Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for authentication based on physical interaction and characteristic noise patterns. Execution of a requested transaction may be conditioned upon satisfaction of an authentication requirement. For example, the requesting user may be prompted to perform a physical interaction such as a swipe across a screen of a client device. The sensor data includes a characteristic noise pattern caused by manufacturing deviations of the set of sensors that captured the sensor data. The sensor data describing the physical interaction and the characteristic noise pattern are used to determine whether the authentication requirement has been satisfied. For example, the sensor data and characteristic noise pattern are used to determine whether the user that performed the physical interaction is an authorized user. The authentication requirement is satisfied upon determining that the user that performed the physical interaction is an authorized user.

Abstract: A method may involve, for each of one or more messages that are selected from a plurality of messages from an account: (a) extracting one or more phrases from a respective selected message; (b) determining that a conversation includes the respective selected message and one or more other messages from the plurality of messages; (c) generating a first feature vector based on the conversation, wherein the first feature vector includes one or more first features, wherein the one or more first features include one or more words from the conversation; and (d) generating, by a computing system, one or more training-data sets, wherein each training-data set comprises one of the phrases and the first feature vector.

Abstract: A device authenticates a request to verify a user. The device accesses a face image that depicts a face of the person and includes a characteristic noise pattern inserted by a camera of the device. The device also accesses a geolocation at which the device captured the face image and inputs the face image and the geolocation into an artificial intelligence engine that outputs a face score, a device score, and a location score. The device next submits the request with the scores to a server machine and obtains an authentication score from the server machine. The device then presents an indication that the request to verify the person is authentic based on a comparison of the obtained authentication score to a threshold authentication score.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for detecting fraudulent transactions. A fraud detection system determines whether transactions are fraudulent based on a machine learning model framework that leverages a combination of transaction data describing a transaction and sequence data describing a sequence of event preceding the transaction. The machine learning model framework includes multiple feature models that each provide an output based on a different set of feature data describing the transaction, as well an events sequence model that provides an output based on the sequence data describing the sequence of event preceding the transaction. The output of these machine learning models is used to generate a cumulative input that is provided into a secondary machine learning model that outputs a probability value indicating a likelihood that the transaction is fraudulent.

Abstract: A door assembly for an appliance includes an inner door panel and outer door panel that define an interior chamber for receiving at least a first light source and a second light source. An indicator lens extends from a split receiving surface facing the light sources to an indicator aperture defined in the outer door panel. The split receiving surface has a first surface for receiving light from the first light source and a second surface for receiving light from the second light source. In addition, an opaque partition is positioned between the first light source and the second light source for at least partially blocking light from first light source and the second light source from bleeding onto the second surface and the first surface, respectively.

Abstract: A method may involve, for each of one or more messages that are selected from a plurality of messages from an account: (a) extracting one or more phrases from a respective selected message; (b) determining that a conversation includes the respective selected message and one or more other messages from the plurality of messages; (c) generating a first feature vector based on the conversation, wherein the first feature vector includes one or more first features, wherein the one or more first features include one or more words from the conversation; and (d) generating, by a computing system, one or more training-data sets, wherein each training-data set comprises one of the phrases and the first feature vector.

Abstract: A device authenticates a request to verify a user. The device accesses a face image that depicts a face of the person and includes a characteristic noise pattern inserted by a camera of the device. The device also accesses a geolocation at which the device captured the face image and inputs the face image and the geolocation into an artificial intelligence engine that outputs a face score, a device score, and a location score. The device next submits the request with the scores to a server machine and obtains an authentication score from the server machine. The device then presents an indication that the request to verify the person is authentic based on a comparison of the obtained authentication score to a threshold authentication score.

Abstract: The described techniques relate to coordinating and managing faults of systems of a vehicle, such as an autonomous vehicle, to enable the vehicle to respond safely and appropriately to the faults. In examples, a centralized fault monitor system receives faults from different vehicle systems, maps the received faults to associated fault constraints, prioritizes different and shared parameters between the fault constraints, and communicates the constraint parameters to appropriate vehicle systems accordingly.

Abstract: The present disclosure is generally directed to systems and methods for providing privacy to a user of a user device that is used for interacting with a networked software platform. A server computer coupled to the user device receives a hashed device ID of the device and generates a unique user ID in the form of a unique number. The user ID can be used by the server and other entities to gather information related to the activities of the user with respect to the networked software platform, which can be, for example, a video game platform, a social media platform, or a health-related diagnostic tool. The identity of the user remains anonymous during the information gathering procedures because neither the device ID nor the identity of the user is transmitted over the network when the user is participating in activities of the networked software platform.

Abstract: Malicious attacks by certain devices against a radio access network (RAN) can be detected and mitigated, while allowing communication of priority messages. A security management component (SMC) can determine whether a malicious attack against the RAN is occurring based on a defined baseline that indicates whether a malicious attack is occurring. The defined baseline is determined based on respective characteristics associated with respective devices that are determined based on analysis of information relating to the devices. In response to determining there is a malicious attack, SMC determines whether to block connections of devices to the RAN based on respective priority levels associated with respective messages being communicated by the devices.

