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    • 2. 发明授权
    • Method to predict session duration on mobile devices using native machine learning
    • 使用本地机器学习预测移动设备上的会话持续时间的方法
    • US08510238B1
    • 2013-08-13
    • US13585503
    • 2012-08-14
    • Hrishikesh AradhyeRuei-sung Lin
    • Hrishikesh AradhyeRuei-sung Lin
    • G06F15/18
    • G06N99/005H04W4/50H04W16/22
    • Methods and apparatus for predicting time spans for mobile platform activation are presented. A machine-learning service executing on a mobile platform receives feature-related data. The feature-related data includes usage-related data about time spans that the mobile platform is activated and platform-related data received from the mobile platform. The usage-related data and the platform-related data can differ. The machine-learning service determines whether the machine-learning service is trained to perform machine-learning operations related to predicting a time span that the mobile platform will be activated. In response to determining that the machine-learning service is trained, the machine-learning service: receives a request for a predicted time span that the mobile platform will be activated, determines the predicted time span by the machine-learning service performing a machine-learning operation on the feature-related data, and sends the predicted time span.
    • 提出了用于预测移动平台激活时间跨度的方法和装置。 在移动平台上执行的机器学习服务接收特征相关数据。 功能相关数据包括关于移动平台被激活的时间跨度和从移动平台接收的与平台相关的数据的使用相关数据。 使用相关数据和平台相关数据可能不同。 机器学习服务确定机器学习服务是否被训练以执行与预测移动平台将被激活的时间跨度相关的机器学习操作。 响应于确定机器学习服务被训练,机器学习服务:接收对移动平台将被激活的预测时间跨度的请求,通过执行机器学习服务的机器学习服务来确定预测的时间间隔, 对功能相关数据进行学习操作,并发送预测时间段。
    • 3. 发明授权
    • Determining a dominant hand of a user of a computing device
    • 确定计算设备的用户的优势手
    • US08665238B1
    • 2014-03-04
    • US13658632
    • 2012-10-23
    • Richard Carl Gossweiler, IIIHrishikesh Aradhye
    • Richard Carl Gossweiler, IIIHrishikesh Aradhye
    • G06F3/041
    • G06F3/0481G06F3/0482G06F3/0488G06F3/04886
    • In one example, a method includes determining, by a computing device, a plurality of features. Each feature from the plurality of features may be usable to determine a dominant hand of a user of the computing device. The method also includes receiving, by the computing device, a plurality of input values, each input value from the plurality of input values corresponding to the respective plurality of features, and determining, using a probabilistic model and based at least in part on at least one input value from the plurality of input values corresponding to the respective feature from the plurality of features, a hand of the user as a dominant hand of the user. The method also includes generating, based at least in part on the determined dominant hand of the user, a graphical user interface for display at a presence-sensitive display operatively coupled to the computing device.
    • 在一个示例中,方法包括由计算设备确定多个特征。 来自多个特征的每个特征可以用于确定计算设备的用户的优势手。 所述方法还包括由所述计算设备接收多个输入值,来自所述多个输入值的对应于所述相应多个特征的每个输入值,以及使用概率模型并且至少部分至少基于至少 来自与来自多个特征的各个特征相对应的多个输入值中的一个输入值,作为用户的优势手的用户的手。 该方法还包括至少部分地基于所确定的用户的优势手,生成用于在可操作地耦合到计算设备的存在敏感显示器上显示的图形用户界面。
    • 4. 发明授权
    • Automatic multi-device localization and collaboration using cameras
    • 使用相机自动多设备本地化和协作
    • US08531519B1
    • 2013-09-10
    • US13605194
    • 2012-09-06
    • Yifan PengWei HuaHrishikesh AradhyeRodrigo Carceroni
    • Yifan PengWei HuaHrishikesh AradhyeRodrigo Carceroni
    • H04N7/18G06K9/66
    • H04N7/18
    • Implementations relate to a computer-implemented method and a device for determining a relative posed between devices. The method can include receiving data representing first keypoint features from a first image captured by a camera of a second mobile computing device; capturing, by a camera of a first mobile computing device, a second image, wherein the first image and the second image comprise a substantially common scene having an area of overlap; computing, by the first mobile computing device, data representing second keypoint features from the second image; determining, by the first mobile computing device, based at least in part on the data representing first keypoint features and the data representing second keypoint features, a relative pose of the first mobile computing device and the second mobile computing device; and communicating the relative pose to the second mobile computing device.
