SYSTEM AND METHOD FOR AUTOMATED CHAT TESTING
    31.
    发明申请
    SYSTEM AND METHOD FOR AUTOMATED CHAT TESTING 审中-公开
    用于自动测试的系统和方法

    公开(公告)号:US20160006874A1

    公开(公告)日:2016-01-07

    申请号:US14854023

    申请日:2015-09-14

    Abstract: A system for flexible and scalable automated end-to-end chat-based contact center testing, having a test case management platform, a chat cruncher, a contact center manager, a chat classifier, a desktop automation engine, and headless browser-based virtual agents and customers. The test case management platform allows a user to configure operation of the system. The chat cruncher operates a plurality of virtual customers. The contact center manager operates a plurality of virtual agents to participate in chat session with virtual customers.

    Abstract translation: 一个灵活和可扩展的自动端到端基于聊天的联络中心测试的系统,具有测试用例管理平台,聊天处理器,联络中心管理器,聊天分类器,桌面自动化引擎和无头基于浏览器的虚拟 代理商和客户。 测试用例管理平台允许用户配置系统的操作。 聊天处理器操作多个虚拟客户。 联络中心经理操作多个虚拟代理以参与与虚拟客户的聊天会话。

    SYSTEM AND METHOD FOR AUTOMATED CHAT TESTING
    32.
    发明申请
    SYSTEM AND METHOD FOR AUTOMATED CHAT TESTING 有权
    用于自动测试的系统和方法

    公开(公告)号:US20160006873A1

    公开(公告)日:2016-01-07

    申请号:US14854003

    申请日:2015-09-14

    Abstract: A system for flexible and scalable automated chat-based contact center testing, comprising a test case management platform, “chat cruncher”, contact center manager, chat classifier, and desktop automation engine, and method for using such a system for automated testing of a contact center's chat-based interactions environment and reporting of test results.

    Abstract translation: 一种灵活和可扩展的基于自动聊天的联络中心测试系统,包括一个测试用例管理平台,“聊天室”,联络中心经理,聊天分类器和桌面自动化引擎,以及使用这样的系统进行自动化测试的方法 联络中心的基于聊天的互动环境和测试结果报告。

    SYSTEM AND METHOD FOR AUTOMATED CHAT TESTING
    33.
    发明申请
    SYSTEM AND METHOD FOR AUTOMATED CHAT TESTING 有权
    用于自动测试的系统和方法

    公开(公告)号:US20140108583A1

    公开(公告)日:2014-04-17

    申请号:US14141424

    申请日:2013-12-27

    Abstract: A system for flexible and scalable automated end-to-end chat-based contact center testing, having a test case management platform, a chat cruncher, a contact center manager, a chat classifier, a desktop automation engine, and headless browser-based virtual agents and customers. The test case management platform allows a user to configure operation of the system. The chat cruncher operates a plurality of virtual customers. The contact center manager operates a plurality of virtual agents to participate in chat session with virtual customers.

    Abstract translation: 一个灵活和可扩展的自动端到端基于聊天的联络中心测试的系统,具有测试用例管理平台,聊天处理器,联络中心管理器,聊天分类器,桌面自动化引擎和无头基于浏览器的虚拟 代理商和客户。 测试用例管理平台允许用户配置系统的操作。 聊天处理器操作多个虚拟客户。 联络中心经理操作多个虚拟代理以参与与虚拟客户的聊天会话。

    System and method for autogenerated synthetic operational customer satisfaction scoring and analysis

    公开(公告)号:US11948160B2

    公开(公告)日:2024-04-02

    申请号:US17579205

    申请日:2022-01-19

    CPC classification number: G06Q30/0201

    Abstract: A system and method for autogenerated synthetic operational customer satisfaction scoring and analysis, that generates synthetic calls and follows customer interactions across multiple channels as a single journey, which monitors synthetic calls for a plurality of failure events that may represent infrastructure related operational deficiencies that may adversely affect customer satisfaction and sentiment, which assigns a synthetic operational customer satisfaction score for each synthetic call, and which uses machine learning to optimize infrastructure configurations to improve customer satisfaction and sentiment.

    SYSTEM AND METHOD FOR AUTOGENERATED SYNTHETIC OPERATIONAL CUSTOMER SATISFACTION SCORING AND ANALYSIS

    公开(公告)号:US20230252496A1

    公开(公告)日:2023-08-10

    申请号:US17579205

    申请日:2022-01-19

    CPC classification number: G06Q30/0201

    Abstract: A system and method for autogenerated synthetic operational customer satisfaction scoring and analysis, that generates synthetic calls and follows customer interactions across multiple channels as a single journey, which monitors synthetic calls for a plurality of failure events that may represent infrastructure related operational deficiencies that may adversely affect customer satisfaction and sentiment, which assigns a synthetic operational customer satisfaction score for each synthetic call, and which uses machine learning to optimize infrastructure configurations to improve customer satisfaction and sentiment.

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