Abstract: Present disclosure discloses method and system for automatically generating virtual driving environment using real-world data for autonomous vehicle (AV). Method extracts object feature data and road feature data from information related to an environment surrounding the AV. Thereafter, method generates a dynamic tree-based model for one or more objects in the environment with reference to the AV based on the object feature data and road network information based on the road feature data. Subsequently, method generates a pre-virtual driving environment by combining the dynamic tree-based model for the one or more objects and the road network information using a tree traversal technique and validates the pre-virtual driving environment for inaccuracies based on configurable validation rules.

Abstract: Embodiments of present disclosure relates to method and gesture prediction system for predicting gesture of subjects for controlling AV during navigation. The gesture prediction system obtains data from sensors and generates context information of environment of AV. The gesture prediction system generates parameters for sampling frames for determining gesture of subjects. The gesture prediction system estimates current pose, and subsequent poses by extrapolating current pose and subsequent poses using deep learning techniques. Further, the gesture prediction system infers current behaviour of subjects by classifying current pose and subsequent poses into one of predefined gesture categories. The gesture prediction system predicts gesture of subjects based on current behaviour, context information and location information of subjects for controlling AV during navigation. Thus, the present disclosure forecast gestures of subjects at a faster rate and controls the AV during the navigation.

Abstract: Disclosed embodiments provide a framework for processing sets of credentials in real-time using machine learning models to identify requisitions that can be recommended to users. In response to receiving a set of credentials from a user, a system assigns a classification to the set of credentials based on a set of characteristics associated with the set of credentials. A machine learning model is used to assign tags to the text of the set of credentials. These tags correspond to the set of characteristics. Using these tags, a set of open requisitions are identified and provided to the user.

Abstract: A method for machine learning includes learning, during a training stage, network parameter values of a neural network to obtain a trained neural network configured to perform reconstruction of medical images; refining, during a subsequent refinement stage, the learned network parameter values to generate refined network parameter values defining a refined neural network; and applying input medical image data to the refined neural network to generate a reconstructed medical image. The method retains benefits of machine learning image reconstruction to obtain a desired reconstructed image.

Abstract: A natural language workflow policy is generated for a workflow to solve customer support tickets is automatically generated from representative tickets. Tools, such as API calls, may also be automatically generated from representative tickets. In one implementation, a clustering technique may be used to identify representative answers for particular customer support topics. The generated workflows may be used by a large language model to generate answers for customer questions for an autonomous AI chatbot agent.

Abstract: An autonomous customer support Chatbot Agent utilizes a large language model to aid in implementing a workflow to solve a customer issue. A natural language workflow policy may be selected by an admin, along with tools such as API calls. The large language model determines the implementation details for the workflow based on the workflow policy and the selected tools.

Abstract: A system and method are disclosed for detecting the intent of a freeform customer support email question and performing a workflow to respond to the customer support email. In one implementation, a granular taxonomy of topics is generated and intent detection includes mapping a summary of the email to a topic in the taxonomy. An interactive workflow may be selected based on the topic of the email. The interactive workflow may include slot-filling using generative artificial intelligence. The interactive workflow may include API calls.

Abstract: A method for selection of a marker in an augmented reality (AR) environment is provided. The method includes capturing a scene in the augmented reality environment; extracting a set of region of interest from the scene captured; identifying a text in the region of interest or from a document associated to the region of interest; determining a set of phrase-action pairs from the text; generating a representation of a set of region of interest and a representation of a set of phrase-action pairs; calculating inter model similarity using the set of region of interest and the set of phrase-action pairs in common embedding space; computing intra model similarity by comparing the extracted ROI with a generated ROI and the extracted phrase-action with generated phrase actions; and selecting a phrase-action-ROI tuple having the highest intra modal similarity as the marker.

Abstract: A computer-implemented method is disclosed for using generative AI for customer support. An AI model may be fine-tuned on the task of generating a template workflow answer given a prompt of real answers. In some implementations, an AI empathy model is trained/fine-tuned to customize template answers to be more empathic. In some implementations, the template workflow answer may include an API call step.

Abstract: An apparatus for reconstructing or filtering medical image data is provided. The apparatus includes processing circuitry to receive a first medical image data and meta-parameters related to the first medical image data; apply the received meta-parameters to inputs of a first trained machine-learning (ML) network, e.g., a multilayer perceptron, to obtain, from outputs of the first trained ML network, tuning parameters of a second ML network (e.g., a convolutional neural network) different from the first ML network; apply the received first medical image data to inputs of the second ML network, as tuned by the obtained tuning parameters output from the first ML network, to obtain, from outputs of the second ML network, second medical image data; and output the second medical image data. In one embodiment, the first medical image data is magnetic-resonance k-space data and the second medical data is a magnetic-resonance image.

