METHOD AND SYSTEM FOR DIGITAL PRINTING OF IMAGES ON PHYSICAL SUPPORTS

    公开(公告)号:EP4404047A1

    公开(公告)日:2024-07-24

    申请号:EP24152672.2

    申请日:2024-01-18

    IPC分类号: G06F3/12 G06F40/10 G06N3/00

    摘要: Method for digital printing of images (P) on physical supports (S), the method comprising the step i) of generating a digital image (D1) with a predetermined format, the step ii) of converting the digital image (D1) into an image (D2) with a format suitable for printing on a physical support (S) and the step iii) of printing the converted image (D2) on the physical support (S) in order to obtain the physical support (S) with a printed image (P). At least the step i) is performed by means of a process for the processing, by at least one artificial intelligence software algorithm (4), of a series of inputs (T1, T2, Tn...) comprising images and/or texts and/or keywords, the digital image (D1) representing the output of the processing process in step i). The invention also relates to a system (1) for digital printing of images (P) on physical supports (S).

    METHOD AND SYSTEM FOR GENERATING CONTEXTUAL EXPLANATION FOR MODEL PREDICTIONS

    公开(公告)号:EP4379612A1

    公开(公告)日:2024-06-05

    申请号:EP23212486.7

    申请日:2023-11-28

    IPC分类号: G06N5/045 G06N20/00 G06F40/10

    CPC分类号: G06N20/00 G06N5/045 G06F40/10

    摘要: Human-understandable explanations of Artificial Intelligence (AI) based models are crucial to building transparency and trust in AI based solutions. More importantly, these explanations need to be contextual, applicable to the domain the model is used in and relevant to the concerned stakeholder. Conventionally, there is a lack of communicating these explanations to various stakeholders in a language that they can understand and relate to. The present disclosure facilitates the conversational agents (chat bots) with intelligence and actions that would help them communicate the right information to the right stakeholder in the right way. In the present disclosure, contextual explanation for user queries is generated based on the output from AI models. Here, the impacting features are obtained from the explainer model associated with the prediction model and the contextual information is generated. Further, the contextual information is converted to the contextual explanation to the user.