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公开(公告)号：US20230325742A1
公开(公告)日：2023-10-12
申请号：US18332820
申请日：2023-06-12
申请人： Verint Americas Inc.
IPC分类号： G06Q10/0631 , G06Q10/0633
CPC分类号： G06Q10/06315 , G06Q10/0633 , G06Q10/063116 , G06Q10/063118
摘要： In an entity such as a call center, back office, or retail operation, external event data is recorded along with call volume information for a plurality of time intervals. Based on the recorded event data and call volume for the plurality of intervals, a model is trained to predict call (or other communication) volume for a specified time interval using the external event data. The external event data may include data about one or more events that may affect the demand received by the entity. When the predicted call volume is significantly above or below what would be predicted for the entity using historical data alone, an indicator may be displayed to a user or administrator that identifies the external event that is responsible for the lower or higher prediction. The call volume prediction may be used to schedule one or more agents (or other employees) to work during the specified time interval.
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公开(公告)号：US20220180276A1
公开(公告)日：2022-06-09
申请号：US17114602
申请日：2020-12-08
申请人： Verint Americas Inc.
IPC分类号： G06Q10/06
摘要： In an entity such as a call center, back office, or retail operation, external event data is recorded along with call volume information for a plurality of time intervals. Based on the recorded event data and call volume for the plurality of intervals, a model is trained to predict call (or other communication) volume for a specified time interval using the external event data. The external event data may include data about one or more events that may affect the demand received by the entity. When the predicted call volume is significantly above or below what would be predicted for the entity using historical data alone, an indicator may be displayed to a user or administrator that identifies the external event that is responsible for the lower or higher prediction. The call volume prediction may be used to schedule one or more agents (or other employees) to work during the specified time interval.
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公开(公告)号：US20240020545A1
公开(公告)日：2024-01-18
申请号：US17812312
申请日：2022-07-13
申请人： Verint Americas Inc.
IPC分类号： G06N5/02
CPC分类号： G06N5/022
摘要： The present disclosure describes methods and systems for selecting the forecasting algorithm to use for a prediction based on motifs. A motif is a pattern of interval values that is found to repeat in time series data. Time series data that includes historical demand data (e.g., average communication volume) for an entity at various time intervals in the past is received. The time series data is processed to identify motifs. For each identified motif, the forecasting algorithm that best predicts the historical demand data for time intervals associated with the motif is determined. Later, when the entity desires to receive a forecast for a future time interval, the motif associated with the future time interval is determined. The forecasting algorithm determined to best predict demand for the determined motif is then used to predict the demand for the future time interval.
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公开(公告)号：US20240020589A1
公开(公告)日：2024-01-18
申请号：US17812282
申请日：2022-07-13
申请人： Verint Americas Inc.
摘要： Methods and systems for selecting a forecasting algorithm to use for a forecast for a time interval are provided. A class is a series of time intervals that is selected by an entity from time series data that relates to external data or is a series of time intervals from the time series data that corresponds to a motif. The time series data is processed by a computer to identify motifs, and classes are generated based on each identified motif. A user may further identify one or more classes in the time series data. For each class, the forecasting algorithm that best predicts the historical demand data for time intervals associated with the class is determined. Later, when the entity desires to receive a forecast for a future time interval, the class associated with the future time interval is determined. The forecasting algorithm determined to best predict demand for the determined class is then used.
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