Abstract: A computer-implemented method for augmenting customer support is disclosed in which a granular taxonomy is formed to classify tickets based on customer issue topic. A dashboard and user interface of performance metrics may be generated for the topics in the taxonomy. Recommendations may also be generated to aid servicing customer support issues for topics in the taxonomy. This may include generating information to aid in determining topics for generating automated responses or generating recommended answers for particular topics. In some implementations, an archive of historic tickets is used to generate training data for a machine learning model to classify tickets.

Abstract: An apparatus for incremental motion correction in medical imaging. The apparatus for motion correction in magnetic resonance imaging includes processing circuitry configured to estimate an intermediate image from a first section of k-space, the first section of the k-space corresponding to acquisition time points within a magnetic resonance scan of a subject, the corresponding acquisition time points within the magnetic resonance scan being associated with shots of the k-space determined to have minimal motion, estimate motion parameters of a second section of the k-space using the estimated intermediate image, combine data from the first section of the k-space with data from the second section of the k-space according to the estimated motion parameters, and reconstruct the combined data of the k-space to generate a final image.

Abstract: Disclosed embodiments provide a framework for processing sets of credentials in real-time using machine learning models to identify requisitions that can be recommended to users. In response to receiving a set of credentials from a user, a system assigns a classification to the set of credentials based on a set of characteristics associated with the set of credentials. A machine learning model is used to assign tags to the text of the set of credentials. These tags correspond to the set of characteristics. Using these tags, a set of open requisitions are identified and provided to the user.

Abstract: The present disclosure relates to a method and apparatus correcting a metal artifact in magnetic resonance imaging (MRI) data. The method and apparatus acquire plural slices along a slice direction of a scanned region associated with a body part, estimate a spatial extent of a signal dispersion of the acquired plural slices along the slice direction, and combine the signal of the acquired plural slices along the slice direction based on the estimated spatial extent of the signal dispersion to generate a reconstructed image of the scanned region.

Abstract: A method for selection of a marker in an augmented reality (AR) environment is provided. The method includes capturing a scene in the augmented reality environment; extracting a set of region of interest from the scene captured; identifying a text in the region of interest or from a document associated to the region of interest; determining a set of phrase-action pairs from the text; generating a representation of a set of region of interest and a representation of a set of phrase-action pairs; calculating inter model similarity using the set of region of interest and the set of phrase-action pairs in common embedding space; computing intra model similarity by comparing the extracted ROI with a generated ROI and the extracted phrase-action with generated phrase actions; and selecting a phrase-action-ROI tuple having the highest intra modal similarity as the marker.

Abstract: An apparatus for incremental motion correction in medical imaging. The apparatus for motion correction in magnetic resonance imaging includes processing circuitry configured to estimate an intermediate image from a first section of k-space, the first section of the k-space corresponding to acquisition time points within a magnetic resonance scan of a subject, the corresponding acquisition time points within the magnetic resonance scan being associated with shots of the k-space determined to have minimal motion, estimate motion parameters of a second section of the k-space using the estimated intermediate image, combine data from the first section of the k-space with data from the second section of the k-space according to the estimated motion parameters, and reconstruct the combined data of the k-space to generate a final image.

Abstract: A magnetic resonance imaging apparatus according to an embodiment includes a processing circuit. The processing circuit acquires a plurality of echo signals not to be encoded corresponding to a plurality of respective shots about acquisition of a plurality of echo signals having been encoded by magnetic resonance imaging for a subject, compares the echo signals not to be encoded with each other about the shots, and identifies a shot to be removed about generation of a magnetic resonance image about the subject out of the shots based on a comparison result of the echo signals not to be encoded.

Abstract: An apparatus for incremental motion correction in medical imaging. The apparatus for motion correction in magnetic resonance imaging includes processing circuitry configured to estimate an intermediate image from a first section of k-space, the first section of the k-space corresponding to acquisition time points within a magnetic resonance scan of a subject, the corresponding acquisition time points within the magnetic resonance scan being associated with shots of the k-space determined to have minimal motion, estimate motion parameters of a second section of the k-space using the estimated intermediate image, combine data from the first section of the k-space with data from the second section of the k-space according to the estimated motion parameters, and reconstruct the combined data of the k-space to generate a final image.

Abstract: An apparatus and method are provided to simultaneously provide good image quality and fast image reconstruction from magnetic resonance imaging (MRI) data by selecting an appropriate value for the regularization parameter used in compressed sensing (CS) image reconstruction. In CS reconstruction a high-resolution image can be reconstructed from randomized undersampled data by imposing sparsity in multi-scale transformation (e.g., wavelet) domain. Further, in the transformation domain, a threshold can be determined between signal and noise levels of the transform coefficients. A regularization parameter based on this threshold scales the regularization term, which imposes sparsity, relative to the data fidelity term in an objective function, thereby balancing the tradeoff between noise and smoothing.

Abstract: Disclosed embodiments provide a framework to facilitate recruitment and processing of applicants. A bot recruiting agent is implemented that engages in a communications session with applicants to solicit responses to questions for a job opening. The responses are scored according to a set of metrics and a fitness score for each applicant is provided. Based on the fitness score, a recommendation for advancing an applicant for the job opening is generated.