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31.发明申请公开(公告)号：US20230140004A1
公开(公告)日：2023-05-04
申请号：US17514768
申请日：2021-10-29
Applicant: Adobe Inc.
Inventor： James Kostas , Georgios Theocharous
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate digital item recommendations for client devices utilizing coagent recommendation models of a distributed asynchronous coagent network. Indeed, in one or more embodiments, the disclosed systems operate on an edge computing device of a distributed asynchronous coagent network. In some cases, the disclosed systems utilize recommendation scores generated at the edge computing device via local coagents and additional recommendation scores received from other coagents of other edge computing devices to generate a digital item recommendation. In some cases, the disclosed systems progressively refines the recommendation as delayed scores from the other coagents are received. Further, in some embodiments, the disclosed systems update parameters of the local coagents using local policy gradients determined from responses to the generated recommendations.
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公开(公告)号：US11640617B2
公开(公告)日：2023-05-02
申请号：US15465449
申请日：2017-03-21
Applicant: Adobe Inc.
Inventor： Chunyuan Li , Hung Hai Bui , Mohammad Ghavamzadeh , Georgios Theocharous
IPC: G06Q30/0202 , G06N3/08 , G06N3/044 , G06Q30/0204
Abstract: Metric forecasting techniques and systems in a digital medium environment are described that leverage similarity of elements, one to another, in order to generate a forecast value for a metric for a particular element. In one example, training data is received that describes a time series of values of the metric for a plurality of elements. The model is trained to generate the forecast value of the metric, the training using machine learning of a neural network based on the training data. The training includes generating dimensional-transformation data configured to transform the training data into a simplified representation to determine similarity of the plurality of elements, one to another, with respect to the metric over the time series. The training also includes generating model parameters of the neural network based on the simplified representation to generate the forecast value of the metric.
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公开(公告)号：US11449763B2
公开(公告)日：2022-09-20
申请号：US15914285
申请日：2018-03-07
Applicant: Adobe Inc.
Inventor： Frits de Nijs , Georgios Theocharous
IPC: G06F16/951 , G06N5/02
Abstract: The present disclosure relates to recommending points of interest to a plurality of users based on a type of each user as well as constraints associated with the points of interest. For example, one or more embodiments determine a user type for each user and determine user preferences based on the user type. Additionally, the system can determine resource constraints associated with each point of interest, indicating limitations on the capacity of each associated resource. The system can then provide recommendations to the plurality of users based on the user types and the resource constraints. In particular, the system can recommend points of interest that satisfy the preferences corresponding to each user type subject to the resource constraints of each point of interest. For example, one or more embodiments involve solving a linear program that takes into account user types to obtain recommendation policies subject to the resource constraints.
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公开(公告)号：US20220188895A1
公开(公告)日：2022-06-16
申请号：US17120583
申请日：2020-12-14
Applicant: Adobe Inc.
Inventor： Nedim Lipka , Michele Saad , Georgios Theocharous
IPC: G06Q30/06 , G06F40/289 , G06N5/02 , G06N5/04
Abstract: Unstructured texts associated with a product is received, where the unstructured texts include, for example, a title of the product, one or more reviews of the product, questions and/or answers associated with the product. A phrase in an unstructured text is identified. A first knowledge base is searched, to identify that the phrase is a feature value that is associated with a feature. For example, the first knowledge base lists the feature value to be an instance of the feature. Accordingly, a tuple is generated, where the tuple includes the product as a subject, the feature as a predicate, and the feature value comprising the phrase as an object. A second knowledge base is updated with the tuple. The second knowledge base is usable for processing queries about the product. For example, the second knowledge base is used to generate a result of a query about the product.
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公开(公告)号：US20220147300A1
公开(公告)日：2022-05-12
申请号：US17091335
申请日：2020-11-06
Applicant: Adobe Inc.
Inventor： Jennifer Healey , Haoliang Wang , Georgios Theocharous
IPC: G06F3/14 , G06F40/279 , G06Q30/06 , G06Q10/08 , G06K7/10 , G06K19/07 , G06Q30/02 , G06F16/54 , G06F16/535 , G01C21/20
Abstract: This disclosure describes embodiments of methods, systems, and non-transitory-computer readable media that personalize visual content for display on digital signage near a projected location of a person by mapping visual content to physical items selected by the person. In some examples, the disclosed system identifies physical items selected by a person based on signals from the physical items, such as signals emitted by RFID tags affixed to (or other devices associated with) the physical items. The disclosed system analyzes the collection of physical items—as identified by the signals—to tailor digital signage content specific to the person. The disclosed system further tracks the location of the person as the person moves through a physical space and interacts with the physical items. Based on the tracked positions, the disclosed system determines a digital sign in proximity to a predicted location of the person to display the personalized visual content.
