公开(公告)号：US20230196109A1

公开(公告)日：2023-06-22

申请号：US18172419

申请日：2023-02-22

Applicant: FUJITSU LIMITED

Inventor： Masaru TODORIKI ,  Masafumi SHINGU ,  Koji MARUHASHI

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: A non-transitory computer-readable recording medium storing a model generation program for causing a computer to perform processing including: changing first data and generating a plurality of pieces of data; calculating a plurality of values indicating a distance between the first data and each of the plurality of pieces of data; determining whether or not a value indicating uniformity of distribution of the distance between the first data and each of the plurality of pieces of data is equal to or greater than a threshold based on the plurality of values; and in a case where the value indicating the uniformity is determined to be equal to or greater than the threshold, generating a linear regression model using a result obtained by inputting the plurality of pieces of data into a machine learning model as an objective variable and using the plurality of pieces of data as explanatory variables.

公开(公告)号：US20230133868A1

公开(公告)日：2023-05-04

申请号：US17945102

申请日：2022-09-15

Applicant: Fujitsu Limited

Inventor： Masaru TODORIKI ,  Koji MARUHASHI

IPC: G06N20/00 ,  G06K9/62 ,  G06N5/04

Abstract: A recording medium storing an explanatory program for causing a computer to execute an explanatory process. The process includes: generating a plurality of pieces of data based on first data; calculating a ratio of output results, among a plurality of results output in a case that each of the plurality of pieces of data is input to a machine learning model, different from first results output in a case that the first data is input to the machine learning model; generating a linear model based on the plurality of pieces of data and the plurality of results in a case that the calculated ratio satisfies a criterion; and outputting explanatory information with respect to the first results based on the linear model.

公开(公告)号：US20200151574A1

公开(公告)日：2020-05-14

申请号：US16670322

申请日：2019-10-31

Applicant: FUJITSU LIMITED

Inventor： TATSURU MATSUO ,  Koji MARUHASHI

IPC: G06N3/08 ,  G06K9/62

Abstract: A learning method includes: acquiring input data and correct answer information, the input data including a set of multiple pieces of relationship data in which relationships between variables are recorded respectively; determining conversion rule corresponding to each of the multiple pieces of relationship data such that relationships before and after a conversion of a common variable commonly in the multiple pieces of relationship data are the same, when converting a variable value in each of the multiple pieces of relationship data into converted data rearranging the variable values in an order of input; converting each of the multiple pieces of relationship data into a multiple pieces of the converted data according to each corresponding conversion rule; and inputting a set of the multiple pieces of converted data to the neural network and causing the neural network to learn a learning model based on the correct answer information.

公开(公告)号：US20160357846A1

公开(公告)日：2016-12-08

申请号：US15166945

申请日：2016-05-27

Applicant: FUJITSU LIMITED

Inventor： Koji MARUHASHI

IPC: G06F17/30

CPC classification number: G06F16/285

Abstract: A data classification apparatus includes an acquisition section for acquiring data including records; a classification section for classifying the records, wherein the classification section generates groups in which each of the records is arranged, calculates a first and a second evaluation values, determines whether or not to rearrange the first record based on the first and the second evaluation values, and performs rearrangement of the first record when it is determined that the first record is to be rearranged, the first evaluation value being based on an arrangement status of the records when a first record arranged in a first group in the groups is rearranged into a second group not included in the groups and the second evaluation value based on an arrangement status of the records when each record arranged in the first group is rearranged into either the first group or the second group.

Abstract translation: 数据分类装置包括用于获取包括记录的数据的获取部分; 用于分类记录的分类部分，其中所述分类部分生成其中每个记录被布置的组，计算第一和第二评估值，基于所述第一和第二评估值来确定是否重新排列所述第一记录 并且当确定要重新排列第一记录时，执行第一记录的重新排列，第一评估值基于排列在组中的第一组中的第一记录被重新排列成为的记录的排列状态 当将排列在第一组中的每个记录重新布置在第一组或第二组中时，基于记录的排列状态，不包括在组中的第二组和第二评估值。

公开(公告)号：US20240394733A1

公开(公告)日：2024-11-28

申请号：US18791619

申请日：2024-08-01

Applicant: Fujitsu Limited

Inventor： Yuta FUJISHIGE ,  Tatsuya ASAI ,  Kento UEMURA ,  Hirofumi SUZUKI ,  Yusuke KOYANAGI ,  Koji MARUHASHI

IPC: G06Q30/0201

Abstract: A recording medium stores a program for causing a computer to execute a process including: referring to a memory storing data constituted by combinations of features to extract data groups of which the combinations satisfy each condition; identifying relationships between the features included in the data groups; classifying the relationships into a first clusters, based on first similarity; classifying the data groups into second clusters, based on second similarity; classifying the data groups into third clusters so as to classify, into a same cluster, data groups that are in a same one first cluster obtained by classifying the relationships corresponding correspond to each data group and are in a same one second cluster obtained by classifying each data group; identifying first conditions for classifying the data groups classified into each cluster and the data groups classified into other clusters; and outputting the identified first conditions with a classification result.

