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公开(公告)号：US10546003B2
公开(公告)日：2020-01-28
申请号：US15808498
申请日：2017-11-09
Applicant: Adobe Inc.
Inventor： Prakhar Gupta , Iftikhar Ahamath Burhanuddin , Harvineet Singh , Atanu Ranjan Sinha
IPC: G06F16/33 , G06F16/242 , G06F16/332 , G10L15/22 , G10L15/26
Abstract: This disclosure covers methods, non-transitory computer readable media, and systems that use an intelligent analytics interface to process natural-language and other inputs to configure an analytics task for the system. The disclosed methods, non-transitory computer readable media, and systems provide the intelligent analytics interface to facilitate an exchange between the systems and a user to determine values for the analytics task. The methods, non-transitory computer readable media, and systems then use these values to execute an analytics task.
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公开(公告)号：US20190138648A1
公开(公告)日：2019-05-09
申请号：US15808498
申请日：2017-11-09
Applicant: Adobe Inc.
Inventor： Prakhar Gupta , Iftikhar Ahamath Burhanuddin , Harvineet Singh , Atanu Ranjan Sinha
Abstract: This disclosure covers methods, non-transitory computer readable media, and systems that use an intelligent analytics interface to process natural-language and other inputs to configure an analytics task for the system. The disclosed methods, non-transitory computer readable media, and systems provide the intelligent analytics interface to facilitate an exchange between the systems and a user to determine values for the analytics task. The methods, non-transitory computer readable media, and systems then use these values to execute an analytics task.
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公开(公告)号：US10984058B2
公开(公告)日：2021-04-20
申请号：US15892085
申请日：2018-02-08
Applicant: Adobe Inc.
Inventor： Branislav Kveton , Zheng Wen , Prakhar Gupta , Iftikhar Ahamath Burhanuddin , Harvineet Singh , Gaurush Hiranandani
IPC: G06F16/00 , G06F16/9535 , G06N20/00 , G06F16/248 , G06F16/2457 , G06Q30/02
Abstract: A machine-learning framework uses partial-click feedback to generate an optimal diverse set of items. An example method includes estimating a preference vector for a user based on diverse cascade statistics for the user, the diverse cascade statistics including previously observed responses and previously observed topic gains. The method also includes generating an ordered set of items from the item repository, the items in the ordered set having highest topic gain weighted by similarity with the preference vector, providing the ordered set for presentation to the user, and receiving feedback from the user on the ordered set. The method also includes, responsive to the feedback indicating a selected item, updating the diverse cascade statistics for observed items, wherein the updating results in penalizing the topic gain for items of the observed items that are not the selected item and promoting the topic gain for the selected item.
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公开(公告)号：US10515379B2
公开(公告)日：2019-12-24
申请号：US15384639
申请日：2016-12-20
Applicant: Adobe Inc.
Inventor： Prakhar Gupta , Nalam V S S Krishna Chaitanya , Debraj Basu , Aayush Ojha
Abstract: A computer system stores digital media content such as images and video along with associated tags and timestamps. The system detects trends in the media content by semantic analysis which includes generation of a temporal tag graph that includes data indicative of a semantic representation of the tags over a plurality of time periods. The data in the tag graph is clustered to generate a set of identified trends reflected by the tags over the plurality of time periods. The set of identified trends is stored in data storage and is available for characterization which includes labeling of the trends, scoring the trends, evaluating changes in the trends over time, and identifying images representative of the detected trends. The temporal tag graph may take the form of a weighted undirected graph where each node in the graph is associated with one of the tags and the edges connecting the nodes represents a temporal correlation between the nodes associated with each edge.
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公开(公告)号：US20190243923A1
公开(公告)日：2019-08-08
申请号：US15892085
申请日：2018-02-08
Applicant: Adobe Inc.
Inventor： Branislav Kveton , Zheng Wen , Prakhar Gupta , Iftikhar Ahamath Burhanuddin , Harvineet Singh , Gaurush Hiranandani
CPC classification number: G06F16/9535 , G06F16/24578 , G06F16/248 , G06N20/00
Abstract: A machine-learning framework uses partial-click feedback to generate an optimal diverse set of items. An example method includes estimating a preference vector for a user based on diverse cascade statistics for the user, the diverse cascade statistics including previously observed responses and previously observed topic gains. The method also includes generating an ordered set of items from the item repository, the items in the ordered set having highest topic gain weighted by similarity with the preference vector, providing the ordered set for presentation to the user, and receiving feedback from the user on the ordered set. The method also includes, responsive to the feedback indicating a selected item, updating the diverse cascade statistics for observed items, wherein the updating results in penalizing the topic gain for items of the observed items that are not the selected item and promoting the topic gain for the selected item.
