A computing device divides a training image into a plurality of training blocks, and the training image includes a training object. The computing device calculates, for each of the training blocks, a correct confidence score of the training object covering the training block according to an image-marking data and a confidence-score-translating function, and the image-marking data includes a piece of location information of the training object in the training image. Then, the computing device trains a deep-learning model with the training image, the correct confidence scores and the image-marking data to generate the object-detecting model.

An apparatus includes a display and a processor. The processor displays a virtual shopping cart. The processor also receives information indicating that an algorithm determined that a physical item was selected by a person during a shopping session in a physical store, based on a set of inputs received from sensors located within the store. In response, the processor displays a virtual item, which includes a graphical representation of the physical item. The processor additionally displays a rack video captured during the shopping session by a rack camera located in the store. The rack camera is directed at a physical rack located in the store, which includes the physical item. In response to displaying the rack video, the processor receives information identifying the virtual item, where the rack video depicts that the person selected the physical item. The processor then stores the virtual item in the virtual shopping cart.

Disclosed herein an artificial intelligence apparatus for determining inattention of a driver including a vibration sensor or a gyro sensor configured to sense movement of a driver's seat of a vehicle, a camera configured to receive image data including a face of a driver, a communication modem configured to receive vehicle status information from an ECU (Electronic Control Unit) of the vehicle, and a processor configured to generate movement information of the driver's seat using vibration sensor information received from the vibration sensor or gyro sensor information received from the gyro sensor, generate driver status information corresponding to the driver from the received image data, determine whether the driver is in an inattention status based on the movement information of the driver's seat, the driver status information and the vehicle status information, and output an inattention alarm if the driver is in the inattention status.

Methods and systems for improving the interactivity of media content. The methods and systems are particularly applicable to the e-learning space, which features unique problems in engaging with users, maintaining that engagement, and allowing users to alter media assets to their specific needs. To address these issues, as well as improving interactivity of media assets generally, the methods and systems described herein provide for annotation and truncation of media assets. More particularly, the methods and systems described herein provide features such as annotation guidance and video condensation.

A first and second scoring endpoint with payload logging are deployed. At the second scoring endpoint, native data and a user-generated score for the native data are received, the native data is pre-processed into readable data for the deep-learning model, and the user-generated score and the readable data are output to the first scoring endpoint, which is associated directly with the deep-learning model. A raw payload that includes the native data is output to a payload store. At the first scoring endpoint, the readable data and the user-generated score are processed by the deep-learning model, which outputs a transformed payload and a prediction, respectively, to the payload store. The raw payload is matched with the transformed payload and the prediction to produce a comprehensive data set, which is evaluated to describe a set of transformation parameters. The deep-learning model is retrained to account for the set of transformation parameters.

A query change system includes: a processor configured to correct, in a case where a first query image inputted by a user includes a contradicting part that contradicts a first condition related to a search target, the contradicting part of the first query image in accordance with the first condition to generate a second query image.

A system comprising a database and a user device. The database may be configured to (i) store metadata generated in response to objects detected in a video, (ii) store a confidence level associated with the metadata, (iii) provide to a plurality of users (a) data portions of the video and (b) a request for feedback, (iv) receive the feedback and (v) update the confidence level associated with the metadata in response to the feedback. The user device may be configured to (i) view the data portions, (ii) accept input to receive the feedback from one of said plurality of users and (iii) communicate the feedback to the database. The confidence level may indicate a likelihood of correctness of the objects detected in response to video analysis performed on the video. The database may track user statistics for the plurality of users based on the feedback.

Systems, methods and apparatus of drowsiness detection for vehicle control. For example, a vehicle includes: a camera configured to face a driver of the vehicle and generate a sequence of images of the driver driving the vehicle; an artificial neural network configured to analyze the sequence of images and classify, based on the sequence of images, whether the driver is in a drowsy state; and an infotainment system configured to provide instructions to the driver in response to a classification by the artificial neural network that the driver is in the drowsy state.

In this patent, a smart consulting process is established to improve workflow and human classification of images. A key innovative aspect is the process for selecting an optimal consultant for an image. Such a process may improve human image classification, such as diagnostic radiology. Furthermore, such a process may improve education to more novice imagers. A modified workflow is established where users have general and specific consult pools. Additionally, a modified relative value unit (RVU) system is created to appropriately compensate users for the modified workflow, which is established.

Automatic activation and configuration of robotic process automation (RPA) workflows using machine learning (ML) is disclosed. One or more parts of an RPA workflow may be turned on or off based on one or more probabilistic ML models. RPA robots may be configured to modify parameters, determine how much of a certain resource to provide, determine more optimal thresholds, etc. Such RPA workflows implementing ML may thus be hybrids of both deterministic and probabilistic logic, and may learn and improve over time by retraining the ML models, adjusting the confidence thresholds, using local/global confidence thresholds, providing or adjusting modifiers for the local confidence thresholds, implement a supervisor system that monitors ML model performance, etc.