Abstract: An item stowed at an inventory location may rest or be supported by a surface, such as a shelf, that includes an optical sensor array. The optical sensor array comprises a plurality of optical sensors that detect light intensity values. When a light source illuminates the item resting on the surface, a footprint or shadow is cast by the item onto the surface. Such a shadow is detected by the light intensity values at the optical sensors in the optical sensor array. Once the shadow is detected, information about the area, shape, and perimeter of the shadow can be determined and saved as footprint data for a single item. Such a process can be performed at intake of the item to the facility and later accessed to determine a quantity of the item on a shelf of a facility.

Abstract: Systems and methods are disclosed herein for creating a visual guide for developing training data for a classification of image, where the training data includes images tagged with labels for the classification of the images. A processor may prompt a user to define a framework for the classification. For an initial set of images within the training data, qualified human classifiers are prompted to locate the images within the framework and to tag the images with labels. The processor determines whether the tagged images have consistent labels, and, if so, the processor adds images to the training data. The processor may add the images by providing a visual guide, the visual guide including tagged images arranged according to their locations within the framework their labels, and prompting human classifiers to tag the additional images with labels for the classification, according to the visual guide.

Abstract: This disclosure describes an item drop location for placing items that have been picked from an inventory location within a material handling facility but have not yet been transitioned from the materials handling facility and a return location for returning items that have been transitioned from the materials handling facility. Likewise, described is a system and method for identifying an item placed at a drop location or an item placed at a return location, processing the placed item and providing confirmation to a user that placed the item and facilitating a return of the item or a removal of the item from an item identifier list associated with the user.

Abstract: An inventory location such as a shelf may be used to stow different types of items. Interactions may take place, such as the pick or place of one or more items from the inventory location. Weight data may be acquired from weight sensors coupled to the shelf. A measured change in weight is used to determine a first group of hypotheses. Each of the hypotheses may be descriptive of a different permutation of products being added to or removed from the shelf that could account for the measured change in weight. The weight data is also processed to determine a weight distribution of the items on the shelf. Information about the weight distribution may be used to select a subset of the first group of hypotheses. The subset may be used to determine what items were interacted with, quantity involved, and so forth.

Abstract: Described is a system for counting stacked items using image analysis. In one implementation, an image of an inventory location with stacked items is obtained and processed to determine the number of items stacked at the inventory location. In some instances, the item closest to the camera that obtains the image may be the only item viewable in the image. Using image analysis, such as depth mapping or Histogram of Oriented Gradients (HOG) algorithms, the distance of the item from the camera and the shelf of the inventory location can be determined. Using this information, and known dimension information for the item, a count of the number of items stacked at an inventory location may be determined.

Abstract: Described are systems for processing sensor data associated with an inventory location for the purpose of identifying stable and unstable states in the sensor data. The stable and unstable states can be determined by fitting a line or curve to a portion of the sensor data occurring during a window of time and calculating a corresponding slope value of the line or curve. By comparing the slope value of the line or curve to a threshold value, stable and unstable states can be determined for the sensor data. Once stable states have been identified, a change in sensor data can be determined and used to identify a change in quantity of items at the inventory location.

Abstract: Described is a system for counting stacked items using image analysis. In one implementation, an image of an inventory location with stacked items is obtained and processed to determine the number of items stacked at the inventory location. In some instances, the item closest to the camera that obtains the image may be the only item viewable in the image. Using image analysis, such as depth mapping or Histogram of Oriented Gradients (HOG) algorithms, the distance of the item from the camera and the shelf of the inventory location can be determined. Using this information, and known dimension information for the item, a count of the number of items stacked at an inventory location may be determined.

Abstract: Described are systems for processing sensor data obtained from a sensor such as a weight sensor. In one implementation, a line may be fit to a set of sensor data occurring during a window of time. A slope value of the line may be calculated and provided as input to a change detector. The change detector may implement a cumulative sum (CUSUM) algorithm to determine when the slope value is indicative of a stable or unstable state. Values of sensor data associated with times indicated as having a stable state may be used to determine weight data.

Abstract: An item stowed at an inventory location may rest on a surface such as a shelf or may be supported above the surface. The item may exhibit a footprint that is representative of a physical perimeter of the item. For example, the footprint may be an edge of a shadow cast by the item. Information about a size and shape of a footprint associated with a particular item may be stored. An optical sensor array may acquire sensor image data indicative of the footprint of one or more of the items stored at an inventory location. A quantity of items at the inventory location may be determined by comparing the sensor image data with the stored data.

