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1. 发明申请

US20140372344A1 Adaptive User Interfaces 审中-公开
标题翻译：自适应用户界面
公开(公告)号：US20140372344A1
公开(公告)日：2014-12-18
申请号：US14277316
申请日：2014-05-14
申请人： InsideSales.com, Inc.
发明人： Richard Glenn Morris , Xinchuan Zeng , David Randal Elkington
IPC分类号： G06N99/00
CPC分类号： G06N20/00 , G06N5/02
摘要： According to various embodiments, user performance and/or motivation for a computing system may be maximized by optimizing one or more target components of a user interface of the computing system. The target components may be aspects of the user interface that is perceived by the user. One or more input features and one or more output features may be identified, and data regarding these input and output features may be gathered. This data may be compared with the results generated by a set of candidate artificial intelligence algorithms to determine which of them provides the best fit with the data collected. Then, the selected artificial intelligence algorithm may be applied to the user interface to iteratively change the target components over time until the optimal settings for each user are discovered.
摘要翻译：根据各种实施例，可以通过优化计算系统的用户界面的一个或多个目标组件来最大化计算系统的用户性能和/或动机。目标组件可以是用户感知的用户界面的方面。可以识别一个或多个输入特征和一个或多个输出特征，并且可以收集关于这些输入和输出特征的数据。该数据可以与由一组候选人工智能算法产生的结果进行比较，以确定其中的哪一个与收集的数据提供最佳匹配。然后，所选择的人工智能算法可以被应用于用户界面，以随着时间的推移来迭代地改变目标分量，直到发现每个用户的最佳设置。

2. 发明申请

US20140143344A1 Systems and Methods for Transferring Personal Session Information for Telephonic Use 审中-公开
标题翻译：传输个人会话信息用于电话使用的系统和方法
公开(公告)号：US20140143344A1
公开(公告)日：2014-05-22
申请号：US14022191
申请日：2013-09-09
申请人： InsideSales.com, Inc.
发明人： Thomas Jeffrey Purdy , David Randal Elkington , Matthew Coston Parker
IPC分类号： H04L29/06
CPC分类号： H04L65/403 , H04M3/5191 , H04M7/003
摘要： Disclosed herein are systems and associated methods for operating web interactive services in conjunction with communication services, linking the communication with the interaction by means of a session-specific identifier such as a telephone number. During the course of a web session, user interaction information may be collected, that information potentially indicating subjects of interest to a user associated with the session-specific identifier. In the event the user uses the identifier make a contact regarding the information, the identifier can be used to associate the interaction information and subjects of interest, such that the contact may have that information and those subjects available to assist a user making contact. Interaction and subject information may also be used to customize the interaction with a contacting user with regard to the routing of a telephone call, a greeting, a product or service offering, or other communication.
摘要翻译：本文公开的是用于与通信服务一起操作web交互式服务的系统和相关联的方法，通过诸如电话号码的会话特定标识符将通信与交互相关联。在网络会话期间，可以收集用户交互信息，该信息潜在地指示与会话特定标识符相关联的用户感兴趣的主题。如果用户使用该标识符来形成关于该信息的联系人，那么该标识符可用于将交互信息和感兴趣的主题相关联，使得该联系人可以具有该信息，并且该些主题可用于协助用户进行联系。还可以使用交互和主题信息来定制关于电话呼叫，问候，产品或服务提供或其他通信的路由的与联系用户的交互。

