In today’s competitive business environment creating memorable experiences and emotional connections (Creating customer value through service experiences: An empirical study in the hotel industry. Tourism and Hospitality Management 18, no. 1 (2012): 37–53) with consumers is critical to win consumer spending and long-term brand loyalty [1]. Brands want their customers to be in pleasing subliminal scented (Robert Klara, “Something in the air,”http://www.adweek.com/brandmarketing/something-air-138683/ creation date: March 2012, access date: January 02, 2017) environments because, as research has shown, even a few microparticles of scent can do a lot of marketing’s heavy lifting, from improving consumer perceptions of quality to increasing the number of store visits. Hence, customer venues such as hotels, retail showrooms, casinos, hospitable and other captive audience places employ HVAC (Heating, ventilation and air conditioning) based scent diffusion system that delivers a seamless olfactory [2] experience to connect with consumers on a deeper emotional level, resulting in a more memorable experience. Current scent diffusion systems, however, use power hungry deployments and dispense periodically, without accounting social mood, geographic local etiquettes, venue-patron occupancy ratios and sudden changes in foot traffic numbers. Thus, resulting sub-optimal user experience that might lead to a poor brand engagement and could incur higher operational costs and thus reduce over all return on the investment (ROI). In this research paper, we propose an innovative approach to create artificial intelligence (AI) infused Fragrance Systems that improve venue experience and operational efficiencies through the application of data science, Big Data Technologies, Edge processing, Supervised machine learning and IoT Sensing. Our system combines pragmatic data science and machine learning algorithms with arty social and mood drivers, albeit data science computed, to create adaptive and artistic fragrance system. The amalgamation data science with human mood influencers is our formula to the innovation that we propose and present a prototyping solution design as well as its application and certain experimental results.

1. 1.

   Kalman Filter -http://commons.apache.org/proper/commons-math/apidocs/org/apache/commons/math4/filter/KalmanFilter.html.

2. 2.

   ATmega328P -http://www.microchip.com/wwwproducts/en/ATmega328P.

Authors and Affiliations

1. Hanumayamma Innovations and Technologies Inc., 628 Crescent Terrace, Fremont, CA, 94536, USA

   Anitha Ilapakurti, Jaya Shankar Vuppalapati, Santosh Kedari, Sharat Kedari, Rajasekar Vuppalapati & Chandrasekar Vuppalapati

Corresponding author

Correspondence toChandrasekar Vuppalapati.

Editors and Affiliations

1. University of Central Florida, Orlando, Florida, USA

   Waldemar Karwowski

2. University of Central Florida, Orlando, Florida, USA

   Tareq Ahram

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