Abstract: Embodiments relate to a system comprising a device for temperature recording; an integrated communication module; a memory; a processor coupled with the memory. The processor is configured to: receive data from the device, analyze the data through an analytics module; predict an anomaly in the data through the analytics module; generate an alert for ensuring quality of food and safety of the food; and predict a failure in the device.

Abstract: Embodiments relate to a system comprising a device for temperature recording; an integrated communication module; a memory; a processor coupled with the memory. The processor is configured to: receive data from the device, analyze the data through an analytics module; predict an anomaly in the data through the analytics module; generate an alert for ensuring quality of food and safety of the food; and predict a failure in the device.

Abstract: Embodiments relate to a system comprising a device for temperature recording; an integrated communication module; a memory; a processor coupled with the memory. The processor is configured to: receive data from the device, analyze the data through an analytics module; predict an anomaly in the data through the analytics module; generate an alert for ensuring quality of food and safety of the food; and predict a failure in the device.

Abstract: Systems and methods for identifying a pattern in data to detect a behavior of interest. The systems and methods receive a data stream representing a series of events occurring over a time interval. The systems and methods determine, for the interval, a depth indicating the amount of interest during the interval, where the interest represents an amount of a selected activity during that interval for a selected parameter. The systems and methods also generate a depth chart for the activities at different values of the selected parameter over a series of intervals, train a machine-learning image-classification model using depth chart images; identify the behavior of interest using predictions from the trained machine-learning model and target pattern; and provide an indication of the presence or absence of the behavior of interest. The parameter can represent a quantity and associated price of a commodity in a market.

Abstract: In some embodiments, an apparatus includes a set of memory modules configured to store data and a reprogrammable circuit module operatively coupled to the set of memory modules. The reprogrammable circuit module is configured to receive, from a host device, information associated with a search request. The reprogrammable circuit module is configured to change from a first configuration to a second configuration in response to receiving the information. The reprogrammable circuit module is configured to retrieve at least a portion of the data stored at the set of memory modules associated with the second configuration. The reprogrammable circuit module is configured to generate a search result based on the portion of the data, and transmit the search result to the host device.

Abstract: In some embodiments, an apparatus includes a set of memory modules configured to store data and a reprogrammable circuit module operatively coupled to the set of memory modules. The reprogrammable circuit module is configured to receive, from a host device, information associated with a search request. The reprogrammable circuit module is configured to change from a first configuration to a second configuration in response to receiving the information. The reprogrammable circuit module is configured to retrieve at least a portion of the data stored at the set of memory modules associated with the second configuration. The reprogrammable circuit module is configured to generate a search result based on the portion of the data, and transmit the search result to the host device.