Abstract: Described are systems and techniques for identifying users arriving at a facility based at least in part on a vehicle in which they arrive. In one implementation, vehicles are identified as they arrive at the facility. A candidate set of users previously associated with the identified vehicle is generated. The recognition system may then detect and identify the occupants of the vehicle using the candidate set. The identity of the vehicle may improve the accuracy of the user identification, reduce time to identify the user, or both.

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: This disclosure describes a system for determining an estimated user departure time from a materials handling facility. For example, a user may enter a materials handling facility to pick one or more items from the materials handling facility. Those items may be provided to an agent for processing, such as packing, while the user picks other items within the materials handling facility. To ensure that the items are processed in a timely manner and made available to the user when the user is ready to depart from the materials handling facility, the implementations discussed determine an anticipated user departure time and compare that time with an estimated processing time needed to process the items.

Abstract: Described is a system and method for monitoring the expiration date of items and determining if an item previously picked by a user will expire before the next anticipated arrival date of the user at a materials handling facility. If the previously picked item will expire before the next anticipated arrival date of the user, the user may receive a recommendation to pick a replacement item so they do not have to return to the materials handling facility early to replace the expired item.

Abstract: An event at a shelf, such as a user interacting with items on the shelf, may be detected using sensors associated with the shelf. Hypotheses to describe the event are generated based on the collected sensor data. However, hypotheses determined based on only one type of sensor data may not have a confidence value that is sufficiently reliable. Hypotheses derived from different sensor types may be combined to generate new hypotheses that have higher confidence values. For example, hypotheses based on image data may be combined with hypotheses based on weight data to produce hypotheses with higher confidence values that are sufficiently reliable. A hypothesis, from the set of combined hypotheses, having the highest confidence value may then be used to describe the interaction at the shelf, such as identifying the item and quantity of item the user interacted with during the event.

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: Systems and methods to modify functions of computing devices based on movements and/or locations of users of such computing devices may include various types of sensors, such as movement sensors, imaging sensors, and location sensors. The sensor data may be processed by various algorithms to determine either or both of movement and location of the user within an environment. Then, based at least on the determined movement or location of the user, a particular function level may be selected for the computing device to ensure safety and environmental awareness of the user. In addition, one or more functions of the computing device may be modified, enabled, disabled, automated, alerted, or otherwise changed based on the selected function level.

Abstract: This disclosure describes a system for utilizing multiple image processing techniques to identify an item represented in an image. In some implementations, one or more image processing algorithms may be utilized to process a received image to generate item image information and compare the item image information with stored item image information to identify the item. When a similarity score identifying the similarity between the item image information and at least one of the stored item image information is returned, a determination may be made as to whether the similarity score is high enough to confidently identify the item. If it is determined that the similarity score is high enough to confidently identify the item, the other algorithms may be terminated and the determined identity of the item returned.

Abstract: Items may be stowed in an inventory location, such as a shelf. An interaction may take place where one of the items is picked from or placed on the inventory location. Sensor data can be acquired from two or more sensors, such as cameras or weight sensors, configured to collect such sensor data for the inventory location. Hypotheses to describe interactions at the inventory location may be determined using sensor data from each different sensor. However, the confidence value for such hypotheses may not be reliable. Fusion of sensor data may be performed where sets of hypotheses from the different sensors are combined and the resulting hypotheses are more accurate.

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: 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 device notification aggregation service (“DNAS”) that aggregates device state information, such as alarms, from multiple distributed devices and then provides device state information and/or device set status information to services that rely on data from the distributed devices to make decisions on other events.

Abstract: Described are systems and techniques for identifying users arriving at a facility based at least in part on a vehicle in which they arrive in. In one implementation, vehicles are identified as they arrive at the facility. A candidate set of users previously associated with the identified vehicle is generated. The recognition system may then detect and identify the occupants of the vehicle using the candidate set. The identity of the vehicle may improve the accuracy of the user identification, reduce time to identify the user, or both.

Abstract: Described is a system and method for assisting users in properly placing and/or positioning items at inventory locations within a materials handling facility. In one example, a placement location may be distinguished for the user through use of illumination or other techniques to assist the user in quickly identifying the proper location at which to place the item. Likewise, a proper position of the item may be presented to the user to assist the user in properly positioning the item at the placement 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 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: This disclosure describes a system for processing an image of an item and correctly identifying the item from a group of candidate items. In one implementation, as item image information for a new item is added to an item images data store, a determination is made as to the weight of the item represented by the image, and the item may be associated with a weight class. Each weight class represents items within a defined weight range. Item image information for items in the same weight class may then be used when new items are added to inventory and/or when identifying an item represented in an image.

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: This disclosure describes a system for utilizing multiple image processing techniques to identify an item represented in an image. In some implementations, one or more image processing algorithms may be utilized to process a received image to generate item image information and compare the item image information with stored item image information to identify the item. When a similarity score identifying the similarity between the item image information and at least one of the stored item image information is returned, a determination may be made as to whether the similarity score is high enough to confidently identify the item. If it is determined that the similarity score is high enough to confidently identify the item, the other algorithms may be terminated and the determined identity of the item returned.

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.