Panasonic Intellectual Property Management Co., Ltd.

Panasonic Intellectual Property Corporation of America

Panasonic Corporation

Matsushita Electric Industrial Co., Ltd.

Katsuyoshi Yamagami

Takashi Ushio

Mitsuru Endo

Toru Tanigawa

Jun Ozawa

Jeffry Fernando

Yasunori Ishii

Eiichi Naito

Yuri Nishikawa

Hongjie Shi

Koji Morikawa

Yumiko Kato

Yumi Wakita

Akinori Matsumoto

Mayu Yokoya

Shinobu Adachi

Aki Yoneda

Takahiro Kamai

Kenji Matsui

There is provided an information processing method for performing, through interaction with a user, narrowing down regarding information that the user desires to search for. The method includes: outputting a first question, which is an open question, about a target of search; obtaining a first answer to the first question, the first answer being input by the user; if a determination is made indicating that the first answer does not satisfy a first condition and that a word or phrase corresponding to a word or phrase included in the first answer is not included in a database, outputting a second question, which is an open question, for requesting an explanation about the word or phrase included in the first answer and not included in the database; and if a determination is made indicating that the first answer satisfies the first condition, outputting a closed question about the target of search.

Information processing method, information processing apparatus, and non-transitory recording medium

An information processing method in an interactive apparatus that questions a user's symptom through interaction with a user includes: outputting first question information of a first type concerning the user's symptom to a display or a speaker connected to the interactive apparatus, receiving first answer information indicating an answer to the question in the first question information from a keyboard, a touch panel, or a microphone connected to the interactive apparatus, outputting second question information of the first type concerning the user's symptom to the display or the speaker when it is determined that the answer in the first answer information does not include a word in a negative expression, and outputting third question information of a second type concerning the user's symptom to the display or the speaker when it is determined that the answer in the first answer information includes the word in the negative expression.

Information processing method and recording medium

A decision tree generating apparatus includes an information gain calculator and a decision tree generator. When a classification target data set including a plurality of pieces of classification target data respectively having different attributes with attribute values assigned thereto is segmented into subsets in a form of a decision tree, the information gain calculator calculates an amount of entropy reduction on each attribute, and calculates an information gain, based on the amount of reduction in the entropy and reliability of a user's answer responsive to an inquiry asking about the attribute. The decision tree generator successively determines an attribute having a maximum information gain to be a node of the decision tree by recursively iterating the segmentation of the pre-segmentation data set, and generates the decision tree that is to be used to determine an order of the inquiries.

Decision tree generating apparatus, decision tree generating method, non-transitory computer-readable recording medium, and inquiry system

An information processing method for information stored in a storage includes: holding a dialog history of a dialog including a question to a user and a reply from the user to the question, determining whether a manner in which a third reply indicating neither a first reply nor a second reply appears in a reply history of the reply included in the held dialog history satisfies a predetermined condition, the first reply indicating an affirmative in response to the question, the second reply indicating a negative in response to the question; and performing presentation regarding the information stored in the storage if the manner is determined to satisfy the predetermined condition.

A speech continuation determination method includes acquiring dialogue data including a system sentence spoken to a user at a first time, a user sentence spoken by the user at a second time following the system sentence, and system intention indicating intention of the system sentence; estimating a predicted response delay amount indicating a wait time for a response to the user sentence by applying the dialogue data to a model obtained by machine learning; acquiring user status information indicating the status of the user; and determining whether a speech sentence by the user continues following the user sentence in accordance with the user status information in the wait time indicated by the predicted response delay amount.

Speech continuation determination method, speech continuation determination device, and recording medium

In an information processing method, display of a question and acquisition of an answer from a user to the question are repeated to narrow down information the user desires to make a search. A question regarding a target of the search is displayed in a first mode on a display included in a terminal. An answer inputted from the user to the question is acquired through the terminal. When the question is displayed, first information regarding a situation of a narrowing-down progress is displayed above the question in a second mode, and second information regarding a guide for answering the question is displayed below the question in a third mode.

Information processing method, information processing apparatus, and recording medium having program recorded thereon

A method includes determining a content of a speech of a user on the basis of text data including a word string acquired as a recognition result of the speech, selecting a correction method suitable for the determined content of the speech from among multiple correction methods for correcting false recognition contained in the text data, and correcting the false recognition contained in the text data using the selected correction method.

Method for correcting false recognition contained in recognition result of speech of user

A method includes: acquiring first voice information indicating a voice of a user input from a microphone; outputting, to a server via a network, first text string information generated from the first voice information, when the first text string information does not match any of pieces of text string information in the first database; acquiring, from the server, first semantic information and/or a control command corresponding to the first semantic information, when a second database includes a piece of text string information matched with the first text string information and the matched piece of text string information is associated with the first semantic information therein; instructing at least one device to execute an operation based on the first semantic information and/or the control command; and outputting, to a speaker, second voice information generated from second text string information, the second text string information being registered and associated with the first semantic information in the first database.

Information processing method and non-temporary storage medium for system to control at least one device through dialog with user

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.

Method for controlling identification and identification control apparatus

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.

Dialogue act estimation with learning model

A method is provided for controlling a device based on acquired text data. The method includes acquiring the text data indicating a voice spoken by a user, and analyzing a meaning of the text data based on a table, in which a word and a vector representing a meaning of the word in a vector space of predetermined dimensions are associated. The method also includes generating a command to control the device based on the analyzed meaning of the text data. The table is generated by performing a learning process by assigning to a first word a first vector representing a meaning of the first word in the vector space, and by assigning to a second word a second vector representing a meaning of the second word in the vector space, in accordance with an arrangement of a word string in a first text corpus and a second text corpus.

Method for assigning semantic information to word through learning using text corpus

A meaning generation method, in a meaning generation apparatus, includes acquiring meaning training data including text data of a sentence that can be an utterance sentence and meaning information indicating a meaning of the sentence and associated with the text data of the sentence, acquiring restatement training data including the text data of the sentence and text data of a restatement sentence of the sentence, and learning association between the utterance sentence and the meaning information and the restatement sentence. The learning includes learning of a degree of importance of a word included in the utterance sentence, and the learning is performed by applying the meaning training data and the restatement training data to a common model, and storing a result of the learning as learning result information.

Meaning generation method, meaning generation apparatus, and storage medium

A method includes acquiring a first corpus, including first text of a first sentence including a first word and described in a natural language, and second text of a second sentence including a second word different in meaning from the first word, a second word distribution of the second word being similar to a first word distribution of the first word, acquiring a second corpus including third text of a third sentence, including a third word identical to the first word and/or the second word, a third word distribution of the third word being not similar to the first word distribution, based on an arrangement of a word string in the first corpus and the second corpus, assigning to the first word a first vector representing a meaning of the first word and assigning to the second word a second vector representing a meaning of the second word.

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.

Subject estimation system for estimating subject of dialog

A method for controlling an operation of a target device using a plurality of input devices is disclosed. The method comprises: receiving from one of the plurality of the input devices a first operation instruction issued to the target device, with a first data format; recognizing the first operation instruction and the first data format; determining that the one of the plurality of the input devices is a first input device corresponding to the first data format; and providing to a user of the target device a recommendation for a second input device, a type of the second input device being different from a type of the first input device, when it is determined that a type of the first operation instruction is identical to a type of a second operation instruction received from the second input device earlier than the reception of the first operation instruction.

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Osaka, Japan

Katsuyoshi Yamagami