3. 发明申请

US20150181041A1 Intelligently Routing Inbound Communication 审中-公开
标题翻译：智能路由入站通信
公开(公告)号：US20150181041A1
公开(公告)日：2015-06-25
申请号：US14140439
申请日：2013-12-24
申请人： InsideSales.com, Inc.
发明人： David Randal Elkington , Xinchuan Zeng
IPC分类号： H04M3/523
CPC分类号： H04M3/5235 , G06N3/0454 , G06N3/084
摘要： Intelligently routing inbound communication. In one example embodiment, a method for routing an inbound communication includes several steps. First, a notification of the inbound communication is received that includes intrinsic information about the initiator of the communication. Next, the intrinsic information about an initiator of the communication is used to retrieve non-intrinsic information about the initiator of the communication from a data store. Finally, the non-intrinsic information is used to determine a probable destination of the communication by inputting at least some non-intrinsic information into a machine learning model to rank the available destinations.
摘要翻译：智能路由入站通信。在一个示例实施例中，用于路由入站通信的方法包括若干步骤。首先，接收到包括关于通信发起者的固有信息的入站通信的通知。接下来，使用关于通信的发起者的固有信息来从数据存储器检索关于通信的发起者的非内在信息。最后，非固有信息用于通过将至少一些非固有信息输入到机器学习模型来对可用目的地进行排序来确定通信的可能目的地。

4. 发明申请

US20140279739A1 RESOLVING AND MERGING DUPLICATE RECORDS USING MACHINE LEARNING 审中-公开
标题翻译：使用机器学习解决和合并重复记录
公开(公告)号：US20140279739A1
公开(公告)日：2014-09-18
申请号：US13838339
申请日：2013-03-15
申请人： INSIDESALES.COM, INC.
发明人： David Randal Elkington , Xinchuan Zeng , Richard Glenn Morris
IPC分类号： G06N99/00
CPC分类号： G06N20/00
摘要： According to various embodiments of the present invention, an automated technique is implemented for resolving and merging fields accurately and reliably, given a set of duplicated records that represents a same entity. In at least one embodiment, a system is implemented that uses a machine learning (ML) method, to train a model from training data, and to learn from users how to efficiently resolve and merge fields. In at least one embodiment, the method of the present invention builds feature vectors as input for its ML method. In at least one embodiment, the system and method of the present invention apply Hierarchical Based Sequencing (HBS) and/or Multiple Output Relaxation (MOR) models in resolving and merging fields. Training data for the ML method can come from any suitable source or combination of sources.
摘要翻译：根据本发明的各种实施例，给出一组表示相同实体的重复记录，实现了自动化技术来精确和可靠地解析和合并字段。在至少一个实施例中，实现使用机器学习（ML）方法从训练数据训练模型并从用户学习如何有效地解析和合并字段的系统。在至少一个实施例中，本发明的方法构建特征向量作为其ML方法的输入。在至少一个实施例中，本发明的系统和方法在解析和合并领域中应用基于分层的排序（HBS）和/或多输出松弛（MOR）模型。 ML方法的训练数据可以来自任何合适的来源或来源的组合。

5. 发明授权

US08788439B2 Instance weighted learning machine learning model 有权
标题翻译：实例加权学习机器学习模型
公开(公告)号：US08788439B2
公开(公告)日：2014-07-22
申请号：US13725653
申请日：2012-12-21
申请人： InsideSales.com, Inc.
发明人： Tony Ramon Martinez , Xinchuan Zeng
IPC分类号： G06F15/18
CPC分类号： H04W4/08 , G06N3/0454 , G06N99/005 , H04L63/302 , H04L67/1097 , H04L67/42
摘要： An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model to train a classifier may include determining a quality value that should be associated with each machine learning training instance in a temporal sequence of reinforcement learning machine learning training instances, associating the corresponding determined quality value with each of the machine learning training instances, and training a classifier using each of the machine learning training instances. Each of the machine learning training instances includes a state-action pair and is weighted during the training based on its associated quality value using a weighting factor that weights different quality values differently such that the classifier learns more from a machine learning training instance with a higher quality value than from a machine learning training instance with a lower quality value.
摘要翻译：实例加权学习（IWL）机器学习模型。在一个示例性实施例中，使用IWL机器学习模型来训练分类器的方法可以包括：在加强学习机器学习训练实例的时间序列中确定应该与每个机器学习训练实例相关联的质量值，将相应确定的每个机器学习训练实例的质量值，并使用每个机器学习训练实例训练分类器。机器学习训练实例中的每一个包括状态对，并且在训练期间基于其相关联的质量值使用加权不同质量值的权重因子进行加权，使得分类器从具有更高级别的机器学习训练实例中学习更多质量价值比具有较低质量价值的机器学习训练实例。

