Kabushiki Kaisha Toshiba

Toshiba Solutions Corporation

Toshiba Medical Systems Corporation

Toshiba Digital Solutions Corporation

Rumi Hayakawa

Shigeaki Sakurai

Shigeru Matsumoto

Seiji Egawa

Shozo Isobe

Kazuyoshi Nishi

Kyoko Makino

Hideki Iwasaki

Takashi Kondo

Toru Ito

Akira Iwasa

Toshiyuki Katou

Shintarou Takahashi

A manufacturing condition output apparatus of an embodiment is a manufacturing condition output apparatus which outputs a manufacturing condition of a product. The manufacturing condition output apparatus outputs change degree information which is information regarding degrees of change of values regarding defect probabilities for a plurality of variables relating to manufacturing of the product from model information of a model generated through machine learning on a basis of manufacturing data of the product and inspection result data of the product, as a manufacturing condition.

Manufacturing condition output apparatus, quality management system, and storage medium

A sequential data analysis apparatus extracts a pattern of two or more sets of items based on an appearance frequency of each of different sets of items in first sequential data, selects a pattern of two or more sets of items based on an appearance frequency of a sub-pattern formed of a portion of the extracted pattern, creates a related pattern including the same last set of items as and the other sets of items different from the selected characteristic pattern, calculates an evaluation value of the related pattern, creates a prediction model by organizing data of the characteristic pattern and the related pattern, and applies second sequential data to the prediction model to determine a result which the second sequential data is likely to lead to.

Sequential data analysis apparatus and program

A data analysis supporting apparatus according to an embodiment comprises a variable-type determining device, a variable-variation generating device, and a variable-contribution rate determining device. The variable-type determining device determines the type of each variable. The variable-variation generating device generates variations of each variable in accordance with the variable type determined and with a variable variation rule that defines a method of generating the variations of each variable of any type. The variable-contribution rate determining device calculates the rate at which the variations and analysis data contribute to the objective variable, and determines, from the rate, whether the variations should be used or deleted.

Data analysis supporting apparatus

A data analyzing apparatus of an embodiment generates a format variation, analytical algorithm name, and analytical parameter not stored in a first storage device, and executes analysis. The data analyzing apparatus determines whether the application accuracy is lower than the knowledge model accuracy. If the determination result is 'not lower', the data analyzing apparatus reactivates a format variation generating device and an analytical parameter generating device. If the determination result is 'lower', the data analyzing apparatus reads out a format variation and knowledge model name associated with the highest priority order in the first storage device, and executes analysis.

Data analyzing apparatus and program

A pattern extracting apparatus includes a first storing section storing plural target information pieces, a candidate pattern producing section producing candidate patterns each including two or more items different from each other based on each of the items included in the plural target information pieces, a candidate evaluation value calculating section calculating an extraction evaluation value of the candidate pattern based on a frequency of appearance at which the produced candidate pattern appears in the plural target information pieces, a pattern extracting section determining and extracting any of the candidate patterns having the calculated extraction evaluation value satisfying a predetermined threshold value, and a second storing section storing an association degree between the items. The candidate evaluation value calculating section calculates the extraction evaluation value based on a weight based on an identified association degree between the items included in the candidate pattern and the frequency of appearance.

Pattern extracting apparatus and method

A report check apparatus includes an input unit which inputs data of a diagnostic reading report as a check target, a check unit which checks whether the input diagnostic reading report contains a semantic error related to diagnostic reading, and a display unit which displays a check result by the check unit.

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Yokohama, Japan

Rumi Hayakawa