Fujitsu Limited

Japon

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Propriétaire / Filiale
[Owner] Fujitsu Limited 9 584
Fujitsu Advanced Engineering Limited 6
Fujitsu Semiconductor Limited 5
Fujitsu Component Limited 4
Fujitsu VLSI Limited 4
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Date
Nouveautés (dernières 4 semaines) 21
2021 juin (MACJ) 20
2021 mai 25
2021 avril 52
2021 mars 13
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Classe IPC
H04W 72/04 - Affectation de ressources sans fil 399
G06F 13/00 - Interconnexion ou transfert d'information ou d'autres signaux entre mémoires, dispositifs d'entrée/sortie ou unités de traitement 258
G06F 12/00 - Accès à, adressage ou affectation dans des systèmes ou des architectures de mémoires 202
G06F 17/30 - Recherche documentaire; Structures de bases de données à cet effet 196
G06F 3/06 - Entrée numérique à partir de, ou sortie numérique vers des supports d'enregistrement 146
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1.

INFORMATION PROCESSING SYSTEM, INFORMATION PROCESSING TERMINAL, AND DATA ACCESS CONTROL PROGRAM

      
Numéro d'application JP2019049483
Numéro de publication 2021/124465
Statut Délivré - en vigueur
Date de dépôt 2019-12-17
Date de publication 2021-06-24
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Suzuki, Kosuke
  • Lim, Han Seng

Abrégé

In an information processing terminal (101), when an access operation to access data in a remote storage region has been input by a user, an access control unit (AC) determines the type of the input access operation. If the type of access operation is neither move nor copy, the access control unit (AC) passes the input access operation to a WebDAV virtual driver (VD) via a file system (FS). The WebDAV virtual driver (VD) issues an instruction corresponding to the received access operation to a WebDAV server (102). In contrast, if the type of access operation is move or copy, the access control unit (AC) issues a MOVE or COPY instruction corresponding to the input access operation directly to the WebDAV server (102) without the MOVE or COPY instruction passing through kernel land (KL).

Classes IPC  ?

  • G06F 11/34 - Enregistrement ou évaluation statistique de l'activité du calculateur, p.ex. des interruptions ou des opérations d'entrée–sortie
  • G06F 11/30 - Surveillance du fonctionnement
  • G06F 16/10 - Systèmes de fichiers; Serveurs de fichiers
  • G06F 21/16 - Traçabilité de programme ou de contenu, p.ex. par filigranage

2.

JOB PREDICTION PROGRAM, SYSTEM, AND METHOD

      
Numéro d'application JP2019049183
Numéro de publication 2021/124397
Statut Délivré - en vigueur
Date de dépôt 2019-12-16
Date de publication 2021-06-24
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Suzuki, Shigeto

Abrégé

The individual topic distributions of a job to be predicted and past jobs for which IO data are known are calculated on the basis of an overall topic model (21) trained by using job information concerning a plurality of jobs and a large-IO topic model (22) trained by using job information concerning large-IO jobs, which constitutes a subset of the overall topic model, and a first job and a second job having topic distributions with the greatest degrees of similarity with the topic distribution of the job to be predicted are extracted from the past jobs. Of the first job and the second job extracted, the IO data of the job having a topic distribution with a greater degree of similarity is output as a predictive value of the IO data of the job to be predicted, which improves the accuracy of prediction of the amounts of input and output for jobs.

Classes IPC  ?

  • G06F 9/48 - Lancement de programmes; Commutation de programmes, p.ex. par interruption
  • G06F 17/18 - Opérations mathématiques complexes pour l'évaluation de données statistiques

3.

INFORMATION PROCESSING PROGRAM, INFORMATION PROCESSING METHOD, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019049967
Numéro de publication 2021/124535
Statut Délivré - en vigueur
Date de dépôt 2019-12-19
Date de publication 2021-06-24
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Kataoka, Masahiro
  • Ohyama, Shogo
  • Onoue, Satoshi

Abrégé

This information processing device embeds a plurality of words in a vector space on the basis of similar word information that associates words which belong to different word classes and which are synonymous or have more than a prescribed degree of similarity. The information processing device calculates a sentence vector for each of a first sentence and a second sentence on the basis of a vector of each word of a plurality of words included in the sentence, said word vector being defined in said vector space. The information processing device determines the similarity between the first sentence and the second sentence on the basis of the vector of the first sentence and the vector of the second sentence.

Classes IPC  ?

  • G06F 16/31 - Indexation; Structures de données à cet effet; Structures de stockage
  • G06F 16/383 - Recherche caractérisée par l’utilisation de métadonnées, p.ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
  • G06F 16/901 - Indexation; Structures de données à cet effet; Structures de stockage
  • G06F 16/908 - Recherche caractérisée par l’utilisation de métadonnées, p.ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu

4.

GUARANTEE CONTROL METHOD, INFORMATION PROCESSING DEVICE, AND GUARANTEE CONTROL PROGRAM

      
Numéro d'application JP2019049715
Numéro de publication 2021/124498
Statut Délivré - en vigueur
Date de dépôt 2019-12-18
Date de publication 2021-06-24
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Sakamoto, Takuya

Abrégé

A user device (1) acquires a certificate (210) including multiple pieces of attribute information, transmits hash values of the multiple pieces of attribute information to a data providing device, and discloses first attribute information included in the multiple pieces of attribute information and identifying a person to the data providing device by use of a zero-knowledge proof using the hash values of the multiple pieces of attribute information. The user device (1) receives from the data providing device and transmits to a data using device: provided data corresponding to the first attribute information; the hash values of the multiple pieces of attribute information; and signature information of data including the provided data and also including the hash values of the multiple pieces of attribute information. Using the zero-knowledge proof using the hash values of the multiple pieces of attribute information, the user device (1) discloses, to the data using device, second attribute information included in the multiple pieces of attribute information and desired by the data using device. In this way, the user device (1) can provide a combination of correct data and attribute information, while restricting the disclosure of the attribute information of the person.

Classes IPC  ?

  • H04L 9/32 - Dispositions pour les communications secrètes ou protégées comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système

5.

INFORMATION PROCESSING PROGRAM, INFORMATION PROCESSING METHOD, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019049664
Numéro de publication 2021/124490
Statut Délivré - en vigueur
Date de dépôt 2019-12-18
Date de publication 2021-06-24
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Kataoka, Masahiro
  • Hori, Shinji
  • Matsumura, Ryo
  • Takebe, Nobuyuki

Abrégé

An information processing device according to the present invention extracts a first sentence vector for a plurality of first sentences in a first text. On the basis of the extracted first sentence vector and a second sentence vector of a plurality of second sentences in a second text, the information processing device identifies a second sentence, out of the plurality of second sentences, for which the vector trends differently than for the plurality of first sentences. From the words in the identified second sentence, the information processing device extracts words that match homophones or conjunctions stored in a storage device. The information processing device converts the extracted words to words that are associated with the homophones or conjunctions stored in the storage device to thereby generate a second sentence having a vector trend the same as or similar to that of the plurality of first sentences.

Classes IPC  ?

  • G06F 40/232 - Correction orthographique, p.ex. vérificateurs d’orthographe ou insertion des voyelles
  • G06F 40/247 - Thésaurus; Synonymes
  • G06F 40/268 - Analyse morphologique

6.

WATER ELECTROLYSIS DEVICE AND WATER ELECTROLYSIS SYSTEM

      
Numéro d'application JP2019048116
Numéro de publication 2021/117096
Statut Délivré - en vigueur
Date de dépôt 2019-12-09
Date de publication 2021-06-17
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Yonezawa, Yu
  • Takauchi, Hideki
  • Nakashima, Yoshiyasu

Abrégé

This water electrolysis device comprises: a plurality of water electrolysis cells that are connected in series; a plurality of detection circuits that are respectively provided to the plurality of water electrolysis cells, and that each detect the current flowing in a by-pass circuit connected in parallel to the corresponding water electrolysis cell; and a signal output circuit that outputs a signal when the current value of the current detected by at least one detection circuit among the plurality of detection circuits exceeds a threshold.

Classes IPC  ?

  • C25B 15/02 - Commande ou régulation des opérations
  • H02J 15/00 - Systèmes d'accumulation d'énergie électrique
  • H02J 3/38 - Dispositions pour l'alimentation en parallèle d'un seul réseau, par deux ou plusieurs générateurs, convertisseurs ou transformateurs

7.

BASE STATION, TERMINAL DEVICE, AND WIRELESS COMMUNICATION SYSTEM

      
Numéro d'application JP2019048155
Numéro de publication 2021/117111
Statut Délivré - en vigueur
Date de dépôt 2019-12-09
Date de publication 2021-06-17
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Hirata, Akira

Abrégé

A base station (100) is provided with a wireless control device (110) and a wireless device (120). The wireless control device (110) has: a first processor (112) which, when a random access process by a terminal device (200) does not finish normally, determines that a random access setting in the wireless device (120) is to be changed; and a transmission path interface (113) which requests the wireless device (120) to change the random access setting in accordance with the determination by the first processor (112). The wireless device (120) has: a second processor (122) which changes the random access setting in response to the request from the wireless control device (110); and a wireless interface (123) which wirelessly provides notification of the random access setting change by the second processor.

Classes IPC  ?

  • H04W 74/08 - Accès non planifié, p.ex. accès aléatoire, ALOHA ou accès multiple par détection de porteuse [CSMA Carrier Sense Multiple Access]

8.

EVOLUTION CALCULATION PROGRAM FOR PARALLEL CALCULATION, INFORMATION PROCESSING DEVICE, AND EVOLUTION CALCULATION METHOD FOR PARALLEL CALCULATION

      
Numéro d'application JP2019048388
Numéro de publication 2021/117150
Statut Délivré - en vigueur
Date de dépôt 2019-12-11
Date de publication 2021-06-17
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Mori, Toshihiko
  • Tsunoda, Yukito

Abrégé

A computation processing device (1) executes evolution calculations for calculating, by parallel processing, the conformity of a plurality of individuals based on a plurality of inputs. The computation processing device (1): calculates, in parallel, the conformity of a plurality of individuals in one generation; if a threshold value is exceeded by a prescribed reference value at the end of a search for an intrinsic solution to be used for calculating the conformity of each individual, ends the calculations for the individual for which the prescribed reference value exceeded the threshold; and imparts an estimated conformity value to the individual for which the calculations were ended. Due to the foregoing, the computation processing device (1) is able to increase the speed of conformity evaluation calculations while maintaining a variety of intrinsic solutions essential to conformity calculations, when performing parallel processing of conformity calculation for each individual in evolution calculation.

Classes IPC  ?

  • G06N 3/12 - Systèmes de calculateurs basés sur des modèles biologiques utilisant des modèles génétiques

9.

GENERATION METHOD, GENERATION PROGRAM, AND INFORMATION PROCESSING SYSTEM

      
Numéro d'application JP2019048515
Numéro de publication 2021/117165
Statut Délivré - en vigueur
Date de dépôt 2019-12-11
Date de publication 2021-06-17
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Murakami, Ryo

Abrégé

In the present invention, an information processing device acquires three-dimensional point group data on the basis of a measurement result by a 3D sensor that performs three-dimensional measurement of a subject. On the basis of results obtained by fitting, to the three-dimensional point group data, cylindrical models that respectively express portions of a human body with a plurality of cylinders, the information processing device assesses noise influence on the portions in measurement results. The information processing device repeatedly executes a process of excluding, from the three-dimensional point group data, point groups around a cylindrical model corresponding to the portions on which noise influence is determined to be not less than a predetermined level, and fitting again the cylindrical model to the three-dimensional point group data from which point groups have been excluded. On the basis of the result of fitting the cylindrical model to the three-dimensional point group data in a case where the noise influence of each portion is less than a predetermined level, the information processing device generates a skeleton recognition result of the subject and outputs the skeleton recognition result.

Classes IPC  ?

10.

WATER ELECTROLYSIS SYSTEM AND WATER ELECTROLYSIS DEVICE

      
Numéro d'application JP2019048117
Numéro de publication 2021/117097
Statut Délivré - en vigueur
Date de dépôt 2019-12-09
Date de publication 2021-06-17
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Yonezawa, Yu
  • Nakashima, Yoshiyasu

Abrégé

Provided is a water electrolysis system comprising: a power generation device; a water electrolysis cell; and a power converter that converts the power supplied from the power generation device and generates an output voltage to be applied to the water electrolysis cell, wherein the power converter restricts fluctuation of the output voltage to a range equal to or greater than a threshold voltage so that the output voltage does not fall below the threshold voltage of the water electrolysis cell.

