Walmart Apollo, LLC

Honeywell International Inc.

Cisco Technology, Inc.

Srinivas Muktevi

Amit Jhunjhunwala

Lingfeng Zhang

Avinash Madhusudanrao Jade

Raghava Balusu

Zhaoliang Duan

Ashlin Ghosh

Benjamin R Ellison

Abhinav Pachauri

Uday Pulleti

Atul Laxman Katole

Mingquan Yuan

Han Zhang

William Craig Robinson

Feiyun Zhu

Tianyi Mao

William Craig Robinson, Jr

Siddhartha Chakraborty

Shiva Shankar J

Zhiwei Huang

Ottalingam Satyanarayan

Naresh Chand

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.

Methods and systems for creating reference image templates for identification of products on product storage structures of a product storage facility

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.

Systems and methods for identifying different product identifiers that correspond to the same product

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.

Systems and methods for processing images captured at a product storage facility

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.

Clustering of items with heterogeneous data points

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. The computing device obtains the keyword and feature model templates associated with a recognized product from the electronic database, extracts the keywords from the products associated with the obtained keyword model templates, identifies products that are similar to the recognized product, and updates the keyword model template for each of the products to include must keywords and negative keywords, facilitating recognition of products in subsequent images captured by the image capture device.

Systems and methods of updating model templates associated with images of retail products at product storage facilities

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.

Systems and methods of identifying individual retail products in a product storage area based on an image of the product storage area

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.

Voice control of components of a facility

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.

Method and system for calculation of QOV metrics

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Bengaluru, India

Srinivas Muktevi