24 results about "Applied artificial intelligence" patented technology

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.

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.

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

This invention relates to the fields of digital therapy, intelligent medical devices, and applied artificial intelligence technologies, and more specifically to a closed-loop pain management method and system integrating diagnostic assistance and interference-resistant sensing. It includes: a wearable device comprising: a neuromodulation signal generator configured to generate neuromodulation stimulation pulses; a biosensor array physically integrated with the neuromodulation signal generator configured to sense physiological data indicating the user's autonomic nervous system activity; a controller configured to execute a time-division multiplexing protocol; a processor; and a non-transitory computer-readable storage medium associated with the processor for generating a diagnostic support report. This invention enables simultaneous effective neural stimulation and high-fidelity ANS signal acquisition on a single wearable device.

Provide a system. 【Solution means】 Means for obtaining learning status, information on educational organization events, and information on sports activities from an electronic device that inputs information; Means for automatically collecting regional examination information and statistical information; Means for integrating the obtained and collected numerical data and performing preprocessing; Means for applying artificial intelligence to identify problem areas using the preprocessed numerical data; Means for generating an educational plan and career proposal based on the results derived by artificial intelligence; Means for transmitting and presenting the generated proposal to an electronic device; Means for incorporating a function in which a home automation device monitors the knowledge absorption status of a learner and assists in proposing educational policies; A system including the above.

According to examples, a system for supplementing user perception and experience via augmented reality (AR), artificial intelligence (AI), and machine-learning (ML) techniques is described. The system may include a processor and a memory storing instructions. The processor, when executing the instructions, may cause the system to receive data associated with at least one of a location, context, or setting and determine, using at least one artificial intelligence (AI) model and at least one machine learning (ML) model, relationships between objects in the at least one of the location, context, or setting. The processor, when executing the instructions, may then apply an artificial intelligence (AI) agent to analyze the relationships and generate a three-dimensional (3D) mapping of the at least one of the location, context, or setting and provide an output to aid a user's perception and experience.

The present invention relates to a data processing apparatus for an occlusal vertical dimension of a patient, a driving method for the data processing apparatus for an occlusal vertical dimension of a patient, and a computer-readable recording medium including a program for executing the driving method, the data processing apparatus comprising: a storage unit storing computed tomography (CT) data obtained by imaging the oral cavity and head of a patient for an occlusal vertical dimension; and a control unit that applies an artificial intelligence (AI) program to the stored CT data to analyze same and adjusts, on the basis of a result of the analysis, a designated 3D tooth model by using the condyle of the patient in the CT data as a reference axis to set the occlusal vertical dimension of the patient.

An AI-supported, intelligent and modular system for monitoring plant health for precision agriculture, consisting of: a deployment device configured for field surveillance, the deployment device being selected either as an airborne drone platform for aerial surveillance or as a ground-based rover platform for ground-based surveillance, depending on the field size and terrain; A detachable sensor module attached to the deployment device for acquiring field monitoring data, wherein the detachable transmitting module comprises: an embedded edge computing unit; a red-green-blue (RGB) camera; a near-infrared (NIR) camera; a temperature sensor; a position sensor; calibration reference targets stored on the edge computing unit; and a wireless communication interface, wherein the embedded edge computing unit is configured to: synchronize data acquisition from the RGB camera, the NIR camera, and the temperature sensor; acquire high-resolution images with the RGB camera and the NIR camera; and record temperature data, humidity data, and metadata including timestamps, elevation, orientation, and coordinates; an external storage medium that is connected via the wireless communication interface to the embedded edge computing unit of the detachable sensor module and receives all data captured by multiple sensors and the camera in real time from the embedded edge computing unit; and A desktop application runs on an external computer device with a processor, and this external computer device is connected to an external storage medium. The data stored on the external storage medium is shared with the desktop application.The desktop application includes a computing module that performs the following functions: retrieving the captured data from the external storage medium; preprocessing the captured data through noise reduction, illumination normalization, radiometric correction, and image alignment; calculating vegetation indices, consisting of the Normalized Difference Vegetation Index (NDVI) and the Green Normalized Difference Vegetation Index (GNDVI), from the preprocessed data; applying artificial intelligence models to identify plant stress states based on the vegetation indices; segmenting an agricultural field into multiple health zones based on the identified plant stress states; assigning a severity level to each health zone; and generating recommendations for each health zone based on the severity level.

The present invention discloses an artificial intelligence (AI)-based system and an AI-based method for processing assets and liabilities with integrated legacy planning. The AI-based system is configured to: a) obtain personal identifiable information (PII) and biometric data, b) obtain assets data and liabilities data, c) obtain financial documents related to the assets data and the liabilities data, d) classify the obtained assets data, liabilities data, and financial documents for generating graphical insights to depict a financial performance, e) authorise users to designate defined assets and defined liabilities to beneficiaries based on the generated graphical insights, f) generate a digital will by exerting artificial intelligence (AI) models and machine learning (ML) models, g) monitor the assets data and the liabilities data for determining predefined triggering events, and h) systematise a transfer of the defined assets and the defined liabilities to the beneficiaries.

Provided are an automatic three-dimensional face scan matching device having artificial intelligence applied thereto, a method for driving the device, and a computer program stored in a medium. According to the present inventive concept, the automatic three-dimensional face scan matching device having artificial intelligence applied thereto includes a communication interface unit receiving first image data obtained by capturing a face center portion during computed tomography (CT) scanning of a face of a user and second image data obtained by scanning the face of a user in a 3D scanning method, and a control unit visualizing the face of a user into 3D by analyzing the first image data and the second image data by having artificial intelligence (AI) applied thereto to find detection points of landmarks that are commonly detected, converting two-dimensional coordinate values for the detection points found on the second image data into three-dimensional coordinate values, and automatically matching the second image data to CT scanning image data based on the converted three-dimensional coordinate values.

