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公开(公告)号：US20190005043A1
公开(公告)日：2019-01-03
申请号：US15637829
申请日：2017-06-29
Applicant: Adobe Systems Incorporated
Inventor： Mayur Hemani , Balaji Krishnamurthy
IPC: G06F17/30 , G06F3/0482
Abstract: Automated digital asset tagging techniques and systems are described that support use of multiple vocabulary sets. In one example, a plurality of digital assets are obtained having first-vocabulary tags taken from a first-vocabulary set. Second-vocabulary tags taken from a second-vocabulary set are assigned to the plurality of digital assets through machine learning. A determination is made that at least one first-vocabulary tag includes a plurality of visual classes based on the assignment of at least one second-vocabulary tag. Digital assets are collected from the plurality of digital assets that correspond to one visual class of the plurality of visual classes. The model is generated using machine learning based on the collected digital assets.
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公开(公告)号：US10129274B2
公开(公告)日：2018-11-13
申请号：US15273213
申请日：2016-09-22
Applicant: Adobe Systems Incorporated
Inventor： Suraj Satishkumar Sheth , Shagun Sodhani , Rohit Bajaj , Nitin Goel , Manoj Awasthi , Kapil Malik , Harsh Rathi , Balaji Krishnamurthy
Abstract: In some embodiments, a processor accesses a metrics dataset, which includes metrics whose values indicate data network activity. The metrics dataset has segments. Each segment is a respective subset of the data items having a common feature. The processor identifies anomalous segments in the metrics dataset. Each anomalous segment has a segment trend that is different from a trend associated with the larger metrics dataset. The processor generates a data graph that includes nodes, which represent anomalous segments, and edges connecting the nodes. The processor applies weights to the edges. Each weight indicates (i) a similarity between a pair of anomalous segments represented by the nodes connected by the weighted edge and (ii) a relationship between the anomalous segments and the metrics dataset. The processor ranks the anomalous segments based on the applied weights and selects one or more segments with sufficiently high ranks.
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公开(公告)号：US10210453B2
公开(公告)日：2019-02-19
申请号：US14828150
申请日：2015-08-17
Applicant: Adobe Systems Incorporated
Inventor： Balaji Krishnamurthy , Tushar Singla
Abstract: Behavioral prediction for targeted end users is described. In one or more example embodiments, a computer-readable storage medium has multiple instructions that cause one or more processors to perform multiple operations. Targeted selectstream data is obtained from one or more indications of data object requests corresponding to a targeted end user. A targeted directed graph is constructed based on the targeted selectstream data. A targeted graph feature vector is computed based on one or more invariant features associated with the targeted directed graph. A behavioral prediction is produced for the targeted end user by applying a prediction model to the targeted graph feature vector. In one or more example embodiments, the prediction model is generated based on multiple graph feature vectors respectively corresponding to multiple end users. In one or more example embodiments, a tailored opportunity is determined responsive to the behavioral prediction and issued to the targeted end user.
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公开(公告)号：US20190034801A1
公开(公告)日：2019-01-31
申请号：US15660547
申请日：2017-07-26
Applicant: Adobe Systems Incorporated
Inventor： Shagun Sodhani , Balaji Krishnamurthy
Abstract: A standardized data model (“SDM”) includes standardized data types that indicate classifications of data elements. In a data service platform, such as a marketing data platform, a data standardization module classifies received data elements. One or more components included in the data standardization module are trained using supervised or unsupervised learning techniques to classify received data elements into a standardized data type included in the SDM. In some cases, an output of an unsupervised learning phase is provided as an input to a supervised learning phase. In some cases, a classified data element is modified by the data standardization module to indicate the standardized data type into which the data element is classified.
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公开(公告)号：US10152655B2
公开(公告)日：2018-12-11
申请号：US15980588
申请日：2018-05-15
Applicant: Adobe Systems Incorporated
Inventor： Balaji Krishnamurthy , Mausoom Sarkar
Abstract: Systems and methods are disclosed herein for automatically identifying a query object within a visual medium. The technique generally involves receiving as input to a neural network a query object and a visual medium including the query object. The technique also involves generating, by the neural network, representations of the query object and the visual medium defining features of the query object and the visual medium. The technique also involves generating, by the neural network, a heat map using the representations. The heat map identifies a location of pixels corresponding to the query object within the visual medium and is usable to generate an updated visual medium highlighting the query object.
