Radiusai, Inc.

Other

Amino Corporation

Abhinav Yarlagadda

Enis Aykut Dengi

Abraham M Othman

Ishan Agrawal

Jeff Kershner

Paul Mills

Peter Martinez

Kamalesh Kalirathinam

Sri Priyanka Madduluri

Rahul Santhosh Kumar Varma

Sai Krishna Bashetty

Aykut Dengi

Rebecca Ackermann

Nicholas C Dunkman

Sumul Mahendra Shah

Thomas Adam Strich

David A Vivero

Jorge A Caballero

Mary Audrey Hampden

Kunal Dhiren Shah

Farid Jamshidian

Eithon Michael Galinato Cadag

James Lyon Fingal

Daniel King

Nathan Kelly

Willard Kirk Strauser

Abraham Othman

Michael H Lin

Systems and methods include extracting item parameters from images of items positioned at a POS system. Item parameters associated with each item when mapped into a feature vector for each item are indicative as to an identification of the item. The feature vectors are analyzed to determine whether item parameters when combined and mapped into the feature vectors match a corresponding feature vector stored in a database. The database stores different combinations of item parameters as mapped into different stored feature vectors with different stored feature vectors associated with different items thereby identifying each item based on each different combination of item parameters as mapped into each stored feature vector associated with each item. Each item positioned at the POS system is identified when the feature vectors associated with each item match a corresponding stored feature vector as stored in the database.

Automatic item identification during assisted checkout based on visual features

Assisted checkout devices, including point-of-sale stations, can use computer vision and machine learning to speed the checkout process while maintaining human verification, assistance, and customer interaction provided by human clerks. A plurality of optical sensors, including cameras, can be arranged with different views of a checkout plane upon which items being purchased by a buyer are placed. Moreover, one or more support towers can be utilized to elevate the optical sensors to vertical heights at which the checkout plane, and items placed thereon, is within the field of view of the optical sensors. The information captured by the plurality of optical sensors can be analyzed using machine learning models to detect and identify the items placed on the checkout plane.

Point of sale station for assisted checkout system

A visual tracking system for tracking and identifying persons within a monitored location, comprising a plurality of cameras and a visual processing unit, each camera produces a sequence of video frames depicting one or more of the persons, the visual processing unit is adapted to maintain a coherent track identity for each person across the plurality of cameras using a combination of motion data and visual featurization data, and further determine demographic data and sentiment data using the visual featurization data, the visual tracking system further having a recommendation module adapted to identify a customer need for each person using the sentiment data of the person in addition to context data, and generate an action recommendation for addressing the customer need, the visual tracking system is operably connected to a customer-oriented device configured to perform a customer-oriented action in accordance with the action recommendation.

System and method for visually tracking persons and imputing demographic and sentiment data

Disclosed are systems and techniques for providing a master health entity index and a data analysis mechanism designed for entity cohort discovery and entity profiling. The entity can be a healthcare facility that diagnoses or treats health conditions and diseases (e.g., hospital, clinic), individuals (e.g., providers, patients, caregivers), healthcare data (e.g., medical conditions, treatments, diagnostic studies, health outcomes), etc. For example, a data analysis mechanism may identify distinctive patient cohorts based on what happened to patients in a hospital and why the occurrence happened, glean insight from the activities of the distinctive patient cohorts, and assist with additional patients with the gleaned insight.

Data analysis mechanism for generating statistics, reports and measurements for healthcare decisions

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San Francisco, CA, United States of America

Abhinav Yarlagadda