WO2026009117 - GENERATION OF IMAGE SETS FOR COGNITIVE ASSESSMENT
National phase entry is expected:
Publication Number
WO/2026/009117
Publication Date
08.01.2026
International Application No.
PCT/IB2025/056587
International Filing Date
27.06.2025
Title **
[English]
GENERATION OF IMAGE SETS FOR COGNITIVE ASSESSMENT
[French]
GÉNÉRATION D'ENSEMBLES D'IMAGES POUR ÉVALUATION COGNITIVE
Applicants **
CUMULUS NEUROSCIENCE LTD.
Inventors
STOTHART, George
MURPHY, Brian Edmond
Priority Data
18/764,056
03.07.2024
US
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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| Translation |
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Quotation for National Phase entry
| Country | Stages | Total | |
|---|---|---|---|
| China | Filing, Examination, Granting | 2276 | |
| EPO | Filing, Examination, Granting | 11108 | |
| Japan | Filing, Examination, Granting | 2328 | |
| South Korea | Filing, Examination, Granting | 2345 | |
| USA | Filing, Examination, Granting | 4740 |
Total:
22,797
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Abstract[English]
Techniques for generating image sets for cognitive assessment are provided. A first machine learning (ML) model may generate a plurality of candidate images that are each expected to meet a set of visual and semantic criteria. Each of the plurality of candidate images that meets the set of visual and semantic criteria may be identified as a test image. A second ML model may be used to generate a set of test images using the plurality of test images by adding to the set of test images, test images whose visual and semantic properties are sufficiently different from the visual and semantic properties of each other test image currently in the set of test images.[French]
L'invention concerne des techniques de génération d'ensembles d'images pour une évaluation cognitive. Un premier modèle d'apprentissage automatique (ML) peut générer une pluralité d'images candidates qui sont chacune censées satisfaire un ensemble de critères visuels et sémantiques. Chacune de la pluralité d'images candidates qui satisfait l'ensemble de critères visuels et sémantiques peut être identifiée en tant qu'image de test. Un second modèle ML peut être utilisé pour générer un ensemble d'images de test à l'aide de la pluralité d'images de test en ajoutant à l'ensemble d'images de test, des images de test dont les propriétés visuelles et sémantiques sont suffisamment différentes des propriétés visuelles et sémantiques de chaque autre image de test actuellement dans l'ensemble d'images de test.