WayBlazer issues statement outlining operations of “Connie the Concierge”

Felix Laboy, CEO of WayBlazer, today provided Etopia News with the following responses to its recently-submitted questions regarding the operations of “Connie the Concierge,” a small, semi-humanoid robot that channels the power of IBM Watson into answering questions about travel and tourism destinations:

How is the project working?

The pilot is a joint collaboration between Hilton and IBM Watson together with WayBlazer.  Connie enables guests at the Hilton McLean in Virginia, to interact with the first Watson-enabled robot concierge, to learn more about in-hotel offerings and local attractions.

When guests ask questions in natural language about restaurants, attractions and things to do “off-property” (Local Area Knowledge) WayBlazer is able to make local recommendations in the form of visual tiles including images and insights about the POI.

How much is it being used?

WayBlazer focuses on the local area knowledge component of Connie’s recommendations.  The technology combines WayBlazer’s extensive travel domain knowledge with IBM Watson's cognitive computing technology platform to enable Connie to greet guests upon arrival and to answer questions about hotel amenities, services and hours of operation. Guests access local area knowledge data daily with a large focus of questions on restaurants in the local area, as well as places to grab a drink and attractions like movie theaters, parks, and museums.

How people are reacting to it?

The WayBlazer team is not on the ground in McLean but from the data we can see that guests are engaged with the local area knowledge piece that WayBlazer provides evidenced by the number of queries, clicks into recommendations cards and clicks for directions.

Plans for the future

WayBlazer is excited to see where Hilton and IBM take this project next and will be ready to support with our natural language search and local area knowledge as well as other technologies where there is a fit.

What has WayBlazer learned?

Our training process has allowed us to learn from guest queries and continually improve the product to be more accurate in understanding the questions and providing a relevant response. We are currently at an accuracy of 90%.