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    • 3. 发明公开
    • METHOD OF MANAGING PROFILES IN A TOKEN
    • VERFAHREN ZUR VERWALTUNG VON PROFILEN在EINEM TOKEN
    • EP3138360A1
    • 2017-03-08
    • EP15718451.6
    • 2015-03-24
    • Gemalto SA
    • FAURE, FrédéricBERARD, XavierDEHLINGER, FranckBORGHINO, Franck
    • H04W92/08H04W8/18
    • H04W92/08H04W8/183
    • The invention is a method for managing the swap of current profile in a UICC comprising several profiles connected to a host machine that includes an application and a baseband which includes a radio functions manager. The method comprising the steps of: - sending a swap command requesting a swap of current profile from the application to the UICC through a first logical channel and - sending a triggering command from the application to the radio functions manager immediately after sending the swap command, the triggering command forcing sending of a first command by the baseband to the UICC through a second logical channel different from the first logical channel.
    • 本发明是一种用于管理UICC中当前简档的交换的方法,包括连接到主机的多个简档,其包括应用和包括无线电功能管理器的基带。 该方法包括以下步骤: - 发送交换命令,请求通过第一逻辑信道从应用程序向UICC交换当前简档;以及 - 在发送交换命令之后立即从应用发送触发命令到无线电功能管理器, 所述触发命令通过不同于所述第一逻辑信道的第二逻辑信道强制将由所述基带发送的第一命令发送到所述UICC。
    • 7. 发明公开
    • A METHOD FOR IMPROVING USER AUTHENTICATION PERFORMED BY A COMMUNICATION DEVICE
    • EP3486818A1
    • 2019-05-22
    • EP17306601.0
    • 2017-11-17
    • GEMALTO SA
    • FAURE, Frédéric
    • G06F21/31G06F21/32H04L29/06H04W12/06G06N3/08
    • This invention relates to a method for improving user authentication efficiency performed by a communication device belonging to an authentication system, the communication device (200) comprising a local machine learning engine (LMLE, 201) comprising a set (220-222) of N artificial neural network ANN 1, i adapted to process N different types of input signals, the method comprising the following steps: receiving (300) a first set of N input signals S _1( i ) for authentication purpose; determining (301) respectively for each of the N input signals S _1( i ) by the N artificial neural networks ANN 1 , i, N estimations LH ( i ) of the likelihood that a given input signal is provided by a legitimate user; determining (302) based on a risk scoring established using the N estimations LH ( i ) if the requesting user is authenticated as the legitimate user; if (303) the requested user is authenticated, determining (304) if at least one likelihood estimation determined for a given input signal S _1( j ) is below a predetermined threshold T C,i , and if it is the case: transmitting (305) the input signal S _1( j ) to a remote server (210) implementing a server machine learning engine (SMLE, 211) adapted to process said N different types of input signals and trained to identify a user U_C ; receiving an input signal S _2( j ) associated to the closest candidate U_C and executing (309) by the local machine learning engine (LMLE, 201) an additional learning phase (309) using the input signal S _2( j ) as an input signal that is not associated to the requesting user.