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Patent Agency Ranking
公开(公告)号：US20220121968A1
公开(公告)日：2022-04-21
申请号：US17072868
申请日：2020-10-16
Applicant: Adobe Inc.
Inventor： Yash Chandak , Georgios Theocharous , Sridhar Mahadevan
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that determine target policy parameters that enable target policies to provide improved future performance, even in circumstances where the underlying environment is non-stationary. For example, in one or more embodiments, the disclosed systems utilize counter-factual reasoning to estimate what the performance of the target policy would have been if implemented during past episodes of action-selection. Based on the estimates, the disclosed systems forecast a performance of the target policy for one or more future decision episodes. In some implementations, the disclosed systems further determine a performance gradient for the forecasted performance with respect to varying a target policy parameter for the target policy. In some cases, the disclosed systems use the performance gradient to efficiently modify the target policy parameter, without undergoing the computational expense of expressly modeling variations in underlying environmental functions.
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公开(公告)号：US20220004898A1
公开(公告)日：2022-01-06
申请号：US16921202
申请日：2020-07-06
Applicant: Adobe Inc.
Inventor： Atanu R Sinha , Tanay Asija , Sunny Dhamnani , Raja Kumar Dubey , Navita Goyal , Kaarthik Raja Meenakshi Viswanathan , Georgios Theocharous
Abstract: The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining a cognitive, action-selection bias of a user that influences how the user will select a sequence of digital actions for execution of a task. For example, the disclosed systems can identify, from a digital behavior log of a user, a set of digital action sequences that correspond to a set of sessions for a task previously executed by the user. The disclosed systems can utilize a machine learning model to analyze the set of sessions to generate session weights. The session weights can correspond to an action-selection bias that indicates an extent to which a future session for the task executed by the user is predicted to be influenced by the set of sessions. The disclosed systems can provide a visual indication of the action-selection bias of the user for display on a graphical user interface.
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38.发明申请公开(公告)号：US20210325193A1
公开(公告)日：2021-10-21
申请号：US17364480
申请日：2021-06-30
Applicant: Adobe Inc.
Inventor： Fan Du , Sana Malik Lee , Georgios Theocharous , Eunyee Koh
Abstract: The present disclosure relates to generating and modifying recommended event sequences utilizing a dynamic user preference interface. For example, in one or more embodiments, the system generates a recommended event sequence using a recommendation model trained based on a plurality of historical event sequences. The system then provides, for display via a client device, the recommendation, a plurality of interactive elements for entry of user preferences, and a visual representation of historical event sequences. Upon detecting input of user preferences, the system can modify a reward function of the recommendation model and provide a modified recommended event sequence together with the plurality of interactive elements. In one or more embodiments, as a user enters user preferences, the system additionally modifies the visual representation to display subsets of the plurality of historical event sequences corresponding to the preferences.
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公开(公告)号：US11085777B2
公开(公告)日：2021-08-10
申请号：US16047908
申请日：2018-07-27
Applicant: Adobe Inc.
Inventor： Fan Du , Sana Malik Lee , Georgios Theocharous , Eunyee Koh
Abstract: The present disclosure relates to generating and modifying recommended event sequences utilizing a dynamic user preference interface. For example, in one or more embodiments, the system generates a recommended event sequence using a recommendation model trained based on a plurality of historical event sequences. The system then provides, for display via a client device, the recommendation, a plurality of interactive elements for entry of user preferences, and a visual representation of historical event sequences. Upon detecting input of user preferences, the system can modify a reward function of the recommendation model and provide a modified recommended event sequence together with the plurality of interactive elements. In one or more embodiments, as a user enters user preferences, the system additionally modifies the visual representation to display subsets of the plurality of historical event sequences corresponding to the preferences.
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公开(公告)号：US10430825B2
公开(公告)日：2019-10-01
申请号：US14997987
申请日：2016-01-18
Applicant: Adobe Inc.
Inventor： Sougata Chaudhuri , Georgios Theocharous , Mohammad Ghavamzadeh
Abstract: A digital medium environment is described to recommend advertisements using ranking functions. A ranking function is configured to compute a score by applying a user context vector associated with a user to individual ranking weight vectors associated with advertisements, and provide the advertisement with the highest score to the user. In order to learn the ranking weight vectors for the ranking function, training data is obtained that includes user interactions with advertisements during previous sessions as well as user context vectors. The ranking weight vectors for the ranking function associated with each advertisement can then be learned by controlling the score generated by the ranking function to be higher for positive interactions than the negative interactions. To do so, the ranking weight vectors may be learned by optimizing an area under the curve ranking loss (AUCL) for the ranking function.
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