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Patent Agency Ranking
公开(公告)号：US11321373B2
公开(公告)日：2022-05-03
申请号：US16698383
申请日：2019-11-27
Applicant: Adobe Inc.
Inventor： Prakhar Gupta , Iftikhar Ahamath Burhanuddin , Harvineet Singh , Atanu Ranjan Sinha
IPC: G06F16/33 , G06F16/242 , G06F16/332 , G10L15/22 , H04L51/02 , G06F40/30 , G10L15/26
Abstract: This disclosure covers methods, non-transitory computer readable media, and systems that use an intelligent analytics interface to process natural-language and other inputs to configure an analytics task for the system. The disclosed methods, non-transitory computer readable media, and systems provide the intelligent analytics interface to facilitate an exchange between the systems and a user to determine values for the analytics task. The methods, non-transitory computer readable media, and systems then use these values to execute an analytics task.
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公开(公告)号：US10824660B2
公开(公告)日：2020-11-03
申请号：US14950550
申请日：2015-11-24
Applicant: ADOBE INC.
Inventor： Kokil Jaidka , Prakhar Gupta , Sajal Rustagi , R. Kaushik
IPC: G06F16/35
Abstract: Techniques are provided for detecting new topics and themes and assigning new posts to existing topic and/or theme clusters in online community discussions. A post posted to an online community is received and a post feature vector representative of the post is created. The post is compared to a plurality of centroid feature vectors, each centroid feature vector being representative of a respective post cluster and associated with a theme. Upon determining that similarity between the post feature vector and one of a plurality of centroid feature vectors satisfies a minimum similarity threshold, the post is assigned to the post cluster of which the centroid feature vector is representative. Upon determining that similarity between the post feature vector and any of the plurality of centroid feature vectors is below the minimum similarity threshold, a new theme cluster is created and the post is assigned to the new theme cluster.
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公开(公告)号：US11663497B2
公开(公告)日：2023-05-30
申请号：US16389628
申请日：2019-04-19
Applicant: Adobe Inc.
Inventor： Atanu Sinha , Prakhar Gupta , Manoj Kilaru , Madhav Goel , Deepanshu Bansal , Deepali Jain , Aniket Raj
IPC: G06N5/02 , G06F16/2457 , G06Q30/0204 , G06F16/901 , G06N3/049 , G06N5/043
CPC classification number: G06N5/02 , G06F16/24578 , G06F16/9024 , G06N3/049 , G06N5/043 , G06Q30/0204
Abstract: A method includes accessing a subject entity and a subject relation of a focal platform and accessing a knowledge graph representative of control performance data. Further, the method includes computing a set of ranked target entities that cause the subject entity based on the subject relation or are an effect of the subject entity based on the subject relation. Computing the set of ranked target entities is performed using relational hops from the subject entity within the knowledge graph performed using the subject relation and reward functions. The method also includes transmitting the set of ranked target entities to the focal platform. The set of ranked target entities is usable for modifying a user interface of an interactive computing environment provided by the focal platform.
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公开(公告)号：US11205111B2
公开(公告)日：2021-12-21
申请号：US15609254
申请日：2017-05-31
Applicant: Adobe Inc.
Inventor： Shiv Kumar Saini , Prakhar Gupta , Harvineet Singh , Gaurush Hiranandani
Abstract: Techniques of forecasting web metrics involve generating, prior to the end of a period of time, a probability of a metric taking on an anomalous value, e.g., a value indicative of an anomaly with respect to web traffic, at the end of the period based on previous values of the metric. Such a probability is based on a distribution of predicted values of the metric at some previous period of time. For example, a web server may use actual values of the number of bounces collected at hourly intervals in the middle of a day to predict a number of bounces at the end of the current day. Further, the web server may also compute a confidence interval to determine whether a predicted end-of-day number of bounces may be considered anomalous. The width of the confidence interval indicates the probability that a predicted end-of-day number of bounces has an anomalous value.
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公开(公告)号：US10664999B2
公开(公告)日：2020-05-26
申请号：US15897807
申请日：2018-02-15
Applicant: Adobe Inc.
Inventor： Prakhar Gupta , Sourav Pal , Shubh Gupta , Ritwik Sinha , Ajaykrishnan Jayagopal
Abstract: A content saliency network is a machine-learned neural network that predicts the saliency of elements of a content item. The content saliency network may be used in a method that includes determining a set of elements in a UI and computing a first context vector for the content. The method may also include, for each element in the set of elements, computing a vector of simple features for the element, the simple features being computed from attributes of the element, computing a second context vector for the element, computing a third context vector for an intermediate context of the element, and providing the vectors to the content saliency network. The content saliency network provides a saliency score for the element. The method further includes generating an element-level saliency map of the content using the respective saliency scores for the set of elements and providing the saliency map to a requestor.
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