Classes IPC  ?

  • C25B 15/02 - Commande ou régulation des opérations
  • H02J 15/00 - Systèmes d'accumulation d'énergie électrique
  • H02J 3/38 - Dispositions pour l'alimentation en parallèle d'un seul réseau, par deux ou plusieurs générateurs, convertisseurs ou transformateurs

11.

RESPONSE PROCESSING PROGRAM, RESPONSE PROCESSING METHOD, INFORMATION PROCESSING DEVICE, AND RESPONSE PROCESSING SYSTEM

      
Numéro d'application JP2019048123
Numéro de publication 2021/117101
Statut Délivré - en vigueur
Date de dépôt 2019-12-09
Date de publication 2021-06-17
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Kawakami, Shinichi

Abrégé

An information processing device (101), in response to the start of a chat related to a business system, transmits as a chat conversation to an information processing terminal (102) a URL for accessing an information managing system (104) associated with the business system. The information processing terminal (102) uses the URL thus received to access the information managing system (104) and inputs to the information managing system (104) authorization information (ID/password) used for login to the business system. The information managing system (104) transmits to the information processing terminal (102) a one-time password corresponding to the authorization information thus input. The information processing terminal (102) transmits to the information processing device (101) the one-time password thus received. The information processing device (101) having received the one-time password from the information processing terminal (102) transmits the one-time password to a processing component group (PG) of an RPA corresponding to the business system.

Classes IPC  ?

  • G06F 21/31 - Authentification de l’utilisateur

12.

INFORMATION MANAGEMENT PROGRAM, INFORMATION MANAGEMENT METHOD, INFORMATION PROCESSING DEVICE, AND INFORMATION MANAGEMENT SYSTEM

      
Numéro d'application JP2019048126
Numéro de publication 2021/117102
Statut Délivré - en vigueur
Date de dépôt 2019-12-09
Date de publication 2021-06-17
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Hitomi, Yoshihiko

Abrégé

An information processing device (101) transmits, upon receipt of an URL generation request from an information processing terminal (102), an obfuscated URL generation request to a data storage device (103). The data storage device (103) generates an obfuscated URL upon receipt of the obfuscated URL generation request, and transmits the obfuscated URL to the information processing device (101). The information processing device (101) transmits to the information processing terminal (102) the received obfuscated URL as a chatbot conversation regarding an online procedure. When an access is made to the obfuscated URL from the information processing terminal (102), a web app (ap) corresponding to the obfuscated URL is activated, and a data storage operation is performed. Upon receipt through the web app (ap) of an input of personal information that is used for the online procedure, the data storage device (103) stores the personal information in a storage destination folder in a personal information database (150). The personal information database (150) is another memory unit different from a memory unit for storing chat logs.

Classes IPC  ?

  • G06F 11/34 - Enregistrement ou évaluation statistique de l'activité du calculateur, p.ex. des interruptions ou des opérations d'entrée–sortie
  • G06F 21/62 - Protection de l’accès à des données via une plate-forme, p.ex. par clés ou règles de contrôle de l’accès

13.

CONVERSATION CONTROL PROGRAM, CONVERSATION CONTROL METHOD, AND CONVERSATION CONTROL DEVICE

      
Numéro d'application JP2019048519
Numéro de publication 2021/117166
Statut Délivré - en vigueur
Date de dépôt 2019-12-11
Date de publication 2021-06-17
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Inoue, Naoya
  • Ishijima, Kiho
  • Ishiguro, Yuki
  • Higashide, Kazuya

Abrégé

In a conversation with a chatbot that provides an inquiry service, this conversation control device determines a script spoken by the chatbot and one or more speaking subject characters, depending on the speech content inputted by the user. A character has associated therewith a role, such as subordinate, manager, specialist or beginner, and an avatar, which is an image of the character. On the basis of the determined script and character, this conversation control device displays a single or multiple avatars and causes said avatars to speak.

Classes IPC  ?

  • G06F 13/00 - Interconnexion ou transfert d'information ou d'autres signaux entre mémoires, dispositifs d'entrée/sortie ou unités de traitement

14.

RESPONSE WORK SUPPORT PROGRAM, RESPONSE WORK SUPPORT DEVICE, AND RESPONSE WORK SUPPORT METHOD

      
Numéro d'application JP2019048535
Numéro de publication 2021/117169
Statut Délivré - en vigueur
Date de dépôt 2019-12-11
Date de publication 2021-06-17
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Nakamura, Kazuhiro
  • Shinozaki, Kazuto

Abrégé

The present invention provides an appropriate operator assistance during a response operation in accordance with voice data acquired. This response operation support program causes a computer to execute the processing of: acquiring voice data during a response operation; converting the acquired voice data to text data; identifying data to be retrieved included in the converted text data; and determining, in accordance with the ratio of the data to be retrieved to the text data, whether to perform a text search concerning the data to be retrieved or transition processing to handle the response operation.

Classes IPC  ?

  • H04M 3/42 - Systèmes fournissant des fonctions ou des services particuliers aux abonnés
  • G10L 15/00 - Reconnaissance de la parole
  • G10L 15/10 - Classement ou recherche de la parole utilisant des mesures de distance ou de distorsion entre la parole inconnue et les gabarits de référence
  • G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine 

15.

ANONYMIZATION DATA SELECTION DEVICE, ANONYMIZATION DATA SELECTION METHOD, AND PROGRAM

      
Numéro d'application JP2019048654
Numéro de publication 2021/117183
Statut Délivré - en vigueur
Date de dépôt 2019-12-12
Date de publication 2021-06-17
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Ogura, Takao
  • Yamaoka, Yuji

Abrégé

This anonymization data selection device comprises: a reception unit which receives a selection condition for selecting a level to be applied to personal data, from among a plurality of levels that relate to degrees of anonymization and that match a provision request for the personal data; an identification unit which refers to a storage unit that associates and stores a plurality of sources of personal data and anonymization levels permitted by each of the sources, and identifies, for each of a plurality of levels that match the selection condition, a source that permits the level; and an output unit which outputs information relating to the identification results obtained by the identification unit. This configuration increases the efficiency of processes for providing anonymized data to users.

Classes IPC  ?

  • G06F 21/62 - Protection de l’accès à des données via une plate-forme, p.ex. par clés ou règles de contrôle de l’accès
  • G06Q 50/10 - Services

16.

DATA GENERATION PROGRAM AND METHOD

      
Numéro d'application JP2019047909
Numéro de publication 2021/111630
Statut Délivré - en vigueur
Date de dépôt 2019-12-06
Date de publication 2021-06-10
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Suzuki, Shigeto

Abrégé

The present invention makes it possible to improve the prediction accuracy of a prediction model. Data padding using a Box-Cox transformation is carried out from the number n of waveform data included in each cluster to a number X at which VAE can be applied, after which data padding using VAE is carried out up to a desired number N per cluster. For each of a plurality of X values, a Gaussian process is applied to a change in the degree of similarity of a first waveform data group (obtained by generating waveform data up to X by means of the BOX-COX transformation and then generating waveform data up to N by means of VAE) and a second waveform data group (obtained by generating waveform data up to N only by means of VAE) with respect to the values of X, and an optimal X is retrieved and determined.

Classes IPC  ?

17.

CLASS GENERATION PROGRAM AND CLASS GENERATION METHOD

      
Numéro d'application JP2019046992
Numéro de publication 2021/111496
Statut Délivré - en vigueur
Date de dépôt 2019-12-02
Date de publication 2021-06-10
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Kurihara, Koji
  • Kawakami, Kentaro
  • Saito, Moriyuki

Abrégé

A class generation program for causing a computer to perform: a process S13 for referring to a storage unit 53 which stores first classes that each represent a first format relating to a vector register vn, second classes that each represent a second format relating to the vector register vn and inherit a first class, and lexical tokens in association with each other, and acquiring a first class, a second class, and a lexical token that are associated with each other; and a process S15 for generating, within the acquired first class, either a first code 77 for generating an instance of the acquired second class, or a second code 78 that overloads the acquired lexical token, depending on the acquired lexical token.

Classes IPC  ?

  • G06F 9/448 - Paradigmes d’exécution, p.ex. implémentation de paradigmes de programmation

18.

EVALUATION METHOD, EVALUATION PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019047358
Numéro de publication 2021/111540
Statut Délivré - en vigueur
Date de dépôt 2019-12-04
Date de publication 2021-06-10
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Shimizu, Toshiya

Abrégé

The present invention makes it possible to appropriately evaluate aggressiveness of training data against machine learning. On the basis of a set (1) of a plurality of pieces of training data (1a, 1b, ...) including pairs comprising input data and labels for machine learning, this information processing device (10) generates a plurality of partial sets (3a, 3b) including one or more of the pieces of training data. The information processing device (10) then carries out, for each of the partial sets (3a, 3b), machine learning using the pieces of training data included in the partial sets (3a, 3b) and thereby generates trained models (4a, 4b) for estimating the label from the input data. The information processing device (10) then carries out, for each of the partial sets (3a, 3b), an evaluation relating to aggressiveness of the pieces of training data included in the partial sets (3a, 3b) against machine learning, on the basis of the precision of estimation by the trained models (4a, 4b).

Classes IPC  ?

  • G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p.ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
  • G06N 20/00 - Apprentissage automatique

19.

LEARNING METHOD, LEARNING DEVICE, AND LEARNING PROGRAM

      
Numéro d'application JP2019046458
Numéro de publication 2021/106118
Statut Délivré - en vigueur
Date de dépôt 2019-11-27
Date de publication 2021-06-03
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Okajima, Seiji
  • Ukai, Takanori

Abrégé

Provided is a learning device (1) that executes a learning method regarding an embedding processing for giving vector representation to instances and ontologies in Knowledge Graph. The leaning device (1) acquires vector representation of a first instance on a Euclidean space (S13), acquires vector representation of a first ontology on a hyperbolic space (S13), generates a second ontology in which the acquired vector representation of the first ontology is changed on the basis of mapping of the first instance into the hyperbolic space (S20, S21), generates a second instance in which the acquired vector representation of the first instance is changed on the basis of mapping of the second ontology into the Euclidean space (S18, S19), and learns correspondence between the second ontology and the second instance for performing link prediction, to thereby realize vector representation with high accuracy for the respective instances and ontologies.

Classes IPC  ?

  • G06N 5/02 - Représentation de la connaissance

20.

BASE STATION DEVICE, TERMINAL DEVICE, AND RADIO COMMUNICATION SYSTEM

      
Numéro d'application JP2019046676
Numéro de publication 2021/106170
Statut Délivré - en vigueur
Date de dépôt 2019-11-28
Date de publication 2021-06-03
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Kawasaki, Yoshihiro

Abrégé

A base station device (200) includes: a radio communication unit (210) that receives a handover-destination notification from a terminal device (100) with which radio communication is being carried out, the handover-destination notification including identification information of a handover-destination base station device (300-1) determined by the terminal device (100) and identification information of a preamble to be transmitted to the handover-destination base station device (300-1) by the terminal device (100); and a transmission unit (230) that transmits an advance notification to the handover-destination base station device (300-1), the advance notification including the identification information of the terminal device (100) and the identification information of the preamble.

Classes IPC  ?

  • H04W 36/08 - Resélection d'un point d'accès
  • H04W 36/30 - La resélection étant déclenchée par des paramètres spécifiques par des données de mesure ou d’estimation de la qualité des liaisons
  • H04W 36/38 - Contrôle de resélection par un équipement fixe du réseau mobile
  • H04W 74/08 - Accès non planifié, p.ex. accès aléatoire, ALOHA ou accès multiple par détection de porteuse [CSMA Carrier Sense Multiple Access]

21.

MACHINE LEARNING METHOD, MACHINE LEARNING DEVICE, AND MACHINE LEARNING PROGRAM

      
Numéro d'application JP2019045670
Numéro de publication 2021/100183
Statut Délivré - en vigueur
Date de dépôt 2019-11-21
Date de publication 2021-05-27
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Liang, Jun

Abrégé

The present invention improves the accuracy of machine learning using a document containing a plurality of words. From among words contained in a document (15), shared words used in both domains (13, 14) are identified. The weights (16) between the domains (13, 14) are calculated on the basis of two or more words other than the shared words among the words contained in the document (15). Feature information (15b) to be assigned to the shared words is calculated by using feature information (13b) calculated by using a model (13a) corresponding to the domain (13), feature information (14b) calculated by using a model (14a) corresponding to the domain (14), and the weights (16). A model (17) is trained by using the feature information (15b) and teacher labels (15a) assigned to the shared words contained in the document (15).

