements and management process records; and the project document management module is used for performing compliance automatic checking and processing on the electronic documents needing to be archived, and dynamically updating and optimizing the project management information base. Through the closed-loop management and control system, the cooperative management and control efficiency of water-power engineering construction participation units is improved.

The invention provides an unmanned aerial vehicle optimal route planning method and system fusing image recognition and M-RRT and APF algorithms. The point cloud and visual data of a fan are obtained in real time through a multi-mode sensor, key parts of blades are recognized based on deep learning, a geometric mapping model is constructed, the blade tip points are corrected in a clustering mode in combination with DBSCAN, and the shutdown posture is predicted. A global path adopts an M-RRT algorithm improved by a direction heuristic factor to guide and search a key area of a blade; a local track is optimized through a dynamic weight APF algorithm, and a repulsive force field is adjusted in real time to cope with attitude changes. An online re-planning mechanism is introduced, NSGA-III multi-target optimization is triggered when the environment suddenly changes or the tracking error exceeds a threshold value, and the optimal track is generated by integrating energy consumption, time and safety. The system integrates high-precision sensing, self-adaptive planning and dynamic optimization, and the inspection coverage rate and the track safety under the complex shutdown attitude are remarkably improved.

Methods, systems, and computer storage media for providing a data analysis pipeline using a data analysis pipeline engine in a data intelligence system are described. A data analysis pipeline refers to a structured sequence of data processing steps that support transforming raw data into meaningful insights or actionable outcomes. The data analysis pipeline engine is an unsupervised learning pipeline based on clustering, topic modeling, and Large Language Models (LLMs). For example, the data analysis pipeline can use advanced machine learning techniques to automatically categorize emails into semantically similar clusters, enabling the data intelligence system to quickly identify and prioritize potentially high-risk emails for further investigation. The data analysis pipeline employs AI agents for context-aware graph induction relevance assessment. The AI agents employ induction and deduction loops to build and refine a data feature hypergraph (e.g., vulnerability hypergraph) that encompasses identified relevant data providing a holistic view of a contextual landscape.

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.

The present disclosure pertains to systems, computer program products, and methods for an automated mesh service-based deployment intelligence system. 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.

The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems. A system and method for providing conversation intelligence services may include pre-processing, transcribing, and post-processing. A conversation recording may be pre-processed generating a conversation record (e.g., conversation object). The pre-processed conversation recording may be transcribed into a transcript. The transcript may be post-processed which may include keyword extraction, topic extraction, feature extraction, event generation, trigger action, and / or search indexing. Conversation information may be presented based on the pre-processing, the transcribing, and the post-processing.

A threat intelligence system utilising language models to generate queries for external threat intelligence systems, receive and filter responses, and generate alerts and reports using further language models.

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.

A model verification system and associated method for employing a multi-party verification technique to verify machine learning models and generative AI systems. The models and associated systems can be deployed in an enterprise and require verification to ensure that cohorts are properly verifying the models and systems and evaluation to ensure that the models and systems operate responsibly and achieve intended outcomes. A dynamic, multi-stakeholder blinded verification process can be employed for the continuous verification and evaluation of machine learning models and the systems that use them. This helps promote unbiased, reproducible verification, evaluation and assessments by preventing potential biases from cohorts form part of the verification process.

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.

The invention provides a software supply chain risk component calling identification method, and belongs to the technical field of basic safety. The method comprises the following steps: loading components in a Java Web application, and dynamically capturing a loaded component list; storing the component list locally and sending the component list to an external intelligence system, acquiring risk component information, and generating a risk calling interface signature according to the risk component information; a preset instrumentation rule file is loaded based on the risk calling interface signature, and accurate monitoring of the risk interface is realized through dynamic method matching and byte code enhancement; screening risk interfaces triggered during operation according to cross comparison of static data and dynamic calling data, and marking high-risk components which must be repaired and risk components which can be postponed to be repaired; the risk report generation module generates repair suggestions and priority reports. According to the method, the accuracy of risk assessment is fundamentally improved, meanwhile, the unnecessary repair cost is remarkably reduced, and an efficient and innovative technical solution is provided for supply chain risk management.

Methods, systems, and computer storage media for providing iterative data processing optimization using an iterative data processing optimization engine in a data intelligence system are described. Iterative data processing refers to handling data where the processing steps are repeated multiple times, across multiple views or modalities, to train machine learning models, filter and score data or generate output. The iterative data processing optimization engine employs expectation step machine learning models that are simple but with fast language models to efficiently and effectively probe and analyze data, while iteratively refining maximization step machine learning models that are optimized and fast to approximate the probing mechanism of the expectation step machine learning models more efficiently, for example, using metadata, external information, and compressed representation. The iterative data processing optimization engine can operate based on an agentic framework using lightweight artificial intelligence (AI) agents to perform model fitting, featurization, and report generation autonomously.

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.

