Abstract: The present disclosure is directed toward eye-tracking by scanning at least one scan beam over a scan region on an eye using a MEMS scanner at a first location, detecting a plurality of glints reflected from the scan region at a plurality of detectors, defining a plane for each glint that includes the location of its respective scanner and its respective detector, and identifying the corneal center of the eye based on the intersection of the plurality of planes. A gaze vector for the eye is then determined based on the corneal center and a pupil center identified using pupillometry.

Abstract: Aspects of the present disclosure describe systems, methods, and structures that provide eye-tracking by 1) steering a beam of light through the effect of a microelectromechanical system (MEMS) onto a surface of the eye and 2) detecting light reflected from features of the eye including corneal surface, pupil, iris—among others. Positional/geometric/feature/structural information pertaining to the eye is determined from timing information associated with the reflected light.

Abstract: Aspects of the present disclosure describe systems, methods, and structures that provide eye-tracking by 1) steering a beam of light through the effect of a microelectromechanical system (MEMS) onto a surface of the eye and 2) detecting light reflected from features of the eye including corneal surface, pupil, iris—among others. Positional/geometric/feature/structural information pertaining to the eye is determined from timing information associated with the reflected light.

Abstract: Aspects of the present disclosure describe systems, methods, and structures that provide eye-tracking by 1) steering a beam of light through the effect of a microelectromechanical system (MEMS) onto a surface of the eye and 2) detecting light reflected from features of the eye including corneal surface, pupil, iris—among others. Positional/geometric/feature/structural information pertaining to the eye is determined from timing information associated with the reflected light.

Abstract: Aspects of the present disclosure describe systems, methods, and structures that provide eye-tracking by 1) steering a beam of light through the effect of a microelectromechanical system (MEMS) onto a surface of the eye and 2) detecting light reflected from features of the eye including corneal surface, pupil, iris—among others. Positional/geometric/feature/structural information pertaining to the eye is determined from timing information associated with the reflected light.

Abstract: A real-time asynchronous event aggregation system, method, and network device are configured to capture real-time asynchronous events, and to pass them as input to one or more aggregation engines to determine a reputation for a target. The aggregation engine(s) may then send out notifications where a reputation category changes for a target, indicating that an action may be taken to inhibit spam messages from the target, highlight a display of content from the target, or the like. As such, the event-driven aggregation engines may be designed to capture real-time asynchronous events, such as reputation reports for a wide variety of activities, including, but not limited to spam and/or not-spam messages, determining a reputation on a posting of comments to a movie, a blog posting, a play list posting, or the like. In one embodiment, a reputation of the sender of the reputation event may also be determined.

Abstract: A real-time asynchronous event aggregation system, method, and network device are configured to capture real-time asynchronous events, and to pass them as input to one or more aggregation engines to determine a reputation for a target. The aggregation engine(s) may then send out notifications where a reputation category changes for a target, indicating that an action may be taken to inhibit spam messages from the target, highlight a display of content from the target, or the like. As such, the event-driven aggregation engines may be designed to capture real-time asynchronous events, such as reputation reports for a wide variety of activities, including, but not limited to spam and/or not-spam messages, determining a reputation on a posting of comments to a movie, a blog posting, a play list posting, or the like. In one embodiment, a reputation of the sender of the reputation event may also be determined.

Abstract: A real-time asynchronous event aggregation system, method, and network device are configured to capture real-time asynchronous events, and to pass them as input to one or more aggregation engines to determine a reputation for a target. The aggregation engine(s) may then send out notifications where a reputation category changes for a target, indicating that an action may be taken to inhibit spam messages from the target, highlight a display of content from the target, or the like. As such, the event-driven aggregation engines may be designed to capture real-time asynchronous events, such as reputation reports for a wide variety of activities, including, but not limited to spam and/or not-spam messages, determining a reputation on a posting of comments to a movie, a blog posting, a play list posting, or the like. In one embodiment, a reputation of the sender of the reputation event may also be determined.

