105114results about "Machine learning" patented technology

A system and method for an AI-controlled sensor network for threat mapping and characterization. The system deploys a network of honeypots and sensors across various geographic locations and network segments, collecting and aggregating data on network traffic and potential threats. An AI orchestrator analyzes this data using advanced machine learning models, generating dynamic honeypot profiles and a comprehensive threat landscape. The system can adapt in real-time to emerging threats, optimize resource allocation, and provide actionable intelligence. By correlating data across multiple points, the system offers enhanced threat detection capabilities and proactive cybersecurity measures, surpassing traditional security information and event management (SIEM) tools.

The invention relates to the technical field of intelligent operation and maintenance, and discloses an intelligent operation and maintenance method fusing multi-modal data and active learning, and the method comprises the steps: deploying a hierarchical Internet of Things equipment network in a target operation and maintenance region, collecting a heterogeneous operation and maintenance data set, and generating an operation and maintenance feature data set through employing a unified building operation and maintenance service framework cooperating with a plurality of MCPs; inputting the operation and maintenance feature data set and the user feedback information into a deep learning intention recognition model for intention recognition and demand analysis to obtain structured user demand data and an operation and maintenance task priority sequence; according to the operation and maintenance feature data set and the structured user demand data, performing real-time evaluation on the equipment operation state to obtain fault risk early warning data; autonomous decision analysis is carried out through an agent type artificial intelligence engine, an equipment maintenance scheme and a resource scheduling scheme are generated, and then the problems that in traditional operation and maintenance, fault prediction is single, the model adaptability is poor, and prediction results are difficult to convert into effective decisions are solved.

An apparatus comprises a memory and a processor communicatively coupled to one another. The memory may be configured to store a data lake and multiple existing spectrum administration service (SAS) configuration commands. The processor may be configured to perform first SAS operations in accordance with the existing SAS configuration commands, collect multiple channel parameters from one or more communication channels configured to provide connectivity between user equipment and a core network, store the channel parameters in the data lake, monitor the channel parameters in the data lake, and generate optimized SAS configuration commands based at least in part upon the channel parameters. Further, the processor is configured to compare the optimized SAS configuration commands to the existing SAS configuration commands and perform second SAS operations in accordance with the optimized SAS configuration commands.

Systems, methods, and apparatuses for providing optimization of network resources. The system is operable to monitor the electromagnetic environment, analyze the electromagnetic environment, and extract environmental awareness of the electromagnetic environment. The system extracts the environmental awareness of the electromagnetic environment by including customer goals. The system is operable to use the environmental awareness with the customer goals and / or user defined policies and rules to extract actionable information to help the customer optimize the network resources.

Systems, methods, and apparatuses for providing optimization of network resources. The system is operable to monitor the electromagnetic environment, analyze the electromagnetic environment, and extract environmental awareness of the electromagnetic environment. The system extracts the environmental awareness of the electromagnetic environment by including customer goals. The system is operable to use the environmental awareness with the customer goals and / or user defined policies and rules to extract actionable information to help the customer optimize the network resources.

A multi-layer artificial intelligence system includes a foundation layer comprising at least one general-purpose large language model (LLM); an expert array layer comprising a plurality of specialized reasoning models; and a meta-reasoning model configured to coordinate operations between the foundation layer and the expert array layer to generate a reasoned analysis.

Systems and methods for improving reimbursement rates from payors for services or procedures provided by a medical care provider are disclosed herein. In some examples, reimbursement can be improved by improving the accuracy of medical coding of services and diagnosis codes through the use of machine learning techniques. In some examples, the system can suggest codes more likely to get reimbursed by a payor through analysis of patient data and / or insurance information. In some examples, the system can determine a likelihood of reimbursement through analysis of patient data and / or insurance information.

The invention relates to the field of security surveying and mapping, in particular to a network security space surveying and mapping method, system and device based on multi-source data fusion, and the method comprises the steps: obtaining network security data in real time, and constructing a dynamic network topological graph; calculating a time-varying vulnerability score based on the topological graph and a historical attack log, and predicting an attack path and a propagation probability through a Bayesian network; performing cross-domain fusion on equipment, service and user behavior characteristics by adopting a federated learning framework to generate a dynamic asset portrait; generating a risk thermodynamic diagram in combination with spatial autocorrelation analysis and a multi-index fusion algorithm; a defense strategy effect is simulated based on an attack graph reconstruction engine, a Pareto optimal strategy combination is generated through an NSGA-II algorithm, and closed-loop verification and dynamic parameter correction are realized by utilizing honeypot deployment and flow traction. Therefore, the problems of topology update lag, single risk assessment dimension, cross-domain threat association fracture, defense strategy static stiffness, non-closed loop of a verification system and the like in the traditional technology are solved.

