WO2025262236 - METHOD AND DEVICE FOR GENERATING SYNTHETIC LOCATION DATA
National phase entry is expected:
Publication Number
WO/2025/262236
Publication Date
26.12.2025
International Application No.
PCT/EP2025/067309
International Filing Date
19.06.2025
Title **
[English]
METHOD AND DEVICE FOR GENERATING SYNTHETIC LOCATION DATA
[French]
PROCÉDÉ ET DISPOSITIF DE GÉNÉRATION DE DONNÉES DE LOCALISATION SYNTHÉTIQUES
Applicants **
ORANGE
Inventors
RENAUD, Denis
LEGENDRE, Arnaud
DUBUS, Clement
BIDET, François
Priority Data
FR2406701
21.06.2024
FR
Application details
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International Searching Authority |
EPO
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| Recordal of a Change of the Applicant's Name/Address |
Change of Applicant's Name and Address
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| Type of Assignment |
The Standard Agent's Assignment
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| Applicant's Legal Status |
Legal Entity
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| Entry into National Phase under |
Chapter I
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| Patent Delivery |
Send the Letters Patent by Courier
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| 译文 |
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Quotation for National Phase entry
| Country | Stages | Total | |
|---|---|---|---|
| China | Filing, Examination, Granting | 1880 | |
| EPO | Filing, Examination, Granting | 8113 | |
| Japan | Filing, Examination, Granting | 1783 | |
| South Korea | Filing, Examination, Granting | 1317 | |
| USA | Filing, Examination, Granting | 4740 |
Total:
17,833
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Abstract[English]
The invention relates to a method and device for anonymising longitudinal location data relating to a plurality of persons. The anonymising method comprises: - a step (E20) of classifying, by means of an unsupervised partitioning algorithm, longitudinal location data into a plurality of clusters having a given minimum size; - a step (E30) of learning, from the longitudinal location data, a generative model conditioned by the plurality of clusters; and - a step (E40) of generating synthetic data within each cluster using the generative model and taking into account a distribution per cluster of the original longitudinal location data.[French]
Procédé et dispositif d'anonymisation de données de localisation longitudinales relatives à une pluralité d'individus. Le procédé d'anonymisation comprend : - une étape (E20) de classement au moyen d'un algorithme de partitionnement non supervisé de données de localisation longitudinales en une pluralité de clusters ayant une taille minimale donnée; - une étape (E30) d'apprentissage à partir des données de localisation longitudinales d'un modèle génératif conditionné par ladite pluralité de clusters; et - une étape (E40) de génération de données synthétiques au sein de chaque cluster en utilisant ledit modèle génératif et en tenant compte d'une distribution par cluster des données de localisation longitudinales d'origine.