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公开(公告)号：US11373210B2
公开(公告)日：2022-06-28
申请号：US16830886
申请日：2020-03-26
Applicant: Adobe Inc.
Inventor： Niranjan Shivanand Kumbi , Ajay Awatramani , Balaji Vasan Srinivasan , Reddy Sreekanth , Niyati Himanshu Chhaya
IPC: G06F40/279 , G06Q30/02 , G06F40/30
Abstract: Techniques and systems are described for content interest from interaction information. Keywords are extracted from digital content, and relevance values are determined based on the keywords that captures both the statistical and semantic significance of topics in the digital content through use of a network representation. Interest values for an entity are determined based on the relevance values and an interaction dataset, which capture both the statistical and semantic significance of the topics with respect to the entity. The interest values may be utilized to control output of digital content to a client device.
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公开(公告)号：US20210304253A1
公开(公告)日：2021-09-30
申请号：US16830886
申请日：2020-03-26
Applicant: Adobe Inc.
Inventor： Niranjan Shivanand Kumbi , Ajay Awatramani , Balaji Vasan Srinivasan , Reddy Sreekanth , Niyati Himanshu Chhaya
IPC: G06Q30/02 , G06F40/279 , G06F40/30
Abstract: Techniques and systems are described for content interest from interaction information. Keywords are extracted from digital content, and relevance values are determined based on the keywords that captures both the statistical and semantic significance of topics in the digital content through use of a network representation. Interest values for an entity are determined based on the relevance values and an interaction dataset, which capture both the statistical and semantic significance of the topics with respect to the entity. The interest values may be utilized to control output of digital content to a client device.
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公开(公告)号：US20210110411A1
公开(公告)日：2021-04-15
申请号：US16600099
申请日：2019-10-11
Applicant: Adobe Inc.
Inventor： Yali Pollak , Vivek Sinha , Ajay Awatramani
Abstract: A method, in which one or more processing devices perform operations, includes executing a content-extraction agent that extracts activity data describing interactions with online resources by one or more user devices associated with a target entity. The method includes organizing the activity data into an input descriptive data structure associated with the target entity. The method includes computing a probability of the target entity belonging to a decision-making group by applying, to the input descriptive data structure, a role-classification model that is trained to determine probabilities that entities belong to the decision-making group. The method further includes transmitting an indication of the probability to a content provider, where transmitting the indication of the probability causes the content provider to customize interactive content to the target entity prior to a transmission of the interactive content to the one or more user devices.
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公开(公告)号：US20200329097A1
公开(公告)日：2020-10-15
申请号：US16384646
申请日：2019-04-15
Applicant: ADOBE INC.
Inventor： Niranjan Shivanand Kumbi , Ajay Awatramani
Abstract: An improved marketing automation system can optimize governance of server resources by managing the execution of campaigns. The marketing automation system can develop intelligence around a given customer's inflow of incoming campaigns, the execution time of the campaigns, and general resource utilization over time. The marketing automation system can learn to predict an expected number and type of campaigns for a pre-defined window of time. This intelligence can be leveraged to ensure that one or more executors remain available to execute predicted high priority campaigns upon placement into an execution queue. Further, this intelligence can be applied such that predicted dormant executors can be used to execute low priority tasks. In this way, the marketing automation system minimizes queue time until execution for high priority campaigns while optimizing use of server resources.
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公开(公告)号：US11727209B2
公开(公告)日：2023-08-15
申请号：US16859780
申请日：2020-04-27
Applicant: Adobe Inc.
Inventor： Ajay Jain , Sanjeev Tagra , Sachin Soni , Niranjan Shivanand Kumbi , Eric Andrew Kienle , Ajay Awatramani , Abhishek Jain
IPC: G06F40/279 , G06N3/08 , G06N3/044
CPC classification number: G06F40/279 , G06N3/044 , G06N3/08
Abstract: In implementations of systems for role classification, a computing device implements a role system to receive data describing a corpus of text that is associated with a user ID. Feature values of features are generated by a first machine learning model by processing the corpus of text, the features representing questions with respect to the corpus of text and the feature values representing answers to the questions included in the corpus of text. A classification of a role is generated by a second machine learning model by processing the feature values, the classification of the role indicating a relationship of the user ID with respect to a product or service. The role system outputs an indication of the classification of the role for display in a user interface of a display device.
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Patent Agency Ranking
公开(公告)号：US20210209629A1
公开(公告)日：2021-07-08
申请号：US16732678
申请日：2020-01-02
Applicant: ADOBE INC.
