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1. 发明授权

US11665281B2 Call recommendation system and call recommendation method based on artificial intelligence 有权
公开(公告)号：US11665281B2
公开(公告)日：2023-05-30
申请号：US17153054
申请日：2021-01-20
申请人： Byung Kwan Jung , Mi Sung Cho
发明人： Byung Kwan Jung , Mi Sung Cho
IPC分类号： H04M3/51 , H04M15/00 , G06N20/00 , G06N3/08
CPC分类号： H04M3/5175 , G06N3/08 , G06N20/00 , H04M15/49 , H04M15/8044 , H04M2215/745
摘要： A call recommendation system based on artificial intelligence is provided. The call recommendation system includes a data collecting unit, a matching time predicting unit, a price determining unit, and a final ranking determining unit. When a service is requested from a service user, the data collecting unit collects first past data indicating a past location of the service user, first present data indicating a present location of the service user, second past data indicating a past location of a service provider, and second present data indicating a present location of the service provider. The matching time predicting unit inputs the first and second past data and the first and second present data to a recurrent neutral network (RNN) leaning model to predict a future location of the service user and a future location of the service provider and inputs first prediction data regarding the future location of the service user and second prediction data regarding the future location of the service provider to a prediction learning model to predict, when the service provider selects a service, a matching time required until the service provider is matched with a next service user after the service provider completes the service. The price determining unit determines a price for the service such that the price increases as the matching time increases. The final ranking determining unit determines a recommendation rating (or a recommendation priority) of a service among services required for the service provider based on preference data indicating preference of the service provider regarding a service and a price. The RNN learning model and the prediction learning model are based on a deep learning algorithm.

2. 发明申请

US20210274043A1 CALL RECOMMENDATION SYSTEM AND CALL RECOMMENDATION METHOD BASED ON ARTIFICIAL INTELLIGENCE 有权
公开(公告)号：US20210274043A1
公开(公告)日：2021-09-02
申请号：US17153054
申请日：2021-01-20
申请人： Byung Kwan Jung , Mi Sung Cho
发明人： Byung Kwan Jung , Mi Sung Cho
IPC分类号： H04M3/51 , H04M15/00 , G06N20/00 , G06N3/08
摘要： A call recommendation system based on artificial intelligence is provided. The call recommendation system includes a data collecting unit, a matching time predicting unit, a price determining unit, and a final ranking determining unit. When a service is requested from a service user, the data collecting unit collects first past data indicating a past location of the service user, first present data indicating a present location of the service user, second past data indicating a past location of a service provider, and second present data indicating a present location of the service provider. The matching time predicting unit inputs the first and second past data and the first and second present data to a recurrent neutral network (RNN) leaning model to predict a future location of the service user and a future location of the service provider and inputs first prediction data regarding the future location of the service user and second prediction data regarding the future location of the service provider to a prediction learning model to predict, when the service provider selects a service, a matching time required until the service provider is matched with a next service user after the service provider completes the service. The price determining unit determines a price for the service such that the price increases as the matching time increases. The final ranking determining unit determines a recommendation rating (or a recommendation priority) of a service among services required for the service provider based on preference data indicating preference of the service provider regarding a service and a price. The RNN learning model and the prediction learning model are based on a deep learning algorithm.

3. 发明申请

US20100036641A1 System and method of estimating multi-tasking performance 审中-公开
标题翻译：估计多任务性能的系统和方法
公开(公告)号：US20100036641A1
公开(公告)日：2010-02-11
申请号：US12318043
申请日：2008-12-19
申请人： Seung Won Lee , Min Soo Ryu , Byung Kwan Jung
发明人： Seung Won Lee , Min Soo Ryu , Byung Kwan Jung
IPC分类号： G06F15/00
CPC分类号： G06F11/3419 , G06F11/3447 , G06F2201/86 , G06F2201/865
摘要： A method and system of estimating multi-tasking performance performed in multi-processor are described. The method includes dividing a plurality of tasks into a plurality of sub tasks in accordance with predefined operation types, arranging the plurality of sub tasks in accordance with a predecessor-successor structure based on the operation types of the plurality of sub tasks, and estimating multi-tasking performance of the plurality of tasks using the arranged plurality of sub tasks.
摘要翻译：描述了在多处理器中执行多任务处理性能的方法和系统。该方法包括根据预定义的操作类型将多个任务划分为多个子任务，基于多个子任务的操作类型根据前导后继结构排列多个子任务，并且估计多个使用所排列的多个子任务来分析多个任务的性能。

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