WO2024134556 - MACHINE LEARNING ARCHITECTURES AND PREDICTION MODELS FOR GENERATING DATA ASSET PREDICTIONS
National phase entry is expected:
Publication Number
WO/2024/134556
Publication Date
27.06.2024
International Application No.
PCT/IB2023/063048
International Filing Date
20.12.2023
Title **
[English]
MACHINE LEARNING ARCHITECTURES AND PREDICTION MODELS FOR GENERATING DATA ASSET PREDICTIONS
[French]
ARCHITECTURES D'APPRENTISSAGE AUTOMATIQUE ET MODÈLES DE PRÉDICTION POUR GÉNÉRER DES PRÉDICTIONS D'ACTIFS DE DONNÉES
Applicants **
ANMUT LTD.
Inventors
COLEMAN, Alexander James
GAMBERI, Luca
SAMUEL, Ananya Ruth
DAVIS, Stefan Montelongo
Priority Data
63/476,340
20.12.2022
US
18/389,618
19.12.2023
US
Application details
| Total Number of Claims/PCT | * |
| Number of Independent Claims | * |
| Number of Priorities | * |
| Number of Multi-Dependent Claims | * |
| Number of Drawings | * |
| Pages for Publication | * |
| Number of Pages with Drawings | * |
| Pages of Specification | * |
| * | |
| Number of Office Actions | * |
| * | |
International Searching Authority |
EPO
* |
| Recordal of a Change of the Applicant's Name/Address |
Change of Applicant's Name and Address
* |
| Type of Assignment |
The Standard Agent's Assignment
* |
| Applicant's Legal Status |
Legal Entity
* |
| * | |
| * | |
| * | |
| * | |
| * | |
| Entry into National Phase under |
Chapter I
* |
| Patent Delivery |
Send the Letters Patent by Courier
* |
| Translation |
|
* The data is based on automatic recognition. Please verify and amend if necessary.
** IP-Coster compiles data from publicly available sources. If this data includes your personal information, you can contact us to request its removal.
Quotation for National Phase entry
| Country | Stages | Total | |
|---|---|---|---|
| China | Filing, Examination, Granting | 2460 | |
| EPO | Filing, Examination, Granting | 11764 | |
| Japan | Filing, Examination, Granting | 2358 | |
| South Korea | Filing, Examination, Granting | 2366 | |
| USA | Filing, Examination, Granting | 4740 |
Total:
23,688
The term for entry into the National Phase has expired. This quotation is for informational purposes only
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Abstract[English]
This disclosure relates to improved systems and methods for predicting the correlation between the performance of an entity and one or more data assets related to that entity. The techniques described herein can be utilized to generate various types of data asset predictions that objectively quantify or measure metrics related to the data assets. In some embodiments, one or more machine learning models can be executed to perform relevancy and/or correlation analyses, and the outputs of the machine learning models can be utilized to generate the data asset predictions. Other embodiments are disclosed herein as well.[French]
La présente divulgation concerne des systèmes et des procédés améliorés pour prédire la corrélation entre les performances d'une entité et un ou plusieurs actifs de données associés à cette entité. Les techniques décrites dans la présente invention peuvent être utilisées pour générer divers types de prédictions d'actifs de données qui quantifient ou mesurent objectivement des paramètres associés aux actifs de données. Dans certains modes de réalisation, un ou plusieurs modèles d'apprentissage automatique peuvent être exécutés pour effectuer des analyses de pertinence et/ou de corrélation et les sorties des modèles d'apprentissage automatique peuvent être utilisées pour générer les prédictions d'actifs de données. L’invention divulgue également d'autres modes de réalisation.