Panasonic Corporation

Panasonic Intellectual Property Corporation of America

Panasonic Intellectual Property Management Co., Ltd.

Pansonic Corporation

Iku Ohama

Shohji Ohtsubo

Ryota Fujimura

Hideo Umetani

Yukie Shoda

Naoyuki Harada

Norihiro Matsui

Tatsumi Oba

Takuya Matsumoto

Rinako Kamei

Naohisa Tanabe

Toshihisa Abe

Hiromi Iida

Hiroki Arimura

Takuya Kida

A demand prediction method using a computer includes calculating an error between a prediction value of an amount of a past demand and a measurement value of the amount of the past demand, the prediction value of the amount of the past demand being calculated by inputting a measurement value of a past explanatory variable to a prediction model that is constructed in accordance with a measurement value of an amount of a demand at a predetermined location and an explanatory variable that serves as an external factor that affects an increase or a decrease in the amount of the demand at the predetermined location, determining whether the calculated error is an abnormal value, acquiring a new explanatory variable if the error is determined to be the abnormal value, updating the prediction model in accordance with the acquired new explanatory variable, and newly predicting an amount of a future demand using the updated prediction model.

Demand prediction method, demand prediction apparatus, and non-transitory computer-readable recording medium

A method is provided for reducing computer memory usage and processing time in assessing effectiveness of a prediction model. The method includes acquiring a plurality of records and sorting the records based on prediction values. The method also includes determining two of three performance data parameters, including a number of selected records and a number of correct decisions, and determining a level of effectiveness of the prediction model based on the two of three performance data parameters. The method further includes generating a graph illustrating the level of effectiveness of the prediction model based on the two of three performance parameters, and visually conveying the level of effectiveness of the prediction model utilizing the generated graph.

Evaluation result display method, evaluation result display apparatus, and non-transitory computer-readable recording medium storing evaluation result display program

An anomaly detection method includes: extracting, for each of a plurality of learning packets obtained, all possible combinations of N-grams in the payload included in the learning packet; counting a first number which is the number of occurrences of each combination in the payloads of the learning packets; calculating, as anomaly detection models, first probabilities by performing smoothing processing based on a plurality of the first numbers; and when the score calculated for each of a plurality of packets exceeds a predetermined threshold that is based on the anomaly detection models stored in a memory, outputting information indicating that the packet having the score has an anomaly.

Anomaly detection method, learning method, anomaly detection device, and learning device

A portable display device comprises: a tilt detection unit detecting an angle of rotation, measured between a vertical plane component of a tilt vector of a predetermined reference line in the display and a gravitational vector, as a display tilt; a facial orientation specification unit recognizing a face of a person facing the display and specifying an upright orientation of the face; a terminal orientation determination unit establishing a plurality of angle ranges, and determining whether the display tilt newly detected by the tilt detection unit exceeds a given angle range and enters another angle range, the given angle range including the angle detected by the tilt detection unit upon updating a previous display orientation; and a control unit causing the facial orientation specification unit to begin the specification when the terminal orientation determination unit makes an affirmative determination, and updating the display orientation according to the specified upright orientation.

Display device and method of switching display direction

An object is to provide an apparatus that accurately estimates an association between content and a user who performed an operation on the content. The apparatus includes: a first association information generation unit that generates first association information in which content information subjected to an operation and a user who performed the operation are associated; a learning unit that generates learnt information for associating a given user with content likely to be operated by the given user by using the first association information; an estimation unit that estimates a user by using unidentified content information for which the user cannot be identified and the learnt information; and a second association information generation unit that generates second association information in which the unidentified content information and the estimated user are associated. The learning unit updates the learnt information by preferentially using the first association information over the second association information.

Estimation apparatus, estimation method, program, and integrated circuit

Provided is a classification device capable of classifying time series data of location information into groups in which continuity of time is secured. The classification device () includes a data storage unit () that stores time-of-day information and location information corresponding to the time-of-day information, a sorting unit () that sorts the location information in order of time using the time-of-day information, a hidden Markov model storage unit () storing therein a left-to-right hidden Markov model containing a plurality of states and in which a parameter is undetermined, a Markov model output unit () that determines the parameter by inputting the location information sorted in order of time into the hidden Markov model, and a classification unit () that classifies each location information in one of the plurality of states by inputting the location information sorted in order of time into the hidden Markov model containing the determined parameter.

Classification device and classification method

A content output device for outputting contents to be displayed to users, including: a storage unit storing a plurality of contents, each being associated with its attribute; a first output control unit for outputting a predetermined content among the contents to a screen; a related content identifying unit for identifying, among the contents stored in the storage unit, a set of related contents whose attributes are related to an attribute of the predetermined content; a user preference identifying unit for identifying, among the contents stored in the storage unit, contents that match the preference of the user; and a second output control unit for outputting, to the screen, one or more contents that belong to both the set of related contents and the contents identified by the user preference identifying unit, thereby outputting images that are most likely to be desired by thy user.

Content output device, content output method, program, program recording medium, and content output integrated circuit

A video recording apparatus that prevents screen malfunctions due to playback of incomplete frame image data, even when a power failure has occurred while recording frame image data received via a network to a recording medium, and incomplete frame image data has been recorded. The video recording apparatus includes a reception unitthat receives management information attached to each piece of data, a first recording unitthat records the received video data to the recording medium, and a second recording unitthat records position information of the video data to the recording medium. The video recording apparatus quickly specifies damaged data positions by referencing a file length of the video data file, and a position information file

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Osaka, Japan

Iku Ohama