Amazon Technologies, Inc.

Landing Ai

Microsoft Technology Licensing, LLC

Gopi Prashanth Gopal

Ohil Krishnamurthy Manyam

Liefeng Bo

Yasser Baseer Asmi

Sudarshan Narasimha Raghavan

Xiaofeng Ren

Avishkar Misra

Christopher Robert Towers

Ramanathan Palaniappan

Jayakrishnan Kumar Eledath

Navid Shiee

Dilip Kumar

Paul Eugene Munger

Gerard Guy Medioni

Nishitkumar Ashokkumar Desai

Nikhil Chacko

Kushagra Srivastava

Robert Crandall

Kun Qian

Sudheer Babu Ramoji

Michel Leonard Goldstein

Long Xuan Nguyen

Andre David Brown

Nadya Dhalla

Sanjeev Satheesh

Daniel Bibireata

Dongyan Wang

Paul Aksenti Savastinuk

Connor Spencer Blue Worley

Roman Talyansky

Yiwen Rong

Andrew Yan-Tak Ng

Michael Dillon

Yu Qing Zhou

Eli Osherovich

Jack G Song

Pingyang He

Karthikeyan Thiruppathisamy Nathillvar

Rustam Hashimov

Dillon Anthony Laird

Alejandro Betancourt

Rachid M Alameh

Gregory Donald Hager

Hugh Evan Williams

Sanaz Ahari

Gaurav Sareen

Nitin Agrawal

Ejaz Ahmed

Milad Shokouhi

Derrick Leslie Connell

Stephen O'Dea

Andrew Peter Oakley

Jay Goyal

Neil Sharman

Christopher A Meyers

Mark William Sabini

Nicholas Craswell

Ankur Rawat

Ajay Kumar Ponnuswamy

Rani Awale

Jun Ai

Edwin Thorne

Wei Fu

Rahul Devraj Solanki

Yuanzhe Dong

Hao Sheng

Kavitha Lingamoorthy

Inventory monitoring systems are configured for mounting within enclosures such as refrigerators, cabinets, closets or sets of shelves. The inventory monitoring systems include cameras, illuminators and processors provided within housings that are formed from plastics and mounted within an enclosure. The cameras capture images of items entering or departing the enclosure, and other data regarding conditions within the enclosure. The images or data are interpreted by either the inventory monitoring system processors or an external computer system to identify an item depicted therein, to determine whether the item has entered or departed the enclosure, or to otherwise identify conditions within the enclosure. A record of inventory or other conditions within the enclosure may be updated to reflect the entry or departure of the item accordingly, and information regarding the inventory or the conditions within the enclosure, as determined by the inventory monitoring systems, may be utilized for any purpose.

Devices for monitoring inventory within enclosures

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.

Determining inventory changes at an inventory location

Tidiness data associated with an inventory location, such as a shelf, indicates whether an item returned to the inventory location is designated for storage at the inventory location. When an item is returned to the inventory location and a user is determined to be near the inventory location, the weight change detected at the inventory location is compared to a corresponding weight of each item in the custody of the user, such as items within cart state data associated with the user. Differences in weight between the weight change and the corresponding weight of each item in the custody of the user are calculated. Such differences may then be compared to one or more metrics, such as threshold values, to determine how well the top candidate item in the custody of the user matches the type of item designated for storage at the inventory location.

Generating tidiness data associated with items at an inventory location

This disclosure describes, in part, a mobile apparatus for identifying items within a facility using an omnidirectional imaging system. For instance, the mobile apparatus may include a main frame, a chassis attached to the main frame, a basket that attaches to the chassis in order to prevent the basket from contacting the main frame, and an omnidirectional imaging system providing image data in and around the mobile apparatus. A user may place item(s) within a receptacle of the basket. The mobile apparatus may further include a handlebar module attached to the main frame, the handlebar module including the omnidirectional imaging system and a computing system for identifying items and events in and around the cart.

Omnidirectional item identifying mobile apparatus

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.