Classes IPC  ?

  • G06F 40/216 - Analyse syntaxique utilisant des méthodes statistiques

22.

OUTPUT METHOD, OUTPUT PROGRAM, AND OUTPUT DEVICE

      
Numéro d'application JP2019044770
Numéro de publication 2021/095212
Statut Délivré - en vigueur
Date de dépôt 2019-11-14
Date de publication 2021-05-20
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Yamada, Moyuru

Abrégé

An output device (100) generates, by using a generation model (101), a correction vector for correcting a vector based on first modal information, the correction vector being generated on the basis of a correlation between a vector based on the first modal information and a vector based on second modal information. The output device (100) couples, by using a coupling model (102), the generated correction vector to the vector based on the first modal information. The output device (100) compresses, by using a compression model (103), the vector based on the first modal information after coupling, in accordance with a prescribed rule. The output device (100) carries out, by using a normalization model (104), a normalization process on the vector based on the first modal information after compression. The output device (100) outputs the vector obtained through the normalization process.

Classes IPC  ?

23.

DISPLAY CONTROL PROGRAM, DISPLAY CONTROL METHOD, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019044963
Numéro de publication 2021/095262
Statut Délivré - en vigueur
Date de dépôt 2019-11-15
Date de publication 2021-05-20
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Maekawa, Takahiro
  • Muraoka, Hiroaki

Abrégé

A management screen image (MS) is one example of a management screen image that is displayed when performing FAQ maintenance. In the management screen image (MS), input words that have resulted in zero hits or no conforming answers are displayed in list form in decreasing order of the number of occurrences (number of zero-hit cases and number of no-conforming-answer cases). Furthermore, in the management screen MS, only input words in positions 1, 2, 3, 7 that were assessed to be queries, among the input words that have resulted in zero hits or no conforming answers, are highlighted. Therefore, a manager of a FAQ system can easily identify which input words are questions (queries) and which input words are free talk (other than queries), among the input words that have resulted in zero hits or no conforming answers, and more easily determines FAQs which should be preferentially maintained.

Classes IPC  ?

  • G06F 16/90 - Recherche d’informations; Structures de bases de données à cet effet; Structures de systèmes de fichiers à cet effet - Détails des fonctions des bases de données indépendantes des types de données cherchés
  • G06F 16/9038 - Présentation des résultats des requêtes
  • G06F 16/906 - Groupement; Classement

24.

CONTROL METHOD, CONTROL PROGRAM, INFORMATION PROCESSING DEVICE, AND CONTROL SYSTEM

      
Numéro d'application JP2019044969
Numéro de publication 2021/095266
Statut Délivré - en vigueur
Date de dépôt 2019-11-15
Date de publication 2021-05-20
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Fujimoto, Shingo
  • Morinaga, Masanobu

Abrégé

A server C (103) has a smart contract function for linking a cryptoasset chain (A) handling Y-coin and a settlement token chain (B) handling D-coin, and when a request from a user to issue D-coin occurs, the server C refers to a credit information database (121) of the user, determines an allowable payment amount of D-coin, and transmits a token indicating the allowable payment amount from a user account (201) in the cryptoasset chain (A) to a credit account (212) in the settlement token chain (B). After the token is received the settlement token chain (B) is able to use the token to execute a user transaction offline with respect to the outside such as the server C (103). When a settlement is carried out the server C (103) collects, from the user account (201) in the cryptoasset chain (A), Y-coin having a value corresponding to the D-coin paid out in the transaction by the user in the settlement token chain (B).

Classes IPC  ?

  • G06Q 20/06 - Circuits privés de paiement, p.ex. impliquant de la monnaie électronique utilisée uniquement entre les participants à un programme commun de paiement

25.

OUTPUT METHOD, OUTPUT PROGRAM, AND OUTPUT DEVICE

      
Numéro d'application JP2019044769
Numéro de publication 2021/095211
Statut Délivré - en vigueur
Date de dépôt 2019-11-14
Date de publication 2021-05-20
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Yamada, Moyuru

Abrégé

An output device (100) corrects a vector based on information about a first modal, the correction being made on the basis of the correlation between the vector based on the information about the first modal and a vector based on information about a second modal. The output device (100) corrects the vector based on the information about the second modal, the correction being made on the basis of the correlation between the vector based on the information about the first modal and the vector based on the information about the second modal. The output device (100) generates a first vector on the basis of the correlation of two vectors of different types obtained from the vector based on the information about the first modal after correction. The output device (100) generates a second vector on the basis of the correlation of two vectors of different types obtained from the vector based on the information about second modal after correction. The output device (100) generates and outputs a third vector on the basis of the correlation of two vectors of different types obtained from a combination vector that includes a prescribed vector, the first vector, and the second vector.

Classes IPC  ?

26.

LEARNING METHOD, LEARNING PROGRAM, AND LEARNING DEVICE

      
Numéro d'application JP2019044771
Numéro de publication 2021/095213
Statut Délivré - en vigueur
Date de dépôt 2019-11-14
Date de publication 2021-05-20
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Kamata, Yuichi
  • Nakagawa, Akira

Abrégé

This learning device (100) extracts a feature quantity from first modal information using an extraction model (111). The learning device (100) converts the extracted feature quantity using a conversion model (112) on the basis of a plurality of parameters and thereby acquires a new feature quantity. The learning device (100) inputs the acquired new feature quantity to a first processing model (121) and thereby acquires a first output value. The learning device (100) inputs other feature quantities extracted from second modal information to a second processing model (122) and thereby acquires a second output value. The learning device (100) inputs the acquired first and second output values to a third processing model (123) and thereby acquires a third output value. The learning device (100) updates the plurality of parameters on the basis of the acquired third output value.

Classes IPC  ?

27.

SERVICE LINKAGE PROGRAM, SERVICE LINKAGE METHOD, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019044964
Numéro de publication 2021/095263
Statut Délivré - en vigueur
Date de dépôt 2019-11-15
Date de publication 2021-05-20
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Iijima, Osuke
  • Matsuyama, Hiroshi
  • Narukawa, Shinji

Abrégé

In the present invention, when input information corresponding to a web service is received, a service linkage function (sc) identifies which input item of the web service the input information corresponds to, and uses a processing component group (PG) of a RPA corresponding to the web service to execute an input information input process for the identified input item. When a processing result of the web service is received the service linkage function (sc) determines whether a reply to an information processing terminal (102) is to be transmitted by a chatbot conversation means or by another means (an email service or the like) different from the chatbot conversation means. For example, when the type of a web service "balance inquiry" is a "synchronization process" and the progress status is "receive processing result," the service linkage function determines that transmission will be carries out by the chatbot conversation means. In this case the service linkage function (sc) transmits the processing result of the web service "balance inquiry" by the chatbot conversation means.

Classes IPC  ?

  • G06F 13/00 - Interconnexion ou transfert d'information ou d'autres signaux entre mémoires, dispositifs d'entrée/sortie ou unités de traitement

28.

COMMUNICATION METHOD AND APPARATUS FOR VEHICLE-TO-EVERYTHING SERVICE, AND COMMUNICATION SYSTEM

      
Numéro d'application CN2019115995
Numéro de publication 2021/087804
Statut Délivré - en vigueur
Date de dépôt 2019-11-06
Date de publication 2021-05-14
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Li, Guorong
  • Jia, Meiyi
  • Ji, Pengyu
  • Wang, Xin

Abrégé

The present application provides a communication method and apparatus for a Vehicle-to-Everything (V2X) service, and a communication system. The apparatus comprises a first communication portion configured to detect that a Radio Link Failure (RLF) occurs in a sidelink, and inform, by means of a Sidelink UE Information message, a network device that RLFs occur in all sidelinks of a terminal device.

Classes IPC  ?

  • H04W 4/40 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p.ex. communication véhicule-piétons

29.

METHOD AND DEVICE FOR RESERVING SIDE LINK RESOURCES

      
Numéro d'application CN2019116303
Numéro de publication 2021/087874
Statut Délivré - en vigueur
Date de dépôt 2019-11-07
Date de publication 2021-05-14
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Ji, Pengyu
  • Zhang, Jian
  • Zhang, Lei
  • Wang, Xin

Abrégé

The embodiments of the present application provide a method and device for reserving side link resources. The method comprises: a first terminal device receives first indication information indicating a second time frequency resource and sent by a second terminal device, wherein the second time frequency resource is at least partially overlapped with a first time frequency resource reserved or allocated by the first terminal device; and the first terminal device sends to a network device second indication information that indicates at least a part of the first time frequency resource is preempted.

Classes IPC  ?

30.

METHOD AND APPARATUS FOR SELECTING SIDELINK RESOURCES

      
Numéro d'application CN2019116320
Numéro de publication 2021/087881
Statut Délivré - en vigueur
Date de dépôt 2019-11-07
Date de publication 2021-05-14
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Ji, Pengyu
  • Zhang, Jian
  • Li, Guorong
  • Zhang, Lei
  • Wang, Xin

Abrégé

Provided in the embodiments of the present application are a method and apparatus for selecting sidelink resources. The method comprises: a terminal device selects one or more first sidelink resources for sidelink data, and according to the one or more first sidelink resources, selects a second sidelink resource for single subchannel transmission; or, selects a second sidelink resource for the single subchannel transmission, and selects one or more first sidelink resources for the sidelink data according to the second sidelink resource, wherein the single subchannel transmission at least indicates reservation information of the foremost starting resource in a time domain among the one or more first sidelink resources.

Classes IPC  ?

31.

WIRELESS COMMUNICATION METHOD, APPARATUS, AND SYSTEM

      
Numéro d'application CN2019116418
Numéro de publication 2021/087922
Statut Délivré - en vigueur
Date de dépôt 2019-11-07
Date de publication 2021-05-14
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Chen, Zhe
  • Zhang, Lei
  • Song, Lei
  • Wang, Xin

Abrégé

The present application provides a wireless communication method and apparatus, and a communication system. The wireless communication method comprises: a terminal device receives configuration information, wherein the configuration information indicates that one or more SPS configurations are associated with an SPS configuration set, and the one or more SPS configurations correspond to the same priority or the same HARQ-ACK codebook.

Classes IPC  ?

32.

METHOD AND APPARATUS FOR SENDING UPLINK TRANSMISSION, AND COMMUNICATION SYSTEM

      
Numéro d'application CN2019116426
Numéro de publication 2021/087930
Statut Délivré - en vigueur
Date de dépôt 2019-11-07
Date de publication 2021-05-14
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Shimomura, Tsuyoshi
  • Chen, Zhe
  • Zhang, Lei

Abrégé

Embodiments of the present application provide a method and apparatus for sending an uplink transmission, and a system. The method for sending an uplink transmission comprises: a terminal device receives second indication information, the second indication information being used for indicating a duration of a second signal corresponding to a second uplink transmission; and the terminal device adjusts the duration of the second signal corresponding to the second uplink transmission, and sends the adjusted second uplink transmission.

Classes IPC  ?

  • H04W 56/00 - Dispositions de synchronisation
  • H04W 72/04 - Affectation de ressources sans fil
  • H04L 27/26 - Systèmes utilisant des codes à fréquences multiples
  • H04L 5/00 - Dispositions destinées à permettre l'usage multiple de la voie de transmission
  • H04J 3/06 - Dispositions de synchronisation

33.

TRANSMISSION PARAMETER DETERMINATION METHOD AND APPARATUS

      
Numéro d'application CN2019116366
Numéro de publication 2021/087895
Statut Délivré - en vigueur
Date de dépôt 2019-11-07
Date de publication 2021-05-14
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Song, Lei
  • Chen, Zhe
  • Zhang, Lei

Abrégé

A transmission parameter determination method and apparatus. The apparatus comprises: a first receiving unit configured to receive at least two TCI state-related parameters or signaling configured or indicated by a network device; and a first determining unit configured to determine a transmission parameter of at least one transmission timing of a transmission block according to a related parameter or signaling comprising a TCI state mapping pattern, and a parameter or signaling indicating that DCI signaling does not comprise a TCI domain, or a parameter or signaling indicating that the DCI signaling comprises the TCI domain, in the at least two TCI state-related parameters or signaling, or to determine a transmission parameter of at least one transmission timing of a transmission block according to a related parameter or signaling comprising a TCI state mapping pattern and DCI signaling that does not comprise a TCI domain in the at least two TCI state-related parameters or signaling, or to determine a transmission parameter of at least one transmission timing of a transmission block according to the TCI state pattern determined by a terminal device and a parameter or signaling indicating that the DCI signaling comprises a TCI domain in the at least two TCI state-related parameters.

