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公开(公告)号：US10657574B2
公开(公告)日：2020-05-19
申请号：US15264068
申请日：2016-09-13
Applicant: ADOBE INC.
Inventor： Hung Bui , Branislav Kveton , Suvash Sedhain , Nikolaos Vlassis , Jaya Kawale
Abstract: Techniques disclosed herein provide more efficient and more relevant item recommendations to users in large-scale environments in which only positive interest information is known. The techniques use a rank-constrained formulation that generalizes relationships based on known user interests in items and/or use a randomized singular value decomposition (SVD) approximation technique to solve the formulation to identify items of interest to users in an efficiently, scalable manner.
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公开(公告)号：US11080732B2
公开(公告)日：2021-08-03
申请号：US15180582
申请日：2016-06-13
Applicant: ADOBE INC.
Inventor： Trevor Paulsen , Craig Mathis , Nikolaos Vlassis , Branislav Kveton , Kristopher Paries , Ivan Andrus , Hung Bui , Michael Rimer
IPC: G06Q30/02 , G06F3/0484
Abstract: Systems and methods are disclosed herein for providing a user interface representing differences between segments of end users. The systems and methods receive user input on a user interface identifying a first segment, the first segment being a subset of the end users having a particular characteristic, determine differences between the first segment and a second segment, and represent, on the user interface, the differences between the first segment and the second segment based on relative significances of the differences. The marketer using the user interface is able to quickly and easily identify the metrics, dimensions, and/or relationships to other segments that most distinguish the compared segments from one another.
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公开(公告)号：US20210050014A1
公开(公告)日：2021-02-18
申请号：US17086805
申请日：2020-11-02
Applicant: Adobe Inc.
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating dialogue responses based on received utterances utilizing an independent gate context-dependent additive recurrent neural network. For example, the disclosed systems can utilize a neural network model to generate a dialogue history vector based on received utterances and can use the dialogue history vector to generate a dialogue response. The independent gate context-dependent additive recurrent neural network can remove local context to reduce computation complexity and allow for gates at all time steps to be computed in parallel. The independent gate context-dependent additive recurrent neural network maintains the sequential nature of a recurrent neural network using the hidden vector output.
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公开(公告)号：US11055317B2
公开(公告)日：2021-07-06
申请号：US15611563
申请日：2017-06-01
Applicant: Adobe Inc.
Inventor： Hamid Dadkhahi , Mohammad Ghavamzadeh , Hung Bui , Branislav Kveton
Abstract: Certain embodiments involve determining and outputting correlations between metrics in large-scale web analytics datasets. For example, a processor identifies pairs of data metrics in a web analytics data set and determines a Maximal Information Coefficient (MIC) score for each pair of data metrics that indicates a strength of a correlation between the pair of data metrics. The processor generates an interactive user interface that graphically displays each pair of correlated data metrics having an MIC score above a threshold and the interactive user interface indicates the strength of the correlation between each displayed pair of correlated data metrics. The processor receives user input indicating an adjustment to the threshold and modifies the interactive user interface in response to receiving the user input by adding pairs of correlated data metrics to, or removing pairs of correlated metrics from, the interactive user interface based on the adjustment to the threshold.
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5.发明申请公开(公告)号：US20200090651A1
公开(公告)日：2020-03-19
申请号：US16133190
申请日：2018-09-17
Applicant: Adobe Inc.
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating dialogue responses based on received utterances utilizing an independent gate context-dependent additive recurrent neural network. For example, the disclosed systems can utilize a neural network model to generate a dialogue history vector based on received utterances and can use the dialogue history vector to generate a dialogue response. The independent gate context-dependent additive recurrent neural network can remove local context to reduce computation complexity and allow for gates at all time steps to be computed in parallel. The independent gate context-dependent additive recurrent neural network maintains the sequential nature of a recurrent neural network using the hidden vector output.
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Patent Agency Ranking
公开(公告)号：US10372821B2
公开(公告)日：2019-08-06
申请号：US15462684
申请日：2017-03-17
Applicant: Adobe Inc.
Inventor： Walter Chang , Trung Bui , Pranjal Daga , Michael Kraley , Hung Bui
Abstract: Certain embodiments identify a correct structured reading-order sequence of text segments extracted from a file. A probabilistic language model is generated from a large text corpus to comprise observed word sequence patterns for a given language. The language model measures whether splicing together a first text segment with another continuation text segment results in a phrase that is more likely than a phrase resulting from splicing together the first text segment with other continuation text segments. Sets of text segments, which include a first set with a first text segment and a first continuation text segment as well as a second set with the first text segment and a second continuation text segment, are provided to the probabilistic model. A score indicative of a likelihood of the set providing a correct structured reading-order sequence is obtained for each set of text segments.
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公开(公告)号：US11354720B2
公开(公告)日：2022-06-07
申请号：US16847156
申请日：2020-04-13
Applicant: Adobe Inc.
Inventor： Hung Bui , Branislav Kveton , Suvash Sedhain , Nikolaos Vlassis , Jaya Kawale
Abstract: Techniques disclosed herein provide more efficient and more relevant item recommendations to users in large-scale environments in which only positive interest information is known. The techniques use a rank-constrained formulation that generalizes relationships based on known user interests in items and/or use a randomized singular value decomposition (SVD) approximation technique to solve the formulation to identify items of interest to users in an efficiently, scalable manner.
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公开(公告)号：US10861456B2
公开(公告)日：2020-12-08
申请号：US16133190
申请日：2018-09-17
Applicant: Adobe Inc.
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating dialogue responses based on received utterances utilizing an independent gate context-dependent additive recurrent neural network. For example, the disclosed systems can utilize a neural network model to generate a dialogue history vector based on received utterances and can use the dialogue history vector to generate a dialogue response. The independent gate context-dependent additive recurrent neural network can remove local context to reduce computation complexity and allow for gates at all time steps to be computed in parallel. The independent gate context-dependent additive recurrent neural network maintains the sequential nature of a recurrent neural network using the hidden vector output.
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公开(公告)号：US20200242678A1
公开(公告)日：2020-07-30
申请号：US16847156
申请日：2020-04-13
Applicant: Adobe Inc.
Inventor： Hung Bui , Branislav Kveton , Suvash Sedhain , Nikolaos Vlassis , Jaya Kawale
Abstract: Techniques disclosed herein provide more efficient and more relevant item recommendations to users in large-scale environments in which only positive interest information is known. The techniques use a rank-constrained formulation that generalizes relationships based on known user interests in items and/or use a randomized singular value decomposition (SVD) approximation technique to solve the formulation to identify items of interest to users in an efficiently, scalable manner.
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10.发明申请公开(公告)号：US20190325068A1
公开(公告)日：2019-10-24
申请号：US15957556
申请日：2018-04-19
Applicant: Adobe Inc.
Inventor： Tuan Manh Lai , Trung Bui , Sheng Li , Quan Hung Tran , Hung Bui
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating digital responses to digital queries by utilizing a classification model and query-specific analysis models. For example, the disclosed systems can train a classification model to generate query classifications corresponding to product queries, conversational queries, and/or recommendation/purchase queries. Moreover, the disclosed systems can apply the classification model to select pertinent models for particular queries. For example, upon classifying a product query, disclosed systems can utilize a neural ranking model (trained based on a set of training product specifications and training queries) to generate relevance scores for product specifications associated with a digital query. The disclosed systems can further compare generated relevance scores to select a product specification and generate a digital response that includes the pertinent product specification to provide for display to a client device.
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