125 results about "Applied artificial intelligence" patented technology

A computer implemented system and method for generating offer ranges for candidates in an interviewing process is disclosed. The system generates an AI-based interviewer simulating human-based interactions for conducting an ongoing interview with candidates. The system analyzes data associated with candidates obtained during ongoing interview. The system process analyzed responses of candidates to determine contextual attributes associated with responses using ML models. The system automatically generates follow-up interview questions to be delivered to candidates during ongoing interview based on analyzed responses from candidates, by applying AI model to contextual attributes associated with responses. The system generates recruitment scores for candidates based on analyzed responses, contextual attributes, and interpreted non-verbal cues, associated with candidates, using AI model. The system generates offer ranges for candidates based on recruitment scores using AI model. The system provides information associated with selected candidates, and offer ranges generated for selected candidates, to users.

The invention relates to the field of artificial intelligence, in particular to an artificial intelligence implementation scheme of a wall building robot. The robot is used for wall engineering of civil buildings and industrial buildings and is the general trend of scientific and technological development. The intelligent wall building robot cannot leave artificial intelligence. Application of artificial intelligence to the wall building robot belongs to emerging frontier science and technology. The invention provides a complete and systematic solution for applying artificial intelligence to the wall building robot, so that the wall building robot can complete wall building intelligently, independently, high-quality and high-efficiency without manual cooperation, 'complicated, dangerous, dirty and heavy 'manual operation of wall engineering is avoided, and the social benefit and the economic benefit are remarkable.

An electronic device may include a communication circuit, a memory, and at least one processor. The memory may store instructions that cause the electronic device to execute an application, determine a property of the application, receive a plurality of data packets for the application from an external device through the communication circuit, determine whether at least one of data throughput information on the external device, state information about a channel transmitting the plurality of data packets, or signal round trip time information satisfies a specified condition, apply artificial intelligence learning-based algorithms to merge and process the plurality of data packets to the plurality of data packets, based on the specified condition being satisfied, and refrain from applying the artificial intelligence learning-based algorithms to the plurality of data packets, based on the specified condition not being satisfied.

The disclosures are directed to processing systems and methods that apply artificial intelligence-based processes to match an input to one of multiple options. In one example, a processor receives input data and, based on inputting the input data to a large language model (LLM), generates graph data that associates each of multiple propositions to one or more entities. Further, based on inputting the graph data to a trained artificial intelligence (AI) model, the processor generates query data characterizing one or more queries. In addition, based on inputting the graph data and the query data to the same or different LLM, the processor generates matching data charactering associations between the multiple propositions and the one or more queries. The processor may then receive a query request, and can match the query request to at least one of the multiple propositions based on the matching data.

Intent-based automation that discovers automatable tasks and / or determines task variants in data is disclosed. Task capture data may be utilized to determine task variants in task mining data. Semantic understanding of user actions by artificial intelligence (AI) / machine learning (ML) model(s), for example, may be applied to determine the intent of the user rather than only focusing on what actions the user is performing on the computing system. Application logs and semantic understanding may be used to facilitate a more accurate determination of what the user actually intends to do. Task capture for individual user flows may be performed. Once these are captured, task capture algorithms and AI / ML models are used to determine which parts of the flows are similar and / or match and which parts are unique. The path through these flows can then be followed to build a process graph that includes decision points representing the unique flows.

Methods and systems are provided for information retrieval pathway guidance. In particular, the systems and methods provided apply artificial intelligence to novel tasks related to retrieving remotely accessible data such as detecting competency levels of users, generating personalized guidance for individual users based on the retrieval goals and initial competency level of a user, generating custom diagnostic assets for those goals based on current strengths and weaknesses, generating content for custom questions for those diagnostic assets, and dynamically tracking and updating the competency level of the user during the course.

A monitoring system, for a charging pile, wherein the charging pile charges an electric vehicle through a power module, includes an image capture module, configured to capture at least one environmental image signal related to the electric vehicle; and a control module, coupled to the image capture module, configured to apple an artificial intelligence technology to analyze the at least one environmental image signal, to generate an analysis result, and control the power module according to the analysis result.

Technology for applying artificial intelligence to decide when to, and / or when not to, send a consumer of a computer system a communication recommending that the computer system be revised to include a more recent version of at least one of the following: a hardware component (for example, microprocessor(s)) and / or a software component (for example, an updated version of an app). The computer system, that is subject to modernization, may be owned outright by the consumer, or it may be purchased as a service (for example, infrastructure as a service, software as a service, package of cloud services). Some embodiments focus on modernization recommendations specifically tailored to cloud orchestration software that deploys containers.