Classes IPC  ?

34.

PROCESSING METHOD AND APPARATUS WHEN FAILING TO ACQUIRE ESSENTIAL SYSTEM INFORMATION

      
Numéro d'application CN2019116410
Numéro de publication 2021/087916
Statut Délivré - en vigueur
Date de dépôt 2019-11-07
Date de publication 2021-05-14
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Jia, Meiyi
  • Zhang, Lei
  • Wang, Xin

Abrégé

A processing method and apparatus when failing to acquire essential system information. The method is applied to a terminal device side. The method comprises: in the process of performing cell reselection evaluation by a terminal device, or when the terminal device applies a system information acquisition procedure, if the terminal device fails to acquire essential system information in at least one cell on an unlicensed frequency, the terminal device considers that all the cells on the frequency are forbidden within a predefined first time period, or within the predefined first time period, the frequency is a lowest priority frequency.

Classes IPC  ?

  • H04W 48/02 - Restriction d'accès effectuée dans des conditions spécifiques
  • H04W 52/02 - Dispositions d'économie de puissance

35.

UPLINK SIGNAL SENDING AND RECEIVING METHOD AND APPARATUS

      
Numéro d'application CN2019116422
Numéro de publication 2021/087926
Statut Délivré - en vigueur
Date de dépôt 2019-11-07
Date de publication 2021-05-14
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Li, Guorong
  • Zhang, Lei

Abrégé

Embodiments of the present application provide an uplink signal sending and receiving method and apparatus. The method comprises: a terminal device instructs, from an MAC layer, a physical layer to send a first uplink signal on a first time-frequency resource; determine, on the MAC layer, that a second time-frequency resource and the first time-frequency resource at least partially overlap on a time domain or a time-frequency domain; and compare, on the MAC layer, a metric of the first time-frequency resource with a metric of the second time-frequency resource.

Classes IPC  ?

  • H04L 29/08 - Procédure de commande de la transmission, p.ex. procédure de commande du niveau de la liaison
  • H04W 72/10 - Affectation de ressources sans fil sur la base de critères de priorité
  • H04W 72/06 - Affectation de ressources sans fil sur la base de critères de classement des ressources sans fil

36.

RANDOM ACCESS METHOD AND APPARATUS, AND COMMUNICATION SYSTEM

      
Numéro d'application CN2019116423
Numéro de publication 2021/087927
Statut Délivré - en vigueur
Date de dépôt 2019-11-07
Date de publication 2021-05-14
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Lu, Yang
  • Li, Guorong
  • Jia, Meiyi

Abrégé

Provided are a random access method and apparatus, and a communication system. The apparatus comprises a first processing unit, wherein the first processing unit determines a random access type according to configuration information of a bandwidth part (BWP) selected by a terminal device and used for random access, and a downlink reference signal received power measured by the terminal device, selects a random access resource, and sends an initial random access message on the random access resource.

Classes IPC  ?

  • H04W 74/00 - Accès au canal sans fil, p.ex. accès planifié, accès aléatoire
  • H04W 74/08 - Accès non planifié, p.ex. accès aléatoire, ALOHA ou accès multiple par détection de porteuse [CSMA Carrier Sense Multiple Access]
  • H04W 72/04 - Affectation de ressources sans fil

37.

METHOD AND DEVICE FOR POWER DISTRIBUTION

      
Numéro d'application CN2019116614
Numéro de publication 2021/087955
Statut Délivré - en vigueur
Date de dépôt 2019-11-08
Date de publication 2021-05-14
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Zhang, Jian
  • Ji, Pengyu
  • Li, Guorong
  • Zhang, Lei
  • Wang, Xin

Abrégé

Provided in the embodiments of the present application are a method and device for power distribution. The method comprises: a terminal device determines whether a sidelink transmission takes precedence over an uplink transmission, where the sidelink transmission comprises the transmission of sidelink information carried by a second uplink physical channel and/or the transmission of a sidelink physical channel/signal, and the uplink transmission comprises the transmission of uplink information carried by the second uplink physical channel and/or the transmission of a first uplink physical channel/signal; and, insofar as the sidelink transmission takes precedence over the uplink transmission, power is distributed with priority to the second uplink physical channel and/or the sidelink physical channel/signal.

Classes IPC  ?

  • H04W 72/14 - Planification du trafic sans fil utilisant un canal d'autorisation

38.

DESIGN ASSISTANCE PROGRAM, DESIGN ASSISTANCE METHOD, AND DESIGN ASSISTANCE DEVICE

      
Numéro d'application JP2019043763
Numéro de publication 2021/090455
Statut Délivré - en vigueur
Date de dépôt 2019-11-07
Date de publication 2021-05-14
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Sakai, Hidehisa
  • Hamazoe, Kazuhiko
  • Ichikawa, Yoshikazu

Abrégé

A design assistance program of an embodiment causes a computer to execute an acquisition process, a correction process, and an output process. In the acquisition process, deformation information indicating the displacement and deformation of each part is acquired by structural analysis based on the condition setting including at least a load of each part to be combined with a product. In the correction process, tolerance information including the dimensional tolerance of each part is corrected on the basis of the acquired deformation information of each part. In the output process, the corrected tolerance information is output.

Classes IPC  ?

  • G06F 30/20 - Optimisation, vérification ou simulation de l’objet conçu
  • G06F 30/23 - Optimisation, vérification ou simulation de l’objet conçu utilisant les méthodes des éléments finis [MEF] ou les méthodes à différences finies [MDF]
  • G06F 30/10 - CAO géométrique

39.

LEARNING METHOD, LEARNING PROGRAM, AND LEARNING DEVICE

      
Numéro d'application JP2019042225
Numéro de publication 2021/084590
Statut Délivré - en vigueur
Date de dépôt 2019-10-28
Date de publication 2021-05-06
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Yamamoto, Takahisa

Abrégé

A learning device (100) acquires an image (101) associated with a label that represents the impression of the image (101). The learning device (100) extracts, from the acquired image (101), a first feature vector (111) relating to the entire image (101). The learning device (100) extracts, from the acquired image (101), a second feature vector (112) relating to an object. The learning device (100) combines the extracted first feature vector (111) with the extracted second feature vector (112) to generate a third feature vector (113). The learning device (100) learns a model on the basis of learning data in which the label representing the impression of the image (101) is associated with the generated third feature vector (113).

Classes IPC  ?

40.

DEGRADATION SUPPRESSION PROGRAM, DEGRADATION SUPPRESSION METHOD, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019042395
Numéro de publication 2021/084623
Statut Délivré - en vigueur
Date de dépôt 2019-10-29
Date de publication 2021-05-06
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Katoh, Takashi
  • Uemura, Kento
  • Yasutomi, Suguru
  • Hayase, Tomohiro
  • Umeda, Yuhei

Abrégé

In the present invention, a degradation suppression device generates a plurality of learning models that have different properties, on the basis of respective learning data pieces which are included in a first learning data set and which are assigned with labels that indicate solution information. If the estimation accuracy of label estimation with respect to input data to be estimated falls below a predetermined standard, the label estimation using any model among the plurality of learning models, the degradation suppression device generates a second learning data set that includes a plurality of learning data pieces obtained by utilizing estimation results using the learning models which have an estimation accuracy of the predetermined standard or greater. The degradation suppression device uses the second learning data set to execute re-learning of the learning models which have an estimation accuracy below the predetermined standard.

Classes IPC  ?

41.

RETRIEVAL METHOD, RETRIEVAL PROGRAM, AND RETRIEVAL DEVICE

      
Numéro d'application JP2019042950
Numéro de publication 2021/084723
Statut Délivré - en vigueur
Date de dépôt 2019-10-31
Date de publication 2021-05-06
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Katae, Nobuyuki

Abrégé

In the present invention, a retrieval device identifies the chemical structure of a compound indicated by a compound name included in an input document. In addition, for each substructure of the chemical structure, the retrieval device counts the number of substructures included in the input document. Furthermore, on the basis of the substructure and the number of substructures, the retrieval device generates a substructure vector of the input document. Furthermore, on the basis of a comparison of the substructure vector and respective substructure vectors of a plurality of documents in which a stored compound name is included, the retrieval device outputs documents similar to the input document from the plurality of documents.

Classes IPC  ?

  • G06F 16/383 - Recherche caractérisée par l’utilisation de métadonnées, p.ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
  • G06F 16/93 - Systèmes de gestion de documents

42.

VOICE PLAYBACK PROGRAM, VOICE PLAYBACK METHOD, AND VOICE PLAYBACK SYSTEM

      
Numéro d'application JP2019042944
Numéro de publication 2021/084718
Statut Délivré - en vigueur
Date de dépôt 2019-10-31
Date de publication 2021-05-06
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Harada, Naoki

Abrégé

In the present invention, a sample playback screen (1400) is an operation screen on which a plurality of performers, "Hanako Yamada, Taro Yamada, Toru Fuji", who correspond to a character "Yamashita" in a scenario (S2), are displayed such that the performer performing the character "Yamashita" can be discerned. Each time a performer is designated by tapping a sample playback button (1401, 1402, 1403) on the sample playback screen (1400), voice data that was recorded by the designated performer is played back for the same dialogue of the character "Yamashita". For example, if the sample playback button (1401) is tapped, the performer "Hanako Yamada" is designated, and the voice data that was recorded by the performer "Hanako Yamada" is played back for the sample playback dialogue of the character "Yamashita". Due to this configuration, when determining a performer for the character "Yamashita", a user can listen to a voice sample of a designated performer by simply designating one of the plurality of performers "Hanako Yamada, Taro Yamada, Toru Fuji".

Classes IPC  ?

43.

AUDIO PLAYBACK PROGRAM, AUDIO PLAYBACK METHOD, AND AUDIO PLAYBACK SYSTEM

      
Numéro d'application JP2019042945
Numéro de publication 2021/084719
Statut Délivré - en vigueur
Date de dépôt 2019-10-31
Date de publication 2021-05-06
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Harada, Naoki

Abrégé

A scenario playback screen (1300) distinguishably displays a state in which the performer for a character "Yamashita" in a scenario is selected as "Hanako Yamada" and the performer for a character "Suzuki" is selected as "Taro Yamada." When a user taps a continuous playback button on the scenario playback screen (1300), the audio data, that is registered by each performer in accordance with the scenario, is continuously played back. When the user taps a button (1303) corresponding to the performer "Hanako Yamada" for the character "Yamashita" if the spoken lines of the performer "Hanako Yamada" are hard to hear during audio playback, a volume control screen (2100) is displayed. The user may move a slider (2102) in a volume control bar (2101) to control the playback volume so that the spoken lines of the performer "Hanako Yamada" are easy to hear.

Classes IPC  ?

44.

VOICE RECORDING PROGRAM, VOICE RECORDING METHOD, AND VOICE RECORDING SYSTEM

      
Numéro d'application JP2019042946
Numéro de publication 2021/084720
Statut Délivré - en vigueur
Date de dépôt 2019-10-31
Date de publication 2021-05-06
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Harada, Naoki

Abrégé

A dialogue recording screen (2200) is an operation screen distinguishably displaying dialogues of characters "Yamashita" and "Suzuki" in a scenario. The dialogue recording screen (2200) highlights dialogues (Sr1) of the character "Yamashita" to be recorded by a user so that the dialogues can be discriminated from dialogues (Sr2) of the other character "Suzuki". Moreover, unrecorded dialogues (Sr1) among the dialogues (Sr1) of the character "Yamashita" to be recorded selected by the user are highlighted. Consequently, the user becomes able to more easily select the dialogues (Sr1) of the character "Yamashita" to be recorded among a plurality of characters. Moreover, the user becomes able to more easily select the unrecorded dialogues (Sr1) among the dialogues (Sr1) of the character "Yamashita".

Classes IPC  ?

45.

VOICE REPRODUCTION PROGRAM, VOICE REPRODUCTION METHOD, AND VOICE REPRODUCTION SYSTEM

      
Numéro d'application JP2019042947
Numéro de publication 2021/084721
Statut Délivré - en vigueur
Date de dépôt 2019-10-31
Date de publication 2021-05-06
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Harada, Naoki

Abrégé

A sample reproduction screen (1400) displays a plurality of performers "Hanako Yamada, Taro Yamada, Yoko Yamada" corresponding to a character "Taro" in a scenario identifiably as performers of the character "Taro". Every time a sample reproduction button for some performer is tapped on the sample reproduction screen (1400), regarding the same dialogue of the character "Taro", voice data registered by the designated performer is reproduced. At the time of this reproduction, a volume adjustment screen (2100) for adjusting the reproduction volume of the voice data of the designated performer by operating a volume adjustment button is displayed, thereby enabling volume adjustment by user operation such that the reproduced volumes of the same dialogue of different performers become the same. Moreover, in automatic volume adjustment, volume adjustment, by which the difference between the reproduced volumes of the same dialogue of the respective performers falls within a predetermined difference, is performed.