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Patent Agency Ranking
公开(公告)号：US20180218009A1
公开(公告)日：2018-08-02
申请号：US15940849
申请日：2018-03-29
Applicant: Adobe Systems Incorporated
Inventor： Vikas Yadav , Balaji Krishnamurthy , Mausoom Sarkar , Rajiv Mangla , Gitesh Malik
IPC: G06F17/30
CPC classification number: G06F16/583 , G06F16/7847
Abstract: A method for clustering product media files is provided. The method includes dividing each media file corresponding to one or more products into a plurality of tiles. The media file include one of an image or a video. Feature vectors are computed for each tile of each media file. One or more patch clusters are generated using the plurality of tiles. Each patch cluster includes tiles having feature vectors similar to each other. The feature vectors of each media file are compared with feature vectors of each patch cluster. Based on comparison, product groups are then generated. All media files having comparison output similar to each other are grouped into one product group. Each product group includes one or more media files for one product. Apparatus for substantially performing the method as described herein is also provided.
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7.发明申请公开(公告)号：US20180068371A1
公开(公告)日：2018-03-08
申请号：US15259832
申请日：2016-09-08
Applicant: Adobe Systems Incorporated
Inventor： Balaji Krishnamurthy , Raghavender Goel , Nikaash Puri
CPC classification number: G06Q30/0631 , G06F17/276 , G06N3/0427 , G06N3/08 , G06N5/041
Abstract: Learning vector-space representations of items for recommendations using word embedding models is described. In one or more embodiments, a word embedding model is used to produce item vector representations of items based on considering items interacted with as words and items interacted with during sessions as sentences. The item vectors are used to produce item recommendations similar to currently or recently viewed items.
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公开(公告)号：US20170134828A1
公开(公告)日：2017-05-11
申请号：US14933762
申请日：2015-11-05
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： Balaji Krishnamurthy , Sunandini Basu , Nutan Sawant
IPC: H04N21/845 , H04N21/45 , H04N21/44 , H04N21/442 , H04N21/439 , H04N21/478 , H04N21/472
CPC classification number: H04N21/8456 , H04N21/4314 , H04N21/439 , H04N21/44008 , H04N21/44222 , H04N21/4532 , H04N21/47202
Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to navigation of a digital video. In one embodiment, a method may begin by partitioning a digital video into a number of sub-stories based at least in part on transition points identified within the digital video. The plurality of sub-stories can then be grouped into video segments based on the content of each sub-story. These video segments can then be packaged into a navigation panel in accordance with a selected template that defines a layout for the navigation panel. Such a navigation panel can present the video segments to a viewer in an interactive graphical manner that enables the viewer to navigate the one or more video segments. Other embodiments may be described and/or claimed.
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公开(公告)号：US20170075977A1
公开(公告)日：2017-03-16
申请号：US14855539
申请日：2015-09-16
Applicant: Adobe Systems Incorporated
Inventor： Vikas Yadav , Balaji Krishnamurthy , Mausoom Sarkar , Rajiv Mangla , Gitesh Malik
IPC: G06F17/30
CPC classification number: G06F17/30247 , G06F17/30799
Abstract: A method for clustering product media files is provided. The method includes dividing each media file corresponding to one or more products into a plurality of tiles. The media file include one of an image or a video. Feature vectors are computed for each tile of each media file. One or more patch clusters are generated using the plurality of tiles. Each patch cluster includes tiles having feature vectors similar to each other. The feature vectors of each media file are compared with feature vectors of each patch cluster. Based on comparison, product groups are then generated. All media files having comparison output similar to each other are grouped into one product group. Each product group includes one or more media files for one product. Apparatus for substantially performing the method as described herein is also provided.
Abstract translation: 提供了一种聚类产品媒体文件的方法。 该方法包括将与一个或多个产品相对应的每个媒体文件划分成多个瓦片。 媒体文件包括图像或视频之一。 为每个媒体文件的每个图块计算特征向量。 使用多个瓦片生成一个或多个补丁群集。 每个补丁群集包括具有彼此相似的特征向量的瓦片。 将每个媒体文件的特征向量与每个分组簇的特征向量进行比较。 基于比较，产生产品组。 具有彼此类似的比较输出的所有媒体文件被分组成一个产品组。 每个产品组包括一个或多个一个产品的媒体文件。 还提供了用于基本上执行如本文所述的方法的装置。
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公开(公告)号：US20190114687A1
公开(公告)日：2019-04-18
申请号：US15785934
申请日：2017-10-17
Applicant: Adobe Systems Incorporated
Inventor： Balaji Krishnamurthy , Nikaash Puri
Abstract: A digital medium environment is described to facilitate recommendations based on vectors generated using feature word embeddings. A recommendation system receives data that describes at least one attribute for a user profile, at least one item, and an interaction between the user profile and the at least one item. The recommendation system associates each user profile attribute, each item, and each interaction between a user profile and an item as a word, using natural language processing, and combines the words into sentences. The sentences are input to a word embedding model to determine feature vector representations describing relationships between the user profile attributes, items, and explicit and implicit interactions. From the feature vector representations, the recommendation system ascertains a similarity between different features. Thus, the recommendation system can provide customized recommendations based on implicit interactions, even for a user profile that is not associated with any historical interaction data.
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