专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

1. 发明授权

US08019702B1 Supervised learning with multi-scale time intervals using a statistical classification model to classify unlabeled events 有权
标题翻译：使用统计分类模型对多标度时间间隔进行监督学习，对未标记的事件进行分类
公开(公告)号：US08019702B1
公开(公告)日：2011-09-13
申请号：US12248784
申请日：2008-10-09
申请人： Ullas Gargi , Jay Yagnik
发明人： Ullas Gargi , Jay Yagnik
IPC分类号： G06F11/00
CPC分类号： G06K9/00523
摘要： A method, a system and a computer program product generate a statistical classification model used by a computer system to determine a class associated with an unlabeled time series event.
摘要翻译：方法，系统和计算机程序产品生成计算机系统使用的统计分类模型来确定与未标记的时间序列事件相关联的类别。

2. 发明授权

US08683521B1 Feature-based video suggestions 有权
标题翻译：基于功能的视频建议
公开(公告)号：US08683521B1
公开(公告)日：2014-03-25
申请号：US12415923
申请日：2009-03-31
申请人： Ullas Gargi , Jay Yagnik
发明人： Ullas Gargi , Jay Yagnik
IPC分类号： G06F13/00
CPC分类号： H04N21/251 , H04N21/4826
摘要： A suggestion server generates suggestions of videos. The suggestion server analyzes log data to create co-watch data identifying pairs of co-watched videos and containing generate values representing the number of times the pairs of videos were co-watched. The suggestion server uses the co-watch data to create feature vectors for the co-watched videos. The suggestion server uses the feature vectors to train a ranker for each video. When trained, the ranker can be applied to a feature vector for a video to produce a ranking score. To produce suggestions for a given video, a set of candidate videos is defined. The suggestion server applies the feature vectors for the candidates to the ranker for the given video to produce ranking scores. The candidate videos are ranked based on their ranking scores, and the highest-ranked candidates are provided as suggestions for the given video.
摘要翻译：建议服务器生成视频的建议。建议服务器分析日志数据以创建识别共同观看视频对的共观察数据，并且包含表示视频对被共同观看的次数的生成值。建议服务器使用共同观察数据为共同观看的视频创建特征向量。建议服务器使用特征向量为每个视频训练游戏者。经过训练后，游戏者可以应用于视频的特征向量以产生排名得分。为了产生给定视频的建议，定义了一组候选视频。建议服务器将候选人的特征向量应用于给定视频的游侠以产生排名得分。候选视频根据其排名分数进行排名，排名最高的候选人被提供给给定视频的建议。

3. 发明授权

US08510252B1 Classification of inappropriate video content using multi-scale features 有权
标题翻译：使用多尺度功能对不适当的视频内容进行分类
公开(公告)号：US08510252B1
公开(公告)日：2013-08-13
申请号：US12248780
申请日：2008-10-09
申请人： Ullas Gargi , Jay Yagnik
发明人： Ullas Gargi , Jay Yagnik
IPC分类号： G06F9/44 , G06N7/02 , G06N7/06
CPC分类号： G06K9/00523
摘要： A method, a system and a computer program product generate a statistical classification model used by a computer system to determine whether a video contains content in a particular class, such as inappropriate content.
摘要翻译：方法，系统和计算机程序产品产生计算机系统使用的统计分类模型，以确定视频是否包含特定类中的内容，例如不适当的内容。

4. 发明授权

US08209270B2 Supervised learning with multi-scale time intervals using a statistical classification model to classify unlabeled events 有权
标题翻译：使用统计分类模型对多标度时间间隔进行监督学习，对未标记的事件进行分类
公开(公告)号：US08209270B2
公开(公告)日：2012-06-26
申请号：US13195755
申请日：2011-08-01
申请人： Ullas Gargi , Jay Yagnik
发明人： Ullas Gargi , Jay Yagnik
IPC分类号： G06F11/00
CPC分类号： G06K9/00523
摘要： A method, a system and a computer program product generate a statistical classification model used by a computer system to determine a class associated with an unlabeled time series event.
摘要翻译：方法，系统和计算机程序产品生成计算机系统使用的统计分类模型来确定与未标记的时间序列事件相关联的类别。