Classes IPC  ?

46.

DATA PRE-PROCESSING METHOD, DATA PRE-PROCESSING DEVICE, DATA PRE-PROCESSING PROGRAM

      
Numéro d'application JP2019041466
Numéro de publication 2021/079425
Statut Délivré - en vigueur
Date de dépôt 2019-10-23
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Okawa, Yoshihiro
  • Ide, Masaru

Abrégé

The present invention suppresses the retraining of a model in response to changes in data trends. Pre-processing (13) that corresponds to a parameter (13a) is performed on measurement data (15) to generate training data (17). The training data (17) is used to train a model (14). The pre-processing (13) is performed on measurement data (16) to generate input data (18). The input data (18) is inputted into the model (14) to generate prediction results (19), and a prediction accuracy is calculated from the prediction results (19) and a supervision label (16a) that is associated with measurement data (16). When the prediction accuracy is below a threshold value, the parameter (13a) for the pre-processing (13) is modified on the basis of a comparison between the training data (17) and the input data (18) generated from measurement data (16).

Classes IPC  ?

47.

DETECTION METHOD, DETECTION PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019041547
Numéro de publication 2021/079436
Statut Délivré - en vigueur
Date de dépôt 2019-10-23
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Okawa, Yoshihiro

Abrégé

When data has been input into a first detection model of a plurality of detection models that have learned a determination boundary for classifying data feature space into a plurality of application regions, this information processing device acquires a first output result indicating in which one of the plurality of application regions the input data is located, on the basis of a plurality of sets of training data corresponding to a plurality of classes. When data has been input into a second detection model of the plurality of detection models, the information processing device acquires a second output result indicating in which one of the plurality of application regions the input data is located. On the basis of the first output result and the second output result, the information processing device detects data that causes degradation of the accuracy of the output result of a learned model as a result of changes in streamed data over time.

Classes IPC  ?

48.

DISPLAY METHOD, DISPLAY PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019041583
Numéro de publication 2021/079444
Statut Délivré - en vigueur
Date de dépôt 2019-10-23
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Ishida, Tsutomu

Abrégé

This information processing device acquires operation data that is input into a trained model. On the basis of the acquired operation data, the information processing device calculates a measure representing the magnitude of change in the output result of the model due to a change in the pattern of the operation data over time. The information processing device determines, on the basis of a comparison between the calculated measure and a preset threshold value, whether or not a concept drift that needs to be addressed by a user has occurred. If it is determined that a concept drift that needs to be addressed by a user has occurred, the information processing device causes a display screen to display information indicating that a concept drift that needs to be addressed by a user has occurred.

Classes IPC  ?

49.

DISPLAY METHOD, DISPLAY PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019041584
Numéro de publication 2021/079445
Statut Délivré - en vigueur
Date de dépôt 2019-10-23
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Ishida, Tsutomu

Abrégé

This information processing device acquires first operation data to be input to a model. The information processing device calculates, on the basis of the acquired first operation data, indexes that indicate the magnitude of change in a result output from the model, which is caused by a temporal change in the trend of the operation data. The information processing device specifies a change in an index in a time series from the calculated indexes. The information processing device determines whether concept drift, requiring handling by a user, occurs on the basis of a comparison between the specified change in the index and a preset threshold. When the concept drift, requiring handling by the user, is determined to have occurred, the information processing device displays, on a display screen, information that indicates the concept drift, requiring handling by the user, has occurred.

Classes IPC  ?

50.

DETECTION METHOD, DETECTION PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019041691
Numéro de publication 2021/079460
Statut Délivré - en vigueur
Date de dépôt 2019-10-24
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Kingetsu, Hiroaki

Abrégé

This information processing device performs learning to obtain an operation model being monitored, by using a plurality of training data items respectively corresponding to a plurality of correct labels. The information processing device learns, on the basis of an output result of the operation model, a determination boundary that divides a data characteristic space into two application regions, and creates an inspector model for calculating respective distances from the determination boundary to inputted data items. The information processing device inputs the training data items to the inspector model, and calculates first distances from the determination boundary to the training data items. The information processing device inputs operation data items to the inspector model, and calculates second distances from the determination boundary to the operation data items. When a difference between the first distances and the second distances is not less than a preset threshold value, the information processing device detects a change of the output result of the operation model caused by a change, over time, of a trend of the operation data items.

Classes IPC  ?

51.

DETERIORATION DETECTION METHOD, DETERIORATION DETECTION PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019041792
Numéro de publication 2021/079478
Statut Délivré - en vigueur
Date de dépôt 2019-10-24
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Yokota, Yasuto

Abrégé

An accuracy deterioration detection device acquires a first output result at a time when input data is input to a learned model, and acquires a second output result at a time when the input data is input to a detection model, which is for detecting performance deterioration of the learned model. The accuracy deterioration detection device calculates a first coincidence result acquired by comparing the first output result and the second output result in a first period. The accuracy deterioration detection device calculates a second coincidence result acquired by comparing the first output result and the second output result in a second period different from the first period. The accuracy deterioration detection device uses the first coincidence result and the second coincidence result to output a change in accuracy deterioration of the learned model.

Classes IPC  ?

52.

ASSESSMENT METHOD, ASSESSMENT PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019041793
Numéro de publication 2021/079479
Statut Délivré - en vigueur
Date de dépôt 2019-10-24
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Yokota, Yasuto

Abrégé

This introduction assessment device acquires a first output result when other environment data created in another environment different from a learning environment is inputted to a learning model. The introduction assessment device acquires a second output result when the other environment data is inputted to a detection model that detects a decrease in the correct-answer rate of the learning model when the learning model is diverted for use in the other environment. The introduction assessment device assesses, on the basis of the first and second output results, whether the learning model should be retrained when the learning model is diverted for use in the other environment.

Classes IPC  ?

53.

DEGRADATION DETECTION METHOD, DEGRADATION DETECTION PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019041803
Numéro de publication 2021/079481
Statut Délivré - en vigueur
Date de dépôt 2019-10-24
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Yokota, Yasuto

Abrégé

This accuracy degradation detection device acquires a first output result obtained when data has been inputted to a learned model. Then, the accuracy degradation detection device acquires a second output result obtained when data has been inputted to a detection model having a narrow model application range for the learned model. Further, the accuracy degradation detection device detects, on the basis of the first output result and the second output result, accuracy degradation of the learned model caused by a change, over time, of a data trend.

Classes IPC  ?

54.

CREATION METHOD, CREATION PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019041806
Numéro de publication 2021/079484
Statut Délivré - en vigueur
Date de dépôt 2019-10-24
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Kobayashi, Kenichi
  • Okawa, Yoshihiro
  • Yokota, Yasuto
  • Nakazawa, Katsuhito

Abrégé

In a creation method according to an embodiment of the present invention, a computer executes an acquisition process, a process for calculating a determination score, a process for calculating a difference, and a creation process. The acquisition process acquires a learning model to serve as a target for detecting an accuracy change. The process for calculating a determination score calculates a determination score, for the acquired learning model, related to determination of sorting class when data has been input. The process for calculating a difference calculates the difference between the determination scores for a first sorting class, for which the value of the calculated determination score is highest, and a second sorting class, for which the value of the calculated determination score is the next highest after the first sorting class. The creation process creates a detection model that determines that a sorting class is undecided when the difference between the calculated determination scores is at or below a preset threshold.

Classes IPC  ?

55.

CREATION METHOD, CREATION PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019041574
Numéro de publication 2021/079440
Statut Délivré - en vigueur
Date de dépôt 2019-10-23
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Okawa, Yoshihiro

Abrégé

This information processing device acquires a first training data set, which was used when learning a model that is subjected to accuracy change detection. The information processing device learns a first detection model using the first training data set, and uses the learned first detection model to calculate a score for each of the plurality of items of training data included in the first training data set. On the basis of the score calculation results, the information processing device creates a second training data set by removing some training data from the first training data set, and learns a second detection model using the second training data set.

Classes IPC  ?

56.

DETECTION METHOD, DETECTION PROGRAM, AND DETECTION DEVICE

      
Numéro d'application JP2019041580
Numéro de publication 2021/079441
Statut Délivré - en vigueur
Date de dépôt 2019-10-23
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Yokota, Yasuto

Abrégé

This detection device specifies a region, from within an inputted image, that has contributed to the calculation of a score of a first class among the scores for each class obtained by inputting an input image to a deep learning model. The detection device also generates a mask image (202b) in which regions in the inputted image other than the specified region are masked. Furthermore, the detection device acquires a score obtained by inputting the mask image (202b) to the deep learning model.

Classes IPC  ?

  • G06T 7/00 - Analyse d'image
  • G06N 3/02 - Systèmes de calculateurs basés sur des modèles biologiques utilisant des modèles de réseaux neuronaux

57.

ESTIMATION PROGRAM, ESTIMATION METHOD, INFORMATION PROCESSING DEVICE, RELEARNING PROGRAM, AND RELEARNING METHOD

      
Numéro d'application JP2019041581
Numéro de publication 2021/079442
Statut Délivré - en vigueur
Date de dépôt 2019-10-23
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Umeda, Yuhei
  • Katoh, Takashi
  • Ike, Yuichi
  • Kajitani, Mari
  • Takenouchi, Masatoshi

Abrégé

This information processing device identifies a representative point for each cluster which corresponds to each label of an estimation target and is generated by the clustering in a characteristic space of a learning data set that includes a plurality of learning data used in the learning of a learning model that estimates the label corresponding to the input data. The information processing device sets the boundary of each cluster, which is the result of the clustering in the characteristic space of a plurality of input data included in an input data set, so as to satisfy the condition that the number of clusters and the number of representative points is the same. The information processing device acquires the label estimation results for the plurality of input data on the basis of the correlation between the input data set cluster determined on the basis of the boundary and the learning data set cluster, and estimates, on the basis of the estimation results, the determination accuracy of the label determined using the learning model assigned to the plurality of input data.

Classes IPC  ?

58.

DETECTION METHOD, DETECTION PROGRAM, AND DETECTION DEVICE

      
Numéro d'application JP2019041582
Numéro de publication 2021/079443
Statut Délivré - en vigueur
Date de dépôt 2019-10-23
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Ishida, Tsutomu

Abrégé

This detection device generates a plurality of second models by changing prescribed parameters of a trained first model. The detection device detects the difference between: the distribution of the outputs obtained from the second models when the learning data that was used to train the first model is input to the second models; and the distribution of the outputs obtained from the second models when prescribed data different from the learning data is input to the second models.

Classes IPC  ?

59.

DISPLAY METHOD, DISPLAY PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019041585
Numéro de publication 2021/079446
Statut Délivré - en vigueur
Date de dépôt 2019-10-23
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Ishida, Tsutomu

Abrégé

This information processing device obtains a plurality of second models in which predetermined parameters of a first model for calculating scores for classifying data items into a plurality of classes have been changed. The information processing device causes a screen to display a total value, for each fixed period, of indexes each indicating the magnitude of a difference between a distribution of scores obtained by inputs, to the second models, of learning data items that are used at the time of performing learning to obtain the first model and are used for performing learning to obtain the first model and a distribution of scores obtained by inputs, to the second models, of data items respectively for a plurality of times after the time of performing learning among time-series data items.

Classes IPC  ?

60.

DISPLAY METHOD, DISPLAY PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019041586
Numéro de publication 2021/079447
Statut Délivré - en vigueur
Date de dépôt 2019-10-23
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Ishida, Tsutomu

Abrégé

This information processing device acquires operation data inputted to a model. The information processing device calculates a first index on the basis of the acquired operation data, the first index indicating the magnitude of an output result of the model attributable to a change over time in a trend of the operation data. The information processing device calculates a second index in which the calculated first index is divided by a cyclical unit time of the operation data. The information processing device assesses, on the basis of comparison of the calculated second index and a preset threshold value, whether a concept drift that needs to be responded to by a user has occurred. When it has been assessed that the concept drift that needs to be responded to by the user has occurred, the information processing device causes information to be displayed on a display screen, the information indicating that the concept drift that needs to be responded to the user has occurred.

Classes IPC  ?

61.

