International Business Machines Corporation

Bing Zhou

Sinem Guven Kaya

Larisa Shwartz

Shu Tao

Seema Nagar

Amitkumar Manoharrao Paradkar

Rohit Madhukar Khandekar

Pooja Aggarwal

Qing Wang

Rohan R Arora

Hongtan Sun

Noah Zheutlin

Dipanwita Guhathakurta

A computer-implemented method and a computer system for identifying persistent anomalies for failure prediction. The computer system receives a time series data stream. The computer system received a predetermined number N and a predetermined number M which is a fraction of N. The computer system segments the time series data stream into N consecutive sliding windows. The computer system performs supervised persistent anomaly detection to determine whether anomalies across the N consecutive sliding windows are persistent, by using a binary classification model. The computer system performs unsupervised persistent anomaly detection to determine whether the anomalies across the N consecutive sliding windows are persistent. The computer system combines results of the supervised persistent anomaly detection and results of the unsupervised persistent anomaly detection to determine persistent anomalies.

Identifying persistent anomalies for failure prediction

Systems, computer-implemented methods, and computer program products to facilitate sparse depth completion with semantic mesh deformation optimization in an augmented reality environment are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components comprise a depth completion component that generates a first depth map from an image and sparse depth samples. The computer executable components further comprise a semantic mesh deformation component that performs a semantic mesh deformation process, using the sparse depth samples, to generate a second depth map comprising a defined image accuracy.

Depth map generation from sparse depth samples in an augmented reality environment

Systems, computer-implemented methods, and computer program products to facilitate automatic multi-dimensional model generation and tracking in an augmented reality environment are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components comprise a label component that annotates a multi-dimensional point cloud representation of an object present in augmented reality data. The computer executable components further comprise a content generation component that generates a multi-dimensional model of a component of the object based on the multi-dimensional point cloud representation.

Automatic multi-dimensional model generation and tracking in an augmented reality environment

A computer system, non-transitory computer storage medium, and a computer-implemented method of assessing technical risk using visual pattern recognition in an Information Technology (IT) Service Management System. A data visualization engine and a time series generation engine receive the operational data, respectively. A first representation of the data is generated by the data visualization engine, and a second representation of the data is generated by the time series generation engine. Anomaly patterns are identified by a pattern recognition engine configured to perform feature extraction and data transformation. An ensembler is configured to accept the outputs from two AI anomaly engines and make a final decision of whether anomaly patterns are captured. Risk scores based on the identified anomaly patterns are output by a pattern recognition engine to an automated management system. The anomalies includes information regarding vulnerabilities of devices or components of the IT Service Management System.

Assessing technical risk in information technology service management using visual pattern recognition

Obtain access to a three-dimensional point cloud representation of an object including poses of a scanning digital camera and corresponding video frames. Down-sample the three-dimensional point cloud representation to obtain a set of region-of-interest candidates. Filter the region-of-interest candidates to select those of the region-of-interest candidates having appearance changes, which distinguish different visual states, as selected regions of interest, based at least in part on the poses of the camera. Generate region of interest images for the selected regions of interest from corresponding ones of the video frames; and train a deep learning recognition model based on the region of interest images. the trained deep learning recognition model can be used, for example, to determine a visual state of the object for repair instructions.

Fine-grained visual recognition in mobile augmented reality

A method, computer system, and a computer program product for intelligently generating an augmented reality (AR)-assisted repair guidance for a user is provided. The present invention may include detecting a scan of an object. The present invention may then include filtering one or more pose-controlled video frames. The present invention may also include extracting at least one new image feature by utilizing a convolutional neural network (CNN) based on the filtered one or more pose-controlled video frames. The present invention may also include aggregating a plurality of visual information associated with the object by utilizing the CNN, wherein the extracted at least one new image feature is included in the aggregated plurality of visual information. The present invention may further include presenting three dimensional (3D) animated instructions to the user, wherein the 3D animated instructions include an AR-assisted repair guidance for the object.

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Port Jefferson Station, NY, United States of America

Bing Zhou