Abstract: A system, methods, and apparatus are provided for ensuring consistency of derived data, relative to primary data, in a distributed data storage system. Primary data and derived data are stored on and/or managed by separate components of the data storage system, such as different storage engines. Primary data are written and updated as specified in write requests, which may be queries directed at the primary storage engine. Results of primary data writes are delivered directly to the derived storage engine. If an update to derived data fails, a record is made; if the update succeeds, any recorded failed writes to the same data are cleared. The derived storage engine also receives write results via a change capture stream of events affecting the primary data, and can use these copies of write results to fix failed updates and to clear failures from the failed write records.

Abstract: A real-time asynchronous event aggregation system, method, and network device are configured to capture real-time asynchronous events, and to pass them as input to one or more aggregation engines to determine a reputation for a target. The aggregation engine(s) may then send out notifications where a reputation category changes for a target, indicating that an action may be taken to inhibit spam messages from the target, highlight a display of content from the target, or the like. As such, the event-driven aggregation engines may be designed to capture real-time asynchronous events, such as reputation reports for a wide variety of activities, including, but not limited to spam and/or not-spam messages, determining a reputation on a posting of comments to a movie, a blog posting, a play list posting, or the like. In one embodiment, a reputation of the sender of the reputation event may also be determined.

Abstract: A real-time asynchronous event aggregation system, method, and network device are configured to capture real-time asynchronous events, and to pass them as input to one or more aggregation engines to determine a reputation for a target. The aggregation engine(s) may then send out notifications where a reputation category changes for a target, indicating that an action may be taken to inhibit spam messages from the target, highlight a display of content from the target, or the like. As such, the event-driven aggregation engines may be designed to capture real-time asynchronous events, such as reputation reports for a wide variety of activities, including, but not limited to spam and/or not-spam messages, determining a reputation on a posting of comments to a movie, a blog posting, a play list posting, or the like. In one embodiment, a reputation of the sender of the reputation event may also be determined.

Abstract: A network device and method are directed towards detecting and blocking spam within a message by rapidly performing a predication analysis based on early received sender reputation reports. Reputation reports may be received from a plurality of users receiving messages that they interpret as either spam or non-spam. A reputation report rate prediction table is employed to predict the total number of user reports that will occur. The total number of spam reports and not spam (ham) reports for may then be used with a sender categorization model to rapidly predict whether the sender is likely to be a spammer. If the sender is determined to be a spammer, various preventative actions may be taken, including, but not limited to blocking messages from the sender to users, alerting third party filter generators of the spammer, or the like.

Abstract: A real-time asynchronous event aggregation system, method, and network device are configured to capture real-time asynchronous events, and to pass them as input to one or more aggregation engines to determine a reputation for a target. The aggregation engine(s) may then send out notifications where a reputation category changes for a target, indicating that an action may be taken to inhibit spam messages from the target, highlight a display of content from the target, or the like. As such, the event-driven aggregation engines may be designed to capture real-time asynchronous events, such as reputation reports for a wide variety of activities, including, but not limited to spam and/or not-spam messages, determining a reputation on a posting of comments to a movie, a blog posting, a play list posting, or the like. In one embodiment, a reputation of the sender of the reputation event may also be determined.

Abstract: A network device and method are directed towards detecting and blocking spam within a message by rapidly performing a predication analysis based on early received sender reputation reports. Reputation reports may be received from a plurality of users receiving messages that they interpret as either spam or non-spam. A reputation report rate prediction table is employed to predict the total number of user reports that will occur. The total number of spam reports and not spam (ham) reports for may then be used with a sender categorization model to rapidly predict whether the sender is likely to be a spammer. If the sender is determined to be a spammer, various preventative actions may be taken, including, but not limited to blocking messages from the sender to users, alerting third party filter generators of the spammer, or the like.