Abstract: An inventory location such as a shelf may be used to stow different types of items, with each type of item in a different partitioned area or section of the shelf. Weight data from weight sensors coupled to the shelf is used to determine a change in weight of the shelf and a change in the center-of-mass (“COM”) of the items on the shelf. Based on the weight data and item data indicative of what items are stowed in particular partitioned areas, activity such as a pick or place of an item and the partitioned area in which the activity occurred may be determined. Data from other sensors, such as a camera, may be used to confirm the occurrence of the activity, disambiguate the determination of the particular partitioned area, and so forth.

Abstract: This disclosure describes an item drop location for placing items that have been picked from an inventory location within a material handling facility but have not yet been transitioned from the materials handling facility and a return location for returning items that have been transitioned from the materials handling facility. Likewise, described is a system and method for identifying an item placed at a drop location or an item placed at a return location, processing the placed item and providing confirmation to a user that placed the item and facilitating a return of the item or a removal of the item from an item identifier list associated with the user.

Abstract: Described is a system for counting stacked items using image analysis. In one implementation, an image of an inventory location with stacked items is obtained and processed to determine the number of items stacked at the inventory location. In some instances, the item closest to the camera that obtains the image may be the only item viewable in the image. Using image analysis, such as depth mapping or Histogram of Oriented Gradients (HOG) algorithms, the distance of the item from the camera and the shelf of the inventory location can be determined. Using this information, and known dimension information for the item, a count of the number of items stacked at an inventory location may be determined.

Abstract: Described is a system for counting stacked items using image analysis. In one implementation, an image of an inventory location with stacked items is obtained and processed to determine the number of items stacked at the inventory location. In some instances, the item closest to the camera that obtains the image may be the only item viewable in the image. Using image analysis, such as depth mapping or Histogram of Oriented Gradients (HOG) algorithms, the distance of the item from the camera and the shelf of the inventory location can be determined. Using this information, and known dimension information for the item, a count of the number of items stacked at an inventory location may be determined.

Abstract: Described is a system for counting stacked items using image analysis. In one implementation, an image of an inventory location with stacked items is obtained and processed to determine the number of items stacked at the inventory location. In some instances, the item closest to the camera that obtains the image may be the only item viewable in the image. Using image analysis, such as depth mapping or Histogram of Oriented Gradients (HOG) algorithms, the distance of the item from the camera and the shelf of the inventory location can be determined. Using this information, and known dimension information for the item, a count of the number of items stacked at an inventory location may be determined.

Abstract: Described is a system for counting stacked items using image analysis. In one implementation, an image of an inventory location with stacked items is obtained and processed to determine the number of items stacked at the inventory location. In some instances, the item closest to the camera that obtains the image may be the only item viewable in the image. Using image analysis, such as depth mapping or Histogram of Oriented Gradients (HOG) algorithms, the distance of the item from the camera and the shelf of the inventory location can be determined. Using this information, and known dimension information for the item, a count of the number of items stacked at an inventory location may be determined.

Abstract: A system, machine-readable storage medium storing at least one program, and a computer-implemented method for icon optimization in a game user interface is provided. A request from a client device to access a game user interface of a player may be received, where the game user interface is associated with one or more computer-implemented games hosted by a game networking system. A first manner of displaying one or more icons each representing a computer-implemented game of the one or more computer-implemented games is determined. The first manner of displaying the one or more icons within the game user interface is determined based on one or more game-related factors specific to the player. Display data to display the one or more icons in the first manner may be generated and provided to the client device.

Abstract: Methods, systems, and media are provided for identifying and clustering queries that are rising in popularity. Resultant clustered queries can be compared to other stored queries using textual and temporal correlations. Fresh indices containing information and results from recently crawled content sources are searched to obtain the most recent query activity. Historical indices are also searched to obtain temporally correlated information and results that match the clustered query stream. A weighted average acceleration of a spike can be calculated to distinguish between a legitimate spike and a non-legitimate spike. Legitimate clusters are combined with other stored clusters and presented as grouped content results to a user output device.

Abstract: Methods, systems, and media are provided for identifying and clustering queries that are rising in popularity. Resultant clustered queries can be compared to other stored queries using textual and temporal correlations. Fresh indices containing information and results from recently crawled content sources are searched to obtain the most recent query activity. Historical indices are also searched to obtain temporally correlated information and results that match the clustered query stream. A weighted average acceleration of a spike can be calculated to distinguish between a legitimate spike and a non-legitimate spike. Legitimate clusters are combined with other stored clusters and presented as grouped content results to a user output device.