公开(公告)号：US20250117581A1

公开(公告)日：2025-04-10

申请号：US18729950

申请日：2022-01-25

Applicant: NEC Corporation

Inventor： Masafumi OYAMADA ,  Taro YANO ,  Kunihiro TAKEOKA ,  Kosuke AKIMOTO

IPC: G06F40/279 ,  G06F40/40

Abstract: In order to classify, stably with high accuracy, a document to be classified, a document classification apparatus (1) includes: a strategy selection section (11) that selects at least one generation strategy from among a plurality of generation strategies for generating a hypothetical sentence related to a candidate classification as which a document is to be classified; a hypothetical sentence generation section (12) that generates, in accordance with the at least one generation strategy selected by the strategy selection section (11), the hypothetical sentence, which is a sentence related to the candidate classification; and a classification section (13) that determines, on the basis of entailment between the document and the hypothetical sentence, a classification as which the document is to be classified.

公开(公告)号：US20240104433A1

公开(公告)日：2024-03-28

申请号：US18370933

申请日：2023-09-21

Applicant: NEC Corporation

Inventor： Kunihiro TAKEOKA

IPC: G06N20/00 ,  G06F40/289

CPC classification number: G06N20/00 ,  G06F40/289

Abstract: A learning system includes: an acquisition unit that obtains document data; a generation unit that generates a key phrase from the document data; a restoration unit that restores the document data from the generated key phrase; and a learning unit that learns parameters of the generation unit on the basis of the document data and the restored document data. According to such a learning system, high-precision learning can be performed even when there is no key phrase as a correct answer.

公开(公告)号：US20250148316A1

公开(公告)日：2025-05-08

申请号：US18832227

申请日：2022-01-31

Applicant: NEC Corporation

Inventor： Genki KUSANO ,  Masafumi OYAMADA ,  Kunihiro TAKEOKA

IPC: G06N5/04 ,  G06F16/80

Abstract: In order to apply a text-based label inference approach to infer a label to be assigned to target data in a common data form, an inference apparatus (1) includes: a data converting section (11) for converting target data subject to label assignment into text; and a label inferring section (12) for inferring a label to be assigned to the target data, in accordance with a label inference model for inferring a label to be assigned to text and the text obtained by the converting carried out by the data converting means.

公开(公告)号：US20250124313A1

公开(公告)日：2025-04-17

申请号：US18703421

申请日：2021-11-11

Applicant: NEC Corporation

Inventor： Kosuke AKIMOTO ,  Kunihiro TAKEOKA ,  Masafumi OYAMADA

IPC: G06N5/048 ,  G06F40/279

Abstract: To determine, with high accuracy, a label to be given to an object even in a case where only a single prediction model exists, an information processing apparatus (1) includes: an acquisition unit (11) that acquires a set of objects; an evaluation unit (12) that evaluates a degree of similarity between objects included in the set of objects and identifies one or a plurality of similar objects which are similar to a prediction target object; and a prediction unit (13) that determines a label to be given to the prediction target object with reference to a similar label(s), the similar label(s) being a label(s) which is/are given to each of the one or a plurality of similar objects and which has/have been predicted by a prediction model.

公开(公告)号：US20240119079A1

公开(公告)日：2024-04-11

申请号：US18276378

申请日：2021-02-26

Applicant: NEC Corporation

Inventor： Taro YANO ,  Kunihiro TAKEOKA ,  Masafumi OYAMADA

IPC: G06F16/35 ,  G06F16/383

CPC classification number: G06F16/35 ,  G06F16/383

Abstract: The input means 181 accepts inputs of test data, a hierarchical structure in which a node of bottom layer represents a target class, and a classification score of a seen class as the classification score indicating a probability that the test data is classified into each class. The unseen class score calculation means 182 calculates the classification score of an unseen class based on uniformity of the classification score of each seen class. The matching score calculation means 183 calculates a matching score indicating similarity between the test data and each class label. The final classification score calculation means 184 calculates a final classification score indicating a probability that the test data is classified into the class so that the larger the classification score of each class, and the matching score, the larger the final classification score.

