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    • 42. 发明授权
    • Natural language classification within an automated response system
    • 自动语言分类在自动化响应系统中
    • US07606714B2
    • 2009-10-20
    • US10939288
    • 2004-09-10
    • David R. WilliamsJeffrey Hill
    • David R. WilliamsJeffrey Hill
    • G10L15/22
    • H04M3/493G06F17/30684G10L13/027G10L15/1822G10L15/22H04M3/5166
    • An automated response system (e.g., an automated voice response system) may employ learning strategies to develop or improve automated response capabilities. Learning strategies may include using communications(e.g., utterances, text messages, etc.) of one party in a conversation (e.g., a customer service agent) to identify and categorize communications of another party in the conversation (e.g., a caller). Classifiers can be build from the categorized communications. Classifiers can be used to identify common communications patterns of a party in a conversation (e.g., an agent). Learning strategies may also include selecting communications as learning opportunities to improve automated response capabilities based on selection criteria (e.g., selection criteria chosen to ensure that the system does not learn from unreliable or insignificant examples).
    • 自动化响应系统(例如,自动语音响应系统)可以采用学习策略来开发或提高自动化响应能力。 学习策略可以包括在会话(例如,客户服务代理)中使用一方的通信(例如,话语,文本消息等)来识别和分类对话中的另一方的通信(例如,呼叫者)。 分类器可以从分类通信中构建。 分类器可以用于识别会话中的一方的通用模式(例如,代理)。 学习策略还可以包括选择通信作为学习机会,以根据选择标准(例如,选择以确保系统不从不可靠或不重要的示例中学习的选择标准)提高自动化响应能力。
    • 46. 发明申请
    • Machine learning
    • 机器学习
    • US20050105712A1
    • 2005-05-19
    • US10939288
    • 2004-09-10
    • David WilliamsJeffrey Hill
    • David WilliamsJeffrey Hill
    • G10L15/22H04M3/00H04M5/00G10L11/00G10L21/00
    • H04M3/493G06F17/30684G10L13/027G10L15/1822G10L15/22H04M3/5166
    • An automated response system (e.g., an automated voice response system) may employ learning strategies to develop or improve automated response capabilities. Learning strategies may include using communications(e.g., utterances, text messages, etc.) of one party in a conversation (e.g., a customer service agent) to identify and categorize communications of another party in the conversation (e.g., a caller). Classifiers can be build from the categorized communications. Classifiers can be used to identify common communications patterns of a party in a conversation (e.g., an agent). Learning strategies may also include selecting communications as learning opportunities to improve automated response capabilities based on selection criteria (e.g., selection criteria chosen to ensure that the system does not learn from unreliable or insignificant examples).
    • 自动化响应系统(例如,自动语音响应系统)可以采用学习策略来开发或提高自动化响应能力。 学习策略可以包括在会话(例如,客户服务代理)中使用一方的通信(例如,话语,文本消息等)来识别和分类对话中的另一方的通信(例如,呼叫者)。 分类器可以从分类通信中构建。 分类器可以用于识别会话中的一方的通用模式(例如,代理)。 学习策略还可以包括选择通信作为学习机会,以根据选择标准(例如,选择以确保系统不从不可靠或不重要的示例中学习的选择标准)提高自动化响应能力。