A class-4 telecommunications switch hosts artificial-intelligence agents that convert live speech to text, translate the text between languages, and synthesize natural speech in real time. The switch proxies calls among public trunks, PBX / media gateways, and cloud ACD / CRM services, embedding diacritic-rich transcripts and user-specific language-model personalization. Deployable at the customer edge, in the PSTN core, or as SaaS, the system supports one-to-one, one-to-many, many-to-one, and many-to-many call patterns. FPGA, ASIC, or SoC accelerators minimize latency and bandwidth, cutting capital cost while improving global voice interoperability and cybersecurity.

Methods and systems are described for imaging and content generation. During a road trip, a driver or passenger observes an object or scene from a vehicle. From a perspective of the driver or passenger, the object or scene flows out of view in a relatively short amount of time. Images and video of the object or scene are captured with cameras. A field of view is extended beyond what the driver or passenger can easily observe. The capture is configurable to focus on particular types of objects or scenes. Recordings of billboards and road signs are easily displayed after passing the object. Object identification, gaze determination, and interest determination are provided. Curated content is generated. Applications to extended reality environments are provided. Artificial intelligence systems, including neural networks, and models are utilized to improve the imaging and content generation. Related apparatuses, devices, techniques, and articles are also described.

A method for a driver assistant image customization of vehicle-related images, a data processing circuit, a computer program, a computer-readable medium, and a vehicle, can include, with at least one sensing device of the vehicle, obtaining an input image to be customized. With at least one human-machine-interface of the vehicle or connected thereto an input determining at least one customization scheme to be performed is received. Using at least one data processing circuit of the vehicle applying artificial intelligence the input image is transformed according to the at least one customization scheme into a transformed output image. With at least one smart mirror of the vehicle or a mobile device connected to the at least one data processing circuit the transformed output image is outputted. The at least one customization scheme includes a plurality of different types of adaptation modes.

The invention provides an interpretable postoperative analgesia formula intelligent recommendation method based on a decision tree, and the method comprises the following steps: S1, according to hospital postoperative analgesia case data, determining patient characteristics and analgesia drugs, the patient characteristics comprising continuous variables and discrete variables, the analgesia drugs being the continuous variables, and preprocessing the case data; s2, performing feature construction and feature selection on the patient features, and taking the selected patient features and analgesic drugs as data sources; s3, taking the preprocessed medical record data as a training sample of a decision tree algorithm, and performing analgesia formula model learning to obtain a learned model; and S4, inputting test case data into the learned model for prediction to obtain a final analgesic formula. Through application of the artificial intelligence technology in the field of postoperative analgesia, medical and industrial crossing is promoted, intelligent recommendation of analgesia formula decisions is achieved, and therefore the workload of doctors in the process of formulating analgesia formulas is greatly reduced.

The invention relates to the application of artificial intelligence and advanced statistical methods in a single software tool to monitor and analyze quality issues throughout the life cycle of a product, the whole life cycle being:-zero kilometer; -the effective service life within the warranty period ("on-site warranty period"); -an effective service life not within the warranty period ("outside warranty period"). In addition, the invention further provides an innovative method for monitoring products beyond the warranty period based on the network capture technology and artificial intelligence.

The application provides a big data intelligent ore prospecting operation method based on multi-source geological relationship data, applies artificial intelligence technology, big data technology and geological theory research achievements to the field of mineral exploration technology, collects or constructs detailed knowledge graphs of single ore type or multiple ore types according to different situations, divides ore prospecting target areas into a plurality of unit modules with spatial information as the most basic geological relationship knowledge graph carrier unit, respectively assigns corresponding attribute contents to the unit modules according to spatial positions, description contents and geological knowledge, references existing related ore type knowledge graphs, performs knowledge fusion and disambiguation on the attribute contents in the unit modules, and constructs the geological relationship knowledge graph of the unit module, and obtains the scores of the unit modules or the scores of the different types of ore-forming possibilities of the unit modules according to weights, so that the ore prospecting prospective areas can be efficiently and accurately delineated or the dominant ore can be determined.

A method, computer program product, and computer system carry out a process of user registration. The process of user registration comprises receiving a registering user input of a drawing process of a drawn image into a graphical user interface and monitoring the first drawing process to gather registration metadata relating to the drawn image. The process also comprises providing, by applying an artificial intelligence image analysis method, a description of the drawn image. Further, the process comprises securely storing the description and a set of the registration metadata for later authentication by comparison to a description of a later drawn image and metadata relating to the later drawn image.