DETECTION METHOD, DETECTION PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019041689
Numéro de publication 2021/079458
Statut Délivré - en vigueur
Date de dépôt 2019-10-24
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Kingetsu, Hiroaki

Abrégé

This information processing device learns an operation model to be monitored, using a plurality of sets of training data that are associated with a first class or a second class. On the basis of knowledge distillation of the operation model, the information processing device learns a decision boundary between a region of the first class and a region of the second class, and creates an inspector model for calculating the distance from the decision boundary to operation data. The information processing device detects a change in the output result of the operation model due to a change in the pattern of data over time, on the basis of the result of inputting the plurality of sets of training data and a plurality of sets of operation data into the inspector model.

Classes IPC  ?

62.

DETECTION METHOD, DETECTION PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019041690
Numéro de publication 2021/079459
Statut Délivré - en vigueur
Date de dépôt 2019-10-24
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Kingetsu, Hiroaki

Abrégé

This information processing device uses a plurality of sets of training data to learn an operation model to be monitored. On the basis of the output result of the operation model, the information processing device learns a decision boundary for classifying data feature space into a plurality of application regions, and creates an inspector model for calculating the distance from the decision boundary to operation data. The information processing device calculates, by means of the inspector model, whether or not the plurality of sets of training data are located near the decision boundary, and obtains a first proportion, which is the proportion of sets of training data that are located near the decision boundary, from among all sets of training data. The information processing device calculates, by means of the inspector model, whether or not a plurality of sets of operation data associated with one of a plurality of correct labels are located near the decision boundary, and obtains a second proportion, which is the proportion of sets of operation data that are located near the decision boundary, from among all sets of operation data. On the basis of the first proportion and the second proportion, the information processing device detects a change in the output result of the operation model due to a change in the pattern of the operation data over time.

Classes IPC  ?

63.

ANOMALY DETECTION METHOD, ANOMALY DETECTION PROGRAM, AND ANOMALY DETECTION DEVICE

      
Numéro d'application JP2019041757
Numéro de publication 2021/079472
Statut Délivré - en vigueur
Date de dépôt 2019-10-24
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Narita, Kenichiroh

Abrégé

In this anomaly detection method, a computer performs the processes of: acquiring a plurality of pieces of waveform data detected by a plurality of sensors placed on a monitoring target; specifying waveform data on a plurality of targets from the plurality of pieces of waveform data, on the basis of the correlation of the shapes of the acquired plurality of pieces of waveform data; integrating the waveform data on the plurality of targets into one; and dividing, by units of time, the waveform data, which has been integrated into one, to make clusters; and detecting an anomaly in a monitoring target, on the basis of the size of the cluster.

Classes IPC  ?

  • G01M 99/00 - Matière non prévue dans les autres groupes de la présente sous-classe

64.

GENERATION METHOD, GENERATION PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019041762
Numéro de publication 2021/079473
Statut Délivré - en vigueur
Date de dépôt 2019-10-24
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Yokota, Yasuto

Abrégé

An accuracy deterioration detection device acquires training data which is classified into a plurality of classes and generates a learned model having an application area of a model on a feature space. On the basis of the training data, the accuracy deterioration detection device generates, with respect to each of an application area of a first model and an application area of a second model in the application area of the learned model, a detection model having an application area of a third model and an application area of a fourth model which are smaller than the application area of the first model and the application area of the second model.

Classes IPC  ?

65.

DETERIORATION DETECTION METHOD, DETERIORATION DETECTION PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019041801
Numéro de publication 2021/079480
Statut Délivré - en vigueur
Date de dépôt 2019-10-24
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Yokota, Yasuto

Abrégé

An accuracy deterioration detection device acquires detection models to detect a change of an output result of a learned model, the detection models respectively corresponding to periods held by data to be input. The accuracy deterioration detection device acquires a first output result at a time when the data is input to the learned model, and acquires second output results at a time when the data is input to the detection models corresponding to the periods, respectively. The accuracy deterioration detection device detects a change of the output result of the learned model on the basis of the second output results and the first output result.

Classes IPC  ?

66.

GENERATION METHOD, GENERATION PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019041804
Numéro de publication 2021/079482
Statut Délivré - en vigueur
Date de dépôt 2019-10-24
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Yokota, Yasuto

Abrégé

In the present invention an accuracy degradation detection device acquires training data used in a learned model. The accuracy degradation detection device then learns a detection model on the basis of the training data. Next, the accuracy degradation detection device determines a state of overtraining of the learned detection model. The accuracy degradation detection device then acquires a feature amount of the detection model for a time at which the determined state of overtraining satisfies a preset condition. The accuracy degradation detection device then generates a detection model on the basis of the acquired feature amount.

Classes IPC  ?

67.

GENERATION METHOD, GENERATION PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019041805
Numéro de publication 2021/079483
Statut Délivré - en vigueur
Date de dépôt 2019-10-24
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Yokota, Yasuto

Abrégé

An accuracy deterioration detection device that obtains first training data that was used in a taught model. The accuracy deterioration detection device then obtains second training data that has set therein labels not included in the first training data. Then the accuracy deterioration detection device, on the basis of the first training data and the second training data, generates a detection model that: outputs a prediction result on the basis of the first training data if within an application area for the trained model; and outputs a label if outside the application area for the trained model.

Classes IPC  ?

68.

GENERATION METHOD, GENERATION PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019041807
Numéro de publication 2021/079485
Statut Délivré - en vigueur
Date de dépôt 2019-10-24
Date de publication 2021-04-29
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Yokota, Yasuto

Abrégé

An accuracy deterioration detection device classifies a plurality of pieces of teacher data to each period according to a preset condition. Subsequently, the accuracy deterioration detection device generates each detection model corresponding to each period, which detects a change in an output result of a trained model for each period by using the teacher data belonging to each period.

Classes IPC  ?

69.

MACHINE LEARNING PROGRAM, MACHINE LEARNING METHOD, AND MACHINE LEARNING DEVICE

      
Numéro d'application JP2019040906
Numéro de publication 2021/075029
Statut Délivré - en vigueur
Date de dépôt 2019-10-17
Date de publication 2021-04-22
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Kato, Takashi
  • Goto, Keisuke
  • Ohori, Kotaro

Abrégé

The present invention discretizes training data so that model accuracy is improved. With regard to a plurality of learning data in which label information is associated with a combination of data item values of a plurality of data items, a data item value is converted for each data item into a discretized data value that has been discretized on the basis of a prescribed criterion. A learning process for learning a model that accepts the discretized data value as input and performs an assessment pertaining to the label information is executed using the post-conversion plurality of learning data. A plurality of different feature information pieces indicating a combination of two or more data items used for assessment among the plurality of data items, and an index value indicating the importance of each of the plurality of feature information pieces, are acquired from the result of execution of the learning process. One or more feature information pieces are selected on the basis of the index value, and the criterion used for the discretization of data item values is altered on the basis of the selected one or more feature information pieces.

Classes IPC  ?

70.

INFORMATION PROCESSING METHOD, INFORMATION PROCESSING PROGRAM, INFORMATION PROCESSING DEVICE, AND INFORMATION PROCESSING SYSTEM

      
Numéro d'application JP2019041159
Numéro de publication 2021/075055
Statut Délivré - en vigueur
Date de dépôt 2019-10-18
Date de publication 2021-04-22
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Asaoka, Masahiro
  • Yasuie, Takeshi
  • Kondo, Reiko
  • Suzuki, Kazuhiro

Abrégé

A computer specifies one or more first physical resources on which a virtual resource used by a first user operates, specifies an apparatus connected to the first physical resource, and one or more second physical resources which are different from the first physical resource and connected to the apparatus and on which a virtual resource used by a user other than the first user operates, and outputs information indicating the first physical resource and information indicating the second physical resource.

Classes IPC  ?

71.

INFORMATION PROCESSING PROGRAM, INFORMATION PROCESSING METHOD, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019039499
Numéro de publication 2021/070224
Statut Délivré - en vigueur
Date de dépôt 2019-10-07
Date de publication 2021-04-15
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Koda, Satoru

Abrégé

extractiondetectionextractiondetectiondetection denotes a loss function for anomaly detection, and λ denotes a positive number. An anomaly score calculation unit calculates an anomaly score using the feature quantity generated by the feature quantity generation unit. An anomaly determination unit compares the anomaly score with a prescribed threshold value, and determines that the categorical data is an anomaly if the anomaly score is equal to or higher than the prescribed threshold value.

Classes IPC  ?

  • G06N 3/04 - Architecture, p.ex. topologie d'interconnexion
  • G06F 21/55 - Détection d’intrusion locale ou mise en œuvre de contre-mesures

72.

SIGNAL TRANSMISSION METHOD AND APPARATUS, AND COMMUNICATION SYSTEM

      
Numéro d'application CN2019109264
Numéro de publication 2021/062575
Statut Délivré - en vigueur
Date de dépôt 2019-09-30
Date de publication 2021-04-08
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Shimomura, Tsuyoshi
  • Jia, Meiyi

Abrégé

Provided by the present application are a signal transmission method and apparatus, and a communication system. The apparatus comprises a first transmission unit, which is configured to: receive a first synchronization signal/physical broadcast channel block (SS/PBCH block, SSB), the first SS/PBCH block not being located on a synchronization raster; and receive a physical downlink control channel (PDCCH), the PDCCH being used to schedule a physical downlink shared channel (PDSCH) used for carrying remaining minimum system information/system information block 1 (RMSI/SIB1).

Classes IPC  ?

  • H04W 72/02 - Sélection de ressources sans fil par un usager ou un terminal

73.

RANDOM ACCESS METHOD AND APPARATUS, AND COMMUNICATIONS SYSTEM

      
Numéro d'application CN2019109265
Numéro de publication 2021/062576
Statut Délivré - en vigueur
Date de dépôt 2019-09-30
Date de publication 2021-04-08
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Jia, Meiyi
  • Jiang, Qinyan

Abrégé

Provided are a random access method and apparatus, and a system. The method comprises: a terminal device selecting (or determining) a first downlink reference signal and/or a second downlink reference signal, wherein the first downlink reference signal and the second downlink reference signal have a quasi-colocation (QCL) relationship; and the terminal device determining (or selecting) a random access resource according to the first downlink reference signal and/or the second downlink reference signal.

Classes IPC  ?

74.

PATTERN SEARCH PROGRAM, PATTERN SEARCH DEVICE, AND PATTERN SEARCH METHOD

      
Numéro d'application JP2019038528
Numéro de publication 2021/064798
Statut Délivré - en vigueur
Date de dépôt 2019-09-30
Date de publication 2021-04-08
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Iwashita Hiroaki

Abrégé

In the present invention, the priority of individual attributes included in a plurality of attribute patterns that individually indicate one or more attributes is determined on the basis of the inclusion relation for occurrence sets in a plurality of samples for the attributes included in the plurality of attribute patterns, and, in accordance with a search sequence based on the determined priority, an assessment is made as to whether each of the plurality of attribute patterns is an emerging pattern. In the assessment-making process, a first attribute pattern is not assessed when the frequency at which the first attribute pattern appears in the plurality of samples is equal to or greater than the frequency at which a second attribute pattern occurs in the plurality of samples, the second attribute pattern having all of the attributes that are included in the first attribute pattern except the lowest-priority attribute.

Classes IPC  ?

  • G06F 16/20 - Recherche d’informations; Structures de bases de données à cet effet; Structures de systèmes de fichiers à cet effet de données structurées, p.ex. de données relationnelles

75.

EVALUATION METHOD, EVALUATION PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019038638
Numéro de publication 2021/064830
Statut Délivré - en vigueur
Date de dépôt 2019-09-30
Date de publication 2021-04-08
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Sakata, Masato

Abrégé

According to the present invention, a recognizing device chronologically acquires skeletal information based on position information about joints of a subject executing a plurality of motions. Next, the recognizing device identifies a transition period between a first motion included in the plurality of motions, and a second motion following the first motion, on the basis of the chronological skeletal information. The recognizing device then inputs the skeletal information corresponding to the identified transition period into an evaluation model trained so as to evaluate the transition period between motions on the basis of the skeletal information, evaluates the transition period, and outputs an evaluation result of the transition period.

Classes IPC  ?

76.

