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公开(公告)号：US10733262B2
公开(公告)日：2020-08-04
申请号：US15726168
申请日：2017-10-05
Applicant: Adobe Inc.
Inventor： Gavin Stuart Peter Miller , Kevin Gary Smith , Kent Andrew Edmonds , Govind P. Balakrishnan
IPC: G06F17/00 , G06F16/958 , G06N20/00
Abstract: Attribute control for updating digital content in a digital medium environment is described. The digital content is updated by incorporating new digital content components from a service provider system, such as a stock content service, to keep the digital content from seeming stale to client device users. The service provider system controls provision of digital content components based on fixed and variable attributes specified for these digital content components. Initially, the service provider system receives a component request, requesting that the service provider system provide the digital content components for incorporation with the digital content. The component request specifies fixed and variable content attributes for the provided digital content components. A fixed content attribute is an attribute that is to be included in the provided digital content components. In contrast, a variable content attribute is an attribute that is allowed to vary from one provided digital content component to another.
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公开(公告)号：US10657118B2
公开(公告)日：2020-05-19
申请号：US15726125
申请日：2017-10-05
Applicant: Adobe Inc.
Inventor： Gavin Stuart Peter Miller , Kevin Gary Smith , Kent Andrew Edmonds , Govind P. Balakrishnan
Abstract: An update basis for updating digital content in a digital medium environment is described. The digital content is updated by incorporating new digital content components from a service provider system, such as a stock content service, to keep the digital content from seeming stale to client device users. The service provider system controls provision of digital content components according to an update basis described in a component request. In part, component requests ask that the service provider system provide digital content components for incorporation with digital content. Component requests also describe a timing basis with which digital content components are to be provided as updates. By way of example, the timing basis may correspond to a time interval (e.g., daily, weekly, monthly, seasonally, times of day, and so on), receiving user input in relation to the digital content (e.g., a navigation input to a web page), and so forth.
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3.发明授权公开(公告)号：US11551257B2
公开(公告)日：2023-01-10
申请号：US15782517
申请日：2017-10-12
Applicant: Adobe Inc.
Inventor： Oliver Isaac Goldman , Thomas William Randall Jacobs , Kent Andrew Edmonds , Kevin Gary Smith , Pradeep Saikalyanachakravarthi Javangula , Ashley Manning Still
Abstract: Techniques and systems are described to enable users to optimize a digital marketing content system by analyzing an effect of components of digital marketing content on audience segments, environments of consumption, and channels of consumption. A computing device of an analytics system receives user interaction data describing an effect of user interaction with multiple items of digital marketing content on achieving an action for multiple audience segments. The analytics system identifies which of a plurality of components are included in respective items of digital marketing content. The analytics system generates data identifying different aspects that likely had an effect on the achieving an action on the items of digital marketing content, such as components of the items of digital marketing content, environments of consumption, channels of consumption. The analytics system outputs a result based on the data in a user interface.
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公开(公告)号：US10795647B2
公开(公告)日：2020-10-06
申请号：US15785298
申请日：2017-10-16
Applicant: Adobe Inc.
Inventor： Thomas William Randall Jacobs , Peter Raymond Fransen , Kevin Gary Smith , Kent Andrew Edmonds , Jen-Chan Jeff Chien , Gavin Stuart Peter Miller
Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.
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公开(公告)号：US20190295004A1
公开(公告)日：2019-09-26
申请号：US15934531
申请日：2018-03-23
Applicant: ADOBE INC.
Inventor： Sorathan Chaturapruek , Georgios Theocharous , Kent Andrew Edmonds
Abstract: Systems and methods provide a recommendation system for recommending sequential content. The training of a reinforcement learning (RL) agent is bootstrapped from passive data. The RL agent of the sequential recommendations system is trained using the passive data over a number of epochs involving interactions between the sequential recommendation system and user devices. At each epoch, available active data from previous epochs is obtained, and transition probabilities are generated from the passive data and at least one parameter derived from the currently available active data. Recommended content is selected based on a current state and the generated transition probabilities, and the active data is updated from the current epoch based on the recommended content and a resulting new state. A clustering approach can also be employed when deriving parameters from active data to balance model expressiveness and data sparsity.
