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公开(公告)号:EP4435603A2
公开(公告)日:2024-09-25
申请号:EP24163248.8
申请日:2024-03-13
发明人: Song, Uiseok , Kim, Seoung Bum , Kim, Jaehoon , Kim, Jungin , Bang, Byungwoo , Lee, Jungmin , Lee, Junyeon , Lee, Jiyoon , Jeong, Jaeyoon
CPC分类号: G06F11/3476 , G06F11/0751 , G06F11/008 , G06N3/08 , G06F40/10
摘要: A method and device for predicting errors in a computing system are disclosed. The error prediction method includes: receiving log data generated by the computing system during operation of the computing system; tokenizing the log data into tokens; inputting the tokens to a discriminator model which generates scores of the respective tokens, each score corresponding to a probability that the corresponding token is an anomaly token; determining an anomaly score based on the scores; and determining a likelihood of future occurrence of an error in the computing system based on the anomaly score.
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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
申请人: Rotas Italia S.r.l.
发明人: CELANTE, Francesco
CPC分类号: G06F3/1242 , G06F3/1204 , G06F3/1208 , G06F3/1244 , 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).
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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
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.
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