TRANSACTION MANAGEMENT DEVICE, TRANSACTION MANAGEMENT PROGRAM, AND TRANSACTION MANAGEMENT METHOD

      
Numéro d'application JP2019038699
Numéro de publication 2021/064852
Statut Délivré - en vigueur
Date de dépôt 2019-10-01
Date de publication 2021-04-08
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Yonekura, Yuki
  • Shimizu, Toshiya
  • Fujimoto, Shingo

Abrégé

This transaction management device comprises: first registration units (52, 53) which, with respect to a first block chain (2) that stores a plurality of transactions in which first information pieces and second information pieces are associated with each other, register the plurality of second information pieces in the plurality of transactions in a database (4) in units of groups based on the first information pieces; and second registration units (51, 52) which register, in a second block chain (3), hash values obtained by hashing the plurality of second information pieces in the units of groups.

Classes IPC  ?

  • G06F 21/64 - Protection de l’intégrité des données, p.ex. par sommes de contrôle, certificats ou signatures
  • G06F 16/182 - Systèmes de fichiers distribués

77.

CORRECTION METHOD, CORRECTION PROGRAM, AND INFORMATION PROCESSING SYSTEM

      
Numéro d'application JP2019038979
Numéro de publication 2021/064912
Statut Délivré - en vigueur
Date de dépôt 2019-10-02
Date de publication 2021-04-08
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Yoshimura, Kazuhiro

Abrégé

An information processing device according to the present invention generates a distance image on the basis of 3D sensor measurement data. From among the pixels contained in the distance image, the information processing device identifies a plurality of first pixels corresponding to a point cloud for the outline of a subject. From among the pixels contained in the distance image, the information processing device identifies a plurality of second pixels contained within a prescribed range from the plurality of first pixels. On the basis of second coordinate information for a second point cloud corresponding to the plurality of second pixels, the information processing device corrects first coordinate information for a first point cloud corresponding to the plurality of first pixels in the measurement data. The information processing device outputs coordinate information for the point cloud constituting the subject, the point cloud including the first point cloud for which the first coordinate information has been corrected.

Classes IPC  ?

  • G01B 11/00 - Dispositions pour la mesure caractérisées par l'utilisation de moyens optiques

78.

INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING PROGRAM, AND INFORMATION PROCESSING METHOD

      
Numéro d'application JP2019039112
Numéro de publication 2021/064937
Statut Délivré - en vigueur
Date de dépôt 2019-10-03
Date de publication 2021-04-08
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Abe Narishige

Abrégé

Provided is an information processing device in an information processing system comprising the information processing device and a photographing device for taking an image. The information processing device comprises: an image acquiring unit for acquiring an image taken by the photographing device; a target detecting unit for detecting a target to be subjected to image analysis from the acquired image; a use determining unit for calculating an image granularity of the detected target and determining, in accordance with the calculated image granularity, a type of the image analysis; and an image analysis unit for performing the determined type of image analysis.

Classes IPC  ?

  • G06T 7/00 - Analyse d'image
  • G01B 11/02 - Dispositions pour la mesure caractérisées par l'utilisation de moyens optiques pour mesurer la longueur, la largeur ou l'épaisseur

79.

EVALUATION METHOD, EVALUATION PROGRAM, AND INFORMATION PROCESSING SYSTEM

      
Numéro d'application JP2019039125
Numéro de publication 2021/064942
Statut Délivré - en vigueur
Date de dépôt 2019-10-03
Date de publication 2021-04-08
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Suzuki, Tatsuya
  • Ikeda, Hiroshi
  • Murakami, Ryo

Abrégé

An information processing device according to the present invention acquires point cloud data pertaining a subject and acquires a three-dimensional model corresponding to the subject. The information processing device executes a first process, a second process, and a third process that each have a different initial value. The information processing device evaluates the result of the first process, the result of the second process, and the result of the third process on the basis of the likelihood of the result of the first process, the likelihood of the result of the second process, and the likelihood of the result of the third process. On the basis of the evaluation result, the information processing device outputs one result among the result of the first process, the result of the second process, and the result of the third process as a skeleton recognition result of the subject.

Classes IPC  ?

80.

EXERCISE RECOGNITION METHOD, EXERCISE RECOGNITION PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019039201
Numéro de publication 2021/064963
Statut Délivré - en vigueur
Date de dépôt 2019-10-03
Date de publication 2021-04-08
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Fujisaki, Masataka
  • Sato, Takuya
  • Yabuki, Akihiko
  • Masui, Shoichi
  • Honda, Takashi

Abrégé

According to the present invention, a recognition device acquires skeleton information in time series, the skeleton information being based on the position information of each of a plurality of joints including a specific joint of a subject doing exercise. The recognition device also estimates, using the position information of the plurality of joints included in the respective time-series skeleton information, an area in which the specific joint is located, among a plurality of areas into which the area of an object used in exercise is divided. Thereafter, the recognition device recognizes the exercise of the subject by using the time-series skeleton information and the estimated position of the specific joint, and outputs the recognition result.

Classes IPC  ?

81.

RANDOM ACCESS METHOD AND DEVICE, AND COMMUNICATION SYSTEM

      
Numéro d'application CN2019109476
Numéro de publication 2021/062639
Statut Délivré - en vigueur
Date de dépôt 2019-09-30
Date de publication 2021-04-08
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Lu, Yang

Abrégé

Provided are a random access method and device, and a communication system. The device comprises a first transmission unit, said first transmission unit being configured for: sending to a network device a first message (MsgA) of a two-step random access procedure, or sending in an unlicensed frequency band a second message (Msg1) of a random access procedure; receiving a random access response sent by the network device; and processing the random access response when a physical downlink common control channel (PDCCH) of the random access response is addressed to a radio network temporary identifier and frame number indication information carried by the random access response matches the radio frame in which the first message was sent or the radio frame in which the second message was sent.

Classes IPC  ?

  • H04W 74/08 - Accès non planifié, p.ex. accès aléatoire, ALOHA ou accès multiple par détection de porteuse [CSMA Carrier Sense Multiple Access]

82.

ATTRIBUTE DETERMINATION DEVICE, ATTRIBUTE DETERMINATION PROGRAM, AND ATTRIBUTE DETERMINATION METHOD

      
Numéro d'application JP2019038734
Numéro de publication 2021/064857
Statut Délivré - en vigueur
Date de dépôt 2019-10-01
Date de publication 2021-04-08
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Uchida Hidetsugu

Abrégé

An attribute determination device of an attribute determination system that comprises the attribute determination device and a plurality of image capture devices for capturing images, said attribute determination device comprising: an image acquisition unit which acquires images captured by the plurality of image capture devices; an image analysis unit which, for each first image capture device that has captured an image showing a first subject, for which an attribute is to be determined, extracts the image showing the first subject, analyzes the image of the first subject shown in the extracted image, and calculates a first probability, which is the probability that the first subject has a first attribute; and an attribute determination unit which determines whether or not the first subject has the first attribute on the basis of the first probabilities and on the basis of second probabilities, each representing the probability that one of the plurality of image capture devices captures an image that shows a subject having the first attribute.

Classes IPC  ?

83.

LASER SENSOR, MIRROR CONTROL METHOD, AND PROGRAM

      
Numéro d'application JP2019038759
Numéro de publication 2021/064863
Statut Délivré - en vigueur
Date de dépôt 2019-10-01
Date de publication 2021-04-08
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Ejiri, Arata
  • Yanai, Kosuke
  • Fujiyoshi, Shinichi
  • Iida, Koichi

Abrégé

In this laser sensor that two-dimensionally scans a measurement target by scanning a scan angle range, an angle-of-view parameter correction circuit (20) is provided with: an angle-of-view change detection unit (201) that receives input of a scan angle range set on the basis of the distance to the measurement target and an azimuth angle, and a shift amount by which the scan angle range is shifted toward the vertical direction, the angle-of-view change detection unit (201) outputting a shift change amount and a signal that indicates a post-change first frame upon detecting a change in angle-of-view based on the inputted shift amount; and a correction amount generation unit (202) that generates a shift correction amount and a scan angle range correction amount that correspond to an expected deviation amount of the first frame by using the shift change amount. In addition, in response to the signal that indicates the post-change first frame: the shift correction amount and the scan angle range correction amount are added to the shift amount and the scan angle range, respectively, and the resultant values are outputted, in the first frame; and the inputted shift amount and scan angle range are outputted without modification in the second and subsequent frames.

Classes IPC  ?

  • G01S 7/481 - Caractéristiques de structure, p.ex. agencements d'éléments optiques

84.

LEARNING METHOD, LEARNING DEVICE, LEARNING PROGRAM, PREDICTION METHOD, PREDICTION DEVICE, AND PREDICTION PROGRAM

      
Numéro d'application JP2019038822
Numéro de publication 2021/064879
Statut Délivré - en vigueur
Date de dépôt 2019-10-01
Date de publication 2021-04-08
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Ukai, Takanori
  • Okajima, Seiji

Abrégé

This learning device: acquires RDF data comprising subjects, predicates, and objects, and retrieves the subject, predicate, and object in a first record constituting the acquired RDF data (S31); and identifies the predicate in a previously retrieved second record that includes a character string identical to the subject or object in the first record (S36). The learning device generates training data comprising RDF data in which the subject or object in the first record is associated with the identified predicate in the second record (S38). With regard to the generated training data, the learning device can enhance the search prediction accuracy for the RDF data by performing learning such that the vector obtained by adding a vector of the predicate to a vector of the subject associated with the RDF data approaches a vector of the object associated with the RDF data.

Classes IPC  ?

  • G06F 16/28 - Bases de données caractérisées par leurs modèles, p.ex. des modèles relationnels ou objet

85.

GENERATION METHOD, GENERATION PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019039019
Numéro de publication 2021/064925
Statut Délivré - en vigueur
Date de dépôt 2019-10-02
Date de publication 2021-04-08
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Yamao, Sosuke

Abrégé

This information processing device specifies, on the basis of whether each of three or more sensors has detected a target at each timepoint, the count of timepoints at which the target was detected at the same timepoint among a plurality of timepoints by each set of two sensors among the three or more sensors. The information processing device generates, on the basis of the count specified for each of the sets of sensors and the variance in the positions at which the target is positioned at the plurality of timepoints, information indicating connectivity related to the precision of estimating the relative positional relationship between the sensors. The information processing device generates, on the basis of the information indicating connectivity, information indicating a first relative positional relationship between two sensors that have connectivity. The information processing device generates, on the basis of the information indicating connectivity and the information indicating the first relative positional relationship, information indicating a second relative positional relationship between two sensors that do not have connectivity, and outputs the generated information.

Classes IPC  ?

  • G01B 11/00 - Dispositions pour la mesure caractérisées par l'utilisation de moyens optiques
  • G01C 3/06 - Utilisation de moyens électriques pour obtenir une indication finale
  • G01C 15/00 - Instruments de géodésie ou accessoires non prévus dans les groupes
  • G06T 15/08 - Rendu de volume

86.

CONTROL METHOD, INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING SYSTEM, AND CONTROL PROGRAM

      
Numéro d'application JP2019039109
Numéro de publication 2021/064936
Statut Délivré - en vigueur
Date de dépôt 2019-10-03
Date de publication 2021-04-08
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Kozakura, Fumihiko

Abrégé

This control method, for controlling electricity trading between a first consumer who has received a power saving request from an aggregator and a second consumer different from the first consumer, is configured to facilitate execution of the electricity trading in accordance with the power saving request by causing a computer to execute a process involving: acquiring a first electric power amount which is the electric power amount to be purchased by the first consumer; acquiring a second electric power amount that can be sold by the second consumer; and controlling the electricity trading between the first consumer and the second consumer in accordance with a result of comparison between a predicted value of a shortage amount of electric power with respect to a power saving target and an actual measurement value of the shortage amount of electric power at the first consumer, even when the second electric power amount is equal to or greater than the first electric power amount.

Classes IPC  ?

  • G06Q 50/06 - Fourniture d'électricité, de gaz ou d'eau

87.

MOTION RECOGNITION METHOD, MOTION RECOGNITION PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019039193
Numéro de publication 2021/064960
Statut Délivré - en vigueur
Date de dépôt 2019-10-03
Date de publication 2021-04-08
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Kusajima, Ikuo

Abrégé

Provided is a recognition device for chronologically acquiring skeletal information including the positions of joints of a subject executing a series of motions including a plurality of basic motions. The recognition device determines whether, depending on the type of the basic motions, to adopt a first motion recognizing technique using a first feature quantity determined as the result of the basic motions, or a second motion recognizing technique using a second feature quantity that transitions in the course of the basic motions. The recognition device, according to the first motion recognizing technique or the second motion recognizing technique that has been determined, determines the type of the basic motions using the skeletal information, and outputs the determined type of the basic motions.

Classes IPC  ?

88.