公开(公告)号：US20240161891A1

公开(公告)日：2024-05-16

申请号：US18461454

申请日：2023-09-05

Applicant: Fujitsu Limited

Inventor： Shuang CHANG ,  Tatsuya ASAI ,  Koji MARUHASHI

IPC: G16H20/00

CPC classification number: G16H20/00

Abstract: A medium storing a program for causing a computer to execute processing including: obtaining pieces of combination data each including attribute information indicating attributes of a person and information indicating which choice is selected from choices; generating, for each choice, converted data obtained by converting the information into information indicating whether the choice is selected; identifying, for each choice, based on the converted data, an attribute, from the attributes, that has a correlation greater than a criterion with the selection of the choice and a condition; identifying a common condition among conditions of different choices based on the condition; and determining a plan for improving a selection result of the choice for a target who matches one of the different choices and who matches the common condition based on a result of an analysis between the attributes and the choice by using the converted data matching the common condition.

公开(公告)号：US20210390623A1

公开(公告)日：2021-12-16

申请号：US17330411

申请日：2021-05-26

Applicant: FUJITSU LIMITED

Inventor： Masaru TODORIKI ,  Yuhei UMEDA ,  Ken KOBAYASHI ,  Koji MARUHASHI

IPC: G06Q40/06 ,  G06K9/00 ,  G06F16/22

Abstract: A non-transitory computer-readable recording medium has stored therein a program that causes a computer to execute a process, the process including determining numerical values indicating features at respective timings having a predetermined time interval with respect to time-series data to be analyzed, numbers of the numerical values at the respective timings being made same, and generating an attractor related to the time-series data based on the determined numerical values.

公开(公告)号：US20240119258A1

公开(公告)日：2024-04-11

申请号：US18363431

申请日：2023-08-01

Applicant: Fujitsu Limited

Inventor： Tao KOMIKADO ,  Koji MARUHASHI

IPC: G06N3/042

CPC classification number: G06N3/042

Abstract: A computer-readable recording medium storing a program for causing a computer to execute processing including: acquiring a first determination result of first graph data by performing determination processing on the first graph data; acquiring one or more first scores regarding a feature of the first graph data by using a trained model, the one or more first scores representing a basis of the first determination result of the first graph data, the trained model being a model configured to output, in response to obtaining graph data, one or more scores regarding the feature of the graph data; in a case where all of the one or more first scores are less than a threshold, specifying second graph data being a second determination result different from the first determination result; and outputting, in association with the first determination result, information regarding the feature of the second graph data.

公开(公告)号：US20210295156A1

公开(公告)日：2021-09-23

申请号：US17201646

申请日：2021-03-15

Applicant: FUJITSU LIMITED

Inventor： Koji MARUHASHI

IPC: G06N3/08

Abstract: A method includes: generating common information to be commonly applied to plural input data each including a combination of a value of each item and an input value in association with one or more items, the common information being for converting a correspondence between each input value and each input node in a machine learner in a case of inputting the plural input data to the machine learner; generating individual information to be individually applied to each input data, the individual information being for converting the correspondence, in association with a remaining item excluding the one or more items, by using a similarity between test data and collation data obtained by converting the correspondence; generating converted data obtained by converting the correspondence by using the generated common conversion information and the generated individual conversion information; and updating the collation data and the machine learner by using the generated converted data.

公开(公告)号：US20200302305A1

公开(公告)日：2020-09-24

申请号：US16821438

申请日：2020-03-17

Applicant: FUJITSU LIMITED

Inventor： YUSUKE OKI ,  Koji MARUHASHI

IPC: G06N3/08 ,  G06N5/04 ,  G06N20/10 ,  G06K9/62 ,  G06F17/16 ,  G06F7/78

Abstract: A learning method implemented by a computer, includes: creating an input data tensor including a local dimension and a universal dimension by partitioning series data into local units, the series data including a plurality of elements, each of the plurality of elements in the series data being logically arranged in a predetermined order; and performing machine learning by using tensor transformation in which a transformation data tensor obtained by transforming the input data tensor with a transformation matrix is outputted using a neural network, wherein the learning includes rearranging the transformation matrix so as to maximize a similarity to a matching pattern serving as a reference in the tensor transformation regarding the universal dimension of the input data tensor, and updating the matching pattern in a process of the machine learning regarding the local dimension of the input data tensor.