Abstract: Workers often forget to enter clockings indicating when they have arrived or left work or transferred between work activities. Various embodiments provide a system that predicts the risk of potential missed worker clockings and delivers alerts to the workers and/or their managers when that risk is high. The system analyzes historical data to find previous instances of missed clockings at an organization, extracts feature information regarding the context of the missed clockings, and calibrates a missing clocking risk model. The model is then used in either an online or batch mode to predict the risk of workers missing upcoming scheduled clockings. Based on these risk scores and system configurations, alerts are generated for employees and managers reminding them about the upcoming clockings and lowering the amount of missed clockings and subsequent remedies.

Abstract: Workers often forget to enter clockings indicating when they have arrived or left work or transferred between work activities. Various embodiments provide a system that predicts the risk of potential missed worker clockings and delivers alerts to the workers and/or their managers when that risk is high. The system analyzes historical data to find previous instances of missed clockings at an organization, extracts feature information regarding the context of the missed clockings, and calibrates a missing clocking risk model. The model is then used in either an online or batch mode to predict the risk of workers missing upcoming scheduled clockings. Based on these risk scores and system configurations, alerts are generated for employees and managers reminding them about the upcoming clockings and lowering the amount of missed clockings and subsequent remedies.

Abstract: Methods for estimating multiple types of retail business volume based on multiple types of data are described. Historical volume data, prior recorded business volume, characteristics of the store including departments, and geographical location are used. Historical data is transformed into multiple features that capture seasonality, trends, the effects of special events and other business characteristics. This data can be pooled based on business characteristics, and then machine learning regression models, e.g., multiple regression trees, are fitted to each pool of data. To estimate future volume, the same features are computed, and the regression model is applied. The estimates are presented back to the user, or transmitted electronically to other systems, including systems for creating worker schedules based on predicted volumes. Systems, apparatus and computer readable media are also described.

Abstract: Workers often forget to enter clockings indicating when they have arrived or left work or transferred between work activities. Various embodiments provide a system that predicts the risk of potential missed worker clockings and delivers alerts to the workers and/or their managers when that risk is high. The system analyzes historical data to find previous instances of missed clockings at an organization, extracts feature information regarding the context of the missed clockings, and calibrates a missing clocking risk model. The model is then used in either an online or batch mode to predict the risk of workers missing upcoming scheduled clockings. Based on these risk scores and system configurations, alerts are generated for employees and managers reminding them about the upcoming clockings and lowering the amount of missed clockings and subsequent remedies.

Abstract: Methods for estimating multiple types of retail business volume based on multiple types of data are described. Historical volume data, prior recorded business volume, characteristics of the store including departments, and geographical location are used. Historical data is transformed into multiple features that capture seasonality, trends, the effects of special events and other business characteristics. This data can be pooled based on business characteristics, and then machine learning regression models, e.g., multiple regression trees, are fitted to each pool of data. To estimate future volume, the same features are computed, and the regression model is applied. The estimates are presented back to the user, or transmitted electronically to other systems, including systems for creating worker schedules based on predicted volumes. Systems, apparatus and computer readable media are also described.

Abstract: A kitchen or cooking utensil has a head portion and a handle portion. A handle shaft connects the head portion and the handle portion of the utensil. The handle shaft is provided with perforations for straining liquid from foodstuffs that are located in a container. The utensil may optionally incorporate a magnet in the handle shaft near the head portion of the utensil to assist in holding the handle shaft against the rim of the container. In an alternative embodiment, the handle shaft has a grater and/or zester function.