Abstract: Systems and methods of monitoring inventory of a product storage facility include an image capture device configured to move about the product storage areas of the product storage facility and capture images of the product storage areas from various angles. A computing device coupled to the image capture device obtains the images of the product storage areas captured by the image capture device and processes the obtained images of the product storage areas to detect individual products captured in the obtained images. Based on detection of the individual products captured in the images, the computing device analyzes each of the obtained images to extract meta data from the packaging the individual products to detect one more keywords and determine the locations of the detected keywords on the packaging, and then utilize this information to predict an identity of the products associated with the packaging.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to processing captured images. In some embodiments, there is provided a system for processing captured images of objects at a product storage facility including a trained machine learning model; and a control circuit. The control circuit may identify a product identifier associated with an object in a captured image; generate predicted product identifiers associated with the object in the captured image based on text identified from the object in the captured image; aggregate the predicted product identifiers; determine a feature of the objects associated with the aggregated predicted product identifiers; determine one or more confusing product identifiers based on a determination of the aggregated predicted product identifiers being associated with the feature; and update a dataset with at least one of the one or more confusing product identifiers and images associated with the one or more confusing product identifiers.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to processing captured images. In some embodiments, there is provided a system for processing captured images of objects at a product storage facility including a trained machine learning model; and a control circuit. The control circuit may group a plurality of product identifiers into one or more clusters based on at least one of visual similarity of corresponding images, textual similarity of corresponding associated descriptions, and associated relationships between product identifiers of the plurality of product identifiers; determine clusters having common elements that are at least within a similarity threshold of each other; merge the clusters with the common elements; and generate a mapping dataset used to retrain the trained machine learning model to identify a plurality of objects. The mapping dataset may include a plurality of associations of associated product identifiers to a single object.

Abstract: Systems and methods of updating templates for use in recognizing individual products in images captured at a product storage facility include an image capture device that captures one or more images of product storage structure at a product storage facility, a computing device in communication with the image capture device, and an electronic database that stores keyword model templates and feature model templates associated with images of previously recognized individual products detected at the product storage facility.

Abstract: Systems and methods of creating reference template images for detecting and recognizing products at a product storage facility include an image capture device having a field of view that includes a product storage structure of the product storage facility, and a computing device including a control circuit and being communicatively coupled to the image capture device. The computing device obtains images of the product storage structure captured by the image capture device, analyzes the obtained images to detect individual ones of the products located on the product storage structure. Then, the computing device identifies the individual ones of the products detected in the images and crops each of the individual ones of the identified products from the images to generate cropped images. The computing device then creates a cluster of the cropped images, and selects one of the cropped images as a reference template image of an identified individual product.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to labeling objects in captured images. In some embodiments, there is provided a system for labeling objects in images captured at a product storage facility including a control circuit and a user interface. The control circuit is configured to select a set of unprocessed images; receive a selected configuration based on data resulting from iteratively processing the set of unprocessed images; cluster each unprocessed image into a corresponding group based on the selected configuration; select a plurality of clustered images from each of the plurality of groups; and output the plurality of clustered images from each group. The user interface is configured to: display each clustered image; and receive a user input labeling one or more objects shown in each clustered image resulting in a labeled dataset used to train a machine learning model.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to processing captured images of objects at a product storage facility. In some embodiments, there is provided a system for processing captured images of objects including a trained machine learning model and a control circuit. In some embodiments, the trained machine learning model is configured to process unprocessed captured images. In some embodiments, the control circuit is configured to associate each of the processed images into one of a first group, a second group, or a third group; remove at least one processed image associated with the first group from the processed images in accordance with a first processing rule; and output remaining processed images associated with the first group and processed images associated with the second group to be used to retrain the trained machine learning model.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to updating inventory of products. In some embodiments, there is provided a system for updating inventory of products including a database; at least one image capture device; and a control circuit. The control circuit is configured to: process an image by: detecting units of one or more products in the image; grouping each detected unit into one or more clusters based on at least one of textual similarities, visual similarities, geometrical similarities, or relative spatial distance; detecting a product identifier for each cluster; identifying a product corresponding to the detected product identifier of each cluster; and counting the detected units associated with each cluster. The control circuit is configured to cause an update to the inventory of products in the database based on the counted detected units of each cluster.

Abstract: Systems and methods of monitoring inventory of a product storage facility include an image capture device configured to move about the product storage areas of the product storage facility and capture images of the product storage areas from various angles. A computing device coupled to the image capture device obtains the images of the product storage areas captured by the image capture device and processes the obtained images of the product storage areas to detect individual products captured in the obtained images. Based on detection of the individual products captured in the images, the computing device analyzes each of the obtained images to extract meta data from the packaging the individual products to detect one more keywords and determine the locations of the detected keywords on the packaging, and then utilize this information to predict an identity of the products associated with the packaging.

Abstract: Devices, methods, and systems for voice control of components of a facility are described herein. One computing device apparatus includes a memory, and a processor configured to execute executable instructions stored in the memory to receive a voice command or voice query from a user, determine location context information associated with the computing device, and determine which component or components of the facility are associated with the voice command or voice query based, at least in part, on the location context information associated with the computing device.

Abstract: Devices, methods, and systems for voice control of components of a facility are described herein. One computing device apparatus includes a memory, and a processor configured to execute executable instructions stored in the memory to receive a voice command or voice query from a user, determine location context information associated with the computing device, and determine which component or components of the facility are associated with the voice command or voice query based, at least in part, on the location context information associated with the computing device.

Abstract: Devices, methods, and systems for voice control of components of a facility are described herein. One computing device apparatus includes a memory, and a processor configured to execute executable instructions stored in the memory to receive a voice command or voice query from a user, determine location context information associated with the computing device, and determine which component or components of the facility are associated with the voice command or voice query based, at least in part, on the location context information associated with the computing device.

Abstract: Devices, methods, and systems for voice control of components of a facility are described herein. One computing device apparatus includes a memory, and a processor configured to execute executable instructions stored in the memory to receive a voice command or voice query from a user, determine location context information associated with the computing device, and determine which component or components of the facility are associated with the voice command or voice query based, at least in part, on the location context information associated with the computing device.

Abstract: A device includes an anchor node configured for placement in a space containing an asset having an asset radio. An anchor node radio is supported by the anchor node. An attenuator is coupled to the anchor node radio to provide a distinctive fingerprint signature of transmissions from the anchor node radio within the space. The distinctive fingerprint is receivable by the asset radio to determine a location of the asset within the space.

Abstract: A method includes receiving sound from an area being monitored by a video surveillance system having a camera and microphone, analyzing the sound to determine a classification of the sound, and capturing video of the area being monitored responsive to the classification of the sound.

Abstract: A method, system and apparatus are provided for measuring the quality of voice (QOV) in a communication network. A time-stamped voice data packet is received. The time stamped on the voice data packet indicates the time at which the voice data packet arrives at intermediate devices. In an embodiment of the invention, the intermediate devices at each hop stamp the time. Thereafter, the voice data packets are analyzed to measure the quality of voice in the communication network.

Abstract: A method, system and apparatus are provided for measuring the quality of voice (QOV) in a communication network. A time-stamped voice data packet is received. The time stamped on the voice data packet indicates the time at which the voice data packet arrives at intermediate devices. In an embodiment of the invention, the intermediate devices at each hop stamp the time. Thereafter, the voice data packets are analyzed to measure the quality of voice in the communication network.