Abstract: Methods and systems are disclosed herein for using anomaly detection in timeseries data of user sentiment to detect incidents in computing systems and identify events within an enterprise. An anomaly detection system may receive social media messages that include a timestamp indicating when each message was published. The system may generate sentiment identifiers for the social media messages. The sentiment identifiers and timestamps associated with the social media messages may be used to generate a timeseries dataset for each type of sentiment identifier. The timeseries datasets may be input into an anomaly detection model to determine whether an anomaly has occurred. The system may retrieve textual data from the social media messages associated with the detected anomaly and may use the text to determine a computing system or event associated with the detected anomaly.

Abstract: The innovation discloses systems, methods and computer program products that reduce complexity and associated measures that are taken to ensure trust in secured transactions as necessitated by requirements present only in an ecommerce environment that do not have a parallel with traditional bricks and mortar commercial transactions. The innovation enables a multitude of mobile wallet applications to engage and obtain services from ecommerce applications wherein backend processing by a mobile wallet server obtains and delivers funds to ecommerce applications without sharing a subset of sensitive data, associated with a mobile wallet application user for the secured transactions. The innovation further provides for a wallet network to be initiated and engaged in which the secured transactions can take place, adopting and adapting social network application connectivity.

Abstract: Methods and systems are disclosed herein for using anomaly detection in timeseries data of user sentiment to detect incidents in computing systems and identify events within an enterprise. An anomaly detection system may receive social media messages that include a timestamp indicating when each message was published. The system may generate sentiment identifiers for the social media messages. The sentiment identifiers and timestamps associated with the social media messages may be used to generate a timeseries dataset for each type of sentiment identifier. The timeseries datasets may be input into an anomaly detection model to determine whether an anomaly has occurred. The system may retrieve textual data from the social media messages associated with the detected anomaly and may use the text to determine a computing system or event associated with the detected anomaly.

Abstract: A pressure monitoring system is disclosed herein comprising at least one pressure and/or flow sensors that is fully integrated into a proximal catheter of a ventriculoperitoneal (VP) shunt. The pressure and/or flow sensor is wired to a transceiver, such as an inductively-charged Bluetooth wireless transceiver, on an external surface of a patient's skull to transmit a detected intracranial pressure (ICP) to a remote monitoring device, wherein the remote monitoring device comprises a computer and/or a mobile device with a compatible application for information analysis.