Abstract: An information processing method includes acquiring first text information from a storage apparatus in which the first text information representing one or more utterance sentences is stored as a learning data set, identifying one or more named entities included in the acquired first text information, replacing each of the one or more identified named entities with an abstract expression abstracted based on a predetermined rule thereby generating second text information from the first text information, and learning a model of a dialogue system using, as training data, the second text information generated in the replacing.

Abstract: A method for controlling identification includes obtaining first text, which is text in a first language, obtaining second text, which is text in a second language obtained by translating the first text into the second language, obtaining correct labels, which indicate content of the first text, inputting the first text and the second text to an identification model common to the first and second languages, and updating the common identification model such that labels identified by the common identification model from the first text and the second text match the correct labels.

Abstract: A dialog act estimation method includes acquiring learning data including a first sentence to be estimated in the form of text data of a first uttered sentence uttered at a first time point, a second sentence which is text data of a second uttered sentence uttered, at a time point before the first time point, successively after the first uttered sentence, act information indicating an act associated to the first sentence, property information indicating a property information associated to the first sentence, and dialog act information indicating a dialog act in the form of a combination of an act and a property associated to the first sentence, making a particular model learn three or more tasks at the same time using the learning data, and storing a result of the learning as learning result information in a memory.

Abstract: An autonomous flying device that tracks a moving object and flies includes a sensor that obtains first information related to a velocity of the moving object, a controller that controls flight of the autonomous flying device, and a driver that drives the autonomous flying device, the controller setting a velocity of the autonomous flying device in accordance with the first information so that the velocity of the autonomous flying device increases as a distance between the moving object and the autonomous flying device increases, the driver causing the autonomous flying device to fly at the velocity set by the controller.

Abstract: A remote communication method of a remote communication system including a first robot deployed in a first place and a first presentation device deployed in a second place, includes capturing an image of a first user in the first place with a camera, transmitting the captured image of the first user from the first robot to the first presentation device, receiving the captured image from the first robot, displaying the captured image, detecting a first amount of change indicating a change in a position of a second user in the second place with reference to a second robot that virtually represents the first user, transmitting the first amount of change to the first robot, receiving the first amount of change from the first presentation device, and moving the first robot to change a point of view of the camera in response to the first amount of change.

Abstract: A processing method executed by a processor that receives an order from a user at a restaurant through interaction includes analyzing information indicating the order, extracting a phrase other than a standard element from the information, with reference to a first database when the extracted phrase is a first phrase in the first database, outputting a first confirmation item corresponding to the first phrase to the user, receiving a first user response corresponding to the first confirmation item, when the extracted phrase is not a first phrase in the first database, referring to a second database, when the extracted phrase is a second phrase in the second database, selecting a third phrase in the first database from second phrase-related phrases, outputting a second confirmation item corresponding to the third phrase to the user referring to the first database, and receiving a second user response corresponding to the second confirmation item.

Abstract: An information processing method includes acquiring first text information from a storage apparatus in which the first text information representing one or more utterance sentences is stored as a learning data set, identifying one or more named entities included in the acquired first text information, replacing each of the one or more identified named entities with an abstract expression abstracted based on a predetermined rule thereby generating second text information from the first text information, and learning a model of a dialogue system using, as training data, the second text information generated in the replacing.

Abstract: A remote communication method of a remote communication system including a first robot deployed in a first place and a first presentation device deployed in a second place, includes capturing an image of a first user in the first place with a camera, transmitting the captured image of the first user from the first robot to the first presentation device, receiving the captured image from the first robot, displaying the captured image, detecting a first amount of change indicating a change in a position of a second user in the second place with reference to a second robot that virtually represents the first user, transmitting the first amount of change to the first robot, receiving the first amount of change from the first presentation device, and moving the first robot to change a point of view of the camera in response to the first amount of change.

Abstract: A method for controlling identification includes obtaining first text, which is text in a first language, obtaining second text, which is text in a second language obtained by translating the first text into the second language, obtaining correct labels, which indicate content of the first text, inputting the first text and the second text to an identification model common to the first and second languages, and updating the common identification model such that labels identified by the common identification model from the first text and the second text match the correct labels.

Abstract: A dialogue act estimation method includes acquiring learning data including a first sentence to be estimated in the form of text data of a first uttered sentence uttered at a first time point, a second sentence which is text data of a second uttered sentence uttered, at a time point before the first time point, successively after the first uttered sentence, act information indicating an act associated to the first sentence, property information indicating a property information associated to the first sentence, and dialogue act information indicating a dialogue act in the form of a combination of an act and a property associated to the first sentence, making a particular model learn three or more tasks at the same time using the learning data, and storing a result of the learning as learning result information in a memory.

Abstract: A subject estimation system includes a convolutional neural network to estimate a subject label of a dialog. The convolution neural network includes: one or more topic-dependent convolutional layers and one topic-independent convolutional layer, each of the one or more topic-dependent convolutional layers performing, on an input of a word-string vector sequence corresponding to dialog text transcribed from a dialog, a convolution operation dependent on a topic, and the topic-independent convolutional layer performing, on the input of the word-string vector sequence, a convolution operation not dependent on the topic; a pooling layer performing pooling process on outputs of the convolutional layer; and a fully connected layer performing full connection process on outputs of the pooling layer.

Abstract: A processing method executed by a processor that receives an order from a user at a restaurant through interaction includes analyzing information indicating the order, extracting a phrase other than a standard element from the information, with reference to a first database when the extracted phrase is a first phrase in the first database, outputting a first confirmation item corresponding to the first phrase to the user, receiving a first user response corresponding to the first confirmation item, when the extracted phrase is not a first phrase in the first database, referring to a second database, when the extracted phrase is a second phrase in the second database, selecting a third phrase in the first database from second phrase-related phrases, outputting a second confirmation item corresponding to the third phrase to the user referring to the first database, and receiving a second user response corresponding to the second confirmation item.

Abstract: An autonomous flying device that tracks a moving object and flies includes a sensor that obtains first information related to a velocity of the moving object, a controller that controls flight of the autonomous flying device, and a driver that drives the autonomous flying device, the controller setting a velocity of the autonomous flying device in accordance with the first information so that the velocity of the autonomous flying device increases as a distance between the moving object and the autonomous flying device increases, the driver causing the autonomous flying device to fly at the velocity set by the controller.

Abstract: A subject estimation system includes a convolutional neural network to estimate a subject label of a dialog. The convolution neural network includes: one or more topic-dependent convolutional layers and one topic-independent convolutional layer, each of the one or more topic-dependent convolutional layers performing, on an input of a word-string vector sequence corresponding to dialog text transcribed from a dialog, a convolution operation dependent on a topic, and the topic-independent convolutional layer performing, on the input of the word-string vector sequence, a convolution operation not dependent on the topic; a pooling layer performing pooling process on outputs of the convolutional layer; and a fully connected layer performing full connection process on outputs of the pooling layer.