5. 发明申请

US20110289033A1 Supervised Learning with Multi-Scale Time Intervals Using a Statistical Classification Model to Classify Unlabeled Events 有权
标题翻译：使用统计分类模型对未标记事件进行分类的多尺度时间间隔的监督学习
公开(公告)号：US20110289033A1
公开(公告)日：2011-11-24
申请号：US13195755
申请日：2011-08-01
申请人： Ullas Gargi , Jay Yagnik
发明人： Ullas Gargi , Jay Yagnik
IPC分类号： G06F15/18
CPC分类号： G06K9/00523
摘要： A method, a system and a computer program product generate a statistical classification model used by a computer system to determine a class associated with an unlabeled time series event.
摘要翻译：方法，系统和计算机程序产品生成计算机系统使用的统计分类模型来确定与未标记的时间序列事件相关联的类别。

6. 发明授权

US08140451B1 Supervised learning using multi-scale features from time series events and scale space decompositions 有权
标题翻译：使用时间序列事件和尺度空间分解的多尺度特征进行监督学习
公开(公告)号：US08140451B1
公开(公告)日：2012-03-20
申请号：US13183375
申请日：2011-07-14
申请人： Ullas Gargi , Jay Yagnik
发明人： Ullas Gargi , Jay Yagnik
IPC分类号： G06F11/00
CPC分类号： G06K9/00536 , G06K9/00516
摘要： Disclosed herein is a method, a system and a computer program product for generating a statistical classification model used by a computer system to determine a class associated with an unlabeled time series event. Initially, a set of labeled time series events is received. A set of time series features is identified for a selected set of the labeled time series events. A plurality of scale space decompositions is generated based on the set of time series features. A plurality of multi-scale features is generated based on the plurality of scale space decompositions. A first subset of the plurality of multi-scale features that correspond at least in part to a subset of space or time points within a time series event that contain feature data that distinguish the time series event as belonging to a class of time series events that corresponds to the class label are identified. A statistical classification model for classifying an unlabeled time series event based on the class corresponding with the class label is generated based at least in part on the at the first subset of the plurality of multi-scale features.
摘要翻译：本文公开了一种用于生成由计算机系统用于确定与未标记的时间序列事件相关联的类别的统计分类模型的方法，系统和计算机程序产品。最初，接收一组标记的时间序列事件。针对所选择的一组标记的时间序列事件识别一组时间序列特征。基于该组时间序列特征生成多个比例空间分解。基于多个刻度空间分解产生多个多尺度特征。所述多个多尺度特征的第一子集至少部分对应于时间序列事件内的空间或时间点的子集，所述时间序列事件包含将所述时间序列事件区分为属于一类时间序列事件的特征数据，所述时间序列事件对应于类标签被识别。至少部分地基于多个多尺度特征的第一子集来生成用于基于与类标签相对应的类来分类未标记时间序列事件的统计分类模型。

7. 发明授权

US08001062B1 Supervised learning using multi-scale features from time series events and scale space decompositions 有权
标题翻译：使用时间序列事件和尺度空间分解的多尺度特征进行监督学习
公开(公告)号：US08001062B1
公开(公告)日：2011-08-16
申请号：US11952436
申请日：2007-12-07
申请人： Ullas Gargi , Jay Yagnik
发明人： Ullas Gargi , Jay Yagnik
IPC分类号： G06F11/00
CPC分类号： G06K9/00536 , G06K9/00516
摘要： Disclosed herein is a method, a system and a computer program product for generating a statistical classification model used by a computer system to determine a class associated with an unlabeled time series event. Initially, a set of labeled time series events is received. A set of time series features is identified for a selected set of the labeled time series events. A plurality of scale space decompositions is generated based on the set of time series features. A plurality of multi-scale features is generated based on the plurality of scale space decompositions. A first subset of the plurality of multi-scale features that correspond at least in part to a subset of space or time points within a time series event that contain feature data that distinguish the time series event as belonging to a class of time series events that corresponds to the class label are identified. A statistical classification model for classifying an unlabeled time series event based on the class corresponding with the class label is generated based at least in part on the at the first subset of the plurality of multi-scale features.
摘要翻译：本文公开了一种用于生成由计算机系统用于确定与未标记的时间序列事件相关联的类别的统计分类模型的方法，系统和计算机程序产品。最初，接收一组标记的时间序列事件。针对所选择的一组标记的时间序列事件识别一组时间序列特征。基于该组时间序列特征生成多个比例空间分解。基于多个刻度空间分解产生多个多尺度特征。所述多个多尺度特征的第一子集至少部分对应于时间序列事件内的空间或时间点的子集，所述时间序列事件包含将所述时间序列事件区分为属于一类时间序列事件的特征数据，所述时间序列事件对应于类标签被识别。至少部分地基于多个多尺度特征的第一子集来生成用于基于与类标签相对应的类来分类未标记时间序列事件的统计分类模型。