6. 发明授权

US09582770B2 Enhancement of machine learning techniques for an electronic message system 有权
标题翻译：增强电子信息系统的机器学习技术
公开(公告)号：US09582770B2
公开(公告)日：2017-02-28
申请号：US15096192
申请日：2016-04-11
申请人： InsideSales.com, Inc.
发明人： Xinchuan Zeng
IPC分类号： G06N99/00
CPC分类号： G06N99/005 , G06Q10/06 , G06Q10/107 , H04L51/02 , H04L51/14
摘要： Techniques are described herein for classifying an electronic message with a particular project from among a plurality of projects. In some embodiments, first and second users associated with the electronic message are identified, and one or more first projects associated with the first user and one and more second projects associated with the second user are determined. Projects that are in common between the first projects and the second projects are determined. When only a single project is in common, the electronic message is associated with the single project. When more than a single project is in common, features associated with each of the projects found to be in common are analyzed by a machine learning model to determine the most likely project to associate with the electronic message from among the projects found to be in common.
摘要翻译：这里描述了用于从多个项目中分类具有特定项目的电子消息的技术。在一些实施例中，识别与电子消息相关联的第一和第二用户，并且确定与第一用户相关联的一个或多个第一项目以及与第二用户相关联的一个和多个第二项目。确定了第一个项目和第二个项目之间共同的项目。当只有一个项目是共同的，电子消息与单个项目相关联。当多个单一的项目是共同的时候，与每个被认为是共同的项目相关联的功能通过机器学习模型进行分析，以确定最有可能的项目，与被发现是共同的项目中的电子信息相关联。

7. 发明申请

US20160357790A1 RESOLVING AND MERGING DUPLICATE RECORDS USING MACHINE LEARNING 审中-公开
标题翻译：使用机器学习解决和合并重复记录
公开(公告)号：US20160357790A1
公开(公告)日：2016-12-08
申请号：US14966422
申请日：2015-12-11
申请人： InsideSales.com, Inc.
发明人： Dave Elkington , Xinchuan Zeng , Richard Morris
IPC分类号： G06F17/30 , G06N7/00 , G06N5/04 , G06N99/00
CPC分类号： G06F16/215 , G06N3/0454 , G06N3/084 , G06N5/025 , G06N20/00
摘要： According to various embodiments of the present invention, an automated technique is implemented for resolving and merging fields accurately and reliably, given a set of duplicated records that represents a same entity. In at least one embodiment, a system is implemented that uses a machine learning (ML) method, to train a model from training data, and to learn from users how to efficiently resolve and merge fields. In at least one embodiment, the method of the present invention builds feature vectors as input for its ML method. In at least one embodiment, the system and method of the present invention apply Hierarchical Based Sequencing (HBS) and/or Multiple Output Relaxation (MOR) models in resolving and merging fields. Training data for the ML method can come from any suitable source or combination of sources.
摘要翻译：根据本发明的各种实施例，给出一组表示相同实体的重复记录，实现了自动化技术来精确和可靠地解析和合并字段。在至少一个实施例中，实现使用机器学习（ML）方法从训练数据训练模型并从用户学习如何有效地解析和合并字段的系统。在至少一个实施例中，本发明的方法构建特征向量作为其ML方法的输入。在至少一个实施例中，本发明的系统和方法在解析和合并领域中应用基于分层的排序（HBS）和/或多输出松弛（MOR）模型。 ML方法的训练数据可以来自任何合适的来源或来源的组合。