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.

This invention discloses a guided intelligent terminal service system, belonging to the field of artificial intelligence technology. The technical problem it addresses is how to apply artificial intelligence to government terminal service systems to provide users with more convenient and intelligent services. The front-end user program module interacts with users and administrators through a front-end interface and connects to the terminal hardware device. It provides a welcome service and displays the login interface, function list, and popular items through a guided service. Working in conjunction with a guided assistant, it provides personalized business recommendation services based on user characteristics and popular items, and receives user operation instructions and business processing-related data to provide business processing services. The back-end management platform provides fault warning services based on machine status data, trains the guided assistant based on user business processing data, and sends the updated guided assistant to the front-end user program module. It also receives fault handling results from maintenance personnel and updates machine operating status information.

An exemplary system for developing generative animation via AI includes one or more storage devices configured to store a static illustration of at least one object. The exemplary system also includes circuitry configured to (1) generate a mesh representation of a segment of the object in the static illustration, (2) simulate motion of the mesh representation by generating a sequence of optical flow fields, (3) extract an initial outline sketch of the object from the static illustration, (4) generate a set of outline sketches that represent the simulated motion by warping the initial outline sketch based at least in part on the sequence of optical flow fields, and (5) apply an AI model to transform the set of outline sketches into a sequence of animation frames that collectively demonstrate the simulated motion. Various other methods, systems, and computer-readable media are also disclosed.

An electronic device for outputting information about an AI project to be performed in a manufacturing process of a semiconductor device, according to the present disclosure, may comprise at least one processor for controlling a hierarchy-based structure to be output to a dashboard for outputting information about the AI project, wherein the dashboard may include: a first dashboard which manages a management parameter of execution equipment input from an input / output module or the AI project corresponding to a product and the execution equipment; a second dashboard which is output to an upper layer of the first dashboard and manages the execution equipment to which the AI project is applied or a recipe of the execution equipment; and a third dashboard which is output to an upper layer of the second dashboard and manages a fab in which the execution equipment is disposed or a process to which the recipe of the execution equipment is applied.

Disclosed is a technology which relates to a method for guaranteeing communication quality of an existing UE when applying AI / ML-based beam prediction. An operation method of a user equipment in a wireless communication system comprises the steps of: receiving information for configuring cell parameters; and on the basis of the information for configuring the cell parameters, acquiring at least one beam set associated with an AI / ML model. The information for configuring the cell parameters comprises bitmap information and transmission interval information for the at least one beam set. According to the present invention, even if overhead of an SSB for beam sweeping is reduced by applying an AI / ML-based beam prediction technology, performance degradation of an existing UE may be minimized.

The application discloses an information processing method, comprising: obtaining push information, object information and scene information; inputting the push information, the object information and the scene information into a target prediction model to output a predicted push effect of target push content in different scenes; the target prediction model is obtained by performing multi-task learning on a preset prediction network based on sample fusion features; the sample fusion features are obtained by performing vector fusion on sample gating features based on sample general features and sample scene features after gate adjustment; and weight parameters of a prediction layer in the prediction network are determined based on sample rearrangement features and global parameters. The application applies artificial intelligence technology, performs representation learning of scene features and replaces global parameters of a subnetwork based on scene adaptive parameters, so that the model can capture difference information and similar information existing between different scenes, thereby improving the accuracy and stability of the target prediction model in predicting push effects in multiple scenes.

A method and a computing apparatus for allocating space in a building are provided. The method includes: receiving a first input that includes information that relates to a plurality of persons that intend to occupy the building; receiving a second input that includes information that relates to building specifications; receiving a third input that includes information that relates to constraints that are mandatory and preferences that are not mandatory; and determining, based on each of the first input, the second input, and the third input, an allocation of each person to a corresponding space within the building and a respective schedule for each person to occupy the corresponding space. The determination of the allocation and the respective schedule may be effected by applying an artificial intelligence (AI) algorithm that is configured to satisfy all of the mandatory constraints and to optimize a satisfaction of the preferences.

An exemplary system for developing generative animation via AI includes one or more storage devices configured to store a static illustration of at least one object. The exemplary system also includes circuitry configured to (1) generate a mesh representation of a segment of the object in the static illustration, (2) simulate motion of the mesh representation by generating a sequence of optical flow fields, (3) extract an initial outline sketch of the object from the static illustration, (4) generate a set of outline sketches that represent the simulated motion by warping the initial outline sketch based at least in part on the sequence of optical flow fields, and (5) apply an AI model to transform the set of outline sketches into a sequence of animation frames that collectively demonstrate the simulated motion. Various other methods, systems, and computer-readable media are also disclosed.

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.

System and method enhances the quality of the raw image capture and applies artificial intelligence analysis on each step of the image enhancement process. An embedded processor includes or executes deep learning algorithms capable of delivering one or more image features by leveraging intermediate representations at any stage of the digital processing pipeline inside the imaging machine. The deep learning algorithm is composed of fully connected, attention, convolutional, or any layers performing linear and non-linear operations for feature extraction and signal processing, or a combination.

Systems and methods are disclosed comprising techniques for personalization in code generation. A platform determines a user persona based on contextual information regarding a user associated with a natural language query. If a corresponding ontology entry is not found, wherein the code unit is identified based on the determined user persona, the platform applies an artificial intelligence model to generate a code unit by prompting the artificial intelligence model with the at least a portion of the natural language query and one or more of the user role information, the historical interaction pattern information, or the determined user persona. The generated code unit is executed against a data source to generate a result set. Responsive to detecting a feedback item in connection with the result set or the code unit, the platform enables incremental training of the artificial intelligence model. Code units can be modified based on additional feedback items.