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Personalized Dining Experience empowered by IoT

IP.com Disclosure Number: IPCOM000246687D
Publication Date: 2016-Jun-28
Document File: 4 page(s) / 44K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a method and system that leverage the technology of the Internet of Things (IoT) to provide dynamically tailored, real time, customized service at any service-based establishment to any customer through historical and real time personalized data collection and analytics.

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Personalized Dining Experience empowered by IoT

When a customer enters a restaurant, the establishment does not automatically know each person's preferences for seating, menu options, special dietary needs, etc. To satisfy all of a customer's needs, the restaurant staff must engage in sometimes lengthy and (in the case of returning customers) repetitive discourse. This can take too much time and delay service, and be frustrating for the customer that has to reiterate preferences.

A method is needed to generate a more efficient and satisfying restaurant experience for the customer. A customized experience can reduce frustration and stress, especially when a customer is visiting a new establishment.

The novel contribution is a method and system that leverage the technology of the Internet of Things (IoT) to provide dynamically tailored, real time, customized service at any restaurant to any customer through historical and real time personalized data collection and analytics. (Although this disclosure speaks to the restaurant experience,

the novel system is applicable to many types of establishments such as fitness centers, stores, hotels, salons, etc., that depend on a positive customer experience to succeed.)

A customer monitor gathers information from various types of sensors, social networks, customers' public calendars, input from servers, etc. If permitted, restaurants can also retrieve personalized information from customers' social network accounts (e.g., posted comments for likes, dislikes, desires, etc.) and public calendar (e.g., scheduled events, available time frames, etc.). In addition, the motion camera in the restaurant should detect whether the guest likes a single dish. The motion camera can detect the customer's gaze movement and then infer the whether the customer likes the cuisine, etc. The data of guest and sensor in the restaurant also provide the seating preference information (e.g., near a window, in the corner, etc.) for better customer satisfaction. The system stores this information in a cloud-based service for better customer service in the future.

A customer database is stored in a secured cloud-based data center. The database stores information (e.g., experience preferences, meal preferences, special needs, time constraints, etc.) collected from the customer monitor as well as from customer input. Through this database, franchised/chain restaurants can share the same customer information.

A dining analytics component analyzes the historical and current status of a customer,

including information retrieved from the social network account and public calendar, and recommends suitable actions to a server. The mode (happiness or sadness) detected of the guest from sensors can provide suggestions to modify the recipe of the cuisines on the menu (e.g., more sugar to mitigate current negative feelings of the guest). The system can recommend meals based on the customer's current mood, to ease the unpleasant fee...