公开(公告)号：US20220180420A1

公开(公告)日：2022-06-09

申请号：US17442161

申请日：2019-03-28

Applicant: NEC Corporation

Inventor： Masafumi OYAMADA ,  Keigo KIMURA ,  Kunihiro TAKEOKA

IPC: G06Q30/06 ,  G06F16/2457

Abstract: The acquisition unit 52B acquires sales request information S1, which is request information regarding a sale of data owned by a data owner, from an owner terminal 2 used by the data owner. The determination unit 53B determines whether or not there is a user who has a past record of performing a task or who is expected to perform the task, the task being subjected to a positive effect when the data is used for the task. The notification unit 54B notifies, in a case where the determination unit 53B determines that there is a user who has a past record of performing the task or who is expected to perform the task, the owner terminal 2 of candidate buyer information S2 indicating information regarding the user.

公开(公告)号：US20210383255A1

公开(公告)日：2021-12-09

申请号：US17288143

申请日：2018-11-01

Applicant: NEC Corporation

Inventor： Kunihiro TAKEOKA ,  Masafumi OYAMADA

IPC: G06N5/04 ,  G06N20/00

Abstract: An input unit 81 inputs an annotation result that is data to which a label is added based on an annotator's answer, and label addition information that indicates an inter-label structure. An answer integration unit 82 integrates the annotation results and estimates the label of the data. A skill estimation unit 83 estimates a skill of the annotator based on a difference between the estimated label and the labels included in the annotation results. An update unit 84 updates, based on the estimated skill of the annotator, the feature of a task for adding a label the inter-label structure of which is specified based on the label addition information to the data, the update being performed so that the feature conforms to the annotation results. An output unit 85 outputs the label estimated by the answer integration unit 82. The answer integration unit 82 estimates the label based on a weight calculated in accordance with closeness of the skill of the annotator and the feature of the task to the label.

公开(公告)号：US20210049483A1

公开(公告)日：2021-02-18

申请号：US16978402

申请日：2018-03-08

Applicant: NEC CORPORATION

Inventor： Masafumi OYAMADA ,  Kunihiro TAKEOKA

IPC: G06N5/04 ,  G06F16/26

Abstract: A column meaning candidate selection means 303 selects a candidate for meaning of a column whose meaning is to be inferred. A column similarity computation means 304 computes, for each candidate for meaning selected by the column meaning candidate selection means 303, a score indicating a similarity between the selected candidate for meaning and meaning of each column other than the column whose meaning is to be inferred contained in a table. A column meaning identification means 305 identifies meaning of the column whose meaning is to be inferred from the candidates for meaning of the column with use of the score computed by the column similarity computation means 304.

公开(公告)号：US20220269953A1

公开(公告)日：2022-08-25

申请号：US17638984

申请日：2019-09-02

Applicant: NEC Corporation

Inventor： Kunihiro TAKEOKA

IPC: G06N5/02

Abstract: An input unit 81 receives input of response data with a response attached to input data by each worker. A learning unit 82 learns a worker model which is a model that predicts a response to new input data using the input response data, for each worker. The input unit 81 receives input of both response data of first response data in which a label included in output candidate label data indicating a candidate label to be assigned to the input data is assigned to the input data, and second response data in which a label not included in the output candidate label data is assigned to the input data, and the learning unit 82 learns the worker model using the both response data of the first response data and the second response data.

公开(公告)号：US20220188926A1

公开(公告)日：2022-06-16

申请号：US17442156

申请日：2019-03-28

Applicant: NEC Corporation

Inventor： Masafumi OYAMADA ,  Keigo KIMURA ,  Kunihiro TAKEOKA

IPC: G06Q40/04

Abstract: The acquisition unit 52A acquires request information S1, which is information regarding possession data owned by a data owner, from an owner terminal 2 used by the data owner. The determination unit 53A determines whether or not there is shortage data other than the possession data required to perform a designation task that is designated. The notification unit 54A notifies, when the determination unit 53A determines that there is the shortage data, the owner terminal 2 of shortage data information S2 that is information regarding shortage information.