Systems, methods, and apparatuses for providing optimization of network resources. The system is operable to monitor the electromagnetic environment, analyze the electromagnetic environment, and extract environmental awareness of the electromagnetic environment. The system extracts the environmental awareness of the electromagnetic environment by including customer goals. The system is operable to use the environmental awareness with the customer goals and / or user defined policies and rules to extract actionable information to help the customer optimize the network resources.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Provided are a text-guided multi-modal relationship extraction method and apparatus. The method comprises: for a given image, obtaining a plurality of local target objects in a global image; obtaining a text feature encoded representation of a given text and visual feature encoded representations of the global image and the local target objects; using the text feature encoded representation as a priori input for a visual encoder, and on the basis of a top-down attention mechanism, further guiding the visual encoder to learn a visual feature encoded representation more relevant to text semantics by means of backward decoding feedback; fusing the text feature encoded representation and the visual feature encoded representation by means of a cross attention mechanism to obtain a cross-modal text feature encoded representation; and performing relationship classification on the basis of the cross-modal text feature encoded representation to obtain a semantic relationship type between two entities in the given text. The present disclosure can effectively reduce the interference of irrelevant visual information, and improve the accuracy of relationship extraction.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a network entity may receive one or more criteria for an artificial intelligence or machine learning (AI / ML) model monitoring operation. The network entity may perform, for an AI / ML model, the AI / ML model monitoring operation based on a first distribution of inference data associated with the AI / ML model satisfying the one or more criteria. The network entity may perform, for the AI / ML model, an action based on the AI / ML model monitoring operation. Numerous other aspects are described.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor that the system validates above a mathematical threshold.

The present disclosure relates to systems and methods for facilitating interoperability between blockchain networks to facilitate bridging of non-fungible tokens (NFTs) across chains. In embodiments, a method includes receiving a bridging request at a first bridge smart contract to transfer a source NFT from a first blockchain to a second blockchain. Upon verification, the first bridge smart contract locks the source NFT. An oracle server determines whether a clone NFT associated with the source NFT exists on the second blockchain. If so, the oracle instructs a second bridge smart contract to unlock the clone NFT. If no clone NFT exists, the oracle instructs minting of the clone NFT and assigning ownership of the clone NFT to the user's account on the second blockchain. The clone NFT remains controllable by the user while the source NFT stays locked on the first blockchain, maintaining a one-to-one relationship between the tokens across chains.

Various embodiments discussed herein are directed to improving existing technologies by detecting a likelihood of hallucination arising from one-shot, few-shot, or outside knowledge contexts. For example, regarding the one-shot or few-shot contexts, some embodiments determine a set of tokens in a language model output that are not found in target content but are found in at least one example. When such phrases are not very common words, this is highly indicative that the model is hallucinating because these phrases should be located in the target content but are not, but are instead located in the examples.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

An example operation may include one or more of receiving a request to test a software program from a user device, the request comprising a description of requirements of the software program, generating a plurality of testing elements based on execution of a generative artificial intelligence (GenAI) model on the description of the requirements of the software program and a repository of test cases, generating a test case for testing the software program where the test case comprises the plurality of testing elements generated by the GenAI model, and storing the test case within a storage device.

Devices and methods enable optimizing a signal of interest based on identifying and analyzing the signal of interest based on radio frequency energy measurements. Signal data is compared with stored data to identify the signal of interest. Signal degradation data is calculated based on noise figure parameters, hardware parameters and environment parameters. The signal of interest is optimized based on the signal degradation data. Terrain data is also operable to be used for optimizing the signal of interest.

