Panasonic Intellectual Property Management Co., Ltd.

Panasonic Intellectual Property Corporation of America

Takashi Ushio

Katsuyoshi Yamagami

Mitsuru Endo

Hongjie Shi

Takamitsu Sasaki

Yasunori Ishii

Hajime Tasaki

Asuka Sakai

A vehicle attack continuity determination method includes: obtaining first vehicle attack event information pertaining to a first vehicle attack event which has occurred in an in-vehicle network, second vehicle attack event information pertaining to a second vehicle attack event which has occurred in the in-vehicle network before the first vehicle attack event, and in-vehicle network information indicating a configuration of the in-vehicle network; determining whether there is continuity between the first vehicle attack event and the second vehicle attack event based on the first vehicle attack event information, the second vehicle attack event information, and the in-vehicle network information; and outputting a result of the determining.

Vehicle attack event continuity determination method, vehicle attack event continuity determination device, and non-transitory computer-readable recording medium

An attack analysis device includes: an obtainer that obtains in-vehicle network information indicating a configuration of an in-vehicle network including a plurality of external communication interfaces and a plurality of control Electronic Control Units (ECUs), and anomaly detection information indicating a result of detecting an anomaly in at least one node in the in-vehicle network; an attack path estimator that, based on the in-vehicle network information and the anomaly detection information, estimates an attack path in an attack on the in-vehicle network, the attack path including an entry point indicating an external communication interface that is a point of intrusion into the in-vehicle network in the attack and an attack target indicating a control ECU that is a target of the attack; and an outputter that outputs the attack path.

Attack analysis device, attack analysis method, and non-transitory computer-readable recording medium

An attack detection method includes determining a sampling rule including a sampling interval and a sampling time on the basis of at least one of a statistic indicating a variation in sensor values included in the sensor data or event information on the mobility entity, which indicates the timing of a change in sensor values; generating sampling data including two or more sensor values selected from the sensor data on the basis of the sampling interval and the sampling time, first order information, and second order information; and calculating a first anomaly score indicating the degree of anomalies in the evaluation target data and a second anomaly score indicating the degree of anomalies in the evaluation target data, determining on the basis of the calculated first and second anomaly scores whether the evaluation target data has resulted from the attack, and outputting a determination result.

Attack detection method, attack detection system, and recording medium

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 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.

Information processing method, information processing apparatus, and program for solving a specific task using a model of a dialogue 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 dialogue act estimation method, in a dialogue act estimation apparatus, includes acquiring first training data indicating, in a mutually associated manner, text data of a first sentence that can be a current uttered sentence, and text data of a second sentence that can be an uttered sentence immediately previous to the first sentence. The method also includes speaker change information indicating whether a speaker of the first sentence is the same as a speaker of the second sentence, and dialogue act information indicating a class of the first sentence. The method further includes learning an association between the current uttered sentence and the dialogue act information by applying the first training data to a model, and storing a result of the learning as learning result information in a memory.

Dialogue act estimation method, dialogue act estimation apparatus, and storage medium

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.

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Tokyo, Japan

Takashi Ushio