Methods, systems, and computer storage media for providing entity-specific data analysis using an entity-specific data analysis engine in a data intelligence system are described. The entity-specific data analysis engine can be an LM-based system that supports generating and communicating entity-specific data analysis output. In operation, a dataset associated with an entity is accessed. A bidirectional volumetric analysis output is generated based on executing a plurality of bidirectional volumetric analysis operations against the dataset. A plurality of probe questions and a plurality of data analysis axes associated with a focus area are generated for analyzing the bidirectional volumetric analysis output. Using the bidirectional volumetric analysis output, the plurality of probe questions, and the plurality of data analysis axes, an entity-specific data analysis output is generated, based in part on identifying false positive trends in the dataset and defining rules to filter out the false positives from the entity-specific data analysis output.

Disclosed is a system for controlling artificial, robotic, or synthetic intelligence systems, the system comprising: a set of ethical and legal standards, a decision logic filter for referring output

A question answering system is configured to extract answers to queries from a collection of data. The system includes an extractive stage configured to receive a query and to determine one or more answers to the query from the collection of data and one or more generative large language model stages configured to pre-process the query before it is provided to the extractive stage and / or to post-process the answers to the query. The collection of data includes a number of document segments and the extractive stage is configured to attribute at least some of the one or more answers to corresponding sub-parts in the document segments.

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.

A mediated artificial superintelligence (mASI) system that uses internal subjective emotions to drive selection, goal setting, and other decision-making as a collective human and artificial intelligence (AI) based mind with internal subjective experience via complex thought models dynamically created by including collective training with an independent core observer model (ICOM) engineering artificial general intelligence (AGI) cognitive architecture is disclosed. The mASI system is a form of an AI system that also uses humans in the form of a collective mind with internal subjective experience. The mASI system extends the ICOM engineering AGI cognitive architecture by adding collective training to allow the system to dynamically created thought models more complex than otherwise possible with current technology while being able to use numerous AI technologies.

The invention belongs to the technical field of medical data, and particularly relates to a medical institution preposed data security monitoring method, which specifically comprises the following steps: data threat detection and analysis: automatically identifying abnormal behaviors and potential threats of medical institution preposed data by using AI and a machine learning algorithm, normal operation modes are identified through a training model, behaviors deviating from the modes are marked as suspicious activities, the AI and machine learning technologies are introduced, legal activities and malicious attempts are accurately distinguished, false alarms caused by excessive sensitivity are avoided, and the false alarm rate is reduced; the continuous learning and updating capability enables the system to identify novel threats more quickly and reduce the risk of missing report, and by means of a dynamic updating mechanism and external threat intelligence, the system can grasp latest threat information in the first time and adjust a defense strategy according to the latest threat information so as to quickly adapt to the new threats.

Methods, systems, and computer storage media for providing a data analysis pipeline using a data analysis pipeline engine in a data intelligence system are described. A data analysis pipeline refers to a structured sequence of data processing steps that support transforming raw data into meaningful insights or actionable outcomes. The data analysis pipeline engine is an unsupervised learning pipeline based on clustering, topic modeling, and Large Language Models (LLMs). For example, the data analysis pipeline can use advanced machine learning techniques to automatically categorize emails into semantically similar clusters, enabling the data intelligence system to quickly identify and prioritize potentially high-risk emails for further investigation. The data analysis pipeline employs AI agents for context-aware graph induction relevance assessment. The AI agents employ induction and deduction loops to build and refine a data feature hypergraph (e.g., vulnerability hypergraph) that encompasses identified relevant data providing a holistic view of a contextual landscape.

The present invention discloses a blockchain-based edge intelligence system resource management method, comprising a system controller sensing the current user device artificial intelligence task information and the system's current wireless environment information; at the same time, the system controller senses the computing resource information of the user device, edge server and cloud server; uploading relevant information from the corresponding device to the system controller via a wireless connection; inputting the current artificial intelligence task information and the system's current wireless environment information into a trained optimization model deployed in the system controller, calculating the user device transmission power control, artificial intelligence task data volume control, user device artificial intelligence task offloading decision based on the current state, and the computing resource allocation to be used in the reasoning process and block generation process of the artificial intelligence task, and transmitting the calculation to each computing entity for execution; the present invention utilizes task offloading and computing resource allocation algorithms to improve system efficiency and user experience.

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.

The invention provides a malicious external connection event detection method and device of a host. The method comprises the steps that multiple pieces of destination address information are collected in a server log of the host; the destination address information comprises a destination IP and / or a destination domain name; the destination IP is used for indicating IP addresses requested to be accessed by a plurality of servers of the host; the destination domain name is used for indicating websites requested to be accessed by a plurality of servers of the host; the server log is generated through a log generation system deployed in the host; determining malicious destination address information in the multiple pieces of destination address information; the malicious destination address information refers to destination address information successfully matched with a malicious address in the threat intelligence system; determining an alarm object according to the malicious destination address information; and outputting the malicious external connection event alarm information to the alarm object. According to the invention, the detection coverage can be improved, and the detection cost can be saved.

Methods and systems for network scanning activity detection are disclosed. The methods and systems include: obtaining darknet data from darknet monitoring sensors; applying the darknet data to a trained machine learning model; obtaining one or more labels of honeypot data corresponding to the darknet data based on the trained machine learning model; and provide a result of threat behaviors of internet protocols based on the one or more labels. Other aspects, embodiments, and features are also claimed and described.

A threat intelligence system utilising language models to generate queries for external threat intelligence systems, receive and filter responses, and generate alerts and reports using further language models.

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.

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.