The present disclosure provides an advanced energy management system that optimizes energy storage unit (ESU) deployment and operation using artificial intelligence. Unlike conventional systems that rely on static configurations or manual adjustments, this system dynamically analyzes data from multiple sources to configure and manage ESUs. By employing AI algorithms, the system can adapt to specific application requirements, predict future energy needs, and efficiently manage energy flow across distributed storage units. The system generates and evaluates multiple ESU configurations, selecting the optimal setup based on performance metrics and application-specific parameters. This approach addresses the limitations of traditional ESU deployment methods, potentially improving overall energy efficiency, enhancing reliability, and reducing costs in various applications, from small-scale residential installations to large industrial and grid-level energy storage solutions. The AI-driven system continuously monitors and optimizes ESU performance, adapting to changing conditions and maintaining ultra-high reliability standards.

A method for creating and applying an AI model (60) for a control device (50) for controlling and / or regulating and / or monitoring a ring rolling process of a workpiece (2), the method comprising: generating a database by executing at least one computer simulation, preferably FEM simulation, of at least one fictitious, partial, or complete ring rolling process based on at least one set of fictitious process parameters; creating the AI ​​model (60) by training a neural network using the generated database; and applying the AI ​​model (60) by inputting process parameters of the ring rolling process of the workpiece (2) into the neural network, wherein the neural network of the AI ​​model (60) generates an output field that is characteristic of the workpiece (2), preferably comprising a temperature and / or deformation distribution of the workpiece (2).

An electron beam defect classification system, that includes (a) an imager that is configured to generate an electron beam image of a region of a sample by illuminating the region with an electron beam; and (b) a processing circuit configured to process the electron beam image to provide a defect classification result. The processing circuit includes an artificial intelligence processor that is configured to apply artificial intelligence processing on the electron beam image to provide the defect classification result. The artificial intelligence processing represented an artificial intelligence process that exhibits an accuracy that is contributed to a training of the artificial intelligence process using a dataset that has undergone a label error reduction process.

A terminal according to one aspect of the present disclosure comprises: a transmission unit that transmits a beam report to which artificial intelligence (AI)-based beam prediction is applied; and a control unit that, when a prediction result and a beam identifier (ID) are to be reported in the beam report, determines the format of the beam ID on the basis of a specific factor.

A system, computer readable medium, apparatus and / or method for non-invasive, non-surgical, digital biopsy. The system, computer readable medium, apparatus and / or method accurately predicts benign kidney lesions from cancers in a patient. A processor may receive patient clinical factors, texture analysis of computer-tomographic imaging, and an artificial intelligence learning model. By implementing artificial intelligence, the processor may then predict or determine a probability of kidney cancer in the patient using the patient clinical factors, the texture analysis of computer-tomographic imaging, and the artificial intelligence learning model. Notably, the prediction is performed without needing invasive biopsy surgery and subsequent pathology analysis to arrive at a diagnosis but relies on radiomics metrics.

Natural language generation technology is disclosed that applies artificial intelligence to structured data to determine content for expression in natural language narratives that describe the structured data. A graph data structure is employed, where the graph data structure comprises a plurality of nodes. The nodes (1) represent corresponding intents so that different nodes represent different corresponding intents, (2) are associated with corresponding analytics for execution to evaluate a given node's corresponding intent and generate a result that represents an evaluation of that node's corresponding intent, and (3) are associated with one or more links with one or more of the nodes to define relationships among the intents. A processor traverses the graph data structure based on defined criteria and a plurality of the links to (1) choose which nodes are to be evaluated and (2) determine content for expression in the narratives based on execution of the chosen nodes' corresponding analytics against the structured data.

Included in the present disclosure is a method, including obtaining data associated with a metric of a user. In some embodiments, the method includes comparing the data to a benchmark associated with the metric, thereby determining a benchmark score. According to some embodiments, the method includes applying artificial intelligence (AI) to determine a course of action based on the benchmark score and demographic information. The course of action may include i) calibrating the benchmark, ii) determining an activity to adjust future data associated with the metric, or iii) both, so as to modify the benchmark score.

The invention discloses a Beidou application system positioning updating method applying artificial intelligence, and relates to the technical field of satellite navigation positioning, and the method comprises the following steps: loading a pre-training AI model when a system is started, and initializing a Beidou receiver and an environment database; beidou satellite observation data and building environment characteristic data are collected in real time; performing quality screening, feature extraction and normalization processing on the collected data; inputting the processed data into an AI model to predict a current positioning error value; correcting the original positioning coordinate by using the prediction error and carrying out smooth filtering; and outputting a final positioning result to a control system and storing related data. According to the method, the attention-enhanced deep learning model is constructed, and the real-time environment characteristics and the Beidou observation data are fused, so that the multipath positioning error in the complex urban environment can be predicted and compensated.

