Walmart Apollo, LLC

Love Good Color LLC

Millerknoll, Inc.

Patrick Harrington

Tao Zhu

Laura Guido-Clark

Lei Tang

Bin Li

Alex K Cammenga

Thanh Ngo

Fnu Manasi

Methods and systems for training a multivariate model predicting utilization of a space. One method includes receiving, over a period of time, signals from each of a plurality of mobile devices located in the space and generating, based on the signals received from each of the plurality of mobile devices, a plurality of location data points for each of the plurality of mobile devices, each of the plurality of location data points for a mobile device including a timestamp and a position within the space of the mobile device. The method also includes accessing metadata of the space, and using, with an electronic processor, the plurality location data points for each of the plurality of mobile devices and the metadata of the space to train machine learning engine. In addition, the method includes predicting a utilization of the space using the machine learning engine.

Space utilization measurement and modeling using artificial intelligence

A system and method for providing color selection assistance and color auditing of an image. The continuous spectrum of colors can be narrowed to a subset of palettes of discrete colors, referred to as temperaments and environments, that facilitate selection of colors that provide a purposeful outcome. Each temperament/environment is associated with a plurality of different discrete colors and a plurality of different descriptive words, including but not limited to identity words and emotive words. The system and method enables the user to objectively transform a desired physiological response into a discrete set of colors from which to make a color selection where the presented colors are related to the desired physiological response or to audit an image to convert the palette of colors in the image to their associated temperaments.

Systems and methods for color selection and auditing

A system and method for optimizing marketing campaigns is presented. Two marketing campaigns are received. Each is presented to a subset of users. The conversion rates of both marketing campaign are used to determine weighting of the two marketing campaigns. The weighting is determined using a range of conversion rates and maximizing the minimum expected value through the range of conversion rates. The process can be iteratively performed to converge upon an optimum weighting of the first and second conversion rates. More than two marketing campaigns can be used. The marketing campaign can be an email marketing campaign, a web marketing campaign, or an advertising keyword campaign. Other embodiments also are disclosed.

System for providing a robust marketing optimization algorithm and method therefor

Providing personalized item recommendations using scalable matrix factorization with randomness. Various embodiments involve ecommerce websites, an item interaction matrix, item interaction weightings, factorizing into a user feature matrix and an item feature matrix, computing a thin matrix, generating a random Gaussian matrix, decomposing a matrix Y by a QR factorization, performing a singular value decomposition, performing alternative least squares, computing item interaction scores, selecting at least one item for recommendation to an Internet user based on the item interaction scores, or a combination thereof.

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Boulder, CO, United States of America

Patrick Harrington