8. 发明申请

US20150278709A1 USING MACHINE LEARNING TO PREDICT BEHAVIOR BASED ON LOCAL CONDITIONS 有权
标题翻译：使用机器学习基于本地条件的预测行为
公开(公告)号：US20150278709A1
公开(公告)日：2015-10-01
申请号：US14530248
申请日：2014-10-31
申请人： INSIDESALES.COM, INC.
发明人： Xinchuan Zeng , Jeffrey Berry , David Elkington
IPC分类号： G06N99/00 , G06N5/04 , G06N7/00
CPC分类号： G06N99/005 , G06N3/084
摘要： Using machine learning to predict behavior based on local conditions. In one example embodiment, a method for using machine learning to predict behavior based on local conditions may include identifying a lead, identifying a target behavior for the lead, identifying a locality associated with the lead, identifying a current local condition of the locality, and employing a machine learning classifier to generate a prediction of a likelihood of the lead exhibiting the target behavior. In this example embodiment, the machine learning classifier may base the prediction on the target behavior, the locality, and the current local condition.
摘要翻译：使用机器学习来预测基于当地条件的行为。在一个示例实施例中，一种使用机器学习来基于局部条件来预测行为的方法可以包括识别潜在客户，识别潜在客户的目标行为，识别与潜在客户相关联的地点，识别当地的当前地区状况，以及使用机器学习分类器来产生展示目标行为的潜在客户的可能性的预测。在该示例实施例中，机器学习分类器可以基于目标行为，局部性和当前局部条件来进行预测。

9. 发明申请

US20140180978A1 INSTANCE WEIGHTED LEARNING MACHINE LEARNING MODEL 审中-公开
标题翻译： INSTANCE加权学习机器学习模型
公开(公告)号：US20140180978A1
公开(公告)日：2014-06-26
申请号：US14189669
申请日：2014-02-25
申请人： INSIDESALES.COM, INC.
发明人： Tony Ramon Martinez , Xinchuan Zeng
IPC分类号： G06N99/00
摘要： An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model may include identifying a temporal sequence of reinforcement learning machine learning training instances with each of the training instances including a state-action pair, determining a first quality value for a first training instance in the temporal sequence of reinforcement learning machine learning training instances determining a second quality value for a second training instance in the temporal sequence of reinforcement learning machine learning training instances, associating the first quality value with the first training instance, and associating the second quality value with the second training instance. In this example embodiment, the first quality value is higher than the second quality value.
摘要翻译：实例加权学习（IWL）机器学习模型。在一个示例实施例中，采用IWL机器学习模型的方法可以包括识别加强学习机器学习训练实例的时间序列，其中每个训练实例包括状态对，确定第一训练实例的第一质量值在强化学习机器学习训练实例的时间序列中，在加强学习机器学习训练实例的时间顺序中确定第二训练实例的第二质量值，将第一质量值与第一训练实例相关联，并且将第二质量值与第二训练实例。在该示例性实施例中，第一质量值高于第二质量值。

10. 发明申请

US20140180975A1 INSTANCE WEIGHTED LEARNING MACHINE LEARNING MODEL 有权
标题翻译： INSTANCE加权学习机器学习模型
公开(公告)号：US20140180975A1
公开(公告)日：2014-06-26
申请号：US13725653
申请日：2012-12-21
申请人： INSIDESALES.COM, INC.
发明人： Tony Ramon Martinez , Xinchuan Zeng
IPC分类号： G06N99/00
CPC分类号： H04W4/08 , G06N3/0454 , G06N99/005 , H04L63/302 , H04L67/1097 , H04L67/42
摘要： An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model to train a classifier may include determining a quality value that should be associated with each machine learning training instance in a temporal sequence of reinforcement learning machine learning training instances, associating the corresponding determined quality value with each of the machine learning training instances, and training a classifier using each of the machine learning training instances. Each of the machine learning training instances includes a state-action pair and is weighted during the training based on its associated quality value using a weighting factor that weights different quality values differently such that the classifier learns more from a machine learning training instance with a higher quality value than from a machine learning training instance with a lower quality value.
摘要翻译：实例加权学习（IWL）机器学习模型。在一个示例性实施例中，使用IWL机器学习模型来训练分类器的方法可以包括：在加强学习机器学习训练实例的时间序列中确定应该与每个机器学习训练实例相关联的质量值，将相应确定的每个机器学习训练实例的质量值，并使用每个机器学习训练实例训练分类器。机器学习训练实例中的每一个包括状态对，并且在训练期间基于其相关联的质量值使用加权不同质量值的权重因子进行加权，使得分类器从具有更高级别的机器学习训练实例中学习更多质量价值比具有较低质量价值的机器学习训练实例。

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