Inventory item release apparatus and method

This disclosure describes, in part, a mobile apparatus that includes capacitive sensor(s) for detecting the presence of a user. For instance, the mobile apparatus, such as a shopping cart, may include capacitive sensor(s) located proximate to a main frame, the handles, and/or the wheel frame. The mobile apparatus may use one or more of these capacitive sensor(s) to determine that the user is located proximate to the mobile apparatus, in contact with the mobile apparatus, and/or located over the main frame of the mobile apparatus. In some examples, the mobile apparatus may then perform one or more actions based on the presence of the user. For example, the mobile apparatus may activate and/or deactivate one or more devices based on the presence of the user. This way, the mobile apparatus is able to conserve power by using the capacitive sensor(s) to detect the present of the user.

Apparatus with presence detection sensors

This disclosure describes systems and techniques for detecting events, determining a result of each respective event using a first hypothesis source, and calculating a likelihood that a second (and/or additional) hypothesis source would determine the same result of the respective event. The calculated likelihood may then be used to be determine whether to request that the second hypothesis source determine the result of the event, determine an amount of resources of the second hypothesis source to use to make this determination, and/or like.

Generating event results using auto-generated sensor data

A process and a system 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.

User-generated visual guide for the classification of images

One or more load cells measure the weight of items at a fixture. Weight changes occur as items are picked from or placed to the fixture and may be used to determine when the item was picked or placed, quantity and so forth. Individual weights for a type of item may vary. A set of data comprising weight changes associated with interactions involving a single one of a particular type of item is gathered. These may be weight changes due to picks, places, or both. A model, such as a probability distribution, may be created that relates a particular weight of that type of item to a probability. The model may then be used to process other weight changes and attempt to determine what type of item was involved in an interaction.

System to determine item weight using data from weight sensors

One or more load cells measure the weight of items on a shelf or other fixture. Weight changes occur as items are picked from or placed to the fixture. Output from the load cells is processed to produce denoised data. The denoised data is processed to determine event data representative of a pick or a place of an item. Hypotheses are generated using information about where particular types of items are stowed, the weights of those particular types of items, and the event data. A high scoring hypothesis is used to determine interaction data indicative of the type and quantity of an item that was added to or removed from the fixture. If ambiguity exists between hypotheses, additional techniques such as data about locations of weight changes and fine grained analysis may be used to determine the interaction data.

Interaction determination using data from weight sensors

A processor receives an image of a syringe. After identifying a background and foreground of the image, where the foreground indicates pixels that may be associated with a defect, the processor subtracts the background to generate an updated image with an accentuated foreground. The processor applies a bounding box to a group of pixels in the foreground and inputs the bounding box into a classifier. The classifier outputs a label indicating whether the syringe is defective.

Automated visual inspection of syringes

Sensor data from load cells at a shelf is processed using a first time window to produce first event data describing coarse and sub-events. Location data is determined that indicates where on the shelf weight changes occurred at particular times. Hypotheses are generated using information about where items are stowed, weights of those of items, type of event, and the location data. If confidence values of these hypotheses are below a threshold value, second event data is determined by merging adjacent sub-events. This second event data is then used to determine second hypotheses which are then assessed. A hypothesis with a high confidence value is used to generate interaction data indicative of picks or places of particular quantities of particular types of items from the shelf.

Joint inference determination of interaction using data from weight sensors

Sensors in a facility obtain sensor data about a user's interaction with a fixture, such as a shelf. The sensor data may include images such as obtained from overhead cameras, weight changes from weight sensors at the shelf, and so forth. Based on the sensor data, one or more hypotheses that indicate the items and quantity may be determined and assessed. The hypotheses may be based on information such as the location of a user and where their hands are, weight changes, physical layout data indicative of where items are stowed, cart data indicative of what items are in the possession of the user, and so forth. A hypothesis having a greatest confidence value may be deemed to be representative of the user's interaction, and interaction data indicative of the item and quantity may be stored.

System to determine user interaction with fixture

Growing community of inventors

Redmond, WA, United States of America

Gopi Prashanth Gopal