Patent ID: 11948472
Assignee: NTT DOCOMO, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A popularity estimation system that estimates the popularity of a particular point which is geographically identified, the popularity estimation system comprising circuitry configured to:
acquire a first map image including a first particular point which is a particular point to be estimated and to acquire a plurality of second map images including a second particular point which is a particular point of which a popularity score indicating popularity is known;
generate a first feature vector and a second feature vector indicating geographical features of the first map image and the second map images by inputting the first map image and the second map images to a geographical feature generation model which has been trained by machine learning with a map image as an input and with a feature vector indicating geographical features of the map image as an output;
calculate a popularity score of the first particular point from a popularity score of the second particular point based on a degree of similarity between the first feature vector and the second feature vector; and
output the calculated popularity score of the first particular point,
wherein the circuitry is further configured to
generate a geographical feature generation model with a map image of a particular point as an input and with a feature vector indicating geographical features of the map image as an output and including a neural network,
acquire, for generating the geographical feature generation model, anchor training data including a map image of a first area, positive-example training data including a map image of a second area with an attribute value of which a difference from a predetermined attribute value of the first area is equal to or less than a predetermined value, and negative-example training data including a map image of a third area with an attribute value of which a difference from the predetermined attribute value of the first area is greater than the predetermined value,
generate, for generating the geographical feature generation model, an anchor feature vector, a positive-example feature vector, and a negative-example feature vector by inputting the anchor training data, the positive-example training data, and the negative-example training data to the geographical feature generation model, and
adjust, for generating the geographical feature generation model, parameters of the neural network such that a difference between the anchor feature vector and the positive-example feature vector approaches zero and a difference between the anchor feature vector and the negative-example feature vector increases.