Abstract: The described techniques relate to coordinating and managing faults of systems of a vehicle, such as an autonomous vehicle, to enable the vehicle to respond safely and appropriately to the faults. In examples, a centralized fault monitor system receives faults from different vehicle systems, maps the received faults to associated fault constraints, prioritizes different and shared parameters between the fault constraints, and communicates the constraint parameters to appropriate vehicle systems accordingly.

Abstract: The described techniques relate to coordinating and managing faults of systems of a vehicle, such as an autonomous vehicle, to enable the vehicle to respond safely and appropriately to the faults. In examples, a centralized fault monitor system receives faults from different vehicle systems, maps the received faults to associated fault constraints, prioritizes different and shared parameters between the fault constraints, and communicates the constraint parameters to appropriate vehicle systems accordingly.

Abstract: A machine trains an artificial intelligence engine to facilitate authentication of a request to verify a user. The machine accesses a reference set of obfuscated geolocations generated from actual geolocations from which a device submitted requests to verify the user. The machine groups the obfuscated geolocations into geographical clusters based on a predetermined cluster radius value and calculates a corresponding representative geolocation for each geographical cluster and a corresponding variance distance from the representative geolocation for each geographical cluster. The machine then generates a reference location score based on the representative geolocations of the geographical clusters and on the variance distances of the geographical clusters. The machine trains an artificial intelligence engine to output that reference location score in response to the reference set being input thereto. The trained artificial intelligence engine may then be provided to one or more devices.

Abstract: A machine trains an artificial intelligence engine to facilitate authentication of a request to verify a user. The machine accesses a reference set of obfuscated geolocations generated from actual geolocations from which a device submitted requests to verify the user. The machine groups the obfuscated geolocations into geographical clusters based on a predetermined cluster radius value and calculates a corresponding representative geolocation for each geographical cluster and a corresponding variance distance from the representative geolocation for each geographical cluster. The machine then generates a reference location score based on the representative geolocations of the geographical clusters and on the variance distances of the geographical clusters. The machine trains an artificial intelligence engine to output that reference location score in response to the reference set being input thereto. The trained artificial intelligence engine may then be provided to one or more devices.

Abstract: A door assembly for an appliance includes an inner door panel and outer door panel that define an interior chamber for receiving at least a first light source and a second light source. An indicator lens extends from a split receiving surface facing the light sources to an indicator aperture defined in the outer door panel. The split receiving surface has a first surface for receiving light from the first light source and a second surface for receiving light from the second light source. In addition, an opaque partition is positioned between the first light source and the second light source for at least partially blocking light from first light source and the second light source from bleeding onto the second surface and the first surface, respectively.

Abstract: A device authenticates a request to verify a user. The device accesses a face image that depicts a face of the person and includes a characteristic noise pattern inserted by a camera of the device. The device also accesses a geolocation at which the device captured the face image and inputs the face image and the geolocation into an artificial intelligence engine that outputs a face score, a device score, and a location score. The device next submits the request with the scores to a server machine and obtains an authentication score from the server machine. The device then presents an indication that the request to verify the person is authentic based on a comparison of the obtained authentication score to a threshold authentication score.

Abstract: A device authenticates a request to user. The device accesses a face image that depicts a face of the person and includes a characteristic noise pattern inserted by a camera of the device. The device also accesses a geolocation which the device captured the face image and puts the face image and the geolocation into an artificial intelligence engine that outputs a face score, device score, and a location score. The device next submits the request with the scores to a server machine and obtains an authentication score from the server machine. The device then presents an indication that the request to verify the person is authentic based on a comparison of the obtained authentication score to a threshold authentication score.

Abstract: An appliance includes a control console. The control console includes a console cover with a circuit board. A front surface of the circuit board is attached to an interior surface of the console cover. A capacitive touch sensor is in operative communication with a face surface of the console cover and an LED is on a back surface of the circuit board. A light guide is mounted on the circuit board and provides optical communication from the LED to the face surface of the console cover.

Abstract: A machine trains an artificial intelligence engine to facilitate authentication of a request to verify a user. The machine accesses a reference set of obfuscated geolocations generated from actual geolocations from which a device submitted requests to verify the user. The machine groups the obfuscated geolocations into geographical clusters based on a predetermined cluster radius value and calculates a corresponding representative geolocation for each geographical cluster and a corresponding variance distance from the representative geolocation for each geographical cluster. The machine then generates a reference location score based on the representative geolocations of the geographical clusters and on the variance distances of the geographical clusters. The machine trains an artificial intelligence engine to output that reference location score in response to the reference set being input thereto. The trained artificial intelligence engine may then be provided to one or more devices.

Abstract: A device authenticates a request to user. The device accesses a face image that depicts a face of the person and includes a characteristic noise pattern inserted by a camera of the device. The device also accesses a geolocation which the device captured the face image and puts the face image and the geolocation into an artificial intelligence engine that outputs a face score, device score, and a location score. The device next submits the request with the scores to a server machine and obtains an authentication score from the server machine. The device then presents an indication that the request to verify the person is authentic based on a comparison of the obtained authentication score to a threshold authentication score.