8. 发明授权

US08959540B1 Predicting engagement in video content 有权
标题翻译：预测参与视频内容
公开(公告)号：US08959540B1
公开(公告)日：2015-02-17
申请号：US12783524
申请日：2010-05-19
申请人： Ullas Gargi , Jay Yagnik , Anindya Sarkar
发明人： Ullas Gargi , Jay Yagnik , Anindya Sarkar
IPC分类号： H04N7/16 , H04H60/32
CPC分类号： H04N21/23418 , H04H20/93 , H04H60/31 , H04H60/46 , H04H60/59 , H04H60/63 , H04N21/237 , H04N21/251 , H04N21/25891 , H04N21/44008 , H04N21/44222 , H04N21/466 , H04N21/4826 , H04N21/6582
摘要： User engagement in unwatched videos is predicted by collecting and aggregating data describing user engagement with watched videos. The data are normalized to reduce the influence of factors other than the content of the videos on user engagement. Engagement metrics are calculated for segments of watched videos that indicate user engagement with each segment relative to overall user engagement with the watched videos. Features of the watched videos within time windows are characterized, and a function is learned that relates the features of the videos within the time windows to the engagement metrics for the time windows. The features of a time window of an unwatched video are characterized, and the learned function is applied to the features to predict user engagement to the time window of the unwatched video. The unwatched video can be enhanced based on the predicted user engagement.
摘要翻译：通过收集和汇总描述与观看视频的用户互动的数据来预测用户对未拍摄的视频的参与。数据被归一化以减少视频内容以外的因素对用户参与的影响。针对所观看视频的细分，计算参与指标，指示用户与每个细分受众群相对于与观看视频的整体用户互动度的参与度。在时间窗口中观看视频的特征被描述，并且学习了将时间窗口内的视频的特征与时间窗口的参与度量相关联的功能。对未拍摄视频的时间窗口的特征进行了表征，并且将学习的功能应用于特征以预测用户对未拍摄视频的时间窗口的参与。可以根据预测的用户参与来增强未拍摄的视频。

9. 发明授权

US09684715B1 Audio identification using ordinal transformation 有权
公开(公告)号：US09684715B1
公开(公告)日：2017-06-20
申请号：US13415704
申请日：2012-03-08
申请人： David Ross , Jay Yagnik
发明人： David Ross , Jay Yagnik
IPC分类号： G06F17/30 , G06F17/00
CPC分类号： G06F17/30743 , G06F17/00 , G06F17/3074
摘要： This disclosure relates to audio identification using ordinal transformations. A media matching component receives a sample audio file. The sample audio file can include, for example, a cover song. The media matching component includes a vector component that computes a set of vectors using auditory feature values included in the sample audio file. A hashing component employs a hash function to generate a fingerprint, including a set of sub-fingerprints, for the sample audio file using the set of vectors. The fingerprint is invariant to variations including but not limited to variations in key, instrumentation, encoding formats, performers, performance conditions, arrangement, and/or recording and processing variations. An identification component determines if any reference audio files are similar to the sample audio file using the fingerprint and/or sub-fingerprints, and identifies any similar reference audio files.

10. 发明授权

US08805090B1 Matching based upon rank 有权
标题翻译：基于等级匹配
公开(公告)号：US08805090B1
公开(公告)日：2014-08-12
申请号：US13368317
申请日：2012-02-07
申请人： Jay Yagnik , Sergey Ioffe
发明人： Jay Yagnik , Sergey Ioffe
IPC分类号： G06K9/68
CPC分类号： G06K9/6212
摘要： Systems and methods for measuring consistency between two objects based upon a rank of object elements instead of based upon the values of those object elements. Objects being compared can be represented by d-dimension feature vectors, U and V, where each dimension includes an associated value. U and V can be converted to rank vectors, P and Q, where values of U and V dimensions are replaced by an ordered rank or a function thereof. Analysis directed to the consistency between U and V can be accomplished by determining consistency between P and Q, which can be more efficient and more accurate, particularly with regard to illumination-invariant comparisons.
摘要翻译：基于对象元素的等级而不是基于这些对象元素的值来测量两个对象之间的一致性的系统和方法。被比较的对象可以由d维特征向量U和V表示，其中每个维度包括相关联的值。 U和V可以被转换为等级向量P和Q，其中U和V维度的值被有序等级或其功能所代替。可以通过确定P和Q之间的一致性来实现对U和V之间的一致性的分析，这可以更有效和更准确，特别是在照明不变比较方面。

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式