    • 实现涉及计算机实现的方法和用于确定设备之间的相对设置的设备。 该方法可以包括从由第二移动计算设备的相机捕获的第一图像接收表示第一关键点特征的数据; 通过第一移动计算设备的相机捕获第二图像,其中所述第一图像和所述第二图像包括具有重叠区域的基本上共同的场景; 由所述第一移动计算设备计算表示来自所述第二图像的第二关键点特征的数据; 至少部分地基于表示第一关键点特征的数据和表示第二关键点特征的数据确定第一移动计算设备和第二移动计算设备的相对姿态; 以及将所述相对姿势传达给所述第二移动计算设备。
    • 5. 发明授权
    • Learning concepts for video annotation
    • 学习视频注释的概念
    • US08396286B1
    • 2013-03-12
    • US12822727
    • 2010-06-24
    • Hrishikesh AradhyeGeorge TodericiJay Yagnik
    • Hrishikesh AradhyeGeorge TodericiJay Yagnik
    • G06K9/62G06K9/66G06K9/00
    • G06K9/00718G06K9/6262
    • A concept learning module trains video classifiers associated with a stored set of concepts derived from textual metadata of a plurality of videos, the training based on features extracted from training videos. Each of the video classifiers can then be applied to a given video to obtain a score indicating whether or not the video is representative of the concept associated with the classifier. The learning process does not require any concepts to be known a priori, nor does it require a training set of videos having training labels manually applied by human experts. Rather, in one embodiment the learning is based solely upon the content of the videos themselves and on whatever metadata was provided along with the video, e.g., on possibly sparse and/or inaccurate textual metadata specified by a user of a video hosting service who submitted the video.
    • 概念学习模块训练与从多个视频的文本元数据导出的存储的一组概念相关联的视频分类器,该训练基于从训练视频中提取的特征。 然后可以将每个视频分类器应用于给定的视频以获得指示视频是否代表与分类器相关联的概念的分数。 学习过程不需要先验知道任何概念,也不需要由人类专家手动应用培训标签的培训视频。 相反,在一个实施例中,学习仅基于视频本身的内容以及与视频一起提供的任何元数据,例如,由提交的视频托管服务的用户指定的可能稀疏和/或不准确的文本元数据 视频。
    • 7. 发明授权
    • Learning to geolocate videos
    • 学习定位视频
    • US08990134B1
    • 2015-03-24
    • US12881078
    • 2010-09-13
    • Jasper SnoekLuciano SbaizHrishikesh AradhyeGeorge Toderici
    • Jasper SnoekLuciano SbaizHrishikesh AradhyeGeorge Toderici
    • G06K9/62
    • G06K9/00697G06F17/30784
    • A classifier training system trains classifiers for inferring the geographic locations of videos. A number of classifiers are provided, where each classifier corresponds to a particular location and is trained from a training set of videos that have been labeled as representing the location. In one embodiment, the training set is further restricted to those videos in which a landmark matching the location label is detected. The classifier training system extracts, from each of these videos, features that characterize the video, such as audiovisual features, text features, address features, landmark features, and category features. Based on these features, the classifier training system trains a location classifier for the corresponding location.Each of the location classifiers can be applied to videos without associated location labels to predict whether, or how strongly, the video represents the corresponding location. The prediction can be used for a variety of purposes, such as automatic labeling of videos with locations, presentation of location-specific advertisements in association with videos, and display of video data on relevant portions of an electronic map.
    • 分类器训练系统训练用于推断视频地理位置的分类器。 提供了多个分类器,其中每个分类器对应于特定位置,并且从被标记为表示位置的训练集合的训练中训练。 在一个实施例中,训练集进一步限于那些其中检测到与位置标签匹配的地标的视频。 分类器训练系统从每个这些视频中提取表征视频的功能,例如视听功能,文本功能,地址功能,地标功能和类别功能。 基于这些特征,分类器训练系统为相应的位置训练位置分类器。 每个位置分类器都可以应用于没有相关位置标签的视频,以预测视频是表示相应位置还是多强。 该预测可以用于各种目的,诸如具有位置的视频的自动标记,与视频相关联的位置特定广告的呈现以及在电子地图的相关部分上的视频数据的显示。