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Browse Prior Art Database

Converged Sensors, Data and Visualization for Optimized Seat Selection in Shared Spaces

IP.com Disclosure Number: IPCOM000240944D
Publication Date: 2015-Mar-13
Document File: 6 page(s) / 373K

Publishing Venue

The IP.com Prior Art Database

Abstract

A system and method to identify, learn and negotiate seating options in public or shared spaces is disclosed. The disclosed system arrives at customized seating recommendations and improved seating options for multiple participants.

This text was extracted from a PDF file.
This is the abbreviated version, containing approximately 34% of the total text.

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Converged Sensors , , Spaces

Disclosed is a system and method to identify, learn and negotiate seating options in public or shared spaces. The system arrives at customized seating recommendations and improved seating options for multiple participants.

Often in shared or public seating areas the audience chooses their own seats. The may occur whether the seat is selected upon arrival, for example, at a typical conference presentation. Seat selection also may occur when the seat is chosen in advance, for example, at a sporting event. A variety of environmental factors and personal preferences can affect the choice of seat for each individual. A lack of efficient communication between people choosing seats can lead to a suboptimal seating arrangement. For example, a couple at a movie theater may expect to need to leave the theater and prefer to sit on the aisle. Another person may have hearing difficulty and prefer to sit near the speakers, or hearing sensitivity and prefer to sit in the quietest portion of the room. Yet for various reasons these audience members may end up sitting in one another's optimal seats.

State of the art in this space focuses on specific aspects such as choosing a window seat on an airline, or selecting a recommended seat for a sporting event.

The disclosed system and method integrates a set of individual components to arrive at an optimal seat selection with the following characteristics:

Learns a user's seating behavior based on input and historical choices.


1.

Cross-references personal preferences with environmental aspects to find ideal


2.

seating options.

Integrates with a centralized reservation database to allows easy on-point


3.

reservation.

Integrates with a centralized reservation database to allow advanced visualization


4.

for seat selection.

Optionally shares seating preferences with other audience members and groups of


5.

audience members to optimize seating arrangement.

Learning Seat Preferences

Some capabilities exist today for seat assignment preferences, such as airline aisle /

window seating options. However these capabilities share a limitation of being tied to the application assigning seats. Preferences such as airline window seats would be better carried on something like a user's smart phone and queried when making seat selections.

A variety of factors can be incorporated into user-defined seating preferences, and only some of the selections would be applicable for different environments. Example factors include:

1

Data and Visualization for Optimized Seat Selection in Shared

Data and Visualization for Optimized Seat Selection in Shared


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1.

Volume (can be subdivided into presentation volume, white noise, audience volume).

Distance to restrooms.

Brightness.

Number of people sitting in front, and behind.

Temperature.

These factors can be input into a preference store by the user or they could be implicitly learned based on user behavior. For example, a smart phone can track...