The method provides for securely harvesting and distributing trusted metadata from devices to artificial intelligence (AI) systems. It enables use of cryptographic hashes and signatures on data for supply chain provenance. It is an agentless method to enhance application security by design, and data protection with data authenticity and confidentiality in device to upstream services communications and data sharing. It helps securely harvest, filter, and forward device metadata to webhooks for AI / ML driven data analytics.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

A digital product network system includes a set of digital products each having a product processor, a product memory, and a product network interface. The digital product network system includes a product network control tower having a control tower processor, a control tower memory, and a control tower network interface. The product processor and the control tower processor collectively include non-transitory instructions that program the digital product network system to generate product level data at the product processor, transmit the product level data from the product network interface, receive the product level data at the control tower network interface, encode the product level data as a product level data structure configured to convey parameters indicated by the product level data across the set of digital products, and write the product level data structure to at least one of the product memory and the control tower memory.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

An example operation may include one or more of receiving, via a user interface, a request to test a software program, reading source code of the software program and identifying a vulnerability in the source code based on the reading, generating repair code for fixing the identified vulnerability based on execution of a generative artificial intelligence (GenAI) model on the source code and a repository of repair code used to repair previous vulnerabilities, and displaying information about the repair code via the user interface.

An example operation may include one or more of generating a large language model via a user interface, executing the large language model on a repository of software test cases and requirements of the software test cases to train the large language model to understand connections between test case components and test case requirements, receiving a description of features of a software program to be tested, and in response to receiving the description of the features, generating a software test case based on execution of the large language model on the received descriptions, and displaying the software test case via the user interface.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Aspects of the subject disclosure may include, for example, obtaining one or more information items or documents, checking the one or more information items or documents for determined sensitivity and determining role-based access for the one or more information items or documents, based on the checking and the determining, performing a preliminary analysis of the one or more information items or documents, wherein the preliminary analysis involves one or more pre-processing procedures, one or more chunking procedures, one or more embedding procedures, one or more customization procedures for particular use cases, or a combination thereof, and storing results of the preliminary analysis in a vector knowledge base for training one or more generative artificial intelligence (AI) large language models (LLMs). Other embodiments are disclosed.

In example embodiments, an industrial technology stack for an industrial environment includes a set of computational resources and a set of layers executed by the set of computational resources, the set of layers including a governance layer, an enterprise layer, an offering layer, a transaction layer, an operations layer, a network layer, a data layer, and a resource layer. In example embodiments, the industrial technology stack may include one or more artificial intelligence models for implementing one or more components of one or more layers of the set of layers.

An example operation may include one or more of monitoring communications that occur with user devices over a shared computer network, detecting a security threat from the monitored communications, generating a software program to simulate the security threat over the shared computer network based on execution of a generative artificial intelligence (GenAI) model on a description of the security threat and a repository of source code, installing the source code for simulating the security threat on a system associated with the computer network, and executing the source code for simulating the security threat via the system.

The invention discloses an Internet of Things multichannel earthquake early warning emergency linkage system, belongs to the technical field of earthquake early warning, and aims to solve the problems of insufficient real-time performance, reliability, scene coverage and safety in the prior art. The sensing layer collects data cooperatively through various sensors, eliminates environmental interference and ensures high-precision seismic wave detection, the edge calculation layer filters noise in real time by adopting an advanced algorithm, and confirms seismic events through multi-dimensional comparison, so that the local processing efficiency is remarkably improved, the cloud load is greatly reduced, a cloud platform fuses multi-source data, and the seismic detection efficiency is improved. A machine learning model is combined to dynamically optimize an early warning threshold value, the accuracy of earthquake type classification and intensity prediction is improved, full-link optimization from data acquisition to intelligent decision making is realized, a multi-channel distribution module is designed, instructions are transmitted according to priority levels, and urban and rural full-scene coverage is ensured through multiple communication technologies.

Systems and methods for assessing machine learning model fairness are provided. A graphical user interface (GUI) is rendered in a web browser. Input data from the web browser is obtained. A first input produced by a first GUI control is received to identify a prediction column in the input data. A second input produced by a second GUI control is received to identify a ground truth column. A third input produced by a third GUI control is received to identify a sensitive attribute column. A plurality of groups within a plurality of sensitive attribute values in the sensitive attribute column are automatically displayed. The system processes the input data based on the prediction column, the ground truth column and the sensitive attribute column to generate one or more algorithmic fairness analysis, which are used to display a visual report.