Abstract: Embodiments of the present disclosure include apparatuses, computer-implemented methods, and computer program products for automatic product verification and shelf product gap analysis. Some embodiments utilize a multi-imager imaging engine to capture at least two image data objects associated with at least a near field and a far field via corresponding near and far-field imagers. The far field image data object in some embodiments is processed to identify, and/or detect and decode, product information on a product label at a shelving location for future processing. The near-field image data object may be processed to identify a product set located within the environment surrounding the product label. The information identified from each image data object may be processed to identify whether one or more product mismatches, pricing mismatches, and/or product gaps are present at the shelving location, with improved likelihood of success for each task.

Abstract: Embodiments are direct to methods and systems for authenticating a user and interpolating user preference embeddings. The systems generate, using a neural network trained to generate features based on training data comprising human voices spoken by a plurality of historical speakers inside a vehicle, input features based on a human voice of a current speaker inside the vehicle, and calculates similarities between an input vector of the input features and historical vectors in voiceprints of one or more enrolled users. After determining a similarity between the input vector and at least one historical vector in a voiceprint of an identified user is less than a threshold similarity, the systems authenticate the current speaker as the identified user, calculate a probabilistic notion based on the similarity, and apply the probabilistic notion to interpolate between downstream user preference embeddings associated with the identified user.

Abstract: Embodiments of the present disclosure include apparatuses, computer-implemented methods, and computer program products for automatic product verification and shelf product gap analysis. Some embodiments utilize a multi-imager imaging engine to capture at least two image data objects associated with at least a near field and a far field via corresponding near and far-field imagers. The far field image data object in some embodiments is processed to identify, and/or detect and decode, product information on a product label at a shelving location for future processing. The near-field image data object may be processed to identify a product set located within the environment surrounding the product label. The information identified from each image data object may be processed to identify whether one or more product mismatches, pricing mismatches, and/or product gaps are present at the shelving location, with improved likelihood of success for each task.

Abstract: Embodiments of the present disclosure include apparatuses, computer-implemented methods, and computer program products for automatic product verification and shelf product gap analysis. Some embodiments utilize a multi-imager imaging engine to capture at least two image data objects associated with at least a near field and a far field via corresponding near and far-field imagers. The far field image data object in some embodiments is processed to identify, and/or detect and decode, product information on a product label at a shelving location for future processing. The near-field image data object may be processed to identify a product set located within the environment surrounding the product label. The information identified from each image data object may be processed to identify whether one or more product mismatches, pricing mismatches, and/or product gaps are present at the shelving location, with improved likelihood of success for each task.

Abstract: A computer-implemented method for determining features of a dataset that are indicative of anomalous behavior of one or more computers in a large group of computers comprises (1) receiving log files including a plurality of entries of data regarding connections between a plurality of computers belonging to an organization and a plurality of websites outside the organization, each entry being associated with the actions of one computer, (2) executing a time series decomposition algorithm on a portion of the features of the data to generate a first list of features, (3) implementing a plurality of traffic dispersion graphs to generate a second list of features, and (4) implementing an autoencoder and a random forest regressor to generate a third list of features.

Abstract: A computer-implemented method for detecting anomalous behavior of one or more computers in a large group of computers comprises (1) receiving log files including a plurality of entries of data regarding connections between a plurality of computers belonging to an organization and a plurality of websites outside the organization, each entry being associated with the actions of one computer, (2) applying a first plurality of algorithms to determine features of the data which may contribute to anomalous behavior of the computers, and (3) applying a second plurality of algorithms to determine which computers are behaving anomalously based upon the features.

Abstract: Embodiments of the present disclosure include apparatuses, computer-implemented methods, and computer program products for automatic product verification and shelf product gap analysis. Some embodiments utilize a multi-imager imaging engine to capture at least two image data objects associated with at least a near field and a far field via corresponding near and far-field imagers. The far field image data object in some embodiments is processed to identify, and/or detect and decode, product information on a product label at a shelving location for future processing. The near-field image data object may be processed to identify a product set located within the environment surrounding the product label. The information identified from each image data object may be processed to identify whether one or more product mismatches, pricing mismatches, and/or product gaps are present at the shelving location, with improved likelihood of success for each task.

Abstract: A system and method for monitoring a vehicle are presented. The system includes a first imaging subsystem for acquiring a plurality of load-carrying-portion images. A cargo-detection subsystem is configured for analyzing each of the plurality of load-carrying-portion images to determine whether cargo is positioned on the load-carrying portion of the vehicle. A power-detection subsystem is configured for determining when the vehicle is running. A motion-detection subsystem is configured for determining when the vehicle is in motion. An analytics subsystem is configured for calculating at least one of (i) the amount of time that the vehicle is running, (ii) the amount of time that the vehicle is running while cargo is positioned on the load-carrying portion, (iii) the amount of time the vehicle is in motion, and (iv) the amount of time the vehicle is in motion while cargo is positioned on the load-carrying portion.