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Patent Agency Ranking
公开(公告)号：US20190163766A1
公开(公告)日：2019-05-30
申请号：US15824836
申请日：2017-11-28
Applicant: ADOBE INC.
Inventor： Samarth Gulati , Brett Butterfield , Baldo Faieta , Bernard James Kerr , Kent Andrew Edmonds
IPC: G06F17/30
Abstract: Systems and methods for searching digital content, such as digital images, are disclosed. A method includes receiving a first search constraint and generating search results based on the first search constraint. A search constraint includes search values or criteria. The search results include a ranked set of digital images. A second search constraint and a weight value associated with the second search constraint are received. The search results are updated based on the second search constraint and the weight value. The updated search results are provided to a user. Updating the search results includes determining a ranking (or a re-ranking) for each item of content included in the search results based on the first search constraint, the second search constraint, and the weight value. Re-ranking the search results may further be based on a weight value associated with the first search constraint, such as a default or maximum weight value.
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公开(公告)号：US11544322B2
公开(公告)日：2023-01-03
申请号：US16389592
申请日：2019-04-19
Applicant: Adobe Inc. , The Regents of the University of California
Inventor： Lubomira Dontcheva , Kent Andrew Edmonds , Cristin Fraser , Scott Klemmer
IPC: G06F16/732 , G06F9/54 , G06F16/783 , G06F3/0484 , G06F9/451 , G06F16/738
Abstract: A method includes detecting control of an active content creation tool of an interactive computing system in response to a user input received at a user interface of the interactive computing system. The method also includes automatically updating a video search query based on the detected control of the active content creation tool to include context information about the active content creation tool. Further, the method includes performing a video search of video captions from a video database using the video search query and providing search results of the video search to the user interface of the interactive computing system.
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公开(公告)号：US11429892B2
公开(公告)日：2022-08-30
申请号：US15934531
申请日：2018-03-23
Applicant: ADOBE INC.
Inventor： Sorathan Chaturapruek , Georgios Theocharous , Kent Andrew Edmonds
Abstract: Systems and methods provide a recommendation system for recommending sequential content. The training of a reinforcement learning (RL) agent is bootstrapped from passive data. The RL agent of the sequential recommendations system is trained using the passive data over a number of epochs involving interactions between the sequential recommendation system and user devices. At each epoch, available active data from previous epochs is obtained, and transition probabilities are generated from the passive data and at least one parameter derived from the currently available active data. Recommended content is selected based on a current state and the generated transition probabilities, and the active data is updated from the current epoch based on the recommended content and a resulting new state. A clustering approach can also be employed when deriving parameters from active data to balance model expressiveness and data sparsity.
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公开(公告)号：US11361018B2
公开(公告)日：2022-06-14
申请号：US15824907
申请日：2017-11-28
Applicant: ADOBE INC.
Inventor： Samarth Gulati , Brett Michael Butterfield , Baldo Faieta , Kent Andrew Edmonds
IPC: G06F16/532 , G06F16/54 , G06F16/56 , G06F16/583 , G06F16/9535
Abstract: Systems and methods for searching digital content are disclosed. A method includes receiving, from a user, a base search constraint. A search constraint includes search values or criteria. A recall set is generated based on the base search constraint. Recommended search constraints are determined and provided to the user. The recommended search constraints are statistically associated with the base search constraint. The method receives, from the user, a selection of a first search constraint included in the plurality of recommend search constraints. The method generates and provides search results to the user that include a re-ordering of the recall set. The re-ordering is based on a search constraint set that includes both the base search constraint and the selected first search constraint. The re-ordering is further based on a weight associated with the base search constraint and another user-provided weight associated with the first search constraint.
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公开(公告)号：US11243747B2
公开(公告)日：2022-02-08
申请号：US17007253
申请日：2020-08-31
Applicant: Adobe Inc.
Inventor： Thomas William Randall Jacobs , Peter Raymond Fransen , Kevin Gary Smith , Kent Andrew Edmonds , Jen-Chan Jeff Chien , Gavin Stuart Peter Miller
Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.
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