A data processing method and a neural network training method and related devices, which can apply artificial intelligence technology to the simulation field, the method comprising: obtaining first information, the first information comprising a constraint condition of a parameter in a PDE to be solved and an initial grid corresponding to the PDE, the PDE being obtained based on an object to be simulated, the shape of the initial grid being determined based on the object to be simulated, and the initial grid comprising N first grid points; inputting the first information into a first neural network to obtain N second grid points output by the first neural network, the N second grid points belonging to an updated grid, the updated grid being used to solve the PDE, the PDE no longer needing to be solved repeatedly, the time consumption being shorter, and the computer resource overhead being reduced; and the mapping relationship between the N second grid points and the N first grid points being reversible, so as to avoid solving meaningless numerical solutions as much as possible.

The invention provides an intelligent power plant fault early warning method and system applying artificial intelligence, the internal relation among a generator set, a sensor network and a control system is comprehensively revealed by constructing a multi-dimensional association knowledge graph, and key cluster identification is carried out on a composite knowledge network, so that the fault early warning efficiency is improved. Key links and potential risk points in operation of a power plant can be accurately positioned, a known fault mode can be timely found through deep analysis of key cluster members and matching in combination with a historical fault mode library, and early warning can be performed on a new and unknown fault mode. Therefore, the intelligent level of power plant fault early warning is greatly improved. Based on the risk grade division of the fault mode and the fault symptom, the early warning information is more refined and hierarchical, the power plant management personnel can conveniently formulate reasonable countermeasures according to the risk grade, and the safe and efficient operation of the power plant is ensured. Therefore, the accuracy and the intelligent level of power plant fault early warning are remarkably improved.

The present disclosure provides a communication method and apparatus, to improve security of applying artificial intelligence in a network. The method includes obtaining first information, where the first information indicates an anti-attack policy of first data, and the first data includes training data and / or inference data of a model; determining the first data based on the first information; and sending the first data, wherein the first data is used to perform model training and / or determine an inference result. The first information can be at least one of the following: data source blocklist information of the first data and / or data source trustlist information of the first data, information indicating a data preprocessing manner of the first data, or information indicating a data enhancement manner of the first data.

The invention relates to a method for enriching a database of medical images intended for a clinical study, comprising the steps of: - retrieving (100) a medical image to be analyzed; - identifying (103) a type of organ in the medical image to be analyzed by implementing an artificial intelligence model generated by training on a database of reference medical images whose predefined parameter values ​​are fixed; - selecting predefined parameters of the image to be analyzed according to the type of organ identified; - identifying (106) the values ​​of the predefined parameters of the medical image to be analyzed by implementing the artificial intelligence model; - storing (108) the identified parameter values ​​in metadata associated with the image to be analyzed. Figure to be published with the abstract: Fig. 1

The application provides a smart power plant fault early warning method and system applying artificial intelligence, which comprehensively reveals the internal relationship among the generator set, the sensor network and the control system by constructing a multi-dimensional associated knowledge graph, can accurately locate the key link and potential risk point in the operation of the power plant by identifying the key cluster of the composite knowledge network, can not only discover the known fault mode in time, but also can early warn the new and unknown fault mode by deeply analyzing the key cluster members and matching with the historical fault mode library, thereby greatly improving the intelligent level of the power plant fault early warning. The risk level division based on the fault mode and the fault symptom makes the early warning information more refined and hierarchical, and facilitates the power plant managers to develop reasonable countermeasures according to the risk level, and ensures the safe and efficient operation of the power plant. Therefore, the accuracy and intelligent level of the power plant fault early warning are significantly improved.

Disclosed herein are example embodiments of an improved narrative generation system where an analysis service that executes data analysis logic that supports story generation is segregated from an authoring service that executes authoring logic for story generation through an interface. Accordingly, when the authoring service needs analysis from the analysis service, it can invoke the analysis service through the interface. By exposing the analysis service to the authoring service through the shared interface, the details of the logic underlying the analysis service are shielded from the authoring service (and vice versa where the details of the authoring service are shielded from the analysis service). Through parameterization of operating variables, the analysis service can thus be designed as a generalized data analysis service that can operate in a number of different content verticals with respect to a variety of different story types.

An AI camera surveillance device includes: a detection region image receiving unit which receives a camera-dependent detection region image; a detection region image preprocessing unit which calculates a distortion correction coefficient for the camera-dependent detection region image and applies the distortion correction coefficient to the camera-dependent detection region image to generate a camera-independent detection region image; a region-of-interest setting unit which sets at least one region-of-interest (ROI) for the camera-independent detection region image; an alarm region setting unit which sets an alarm region by applying an artificial intelligence detection scenario model that monitors each of at least one region-of-interest (ROI) to process occurrence of a user-defined event and execution of a user-defined action; and an administrator-defined scenario execution unit which performs an administrator-defined scenario by detecting occurrence of a user-defined event and processing of a user-defined actions for a specific region-of-interest (ROI) through the artificial intelligence detection scenario model.