Provided is a mobile phone authentication method using implicit authentication, the method including the steps of: by a server, receiving behavior data and environment information data from a user terminal when a user checks an authentication number for authentication of a mobile phone user; by the server, detecting a start point of a behavior of the user for checking the authentication number by performing peak detection in the received behavior data, and storing behavior data from the detected start point; and by the server, extracting feature data from the received environment information data and learning the extracted feature data to build a learning model.

Provided is a mobile phone authentication method using implicit authentication, the method including the steps of: by a server, receiving behavior data and environment information data from a user terminal when a user checks an authentication number for authentication of a mobile phone user; by the server, detecting a start point of a behavior of the user for checking the authentication number by performing peak detection in the received behavior data, and storing behavior data from the detected start point; and by the server, extracting feature data from the received environment information data and learning the extracted feature data to build a learning model.

Systems and processes for user identification using headphones associated with a first device are provided. For example, first movement information corresponding to movement of a second electronic device is detected. Second movement information corresponding to movement of a third electronic device is detected. A similarity score is determined based on the first movement information and the second movement information. In accordance with a determination that the similarity score is above a threshold similarity score, a user is identified as an authorized user of the first electronic device and the second electronic device. Based on the identification, an output is provided to the second electronic device.

A computing system may generate and/or use a behavior photographic identification (“behavior photo ID”) that is based, at least in part, on anonymized parameters related to the behavior of a person. The behavior can include a history of phone calls, texts, or internet browsing. The behavior photo ID, which may be used to uniquely identify the person, may digitally modify a digital photo to encode behaviors or activities of the person. In some implementations, the behavior photo ID may be modified periodically, or from time to time, to produce an updated behavior photo ID that reflects new external events as well as relatively recent behaviors or activities of the person.

A system for embedding users' preferences and behaviors based on interactions with an array of devices.

Method for improving user authentication efficiency performed by a communication device belonging to an authentication system. The communication device includes a local machine learning engine having a set of N artificial neural network ANN1,i adapted to process N different types of input signals. The method includes the steps: receiving a first set of N input signals S_1(i) for authentication purpose; determining respectively for each of the N input signals S_1(i) by the N artificial neural networks ANN1,i, N estimations LH(i) of the likelihood that a given input signal is provided by a legitimate user; determining based on a risk scoring established using the N estimations LH(i) if the requesting user is authenticated as the legitimate user; if the requested user is authenticated, determining if at least one likelihood estimation determined for a given input signal S_1(j) is below a predetermined threshold T.sub.C,i, and if it is the case: transmitting the input signal S_1 (j) to a remote server implementing a server machine learning engine 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 by the local machine learning engine an additional learning phase using the input signal S_2(j) as an input signal that is not associated to the requesting user.

Method for improving user authentication efficiency performed by a communication device belonging to an authentication system. The communication device includes a local machine learning engine having a set of N artificial neural network ANN1,i adapted to process N different types of input signals. The method includes the steps: receiving a first set of N input signals S_1(i) for authentication purpose; determining respectively for each of the N input signals S_1(i) by the N artificial neural networks ANN1,i, N estimations LH(i) of the likelihood that a given input signal is provided by a legitimate user; determining based on a risk scoring established using the N estimations LH(i) if the requesting user is authenticated as the legitimate user; if the requested user is authenticated, determining if at least one likelihood estimation determined for a given input signal S_1(j) is below a predetermined threshold T.sub.C,i, and if it is the case: transmitting the input signal S_1 (j) to a remote server implementing a server machine learning engine 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 by the local machine learning engine an additional learning phase using the input signal S_2(j) as an input signal that is not associated to the requesting user.

Described embodiments provide systems and methods for context aware frictionless authentication. A server may determine authentication method information, contextual scores and contextual weights of a device, in connection with a user request to access a resource via the device. The authentication method information may include a weight and a completion duration for each of a plurality of authentication methods available via the device. The server may determine an authentication score for each of the plurality of authentication methods using the authentication method information, the contextual scores and the contextual weights of the device. The server may identify a first authentication method from the plurality of authentication methods, according to the determined authentication score. The server may authenticate the user request via the first authentication method using a first device that supports the first authentication method.

An authentication system and authentication method for an electronic cigarette and an electronic cigarette configured to be connected within such system so that the authentication method can be applied to the electronic cigarette. The system can be divided into 3 main components, namely the electronic cigarette itself, a mobile terminal in communication with the electronic cigarette and reading a security label from the electronic cigarette, and a service terminal connected to the mobile terminal, for instance through the cloud. The system and method protects particularly from counterfeit cartridges and secures that a cartridge of the intended content is connected in the electronic cigarette. In addition, age verification can be performed.

A method includes detecting proximity between a mobile device and a remote device associated with a transaction reserved by a user of the mobile device and a mode of the electronic device. A verification password is sent to the remote device responsive to detecting the proximity and the mode. A device includes a module to detect proximity between the device and a remote device associated with a transaction reserved by a user of the device occurring within a predefined distance threshold and a processor coupled to the module. A device includes another module to detect a stationary mode of the electronic device occurring for at least a predefined duration threshold. The processor is sends a verification password to the remote device responsive to detecting the proximity and the mode.