International Business Machines Corporation

Priyanka Sarkar

Justin D Eyster

Avery K Rowe

Christopher E Whitridge

Rhonda L Childress

A method, computer system, and a computer program product for generating a location tag for a piece of visual data using deep learning is provided. The present invention may include receiving the piece of visual data. The present invention may also include analyzing the received piece of visual data using a neural network. The present invention may then include retrieving a location for the analyzed piece of visual data from the neural network. The present invention may further include generating a plurality of metadata for the retrieved location associated with the analyzed piece of visual data, wherein the generated plurality of metadata includes the location tag.

Location tagging for visual data of places using deep learning

A mobile computing device such as a smartphone is used to find a parking garage with available spaces for a motor vehicle. The smartphone has a navigation app which receives a destination and computes an estimated time of arrival to that destination. It then broadcasts a request to parking garages in the vicinity of the destination with the ETA. Parking service systems associated with those garages respond with predicted parking availability. A cognitive system analyzes the responses and provides optimal recommendations based on user preferences such as cost, distance and safety. The user selects one of the recommended garages, and the computing device sends a reservation to the selected garage. The navigation app provides first directions to the destination until the vehicle is proximate thereto, and then switches to directions to the garage. The cognitive system can analyze a history of user selections and updating the user's preferences.

Finding available parking spaces using cognitive algorithms

A system facilitates the selection of a restaurant which is suitable for a group having disparate taste preferences. Individual flavor profiles contain information pertaining to flavor preferences of individuals, and a group flavor profile is created based on the flavor profiles of individuals in a particular group. The group flavor profile is matched to one or more restaurant flavor profiles. An individual flavor profile includes numerical values associated with different flavor types, such as savory, sweet, sour, bitter and salty. An individual flavor profile is created by receiving an input indicative of a flavor type (such as a dish or food image) and determining the flavor type using a deep-learning neural network. The group flavor profile is created by averaging numerical values for respective flavor types from the individual flavor profiles of the group. A flavor profile can also include non-food preferences such as cost, traffic, distance, and weather.

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Austin, TX, United States of America

Priyanka Sarkar