VIDEO CODING AND DECODING METHODS AND APPARATUSES, AND ELECTRONIC DEVICE

      
Numéro d'application CN2019107597
Numéro de publication 2021/056211
Statut Délivré - en vigueur
Date de dépôt 2019-09-24
Date de publication 2021-04-01
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Cai, Wenting
  • Zhu, Jianqing

Abrégé

Video coding and decoding methods and apparatuses, and an electronic device. The coding method comprises: for a chrominance coding unit whose size is equal to a virtual pipeline data unit (VPDU), coding a chrominance coding unit split flag (split_chroma_cu_flag); when the value of the chrominance coding unit split flag is 1, performing quaternary tree split on the chrominance coding unit and a luminance coding unit corresponding to the chrominance coding unit; and when the value of the chrominance coding unit split flag is 0, performing quaternary tree split on the luminance coding unit corresponding to the chrominance coding unit but not performing the split on the chrominance coding unit.

Classes IPC  ?

  • H04N 19/176 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c. à d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant une zone de l'image, p.ex. un objet la zone étant un bloc, p.ex. un macrobloc
  • H04N 19/70 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques caractérisés par des aspects de syntaxe liés au codage vidéo, p.ex. liés aux standards de compression
  • H04N 19/186 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c. à d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant une couleur ou une composante de chrominance

89.

METHOD AND DEVICE FOR RECEIVING AND SENDING SIDELINK INFORMATION

      
Numéro d'application CN2019109177
Numéro de publication 2021/056560
Statut Délivré - en vigueur
Date de dépôt 2019-09-29
Date de publication 2021-04-01
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Zhang, Jian
  • Ji, Pengyu
  • Li, Guorong
  • Zhang, Lei
  • Wang, Xin

Abrégé

The embodiments of the present application provide a method and device for receiving and sending sidelink information. The method comprises: a terminal device, in a time unit where it is unnecessary to send sidelink data, determining that at least one physical sidelink feedback channel (PSFCH) needs to be received; and receiving, in the time unit, one of at least one physical sidelink shared channel (PSSCH) and said at least one physical sidelink feedback channel, and/or receiving the at least one physical sidelink shared channel and the at least one physical sidelink feedback channel when a condition is satisfied.

Classes IPC  ?

90.

WIRELESS COMMUNICATION METHOD AND DEVICE, AND SYSTEM

      
Numéro d'application CN2019109180
Numéro de publication 2021/056562
Statut Délivré - en vigueur
Date de dépôt 2019-09-29
Date de publication 2021-04-01
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Chen, Zhe
  • Zhang, Lei
  • Song, Lei
  • Wang, Xin

Abrégé

The present application provides a wireless communication method and device, and a communication system. The wireless communication method comprises: a terminal device receiving first control information, wherein the length of an HARQ process number (HPN) domain of the first control information is configurable, and the length of a redundancy version (RV) domain of the first control information is configurable or predefined; and the terminal device determining, according to the HPN domain and/or the RV domain of the first control information, CG configurations or CG configuration sets corresponding to the first control information; wherein the number M of the configured grant configurations configured on one BWP is less than or equal to 2L+R, or the number N of the configured grant configuration sets configured on one BWP is less than or equal to 2L+R; or, the larger value of the number N of the configured grant configuration sets configured on one BWP and the number M of the configured grant configurations configured on the BWP is less than or equal to 2L+R; L is the length of the HPN domain and R is the length of the RV domain.

Classes IPC  ?

91.

UPLINK SIGNAL SENDING AND RECEIVING METHOD AND APPARATUS

      
Numéro d'application CN2019109230
Numéro de publication 2021/056581
Statut Délivré - en vigueur
Date de dépôt 2019-09-29
Date de publication 2021-04-01
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Li, Guorong
  • Zhang, Lei
  • Jia, Meiyi
  • Lu, Yang
  • Wang, Xin

Abrégé

Provided are an uplink signal sending and receiving method and apparatus. The method comprises: a terminal device giving, on a MAC layer, a physical layer an indication of sending a first uplink signal on a first time-frequency resource; determining, on the MAC layer, that a second time-frequency resource and the first time-frequency resource at least partially overlap on a time domain or a time-frequency domain; and comparing, on the MAC layer, first information related to the first uplink signal with second information related to the second time-frequency resource.

Classes IPC  ?

92.

LEARNING METHOD, LEARNING PROGRAM, AND LEARNING DEVICE

      
Numéro d'application JP2019037370
Numéro de publication 2021/059348
Statut Délivré - en vigueur
Date de dépôt 2019-09-24
Date de publication 2021-04-01
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Kato, Keizo
  • Nakagawa, Akira

Abrégé

ψψ (z) of the feature data z. The learning device (100) decodes, by a decoder (112), the feature data z so as to generate first decoded data x˄. The learning device (100) decodes, by a decoder (114), added data z+ε obtained by adding noise ε to the feature data z so as to generate second decoded data xv. The learning device (100) calculates a first error D1 between first decoded data x˄and the data x, a second error D2 between first decoded data x˄and second decoded data xvψψ (z). The learning device (100) learns an auto-encoder (110) and the probability distribution of the feature data in order to minimize the first error D1, the second error D2, and the information entropy R of the probability distribution.

Classes IPC  ?

93.

LEARNING METHOD, LEARNING PROGRAM, AND LEARNING DEVICE

      
Numéro d'application JP2019037371
Numéro de publication 2021/059349
Statut Délivré - en vigueur
Date de dépôt 2019-09-24
Date de publication 2021-04-01
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Kato, Keizo
  • Nakagawa, Akira

Abrégé

ψψ(z) of the feature data z. The learning device (100) adds a noise ε to the feature data z, and generates after-addition-data z+ε. The learning device (100) decodes the after-addition-data z+ε by means of a decoder (113) and generates decoded data x∨. The learning device (100) generates the first error D1 between the generated decoded data x∨ψψ(z). The learning device (100) is trained with an auto-encoder (100) and the probability distribution of the feature data z in order to minimize the calculated first error D1 and the information entropy R of the probability distribution.

Classes IPC  ?

94.

SIDELINK RESOURCE RESERVATION METHOD AND APPARATUS

      
Numéro d'application CN2019108561
Numéro de publication 2021/056419
Statut Délivré - en vigueur
Date de dépôt 2019-09-27
Date de publication 2021-04-01
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Ji, Pengyu
  • Zhang, Jian
  • Li, Guorong
  • Zhang, Lei
  • Wang, Xin

Abrégé

Embodiments of the present application provide a sidelink resource reservation method and apparatus. The method comprises: a terminal device obtains an initial transmission resource and a retransmission resource used for transmitting sidelink data, and sends indication information indicating the initial transmission resource and/or the retransmission resource, wherein the retransmission resource is used for retransmitting one or more code block groups of the sidelink data; the number of time slots or symbols on a time domain of the retransmission resource is less than the number of time slots or symbols on a time domain of the initial transmission resource; and/or the number of subchannels on a frequency domain of the retransmission resource is less than the number of subchannels on a frequency domain of the initial transmission resource.

Classes IPC  ?

95.

METHOD AND APPARATUS FOR TRANSMITTING PHASE TRACKING REFERENCE SIGNAL

      
Numéro d'application CN2019109151
Numéro de publication 2021/056555
Statut Délivré - en vigueur
Date de dépôt 2019-09-29
Date de publication 2021-04-01
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Song, Lei
  • Chen, Zhe
  • Zhang, Lei
  • Wang, Xin

Abrégé

A method and apparatus for transmitting a phase tracking reference signal. The apparatus is applied to a terminal device side. The apparatus comprises: a first receiving unit (701) for receiving configuration information or indication information sent by a network device (1200), wherein the configuration information or indication information is used for instructing a terminal device to receive at least two versions of a transport block within a scheduling unit; and a first determination unit (702) for determining an association relationship between a PTRS antenna port and a DMRS antenna port according to the configuration information, the indication information or a predefined rule.

Classes IPC  ?

  • H04L 5/00 - Dispositions destinées à permettre l'usage multiple de la voie de transmission

96.

WIRELESS COMMUNICATION METHOD, APPARATUS, AND SYSTEM

      
Numéro d'application CN2019109212
Numéro de publication 2021/056570
Statut Délivré - en vigueur
Date de dépôt 2019-09-29
Date de publication 2021-04-01
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Chen, Zhe
  • Zhang, Lei
  • Song, Lei
  • Wang, Xin

Abrégé

The present application provides a wireless communication method, apparatus, and system. The wireless communication method comprises: a terminal device receiving an indication information set; and the terminal device determining the priority of a first uplink signal according to the indication information set, the first uplink signal being an uplink signal for beam failure recovery.

Classes IPC  ?

  • H04W 16/28 - Structures des cellules utilisant l'orientation du faisceau

97.

UPLINK SIGNAL TRANSMISSION METHOD AND APPARATUS, AND UPLINK SIGNAL RECEPTION METHOD AND APPARATUS

      
Numéro d'application CN2019116345
Numéro de publication 2021/056702
Statut Délivré - en vigueur
Date de dépôt 2019-11-07
Date de publication 2021-04-01
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Li, Guorong
  • Zhang, Lei
  • Jia, Meiyi
  • Lu, Yang
  • Wang, Xin

Abrégé

Embodiments of the present application provide an uplink signal transmission method and apparatus, and an uplink signal reception method and apparatus. The method comprises: a terminal device instructing, from a MAC layer, a physical layer to transmit a first uplink signal on a first time-frequency resource; determining, in the MAC layer, that a second time-frequency resource and the first time-frequency resource at least partially overlap in a time domain or in a time-frequency domain; and comparing, in the MAC layer, first information related to the first uplink signal with second information related to the second time-frequency resource.

Classes IPC  ?

98.

PRIORITY DETERMINATION METHOD AND APPARATUS

      
Numéro d'application CN2019107012
Numéro de publication 2021/051392
Statut Délivré - en vigueur
Date de dépôt 2019-09-20
Date de publication 2021-03-25
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Song, Lei
  • Chen, Zhe
  • Zhang, Lei

Abrégé

Provided in the embodiments of the present application are a priority determination method and apparatus. The method comprises: a terminal device determining that at least two downlink signals need to be received within one time unit; and determining, according to the types and/or spatial quasi-co-location information of the at least two downlink signals, reception priorities of the at least two downlink signals. Therefore, a transmitter and a receiver can reach an agreement on the priorities of at least two signals, thereby reducing and even avoiding signal reception errors.

Classes IPC  ?

  • H04W 72/10 - Affectation de ressources sans fil sur la base de critères de priorité

99.

CONTROL METHOD, INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING SYSTEM, AND CONTROL PROGRAM

      
Numéro d'application JP2019035472
Numéro de publication 2021/048911
Statut Délivré - en vigueur
Date de dépôt 2019-09-10
Date de publication 2021-03-18
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s)
  • Higashikado, Yoshiki
  • Morinaga, Masanobu
  • Fujimoto, Shingo

Abrégé

According to the present invention, a computer performs a process to: receive, from a purchaser terminal used by a purchaser who purchases a product in a store, first information that relates to payment for the purchase of the product, and that is included in code information read at the store by the purchaser terminal; receive, from a store terminal, second information relating to payment for purchase of the product, said store terminal being used for payment for purchase of the product at the store; determine whether or not the first information and the second information relate to the same payment; and, if the determination result is affirmative, perform control to record, on a blockchain, information relating to the purchase payment from the purchaser to the store. This makes it possible to suppress fraudulent billing in code payment.

Classes IPC  ?

  • G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
  • G06Q 50/20 - Services Éducation

100.

SKELETON RECOGNITION METHOD, SKELETON RECOGNITION PROGRAM, AND INFORMATION PROCESSING DEVICE

      
Numéro d'application JP2019035979
Numéro de publication 2021/048988
Statut Délivré - en vigueur
Date de dépôt 2019-09-12
Date de publication 2021-03-18
Propriétaire FUJITSU LIMITED (Japon)
Inventeur(s) Fujimoto, Hiroaki

Abrégé

This recognition device acquires distance images of a subject from a plurality of sensors that respectively sense the subject in a plurality of directions. The recognition device acquires joint information including each joint position of the subject from the plurality of sensors by using the respective distance images acquired from the plurality of sensors and a learning model for estimating each joint position of the subject from the distance images. The recognition device integrates pieces of joint information respectively corresponding to the plurality of sensors to generate skeleton information including three-dimensional coordinates for each joint position of the subject, and outputs the skeleton information on the subject.

Classes IPC  ?

  • G06T 7/00 - Analyse d'image
  • G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
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