Inventor： Niranjan Shivanand Kumbi , Ajay Awatramani , Vaidyanathan Venkatraman , Omar Rahman , Kai Yeung Lau
Abstract: An improved analytics system generates predicted event outcomes for events. The analytics system generates expected registration profiles based on event metadata that indicates predicted audience behavior for an event. This expected registration profile is used to analyze real-time audience behavior of an audience associated with the event. A predicted event outcome can be determined that indicates a time-based conversion propensity related to the audience.
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公开(公告)号：US11775813B2
公开(公告)日：2023-10-03
申请号：US16446386
申请日：2019-06-19
Applicant: Adobe Inc.
Inventor： Niranjan Kumbi , Vaidyanathan Venkatraman , Rajan Madhavan , Omar Rahman , Kai Lau , Badsah Mukherji , Ajay Awatramani
IPC: G06Q10/04 , G06Q30/0202 , G06N3/08 , G06N20/00
CPC classification number: G06N3/08 , G06Q10/04 , G06Q30/0202 , G06N20/00
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a recommended target audience based on determining a predicted attendance utilizing a neural network approach. For example, the disclosed systems can utilize an approximate nearest neighbor algorithm to identify individuals that are within a similarity threshold of invitees for an event. In addition, the disclosed systems can implement an attendance prediction model to determine a probability of an invitee attending the event. The disclosed systems can further determine a predicted attendance for an event based on the individual probabilities. Based on identifying the similar individuals to, and the attendance probabilities for, the invitees, the disclosed systems can generate a recommended target audience to satisfy a target attendance for an event based on a predicted attendance for the event.
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公开(公告)号：US11501161B2
公开(公告)日：2022-11-15
申请号：US16375037
申请日：2019-04-04
Applicant: ADOBE INC.
Inventor： Vaidyanathan Venkatraman , Rajan Madhavan , Omar Rahman , Niranjan Shivanand Kumbi , Brajendra Kumar Bhujabal , Ajay Awatramani
Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for providing factors that explain the generated results of a deep neural network (DNN). In embodiments, multiple machine learning models and a DNN are trained on a training dataset. A preliminary set of trained machine learning models with similar results to the trained DNN are selected for further evaluation. The preliminary set of machine learning models may be evaluated using a distribution analysis to select a reduced set of machine learning models. Results produced by the reduced set of machine learning models are compared, point-by-point, to the results produced by the DNN. The best performing machine learning model with generated results that performs closest to the DNN generated results may be selected. One or more factors used by the selected machine learning model are determined. Those one or more factors from the selected best performing machine learning model may be provided to explain the results of the DNN and increase confidence in the understanding and accuracy of the results generated by the DNN.
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公开(公告)号：US20210334458A1
公开(公告)日：2021-10-28
申请号：US16859780
申请日：2020-04-27
Applicant: Adobe Inc.
Inventor： Ajay Jain , Sanjeev Tagra , Sachin Soni , Niranjan Shivanand Kumbi , Eric Andrew Kienle , Ajay Awatramani , Abhishek Jain
IPC: G06F40/279 , G06N3/08 , G06N3/04
Abstract: In implementations of systems for role classification, a computing device implements a role system to receive data describing a corpus of text that is associated with a user ID. Feature values of features are generated by a first machine learning model by processing the corpus of text, the features representing questions with respect to the corpus of text and the feature values representing answers to the questions included in the corpus of text. A classification of a role is generated by a second machine learning model by processing the feature values, the classification of the role indicating a relationship of the user ID with respect to a product or service. The role system outputs an indication of the classification of the role for display in a user interface of a display device.
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公开(公告)号：US20220343189A1
公开(公告)日：2022-10-27
申请号：US17237892
申请日：2021-04-22
Applicant: Adobe Inc.
Inventor： Niyati Himanshu Chhaya , Niranjan Kumbi , Balaji Vasan Srinivasan , Akangsha Bedmutha , Ajay Awatramani , Sreekanth Reddy
Abstract: Certain embodiments involve using machine-learning methods to generate a recommendation for sequential content items. A method involves accessing a content item associated with an interaction stage in an online environment. A stage graph, which includes a ratio of interactions, of the content item is generated. An additional content item that includes additional stage-transition content is identified. A sequencing function outcome indicating a portion of the ratio of interactions is determined. A transition probability of receiving an interaction with stage-transition content and an additional interaction with the additional stage-transition content is calculated. A content provider system is caused to provide a recipient device with interactive content that includes the additional content item.
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