Abstract: Systems and methods of anomaly detection in time-series are disclosed. A time-series dataset is received and a set of segments is iteratively defined from the time-series dataset by identifying a set of changepoints in the time-series dataset based on a changepoint type and a sensitivity parameter, determining whether the set of segments defined by the changepoints satisfy at least one threshold criteria, and modifying the sensitivity parameter when the threshold criteria is not met or outputting the set of segments when the threshold criteria is met. A segment-specific anomaly detection threshold is determined for each segment in the set of segments and a set of anomaly-flagged segments is generated. The set of anomaly flagged segments are generated by an anomaly detection process based on the segment-specific anomaly detection threshold for a corresponding segment. An anomaly-flagged time-series is generated by combining the set of anomaly-flagged segments.

Abstract: Examples provide a delivery-type predictor for automatically determining a delivery-type of an incoming load of items. The delivery-type predictor identifies a dominating vendor from a plurality of vendors supplying a plurality of items the incoming load bound for a distribution center (DC), fulfillment center (FC) or other delivery destination. The dominating vendor is a vendor providing a majority of items in the load. Historical data associated with previous delivery-types of previous loads associated with the dominating vendor are analyzed to generate a predicated delivery-type for the incoming load. If historical data is unavailable, destination data describing a department in a DC or FC is analyzed to generate the prediction. If historical data and department data are unavailable, the delivery-type predictor analyzes warehouse area data to generate the prediction. The prediction identifies whether the incoming load should be designated a live-type delivery or a drop delivery.

Abstract: Embodiments of the disclosure are directed to the use of controlled randomization in authorizing virtual reality interactions. More specifically, a user of a virtual reality (VR) device may seek to initiate an interaction within the virtual reality environment. In order for the interaction to be allowed for the user, a processing computer may need the user to supply an additional credential. In some cases, the user may enter the additional credential using a series of virtual keypads that are rendered in the virtual reality environment. These keypads may have varying layouts that are determined in a controlled manner (e.g., pseudo-randomly) using pre-determined mathematical procedures. The layout of a subsequent keypad may be partially based on the user's selection in a preceding keypad. The keypad positions for the user's selections may be provided to the processing computer to solve for the credential which can be used for validation purposes.

Abstract: Embodiments of the disclosure are directed to the use of controlled randomization in authorizing virtual reality interactions. More specifically, a user of a virtual reality (VR) device may seek to initiate an interaction within the virtual reality environment. In order for the interaction to be allowed for the user, a processing computer may need the user to supply an additional credential. In some cases, the user may enter the additional credential using a series of virtual keypads that are rendered in the virtual reality environment. These keypads may have varying layouts that are determined in a controlled manner (e.g., pseudo-randomly) using pre-determined mathematical procedures. The layout of a subsequent keypad may be partially based on the user's selection in a preceding keypad. The keypad positions for the user's selections may be provided to the processing computer to solve for the credential which can be used for validation purposes.

Abstract: A computer-implemented system and method may allow primary account holders (e.g., parents) to select merchants along specific routes where a secondary account holder (e.g., child) would be able to use a payment device. Each of these merchants may be categorized to further allow a parent to approve or reject purchases with a particular merchant. Any attempt to consummate a financial transaction with such a device at a disallowed merchant or at a merchant outside the defined geo-zone would not go through and may trigger an alert message to the parent/primary account holder (e.g., an email, SMS, phone call, etc.). Furthermore, setting a threshold may enable a trigger to ask the parent for approval in case the child makes a transaction of an amount exceeding the threshold value.