The present disclosure relates to a device, method and system for calculating, estimating, or monitoring the blood pressure of a subject based on physiological features and personalized models. At least one processor, when executing instructions, may perform one or more of the following operations. A first signal representing a pulse wave relating to heart activity of a subject may be received. A plurality of second signals representing time-varying information on a pulse wave of the subject may be received. A personalized model for the subject may be designated. Effective physiological features of the subject based on the plurality of second signals may be determined. A blood pressure of the subject based on the effective physiological features and the designated model for the subject may be calculated.

Encryption key exchange processes are disclosed. A disclosed method includes initiating communication between a portable communication device including a token and a first limited use encryption key, and an access device. After communication is initiated, the portable communication device receives a second limited use key from a remote server via the access device. The portable communication device then replaces the first limited use key with the second limited use key. The second limited use key is thereafter used to create access data such as cryptograms that can be used to conduct access transactions.

Encryption key exchange processes are disclosed. A disclosed method includes initiating communication between a portable communication device including a token and a first limited use encryption key, and an access device. After communication is initiated, the portable communication device receives a second limited use key from a remote server via the access device. The portable communication device then replaces the first limited use key with the second limited use key. The second limited use key is thereafter used to create access data such as cryptograms that can be used to conduct access transactions.

A display assistant device comprises a display, a camera, a speaker, microphones, a processor and memory. The memory stores programs comprising instructions that, when executed by the processor, enable a plurality of modes of the display assistant device. The modes include a monitoring mode and an assistant mode. In the monitoring mode, the device is configured to perform a remote monitoring function in which first video captured by the camera is streamed to a remote server system for monitoring uses. The monitoring uses include transmission of the first video to remote client devices authorized to access the first video. In the assistant mode, the device is configured to perform a second plurality of functions that excludes the monitoring function and includes a video communication function in which second video captured by the camera is transmitted to second devices participating in a video communication with a first user of the device.

A display assistant device comprises a display, a camera, a speaker, microphones, a processor and memory. The memory stores programs comprising instructions that, when executed by the processor, enable a plurality of modes of the display assistant device. The modes include a monitoring mode and an assistant mode. In the monitoring mode, the device is configured to perform a remote monitoring function in which first video captured by the camera is streamed to a remote server system for monitoring uses. The monitoring uses include transmission of the first video to remote client devices authorized to access the first video. In the assistant mode, the device is configured to perform a second plurality of functions that excludes the monitoring function and includes a video communication function in which second video captured by the camera is transmitted to second devices participating in a video communication with a first user of the device.

Account permissions and data accessibility can be modified based on level of confidence for a login attempt to the account. User activity observations corresponding to one or more login attempts to access a user account can be stored. A confidence score associated with a successful login attempt of the user account can be determined. The confidence score is based on the user activity observations. A level of access to an application with functions and data for the user account can be determined. The level of access is based on the confidence score. The level of access is associated with the functions and the data that are executable and accessible subsequent to the successful login attempt.

A piezo haptic keyboard and touchpad user identification system may comprise a processor receiving an authenticating user input identifying an authorized user of the information handling system, and a controller operably connected to a plurality of piezo electric elements situated beneath the keyboard. The controller may detect haptic hardware typing or touch behavior parameters describing characteristics of a plurality of deformations of the piezo electric elements during interaction between the authorized user and the keyboard, and the processor may use machine learning to identify a repeated pattern of values for a combination of the haptic hardware typing or touch behavior parameters reoccurring during interaction between the authorized user and keyboard. The processor may associate the repeated pattern of values for the combination of the haptic hardware typing or touch behavior parameters with the authorized user for later, passive authentication of a user based on typing dynamics.

A computer-implemented method to identify unauthorized use of a device based on a usage pattern. The method includes tracking usage of a device, wherein the usage includes activity by a user interacting with the device. The method includes identifying a usage pattern, wherein the usage pattern is based on usage data. The method further includes generating, based on the usage pattern, a heatmap. The method includes predicting future usage of the device by the user, wherein the predicting includes generating a Markov chain of the predicted future usage. The method also includes determining actual usage is different than the predicted usage. The method further includes calculating, in response to determining the actual usage is different than the predicted future usage, a difference score. The method includes determining the difference score is above a difference threshold, and activating, in response to the difference score being above the difference threshold, an alert.

A system, method, and computer-readable medium are disclosed for performing a lexicon construction operation. The lexicon construction operation includes: identifying a corpus, the corpus comprising a plurality of training events, each of the plurality of training events comprising a term; grouping terms from the plurality of training events into topic clusters; analyzing the plurality of topic clusters, the analyzing providing a plurality of classified clusters; and, deriving a plurality of learned lexicons from the plurality of classified clusters.

Embodiments are described for a pattern-based control system that learns and applies device usage patterns for identifying and disabling devices exhibiting abnormal usage patterns. The system can learn a user's normal usage pattern or can learn abnormal usage patterns, such as a typical usage pattern for a stolen device. This learning can include human or algorithmic identification of particular sets of usage conditions (e.g., locations, changes in settings, personal data access events, application events, IMU data, etc.) or training a machine learning model to identify usage condition combinations or sequences. Constraints (e.g., particular times or locations) can specify circumstances where abnormal pattern matching is enabled or disabled. Upon identifying an abnormal usage pattern, the system can disable the device, e.g., by permanently destroying a physical component, semi-permanently disabling a component, or through a software lock or data encryption.